CN112259251B - Method and system for simplifying and analyzing pharmacokinetic parameters - Google Patents
Method and system for simplifying and analyzing pharmacokinetic parameters Download PDFInfo
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Abstract
The invention provides a simplified analysis method and a simplified analysis system for pharmacokinetic parameters, and belongs to the technical field of pharmacokinetics. When the pharmacokinetics of a single administration test of a clinical drug test is analyzed, the method carries out self-defined conversion on values which are lower than the lower limit of a measurement method and cannot be measured in a data set, important parameter results such as Cmax, Tmax, mean values of blood concentration of each dose group in the test group at each planned blood sampling time point and the like are obtained, and the operation result is merged into the original data set under the condition that the format of the original data set and other variable data in the data set are not influenced or changed to generate a new data set or directly cover the original data set. The statistical engineer can simply use the method of the invention in SAS software to quickly obtain the required operation result, and can directly carry out the subsequent analysis and research work without spending a large amount of time to import, merge or arrange the data sets among different software.
Description
Technical Field
The invention relates to the technical field of pharmacokinetics, in particular to a method and a system for simplifying and analyzing pharmacokinetic parameters.
Background
The pharmacokinetic analysis of the drug clinical trial is mainly to analyze the blood concentration of each subject at each blood sampling time point and extract pharmacokinetic parameters. Among the numerous pharmacokinetic parameters, Cmax and Tmax belong to one of the most important parameters. Cmax represents the peak blood concentration of each subject, and Tmax represents the blood sampling time point at which the blood concentration of the subject reaches the peak Cmax.
When the blood concentration data of the subject is taken by a statist, some blood concentration data are marked as 'BQL', which means that the blood concentration data are lower than the lower limit of quantification of the measurement method and cannot be measured. Before analyzing the blood concentration data, a statistician usually needs to establish a BQL conversion rule for such "BQL" values in the data.
For the descriptive statistical analysis of blood drug concentration, one of the most important values is the mean value of blood drug concentration at each planned blood sampling time point for each dose group in the test group, and the statistical engineer needs to convert all the "BQL" values before performing the descriptive analysis of blood drug concentration.
A statist usually uses Phoenix WinNonlin software to analyze blood concentration data, extract pharmacokinetic parameters, integrate calculation results, and substitute the integrated data into SAS software to analyze differences among various dosage groups. When Phoenix WinNonlin software is used, a statist needs to firstly determine that the format of a data set file meets the requirements of the WinNonlin software, and if the format of the data set file does not meet the requirements, SAS software is used for format conversion. After the data set is imported into the WinNonlin software, a 'BQL' value conversion rule needs to be established in the software, the rule is applied to the imported data set, and then a statistics engineer needs to establish a non-compartmental model analysis module and a descriptive analysis module in the WinNonlin software to respectively extract pharmacokinetic parameters and perform descriptive analysis on blood concentration. After the data set file after the 'BQL' value conversion is respectively imported into each module, the system respectively generates corresponding result data sets. Some other important variables contained in the blood concentration data set can be reserved to the result data set only by selecting in the WinNonlin software by a statistical engineer, and the statistical engineer may need to introduce each result data set into the SAS software respectively for merging so as to perform subsequent analysis. Some data containing chinese characters are displayed as question marks in WinNonlin software, which may cause data loss when being returned to the SAS.
WinNonlin software may help the statist extract various pharmacokinetic parameters, but is cumbersome and time consuming to operate for a statist who only wants to obtain the Cmax, Tmax and mean plasma concentrations at planned blood collection time points for each dose group in the test group, and may take additional time to merge result data sets or import data between WinNonlin and SAS. WinNonlin is also relatively cumbersome to identify for a subject treatment group, and a statistician may need to extract sub-data sets or set weight variables in WinNonlin software before importing the data set into WinNonlin software.
The prior art has at least the following disadvantages:
1. for the case where only the Cmax, Tmax and mean blood concentration at each blood sampling point of each dose group are to be analyzed, data processing is troublesome and takes a long time.
WinNonlin relates to a series of operations of data set format conversion, treatment group identification, logic establishment of 'BQL', analysis flow establishment, combination of result and original data set, generation of 'NA' value and 'ND' value, and data guide back to SAS
WinNonlin is expensive, and the cost of experimental analysis is high.
Disclosure of Invention
In order to solve the technical problems in the prior art, the invention provides a method and a system for simplifying and analyzing pharmacokinetic parameters, when the method is used for analyzing the pharmacokinetics of a single administration test in a clinical drug test, the value in a data set, which is lower than the lower limit of measurement of a measurement method, can be represented by a user-defined character, such as a 'BQL' value, the 'BQL' value can be subjected to user-defined conversion, important parameter results such as Cmax, Tmax, the average value of blood concentration of each dose group in a test group at each planned blood sampling time point and the like are obtained, and the operation result is combined to the original data set under the condition that the format of the original data set and other variable data in the data set are not influenced or changed at all, so as to generate a new data set or directly cover the original data set. The statistic engineer can simply use the PKBQL Macro program in SAS software to quickly obtain the required operation result, and can directly carry out subsequent analysis and research work without spending a large amount of time on importing, merging or sorting data sets among different software.
The invention converts the value of the blood concentration in the data set which is lower than the lower limit of the measurement method (for the sake of clear and concise explanation, the fourth character is defined as 'BQL', the second character is defined as 'ND', for example) 'BQL', the 'BQL' before reaching Cmax is converted into 0, the 'BQL' value after reaching Cmax is converted into the second character, such as 'ND', and if all the blood concentration data of the subject are 'BQL', the data are all converted into 'ND'. The invention also carries out grouping indication on the treatment group where the subject belongs, is used for indicating the blood sampling time point and the dosage group where the subject belongs, analyzes the Cmax, the Tmax and the average blood concentration of each dosage group of the test group at each planned blood sampling time point, and directly merges the average blood concentration with the data of the original data set to generate a new data set or directly covers the original data set. If all the blood concentration data of the subjects are 'ND', the generated Cmax and Tmax are also marked as 'ND', the average blood concentration of each dose group in the test group at each planned blood collection time point is calculated after converting all the 'BQL' values into 0, the average blood concentration of each dose group in the control group at each planned blood collection time point is marked by 'NA' because of no reference meaning, and the conversion of the BQL values meets the standard of general pharmacokinetic analysis.
The invention provides a simplified analysis method of pharmacokinetic parameters, which comprises the following steps:
s100: importing an original data set, creating a result data set, and performing first data format conversion on characters in variable values required to be called by a program to convert the characters into a digital format;
s200: calculating the peak blood concentration Cmax of each subject, the actual blood sampling time point Tmax when the blood concentration reaches the peak Cmax and the planned blood sampling time point Tmax when the blood concentration reaches the peak Cmax;
s300: calculating the average blood concentration of each dose group at all planned blood sampling time points;
s400: calculating the average blood concentration of each subject;
s500: a second data format conversion step, according to the Cmax and the step calculated in step S200, performing format conversion on the blood concentration value with data of 0 after the blood concentration of each subject reaches Cmax, and performing format conversion on the corresponding Cmax, Tmax and Tmax group values of the subject with average blood concentration of 0;
s600: setting a conversion logic step, which specifically comprises the following steps:
s601: setting a first conversion logic, and converting a missing value first character into a self-defined second character, wherein the second character is used for indicating that a numerical value cannot be measured;
s602: setting a second conversion logic, and converting the missing value first character ' to ' NA ' into a third character;
s700: a third format conversion and sorting step of the format conversion,
automatically converting the missing value first character' in the data of the control group and all the subjects according to the set first conversion logic and second conversion logic;
sorting the result data sets by subject ID and planned blood collection time point;
s800: after the SAS system performs steps S100 to S700 and after step S700, the resulting data set is analyzed and displayed in the SAS system.
Preferably, step S100 specifically includes the following steps:
s101: importing an original data set, and extracting all information in the original data set to create a result data set;
s102: extracting variables required by the operation in the result data set and renaming the variables;
s103: converting a fourth character in variables extracted from a result data set into a missing value first character "-", wherein the fourth character is a self-defined character in an original data set and is used for representing that the blood concentration data is lower than a quantitative lower limit of a measurement method and cannot be measured;
s104: converting all missing value first characters in the result dataset into '0';
s105: creating a grouping indication variable for indicating the blood sampling time point and the dose group to which each line of data in the result dataset belongs, and adding the renamed variable of step S102 and the created grouping indication variable to the result dataset.
Preferably, step S200 specifically includes the following steps:
s201: calculating the peak blood concentration Cmax of each subject;
s202: according to the calculated Cmax of each subject, obtaining an actual blood sampling time point Tmax when the Cmax is reached and a planned blood sampling time point Tmax when the Cmax is reached;
s203: the Cmax, Tmax and Tmax group for each subject were added to the results data set.
Preferably, step S300 specifically includes the following steps:
s301: identifying a combination of dose groups and planned blood collection time points from the grouping indicator variables created in step S105;
s302: calculating the average blood concentration of each dose group at all planned blood sampling time points according to the blood concentration data in each dose group in the result data set;
s303: storing the calculated average blood concentration of each dose group at all planned blood sampling points as a first internal data set, wherein the first internal data set comprises a grouping indicator variable and a mean blood concentration of each dose group, and the mean blood concentration of each dose group is the mean blood concentration of each dose group at all planned blood sampling time points;
s304: and merging the first internal data set into a result data set, and deleting the first internal data set after merging.
Preferably, step S500 specifically includes the following steps: the method specifically comprises the following steps:
s501: after the blood concentration of each subject reached Cmax, the blood concentration value of 0 was converted into the missing value first character "-";
s502: based on the mean blood concentration of each subject calculated in step S400, the Cmax, Tmax and Tmax group values are adjusted as follows:
if the average blood concentration is 0, converting the corresponding Cmax, Tmax and Tmax group values into a first character of a missing value "-";
s503: the mean blood concentration of each subject calculated in step S400 and the grouping indicator variables created in step S105 were deleted.
Preferably, step S400 specifically includes the following steps:
s401: calculating the average blood concentration of each subject;
s402: storing the calculated average blood concentration of each subject as a second internal data set, wherein the second internal data set comprises the ID of the subject and the average blood concentration;
s403: the second internal data set is merged to the result data set and, after merging, the second internal data set is deleted.
Preferably, step S700 specifically includes the following steps:
s701: converting the mean blood concentration of the subjects in the control group at each planned blood sampling time point into a missing value first character "-";
s702: converting data marked as a missing value first character "-" in the Cmax, Tmax and Tmax group values of the subjects in the control group at each planned blood sampling time point into the second character for displaying according to a first conversion logic;
s703: according to a second conversion logic, converting the data marked as a missing value first character 'NA' in the mean blood concentration of the subjects in the control group at each planned blood sampling time point into the third character 'NA' for displaying;
s704: according to a first conversion logic, after the blood concentration of each subject reaches Cmax, converting data of a first character ". multidot.A." of a missing value in the blood concentration value into a second character for displaying;
s705: the resulting data sets are sorted by subject ID and planned blood sampling time point and saved as a new data set or directly overlaid to the original data set.
The present invention provides a simplified pharmacokinetic parameter analysis system using the simplified pharmacokinetic parameter analysis method described above, including:
the system comprises an original data sorting module, a Cmax and Tmax extraction module, a mean value extraction module of blood concentration of each dose group and a mean value extraction module of blood concentration of each subject;
the original data sorting module executes the following operations:
importing an original data set, extracting all information in the original data set, and storing the information as a new result data set;
converting a fourth character in the result dataset into a missing value first character "-";
creating grouping indicator variables of the blood sampling time points and the dosage groups, and combining the grouping indicator variables into a result data set;
the Cmax and Tmax extraction module performs the following operations:
calculating the value of the peak blood concentration Cmax according to the blood concentration of each subject;
according to the calculated Cmax of each subject, obtaining an actual blood sampling time point Tmax when the Cmax is reached and a planned blood sampling time point Tmax when the Cmax is reached;
adding the Cmax, Tmax and Tmax group of each subject to the result data set;
the blood concentration mean value extraction module of each dosage group performs the following operations:
identifying the combination of the dose groups and the planned blood sampling time points according to the grouping indicator variables, and calculating the average blood concentration of each dose group at all the planned blood sampling points according to the blood concentration data;
storing the calculated average blood concentration of each dose group as a first internal data set, wherein the first internal data set comprises a grouping indicator variable and a mean blood concentration of each dose group, and the mean blood concentration of each dose group is the mean blood concentration of each dose group at all planned blood sampling time points; merging the first internal data set into a result data set and deleting the first internal data set;
the blood concentration mean value extraction module of each subject performs the following operations:
calculating the mean blood concentration of each subject;
storing the calculated mean blood concentration for each subject as a second internal data set comprising subject ID and mean blood concentration, and merging the second internal data set into a result data set.
Preferably, the method further comprises the following steps:
the device comprises an analysis result preprocessing module, a conversion logic setting module, a conversion logic application and result sorting module;
the analysis result preprocessing module executes the following operations:
converting the blood concentration value of 0 after the blood concentration of each subject reached Cmax into a missing value first character "-";
and adjusting the Cmax, the Tmax and the Tmax group according to the calculated average blood concentration of each subject:
if the average blood concentration is 0, converting the Cmax, the Tmax and the Tmax group into a first character of a missing value ";
a logic-switching setting module for setting the logic,
setting a first conversion logic, and converting a missing value first character into a second character, wherein the second character is used for indicating that a numerical value cannot be measured;
setting a second conversion logic, and converting the missing value first character ' to ' NA ';
the conversion logic applies a module with a result sorting module,
converting the mean plasma concentration values of each dose group of the control group subjects into missing value first characters ";
applying a first conversion logic, converting the data marked as a missing value first character "-" in the Cmax, Tmax and Tmax group values of the subjects at each planned blood sampling time point in the control group into the second character for displaying;
applying a second conversion logic, converting the data marked as the first character ". multidot." of the missing value in the mean blood concentration of the subjects at each planned blood sampling time point in the control group into the third character "NA" for displaying;
applying a first conversion logic, converting the data of the missing value first character ". multidot.in the blood concentration value of each subject after the blood concentration of each subject reaches Cmax into the second character for displaying;
the resulting data sets are sorted by subject ID and planned blood collection time point and saved as a new data set or directly overlaid to the original data set.
Preferably, the method further comprises the following steps:
and directly calling the pharmacokinetic parameter simplified analysis system by the SAS system, and analyzing and displaying the result in the SAS system.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention converts the value of the blood concentration in the original data set which is lower than the lower limit of the measurement method, is convenient to process, converts the fourth character such as 'BQL' before reaching Cmax into 0, converts the 'BQL' value after reaching Cmax into 'ND', and converts all the blood concentration data of the subject into 'ND' if all the blood concentration data of the subject are 'BQL'. If all the blood concentration data of the subjects are 'ND', the generated Cmax and Tmax are also marked as 'ND', the average blood concentration of each dose group in the test group at each planned blood sampling time point is calculated after converting all 'BQL' values into 0, the average blood concentration of each dose group in the control group at each planned blood sampling time point is marked by 'NA', and the conversion of the BQL values in the invention conforms to the standard of general pharmacokinetic analysis.
2. The invention can be customized by the user for the form of the fourth character and the second character and the name of the result data set, and is more convenient.
3. The program of the method can be directly called by the SAS system, and the generated result data set can be directly analyzed and processed in the SAS system without format conversion, so that the operation of an analyst is more convenient and rapid.
4. The method can directly and quickly obtain results under the condition that only Cmax and Tmax and the average blood concentration of each dose group at each blood sampling point need to be analyzed, has less operation, does not need data format conversion, is more convenient, and does not need data set format conversion, treatment group identification, BQL logic establishment, analysis flow establishment, result and original data set combination, generation of 'NA' value and 'ND' value and data guide-back to a series of operations such as SAS and the like in WinNonlin software analysis.
5. The method can analyze the Cmax, the Tmax and the average blood concentration of each dose group at each blood sampling point without WinNonlin software, omits expensive WinNonlin software, and is a very good helper for researchers only possessing SAS but not purchasing WinNonlin.
Drawings
FIG. 1 is a flow chart of one embodiment of a simplified pharmacokinetic parameter analysis method of the present invention;
FIG. 2 is an exemplary block diagram of one embodiment of a simplified pharmacokinetic parameter analysis system of the invention;
FIG. 3 shows the results of analysis of a raw data set by the method of the present invention in example 3 of the present invention;
FIG. 4 is the analysis result of the mean value of blood concentration at each blood-collecting time point for each dose group in the Phoenix WinNonlin test group of the original data set in example 3 of the present invention;
FIG. 5 shows the results of the analysis of Cmax and Tmax of the original data set with reference to Phoenix WinNonlin subjects in example 3.
Detailed Description
The following detailed description of the embodiments of the present invention is provided in conjunction with the accompanying drawings of fig. 1-5.
The invention provides a simplified analysis method of pharmacokinetic parameters, which comprises the following steps:
s100: importing an original data set, creating a result data set, and performing first data format conversion on characters in variable values required to be called by a program to convert the characters into a digital format;
s200: calculating the peak blood concentration Cmax of each subject, the actual blood sampling time point Tmax when the blood concentration reaches the peak Cmax and the planned blood sampling time point Tmax when the blood concentration reaches the peak Cmax;
s300: calculating the average blood concentration of each dose group at all planned blood sampling time points;
s400: calculating the average blood concentration of each subject;
s500: a second data format conversion step, according to the Cmax and the step calculated in step S200, performing format conversion on the blood concentration value with data of 0 after the blood concentration of each subject reaches Cmax, and performing format conversion on the corresponding Cmax, Tmax and Tmax group values of the subject with average blood concentration of 0;
s600: setting a conversion logic step, which specifically comprises the following steps:
s601: setting a first conversion logic, and converting the missing value first character into a self-defined second character, wherein the second character is used for indicating that a numerical value cannot be measured;
s602: setting a second conversion logic, and converting the missing value first character ' to ' NA ' into a third character;
s700: a third format conversion and sorting step of the format conversion,
automatically converting the missing value first character' in the data of the control group and all the subjects according to the set first conversion logic and second conversion logic;
sorting the result data sets by subject ID and planned blood collection time point;
s800: after the SAS system performs steps S100 to S700 and after step S700, the resulting data set is analyzed and displayed in the SAS system.
As a preferred embodiment, step S100 specifically includes the following steps:
s101: importing an original data set, and extracting all information in the original data set to create a result data set;
the result data set created in step S101 is equivalent to a copy of the original data set, all subsequent operations are performed on the result data set, and the calculation results are also merged into the result data set, and the original data set is not affected after the entire operation (unless the name of the result data set is the same as the name of the original data set, the original data set is overwritten by the result data set). In the result dataset, each column is a variable, and each row is an observed value of each variable. A data set records information of a plurality of variables such as subject id, blood concentration, height, weight, sex and the like, and the purpose of the invention is to add information of new variables obtained by calculation to a result data set, so that a user can obtain all information of original data and information of new variables (such as Cmax, Tmax and the like) from one data set.
S102: extracting variables required by the operation in the result data set and renaming the variables;
the variables required for the operation are renamed here because when the invention is used to operate on different data sets, calculations need to be performed based on information of variables within the data sets, such as subject id, blood concentration, dose group, actual blood collection time point, planned blood collection time point, treatment group, etc., but the names of these variables in different data sets are likely to be different. For example, the blood concentration is a variable, some data sets may be called a concentrationand some data sets may be called a conc, so when using the method of the present invention, the user is required to fill in the names of the variables in the original data set, and then the program recognizes what each variable is required for the operation, for example, conc represents the blood concentration, and then renames the concentrationwhen performing the operation internally, as described in embodiment 2, the specific implementation method and explanation are that all variables required for the operation are recognized and then renamed uniformly in the program operation process, such as data, id, concentration, dose, time, upregroegor, treamentgroup, etc., and the difference in names has no influence on the final generated result.
S103: converting a fourth character in variables extracted from a result data set into a missing value first character "-", wherein the fourth character is a self-defined character in an original data set and is used for representing that the blood concentration data is lower than a quantitative lower limit of a measurement method and cannot be measured;
s104: converting all missing value first characters in the result dataset into '0';
s105: creating a grouping indication variable for indicating the blood sampling time point and the dose group to which each line of data in the result dataset belongs, and adding the renamed variable of step S102 and the created grouping indication variable to the result dataset.
A dose group is a dose to which the subject is administered, such as one group of subjects receiving 1mg of drug and another group receiving 2mg of drug. The treatment group represents whether the group in which the subject is present is the control group or the test group.
Generally, the clinical trial design is such that: each dose group is divided and then each dose group has a control group to which the actual drug is not administered and a test group to which only the actual drug is administered, so that the subject of the control group should have a blood concentration of 0 (no drug) throughout the study, in theory.
The grouping indication variable is a new variable used for indicating the blood sampling time point and the dose group which each row of data belongs to in the result data set, which is equivalent to combining the dose group and the blood sampling time point into one variable to facilitate subsequent operation, but the variable is deleted by a program after the subsequent use, so the variable cannot be displayed in the finally generated result data set. As a preferred embodiment, step S200 specifically includes the following steps:
s201: calculating the peak blood concentration Cmax of each subject;
s202: according to the calculated Cmax of each subject, obtaining an actual blood sampling time point Tmax when the Cmax is reached and a planned blood sampling time point Tmax when the Cmax is reached;
s203: the Cmax, Tmax and Tmax group for each subject were added to the results data set.
Adding Cmax, Tmax and Tmax to the result data set is equivalent to adding three new variables, Cmax, Tmax and Tmax, respectively, to the result data set. The values of the three variables are ".", then, aiming at the subject id, each blood concentration data of the subject is compared, if one blood concentration is greater than the previous blood concentration data, the Cmax is equal to the larger blood concentration, the blood concentration data is compared till the last blood concentration data to find out the maximum blood concentration, and then the actual blood sampling time point and the planned blood sampling time point of the Cmax are respectively Tmax and Tmax group.
As a preferred embodiment, step S300 specifically includes the following steps:
s301: identifying a combination of dose groups and planned blood collection time points from the grouping indicator variables created in step S105;
s302: calculating the average blood concentration of each dose group at all planned blood sampling time points according to the blood concentration data in each dose group in the result data set;
s303: storing the calculated average blood concentration of each dose group at all planned blood sampling points as a first internal data set, wherein the first internal data set comprises a grouping indicator variable and a mean blood concentration of each dose group, and the mean blood concentration of each dose group is the mean blood concentration of each dose group at all planned blood sampling time points;
s304: and merging the first internal data set into a result data set, and deleting the first internal data set after merging.
The first internal data set, which may contain only data sets of mean blood concentration information for each dose group at each blood collection time point for clinical trials, is indexed with an indicator variable that specifies for which dose group and which time point each mean blood concentration data is for (e.g., 1_2 for dose group 1, planned blood collection time point 2 h). The first internal data set and the result data set are also merged with the indicator variable.
As a preferred embodiment, step S500 specifically includes the following steps: the method specifically comprises the following steps:
s501: after the blood concentration of each subject reached Cmax, the blood concentration value of 0 was converted into the missing value first character "-";
s502: based on the mean blood concentration of each subject calculated in step S400, the Cmax, Tmax and Tmax group values are adjusted as follows:
if the average blood concentration is 0, converting the corresponding Cmax, Tmax and Tmax group values into a first character of a missing value "-";
s503: the mean blood concentration of each subject calculated in step S400 and the grouping indicator variables created in step S105 were deleted.
As a preferred embodiment, step S400 specifically includes the following steps:
s401: calculating the average blood concentration of each subject;
s402: storing the calculated average blood concentration of each subject as a second internal data set, wherein the second internal data set comprises the ID of the subject and the average blood concentration;
s403: the second internal data set is merged to the result data set and, after merging, the second internal data set is deleted.
The result data set is all the variables of the original data set plus the newly generated variables, the second internal data set can only comprise two variables of the subject ID and the subject mean blood concentration, the two variables are merged with the result data set by taking the subject ID as an index, after merging, the result data set is equivalent to a variable that the result data set increases the subject mean blood concentration, the subject mean blood concentration is only used for identifying whether the subject blood concentration is always 0, the subsequent operation is convenient, the practical clinical significance is not realized, the blood concentration can be automatically deleted after the use, and the variable is not displayed in the final result data set.
As a preferred embodiment, step S700 specifically includes the following steps:
s701: converting the mean blood concentration of the subjects in the control group at each planned blood sampling time point into a missing value first character "-";
s702: converting data marked as a missing value first character "-" in the Cmax, Tmax and Tmax group values of the subjects in the control group at each planned blood sampling time point into the second character for displaying according to a first conversion logic;
s703: according to a second conversion logic, converting the data marked as a missing value first character 'NA' in the mean blood concentration of the subjects in the control group at each planned blood sampling time point into the third character 'NA' for displaying;
s704: according to a first conversion logic, after the blood concentration of each subject reaches Cmax, converting data of a first character ". multidot.A." of a missing value in the blood concentration value into a second character for displaying;
s705: the resulting data sets are sorted by subject ID and planned blood sampling time point and saved as a new data set or directly overlaid to the original data set.
The missing value first character ". is a uniform expression method for missing data in the SAS, and as long as data is missing, the SAS system displays it as". by default. "NA" stands for "Not Available", i.e. "Not applicable", and similar deletion numbers are generally indicated by "NA". The second character can be customized as "ND", representing "Not specified", corresponding to "unable to quantify". The biggest difference between "NA" and "ND" is that "NA" represents that there is no data or no need to calculate or measure the data, and "ND" represents that the data is measured but no specific value is measured.
For example, if one of the generated variables conc _ mean (mean blood concentration of a subject in a dose group at a certain blood collection time point) is indicated as "NA", this value is the mean blood concentration of a control subject in a dose group at a certain blood collection time point, and is indicated as "NA" because it has no reference meaning and does not need to be calculated. However, "ND" in the blood concentration variable means that the blood concentration was measured, but no specific value was measured.
The fourth character can be customized as "BQL" which represents "Below quantification Limit", that is, "lower than the lower Limit of quantification", that is, for example, the blood concentration is lower than the minimum detectable concentration of the detection instrument (many people will also call it "BLQ"), so the invention can customize the display form for the value lower than the lower Limit of quantification.
For a value labeled as the fourth character (e.g., "BQL"), we do not know whether it represents no drug in the blood (blood level of 0) or whether the blood level is actually too low to quantify (which can be defined as "ND"). The "BQL" value before the blood level reaches the peak Cmax is likely to be converted to 0 because of the absence of drug in the blood (e.g., blood level measured before administration), but the "BQL" value after the blood level reaches the peak Cmax indicates that the blood level is too low (because the blood level gradually decreases after the blood level reaches the peak), and therefore is displayed in an un-quantifiable form (which may be defined as "ND") (a form that allows the user to customize "ND"). All the data of blood concentration were converted to "ND" if they were "BQL" at all time points of the subject.
The present invention provides a simplified pharmacokinetic parameter analysis system using the simplified pharmacokinetic parameter analysis method described above, including:
the system comprises an original data sorting module, a Cmax and Tmax extraction module, a mean value extraction module of blood concentration of each dose group and a mean value extraction module of blood concentration of each subject;
the original data sorting module executes the following operations:
importing an original data set, extracting all information in the original data set, and storing the information as a new result data set;
converting a fourth character in the result dataset into a missing value first character "-";
creating grouping indicator variables of the blood sampling time points and the dosage groups, and combining the grouping indicator variables into a result data set;
the Cmax and Tmax extraction module performs the following operations:
calculating the value of the peak blood concentration Cmax according to the blood concentration of each subject;
according to the calculated Cmax of each subject, obtaining an actual blood sampling time point Tmax when the Cmax is reached and a planned blood sampling time point Tmax when the Cmax is reached;
adding the Cmax, Tmax and Tmax group of each subject to the result data set;
the blood concentration mean value extraction module of each dosage group performs the following operations:
identifying the combination of the dose groups and the planned blood sampling time points according to the grouping indicator variables, and calculating the average blood concentration of each dose group at all the planned blood sampling points according to the blood concentration data;
storing the calculated average blood concentration of each dose group as a first internal data set, wherein the first internal data set comprises a grouping indicator variable and a mean blood concentration of each dose group, and the mean blood concentration of each dose group is the mean blood concentration of each dose group at all planned blood sampling time points; merging the first internal data set into a result data set and deleting the first internal data set;
the blood concentration mean value extraction module of each subject performs the following operations:
calculating the mean blood concentration of each subject;
storing the calculated mean blood concentration for each subject as a second internal data set comprising subject ID and mean blood concentration, and merging the second internal data set into a result data set. As a preferred embodiment, the pharmacokinetic parameter simplification analysis system further comprises:
the device comprises an analysis result preprocessing module, a conversion logic setting module, a conversion logic application and result sorting module;
the analysis result preprocessing module executes the following operations:
converting the blood concentration value of 0 after the blood concentration of each subject reached Cmax into a missing value first character "-";
and adjusting the Cmax, the Tmax and the Tmax group according to the calculated average blood concentration of each subject:
if the average blood concentration is 0, converting the Cmax, the Tmax and the Tmax group into a first character of a missing value ";
a logic-switching setting module for setting the logic,
setting a first conversion logic, and converting a missing value first character into a second character, wherein the second character is used for indicating that a numerical value cannot be measured;
setting a second conversion logic, and converting the missing value first character ' to ' NA ';
the conversion logic applies a module with a result sorting module,
converting the mean plasma concentration values of each dose group of the control group subjects into missing value first characters ";
applying a first conversion logic, converting the data marked as a missing value first character "-" in the Cmax, Tmax and Tmax group values of the subjects at each planned blood sampling time point in the control group into the second character for displaying;
applying a second conversion logic, converting the data marked as the first character ". multidot." of the missing value in the mean blood concentration of the subjects at each planned blood sampling time point in the control group into the third character "NA" for displaying;
applying a first conversion logic, converting the data of the missing value first character ". multidot.in the blood concentration value of each subject after the blood concentration of each subject reaches Cmax into the second character for displaying;
the resulting data sets are sorted by subject ID and planned blood collection time point and saved as a new data set or directly overlaid to the original data set.
As a preferred embodiment, the pharmacokinetic parameter simplification analysis system further comprises:
and directly calling the pharmacokinetic parameter simplified analysis system by the SAS system, and analyzing and displaying the result in the SAS system.
Example 1
Referring to FIGS. 1-5, a simplified method for analyzing pharmacokinetic parameters provided by the present invention is illustrated in detail according to one embodiment of the present invention.
The invention provides a simplified analysis method of pharmacokinetic parameters, which comprises the following steps:
s100: importing an original data set, creating a result data set, and performing first data format conversion on characters in variable values required to be called by a program to convert the characters into a digital format;
step S100 specifically includes the following steps:
s101: importing an original data set, and extracting all information in the original data set to create a result data set;
s102: extracting variables required by the operation in the result data set and renaming the variables;
s103: converting a fourth character in variables extracted from a result data set into a missing value first character "-", wherein the fourth character is a self-defined character in an original data set and is used for representing that the blood concentration data is lower than a quantitative lower limit of a measurement method and cannot be measured;
s104: converting all missing value first characters in the result dataset into '0';
s105: creating a grouping indication variable for indicating the blood sampling time point and the dose group to which each line of data in the result dataset belongs, and adding the renamed variable of step S102 and the created grouping indication variable to the result dataset.
S200: calculating the peak blood concentration Cmax of each subject, the actual blood sampling time point Tmax when the blood concentration reaches the peak Cmax and the planned blood sampling time point Tmax when the blood concentration reaches the peak Cmax;
step S200 specifically includes the following steps:
s201: calculating the peak blood concentration Cmax of each subject;
s202: according to the calculated Cmax of each subject, obtaining an actual blood sampling time point Tmax when the Cmax is reached and a planned blood sampling time point Tmax when the Cmax is reached;
s203: the Cmax, Tmax and Tmax group for each subject were added to the results data set.
S300: calculating the average blood concentration of each dose group at all planned blood sampling time points;
step S300 specifically includes the following steps:
s301: identifying a combination of dose groups and planned blood collection time points from the grouping indicator variables created in step S105;
s302: calculating the average blood concentration of each dose group at all planned blood sampling time points according to the blood concentration data in each dose group in the result data set;
s303: storing the calculated average blood concentration of each dose group at all planned blood sampling points as a first internal data set, wherein the first internal data set comprises a grouping indicator variable and a mean blood concentration of each dose group, and the mean blood concentration of each dose group is the mean blood concentration of each dose group at all planned blood sampling time points;
s304: and merging the first internal data set into a result data set, and deleting the first internal data set after merging.
S400: calculating the average blood concentration of each subject;
step S400 specifically includes the following steps:
s401: calculating the average blood concentration of each subject;
s402: storing the calculated average blood concentration of each subject as a second internal data set, wherein the second internal data set comprises the ID of the subject and the average blood concentration;
s403: the second internal data set is merged to the result data set and, after merging, the second internal data set is deleted.
S500: a second data format conversion step, according to the Cmax and the step calculated in step S200, performing format conversion on the blood concentration value with data of 0 after the blood concentration of each subject reaches Cmax, and performing format conversion on the corresponding Cmax, Tmax and Tmax group values of the subject with average blood concentration of 0;
step S500 specifically includes the following steps: the method specifically comprises the following steps:
s501: after the blood concentration of each subject reached Cmax, the blood concentration value of 0 was converted into the missing value first character "-";
s502: based on the mean blood concentration of each subject calculated in step S400, the Cmax, Tmax and Tmax group values are adjusted as follows:
if the average blood concentration is 0, converting the corresponding Cmax, Tmax and Tmax group values into a first character of a missing value "-";
s503: the mean blood concentration of each subject calculated in step S400 and the grouping indicator variables created in step S105 were deleted.
S600: setting a conversion logic step, which specifically comprises the following steps:
s601: setting a first conversion logic, and converting the missing value first character into a self-defined second character, wherein the second character is used for indicating that a numerical value cannot be measured;
s602: setting a second conversion logic, and converting the missing value first character ' to ' NA ' into a third character;
s700: a third format conversion and sorting step of the format conversion,
automatically converting the missing value first character' in the data of the control group and all the subjects according to the set first conversion logic and second conversion logic;
sorting the result data sets by subject ID and planned blood collection time point;
step S700 specifically includes the following steps:
s701: converting the mean blood concentration of the subjects in the control group at each planned blood sampling time point into a missing value first character "-";
s702: converting data marked as a missing value first character "-" in the Cmax, Tmax and Tmax group values of the subjects in the control group at each planned blood sampling time point into the second character for displaying according to a first conversion logic;
s703: according to a second conversion logic, converting the data marked as a missing value first character 'NA' in the mean blood concentration of the subjects in the control group at each planned blood sampling time point into the third character 'NA' for displaying;
s704: according to a first conversion logic, after the blood concentration of each subject reaches Cmax, converting data of a first character ". multidot.A." of a missing value in the blood concentration value into a second character for displaying;
s705: the resulting data sets are sorted by subject ID and planned blood sampling time point and saved as a new data set or directly overlaid to the original data set.
S800: after the SAS system performs steps S100 to S700 and after step S700, the resulting data set is analyzed and displayed in the SAS system.
The present invention provides a simplified pharmacokinetic parameter analysis system using the simplified pharmacokinetic parameter analysis method described above, including:
the system comprises an original data sorting module, a Cmax and Tmax extraction module, a mean value extraction module of blood concentration of each dose group and a mean value extraction module of blood concentration of each subject;
the original data sorting module executes the following operations:
importing an original data set, extracting all information in the original data set, and storing the information as a new result data set;
converting a fourth character in the result dataset into a missing value first character "-";
creating grouping indicator variables of the blood sampling time points and the dosage groups, and combining the grouping indicator variables into a result data set;
the Cmax and Tmax extraction module performs the following operations:
calculating the value of the peak blood concentration Cmax according to the blood concentration of each subject;
according to the calculated Cmax of each subject, obtaining an actual blood sampling time point Tmax when the Cmax is reached and a planned blood sampling time point Tmax when the Cmax is reached;
adding the Cmax, Tmax and Tmax group of each subject to the result data set;
the blood concentration mean value extraction module of each dosage group performs the following operations:
identifying the combination of the dose groups and the planned blood sampling time points according to the grouping indicator variables, and calculating the average blood concentration of each dose group at all the planned blood sampling points according to the blood concentration data;
storing the calculated average blood concentration of each dose group as a first internal data set, wherein the first internal data set comprises a grouping indicator variable and a mean blood concentration of each dose group, and the mean blood concentration of each dose group is the mean blood concentration of each dose group at all planned blood sampling time points; merging the first internal data set into a result data set and deleting the first internal data set;
the blood concentration mean value extraction module of each subject performs the following operations:
calculating the mean blood concentration of each subject;
storing the calculated mean blood concentration for each subject as a second internal data set comprising subject ID and mean blood concentration, and merging the second internal data set into a result data set.
The pharmacokinetic parameter simplification analysis system further includes:
the device comprises an analysis result preprocessing module, a conversion logic setting module, a conversion logic application and result sorting module;
the analysis result preprocessing module executes the following operations:
converting the blood concentration value of 0 after the blood concentration of each subject reached Cmax into a missing value first character "-";
and adjusting the Cmax, the Tmax and the Tmax group according to the calculated average blood concentration of each subject:
if the average blood concentration is 0, converting the Cmax, the Tmax and the Tmax group into a first character of a missing value ";
a logic-switching setting module for setting the logic,
setting a first conversion logic, and converting a missing value first character into a second character, wherein the second character is used for indicating that a numerical value cannot be measured;
setting a second conversion logic, and converting the missing value first character ' to ' NA ';
the conversion logic applies a module with a result sorting module,
converting the mean plasma concentration values of each dose group of the control group subjects into missing value first characters ";
applying a first conversion logic, converting the data marked as a missing value first character "-" in the Cmax, Tmax and Tmax group values of the subjects at each planned blood sampling time point in the control group into the second character for displaying;
applying a second conversion logic, converting the data marked as the first character ". multidot." of the missing value in the mean blood concentration of the subjects at each planned blood sampling time point in the control group into the third character "NA" for displaying;
applying a first conversion logic, converting the data of the missing value first character ". multidot.in the blood concentration value of each subject after the blood concentration of each subject reaches Cmax into the second character for displaying;
the resulting data sets are sorted by subject ID and planned blood collection time point and saved as a new data set or directly overlaid to the original data set.
Further comprising: and directly calling the pharmacokinetic parameter simplified analysis system by the SAS system, and analyzing and displaying the result in the SAS system.
Example 2
According to a specific embodiment of the present invention, in the process of implementing program operation by the method of the present invention, specific examples of defining variables include:
data: the name of the data set file to be analyzed by the program is used, and after a user fills in the name of the data set to be analyzed by the program, the program automatically calls the blood concentration data set according to the filled-in name.
2, bql: the "BQL" value is labeled in the original data set, the "BQL" value in the blood concentration data set is directly labeled as "BQL", and if the "BQL" in the data set that the user wants to analyze is labeled in other forms, the user can fill in the other forms to identify the program, and it is noted that the labeled form of the "BQL" is enclosed by a single reference number or a double reference number as shown in the above reference code.
And 3, nd: the "BQL" after reaching Cmax needs to be converted into a form, and generally, the "BQL" value after the blood concentration reaches Cmax value needs to be labeled "ND" to represent that the measurement cannot be performed, and the user can also label the "BQL" value as other forms besides "ND" by self-definition, and note that the form of "ND" is enclosed by a single quotation mark or a double quotation mark as is explicitly indicated in the above reference code.
Id: the number of the subject is used and the user then fills in the data set with the name of the variable representing the subject number to identify each subject.
Concentration: the term "blood concentration" refers to the blood concentration of a subject, and the user follows the concentration by filling out the name of a variable representing the blood concentration of the subject in the data set to allow the program to identify the blood concentration data of the subject.
6. dose: the user then fills in the data set with the names of the variables representing the subject dose groups to identify the dose group to which each subject belongs.
Time: the time is the name of the variable representing the blood concentration of the subject in the data set filled later to make the program identify the actual blood sampling time point, which is the difference between the blood concentration analysis time and the administration starting time.
Timegorup: the user fills in the name of the variable representing the planned blood collection time point of the blood concentration of the subject in the data set at the later time to allow the program to identify the planned blood collection time point, which is the planned blood collection time point in the design of the test.
A streammentgroup: is an indicator variable for the treatment group in which the subject is located (0 for the control group and 1 for the test group), and the user then fills in the data set the name of the indicator variable for the treatment group in which the subject is located to allow the program to identify whether each subject belongs to the test group or the control group.
Output putname: the name of the data set generated after operation is the name of the data set generated after the user fills the name of the data set newly generated after program operation after the user fills the outputname, and if the name of the data set is the same as that of the original data set, the user can directly cover the original data set.
New variable meanings in the results dataset:
1, Cmax: is the peak blood concentration of the subject, and if the blood concentration of the subject at each blood sampling time point in the data set is converted into the user-defined "ND" form, the Cmax value of the subject is labeled as the user-defined "ND" form.
Tmax: is the actual blood sampling time point when the blood concentration of the subject reaches Cmax, if the blood concentration of the subject at each blood sampling time point in the data set is converted into the form of 'ND' defined by the user, the Tmax value of the subject is also labeled as the form of 'ND' defined by the user.
Tmax group: is the planned blood sampling time point when the blood concentration of the subject reaches Cmax, if the blood concentration of the subject at each blood sampling time point in the data set is converted into the form of 'ND' defined by the user, the Tmax group value of the subject is marked as the form of 'ND' defined by the user.
Conc _ mean: the average blood concentration of the dose group of the subject at each planned blood sampling time point is calculated after all 'BQL' values of the dose group of the test group at each planned blood sampling time point are converted into 0. If the conc _ mean result of a certain subject at a certain planned blood sampling time point is marked as 'NA', the subject is from a control group, and the mean blood concentration of the subject in the dose group at each planned blood sampling time point has no reference meaning.
Example 3
In accordance with one embodiment of the present invention, the data in Table 1 are used as an example to provide information on blood concentration at each blood sampling time point for a clinical trial subject of a drug administered before treatment by the method of the present invention and the data simulating a single administration. In the data, subj _ id represents a subject number, conc represents a blood concentration, real _ time represents an actual blood sampling time point, time represents a planned blood sampling time point, dose _ group represents a subject dose group, and treatment _ group represents a treatment group in which the subject is positioned, wherein 1 represents a test group, and 0 represents a control group.
TABLE 1 original data set
The following is a code example of the method called in the SAS system, and the implementation program of the method is named as a 'PKBQL Macro program':
the result of the analysis of the PKBQL Macro program by the method of the present invention, see fig. 3, shows that the parameters analyzed by the PKBQL Macro program are well merged with the original data set, and do not affect or change the data storage format or other variables of the original data set. The "BQL" values before the peak blood concentrations of the test subjects 101 and 102 were converted to 0, and the blood concentration of the control subject 103 was converted to "ND" because the blood concentration of each blood collection time point was "BQL", and the mean blood concentration of the dose group of the subject at each planned blood collection time point was not referred to and was labeled as "NA" because the subject was in the control group.
Refer to mean analysis results of blood concentration at each blood sampling time point in each dose group in Phoenix WinNonlin test group, see figure 4. From the results, it can be seen that the analysis result of Phoenix WinNonlin on the mean blood concentration of each dose group at each blood sampling time point in the test group is consistent with that of PKBQL Macro, for example, the mean blood concentration of the dose group 1 at the planned time point 1 in the test group is 49.668, and the mean blood concentration of the dose group 1 at the planned time point 2 in the test group is 40.668. However, other variables in the original data set cannot be simply retained in the operation result of WinNonlin, and the NA value of the average blood concentration value of each dose group of the control group at each blood sampling time point cannot be reflected, so that additional operation needs to be performed on the result data set.
Refer to results of analysis of Cmax and Tmax of Phoenix WinNonlin subjects, see FIG. 5. From the results, the results of analysis of Phoenix WinNonlin on Cmax and Tmax of the subjects are consistent with PKBQL Macro, for example, Cmax and Tmax of 50.123 and 0.9998 of the subject 101 and 49.213 and Tmax of the subject 102 are both 1, but "ND" values of Cmax and Tmax cannot be directly reflected in the results of WinNonlin, and additional operation is required for the result data set.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.
Claims (10)
1. A method for simplified analysis of pharmacokinetic parameters, comprising the steps of:
s100: importing an original data set, creating a result data set, and performing first data format conversion on characters in variable values required to be called by a program to convert the characters into a digital format;
s200: calculating the peak blood concentration Cmax of each subject, the actual blood sampling time point Tmax when the blood concentration reaches the peak Cmax and the planned blood sampling time point Tmax when the blood concentration reaches the peak Cmax;
s300: calculating the average blood concentration of each dose group at all planned blood sampling time points;
s400: calculating the average blood concentration of each subject;
s500: a second data format conversion step, according to the Cmax calculated in step S200 and the average blood concentration calculated in step S400, performing format conversion on the blood concentration value of which the data is 0 after the blood concentration of each subject reaches the Cmax, and performing format conversion on the corresponding Cmax, Tmax and Tmax group values of the subject with the average blood concentration of 0;
s600: setting a conversion logic step, which specifically comprises the following steps:
s601: setting a first conversion logic, and converting a missing value first character into a self-defined second character, wherein the second character is used for indicating that a numerical value cannot be measured;
s602: setting a second conversion logic, and converting the missing value first character ' to ' NA ' into a third character;
s700: a third format conversion and sorting step of the format conversion,
automatically converting the missing value first character' in the data of the control group and all the subjects according to the set first conversion logic and second conversion logic;
sorting the result data sets by subject ID and planned blood collection time point;
s800: after the SAS system performs steps S100 to S700 and after step S700, the resulting data set is analyzed and displayed in the SAS system.
2. The simplified analysis method of pharmacokinetic parameters according to claim 1, wherein the step S100 includes the following steps:
s101: importing an original data set, and extracting all information in the original data set to create a result data set;
s102: extracting variables required by the operation in the result data set and renaming the variables;
s103: converting a fourth character in variables extracted from a result data set into a missing value first character "-", wherein the fourth character is a self-defined character in an original data set and is used for representing that blood concentration data are lower than a quantitative lower limit of a measurement method and cannot be measured;
s104: converting all missing value first characters in the result dataset into '0';
s105: creating a grouping indication variable for indicating the blood sampling time point and the dose group to which each line of data in the result dataset belongs, and adding the renamed variable of step S102 and the created grouping indication variable to the result dataset.
3. The simplified analysis method of pharmacokinetic parameters according to claim 2, wherein the step S200 includes the following steps:
s201: calculating the peak blood concentration Cmax of each subject;
s202: according to the calculated Cmax of each subject, obtaining an actual blood sampling time point Tmax when the Cmax is reached and a planned blood sampling time point Tmax when the Cmax is reached;
s203: the Cmax, Tmax and Tmax group for each subject were added to the results data set.
4. The simplified analysis method of pharmacokinetic parameters according to claim 2, wherein the step S300 specifically includes the steps of:
s301: identifying a combination of dose groups and planned blood collection time points from the grouping indicator variables created in step S105;
s302: calculating the average blood concentration of each dose group at all planned blood sampling time points according to the blood concentration data in each dose group in the result data set;
s303: storing the calculated average blood concentration of each dose group at all planned blood sampling points as a first internal data set, wherein the first internal data set comprises a grouping indicator variable and a mean blood concentration of each dose group, and the mean blood concentration of each dose group is the mean blood concentration of each dose group at all planned blood sampling time points;
s304: and merging the first internal data set into a result data set, and deleting the first internal data set after merging.
5. The simplified analysis method of pharmacokinetic parameters according to claim 2, wherein the step S500 includes the following steps: the method specifically comprises the following steps:
s501: after the blood concentration of each subject reached Cmax, the blood concentration value of 0 was converted into the missing value first character "-";
s502: based on the mean blood concentration of each subject calculated in step S400, the Cmax, Tmax and Tmax group values are adjusted as follows:
if the average blood concentration is 0, converting the corresponding Cmax, Tmax and Tmax group values into a first character of a missing value "-";
s503: the mean blood concentration of each subject calculated in step S400 and the grouping indicator variables created in step S105 were deleted.
6. The simplified analysis method of pharmacokinetic parameters according to claim 1, wherein the step S400 includes the following steps:
s401: calculating the average blood concentration of each subject;
s402: storing the calculated average blood concentration of each subject as a second internal data set, wherein the second internal data set comprises the ID of the subject and the average blood concentration;
s403: the second internal data set is merged to the result data set and, after merging, the second internal data set is deleted.
7. The simplified analysis method of pharmacokinetic parameters according to claim 1, wherein the step S700 includes the following steps:
s701: converting the mean blood concentration of the subjects in the control group at each planned blood sampling time point into a missing value first character "-";
s702: converting data marked as a missing value first character "-" in the Cmax, Tmax and Tmax group values of the subjects in the control group at each planned blood sampling time point into the second character for displaying according to a first conversion logic;
s703: according to a second conversion logic, converting the data marked as a missing value first character 'NA' in the mean blood concentration of the subjects in the control group at each planned blood sampling time point into the third character 'NA' for displaying;
s704: according to a first conversion logic, after the blood concentration of each subject reaches Cmax, converting data of a first character ". multidot.A." of a missing value in the blood concentration value into a second character for displaying;
s705: the resulting data sets are sorted by subject ID and planned blood sampling time point and saved as a new data set or directly overlaid to the original data set.
8. A pharmacokinetic parameter simplification analysis system using the pharmacokinetic parameter simplification analysis method according to claim 1, comprising:
the system comprises an original data sorting module, a Cmax and Tmax extraction module, a mean value extraction module of blood concentration of each dose group and a mean value extraction module of blood concentration of each subject;
the original data sorting module executes the following operations:
importing an original data set, extracting all information in the original data set, and storing the information as a new result data set;
converting a fourth character in the result dataset into a missing value first character "-";
creating grouping indicator variables of the blood sampling time points and the dosage groups, and combining the grouping indicator variables into a result data set;
the Cmax and Tmax extraction module performs the following operations:
calculating the value of the peak blood concentration Cmax according to the blood concentration of each subject;
according to the calculated Cmax of each subject, obtaining an actual blood sampling time point Tmax when the Cmax is reached and a planned blood sampling time point Tmax when the Cmax is reached;
adding the Cmax, Tmax and Tmax group of each subject to the result data set;
the blood concentration mean value extraction module of each dosage group performs the following operations:
identifying the combination of the dose groups and the planned blood sampling time points according to the grouping indicator variables, and calculating the average blood concentration of each dose group at all the planned blood sampling points according to the blood concentration data;
storing the calculated average blood concentration of each dose group as a first internal data set, wherein the first internal data set comprises a grouping indicator variable and a mean blood concentration of each dose group, and the mean blood concentration of each dose group is the mean blood concentration of each dose group at all planned blood sampling time points; merging the first internal data set into a result data set and deleting the first internal data set;
the blood concentration mean value extraction module of each subject performs the following operations:
calculating the mean blood concentration of each subject;
storing the calculated mean blood concentration for each subject as a second internal data set comprising subject ID and mean blood concentration, and merging the second internal data set into a result data set.
9. The system for simplified analysis of pharmacokinetic parameters of claim 8, further comprising:
the device comprises an analysis result preprocessing module, a conversion logic setting module, a conversion logic application and result sorting module;
the analysis result preprocessing module executes the following operations:
converting the blood concentration value of 0 after the blood concentration of each subject reached Cmax into a missing value first character "-"; and adjusting the Cmax, the Tmax and the Tmax group according to the calculated average blood concentration of each subject:
if the average blood concentration is 0, converting the Cmax, the Tmax and the Tmax group into a first character of a missing value ";
a logic-switching setting module for setting the logic,
setting a first conversion logic, and converting a missing value first character into a second character, wherein the second character is used for indicating that a numerical value cannot be measured;
setting a second conversion logic, and converting the missing value first character ' to ' NA ';
the conversion logic applies a module with a result sorting module,
converting the mean plasma concentration values of each dose group of the control group subjects into missing value first characters ";
applying a first conversion logic, converting the data marked as a missing value first character "-" in the Cmax, Tmax and Tmax group values of the subjects at each planned blood sampling time point in the control group into the second character for displaying;
applying a second conversion logic, converting the data marked as the first character ". multidot." of the missing value in the mean blood concentration of the subjects at each planned blood sampling time point in the control group into the third character "NA" for displaying;
applying a first conversion logic, converting the data of the missing value first character ". multidot.in the blood concentration value of each subject after the blood concentration of each subject reaches Cmax into the second character for displaying;
the resulting data sets are sorted by subject ID and planned blood collection time point and saved as a new data set or directly overlaid to the original data set.
10. The system for simplified analysis of pharmacokinetic parameters of claim 8, further comprising:
and directly calling the pharmacokinetic parameter simplified analysis system by the SAS system, and analyzing and displaying the result in the SAS system.
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