1#fengye2001219
Stata 11 help for pkhelp pk------------------------------------------------------------------------------- Title [R] pk -- Pharmacokinetic (biopharmaceutical) data
Description
The term pk refers to pharmacokinetic data and the Stata commands, all of which begin with the letters pk, designed to do some of the analyses commonly performed in the pharmaceutical industry. The system is intended for the analysis of pharmacokinetic data, although some of the commands are for general use.
The pk commands are pkexamine Calculate pharmacokinetic measures pksumm Summarize pharmacokinetic data pkshape Reshape (pharmacokinetic) Latin-square data pkcross Analyze crossover experiments pkequiv Perform bioequivalence tests pkcollapse Generate pharmacokinetic measurement dataset
Remarks
Several types of clinical trials are commonly performed in the pharmaceutical industry. Examples include combination trials, multicenter trials, equivalence trials, and active control trials. For each type of trial, there is an optimal study design for estimating the effects of interest. Currently, the pk system can be used to analyze equivalence trials, which are usually conducted using a crossover design; however, it is possible to use a parallel design and still draw conclusions about equivalence.
Equivalence trials assess bioequivalence between two drugs. Although proving that two drugs behave the same is impossible, the United States Food and Drug Administration believes that if the absorption properties of two drugs are similar, the two drugs will produce similar effects and have similar safety profiles. Generally, the goal of an equivalence trial is to assess the equivalence of a generic drug to an existing drug. This goal is commonly accomplished by comparing a confidence interval about the difference between a pharmacokinetic measurement of two drugs with a confidence limit constructed from U.S. federal regulations. If the confidence interval is entirely within the confidence limit, the drugs are declared bioequivalent. Another approach to accessing bioequivalence is to use the method of interval hypotheses testing. pkequiv is used to conduct these tests of bioequivalence.
Several pharmacokinetic measures that can be used to ascertain how available a drug is for cellular absorption. The most common measure is the area under the time-versus-concentration curve (AUC). Another common measure of drug availability is the maximum concentration (Cmax) achieved by the drug during the follow-up period. Stata reports these and other less common measures of drug availability, including the time at which the maximum drug concentration was observed and the duration of the period during which the subject was being measured. Stata also reports the elimination rate, that is, the rate at which the drug is metabolized, and the drug's half-life, that is, the time it takes for the drug concentration to fall to one-half of its maximum concentration. pkexamine computes and reports all the pharmacokinetic measures that Stata produces, including four calculations of the area under the time-versus-concentration curve. The standard area under the curve from 0 to the maximum observed time (AUC) is computed using cubic splines or the trapezoidal rule. pkexamine also computes the area under the curve from 0 to infinity by extending the standard time-versus-concentration curve from the maximum observed time by using three different methods. The first method simply extends the standard curve by using a least-squares linear fit through the last few data points. The second method extends the standard curve by fitting a decreasing exponential curve through the last few data points. Finally, the third method extends the curve by fitting a least-squares linear regression line on the log concentration. The mathematical details of these extensions are described in [R] pkexamine.
Data from an equivalence trial may also be analyzed using methods appropriate to the particular study design. When you have a crossover design, pkcross can be used to fit an appropriate ANOVA model. As an aside, a crossover design is simply a restricted Latin square; therefore, pkcross can also be used to analyze any Latin-square design.
There are some practical concerns when dealing with data from equivalence trials. Primarily, the data need to be organized in a manner that Stata can use. The pk commands include pkcollapse and pkshape, which are designed to help transform data from a common format to one that is suitable for analysis with Stata.
Examples
--------------------------------------------------------------------------- Setup . webuse auc
List the data . list, abbrev(14)
Calculate pharmacokinetic measures . pkexamine time conc
--------------------------------------------------------------------------- Setup . webuse pkdata
List the variables id, concA, and time, and separate by id. list id concA time, sepby(id)
Produce summary statistics for pharmacokinetic measures . pksumm id time concA
Generate pharmacokinetic dataset . pkcollapse time concA concB, id(id) keep(seq) stat(auc)
Reshape data for use with pkcross and pkequiv. pkshape id seq auc*, order(ab ba)
Perform bioequivalence tests . pkequiv outcome treat period seq id
Analyze crossover experiments . pkcross outcome ---------------------------------------------------------------------------
扫码加好友，拉您进群
全部版块
我的主页
收藏
