Clinical trials statistical analysis

Analysis of most (perhaps 65%) pharmacokinetic data from clinical trials starts and stops with noncompartmental analysis (NCA). NCA usually includes calculating the area under the curve (AUC) of concentration versus time, or under the first-moment curve (AUMC, from a graph of concentration multiplied by time versus time). Calculation of AUC and AUMC facilitates simple calculations for some standard pharmacokinetic parameters and collapses measurements made at several sampling times into a single number representing exposure. The approach makes few assumptions, has few parameters, and allows fairly rigorous statistical description of exposure and how it is affected by dose. An exposure response model may be created. With respect to descriptive dimensions these dose-exposure and exposure-response models... 

BATAVIA M (2001) Clinical research for health professionals, Boston, Buherworth Heinemarm. CAMPBELL M j, MACHiN D (1999) Medical statistics, New York, John Wiley Sons, Inc. CHOW s c, LIU j p (1998) Design and analysis of clinical trials. New York, John Wiley Sons, Inc. 

The E9 discusses the statistical issues in the design and conduct of a clinical trial. It details trial design, trial conduct, and data analysis and reporting. Although most useful... 

In summary, for data to be useful in clinical trial analyses they need to be quantifiable. The data must be either a continuous measure or a categorical value. Free text poses a problem for analysis, and if it is a valuable variable for the statistical analyses it really must be coded. Finally, hardcoding should be used only when absolutely necessary, because it is inherently problematic. Organizations that do allow hardcoding should document in their standard operating procedures (SOPs) that it is an approved business practice and how it is to be used. 

A very common analysis in clinical trials involves the analysis of two binomial variables to see if there is a statistically significant association between them. A binomial variable is one that can have only one of two values. For example, let s assume that we have a variable called treatment whose value is either a 1 to indicate active drug therapy or a 0 to indicate placebo. We also have a variable called headache whose value is a 1 if the patient experiences headache after therapy and a 0 if not. What we want to know is whether a change in the level of therapy is significantly associated with a change in the level of headache. The 2x2 table looks like this ... 

SAS has always had and will maintain a central role in the data management, analysis, and reporting of clinical trial data. Because of the strong suite of SAS statistical procedures and the power of Base SAS programming, SAS remains a favorite of statisticians for the analysis of clinical trial data. Several companies have built their clinical trial data management and statistical analysis systems entirely with SAS software. More recently, SAS has offered SAS Drug Development as an industry solution that provides a comprehensive clinical trial analysis and reporting environment compliant with 21 CRF-Part 11. 

Statistics plays a major role in the design of the clinical trial. The groups or subgroups to be studied, the frequencies, dosages, and the markers used to monitor drug efficacy are all important factors to consider. Statistical analysis provides the means to demonstrate, at a certain confidence level, whether the... 

Statistics analysis is an integral part of a clinical trial. A clinical trial protocol includes information on statistical parameters that the trial is expected to be based on and methods for the analysis of data. 

Cost-minimisation analysis are performed when the clinical outcomes (e.g. efficacy and safety) of the comparator groups are virtually identical and for all practical purposes can be considered to be equal. Because no decision can be made based on differences in the clinical endpoints, decisions are based on the incremental costs of the treatment pathways. Such was the case in a study that assessed the cost-effectiveness of treating proximal deep vein thromboses (DVT) at home with low molecular weight heparin versus standard heparin in hospital therapy. A cost-minimisation approach was chosen for this analysis because the results from a comparative clinical trial confirmed that there were no statistically significant differences in safety or efficacy between the two treatment groups. The study authors concluded that for patients with acute proximal DVTs, treatment at home with low molecular weight heparin was less costly than hospital treatment with standard heparin. ... 

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