Application of Meta-analysis in Clinical Trials

Drug development is a long and expensive process where each step should be carefully planned. During design stage of a clinical trial, the sample size calculation has to be performed based on a primary objective of the trial and target to achieve the desired power for detecting a clinically meaningful difference between test drug and standard/control at a fixed Type I Error rate. However, information about test drug in pilot studies is limited and most of the times statisticians do “the best guess” about effect size of a new drug that leads to wrong sample size calculation and then to failure of the trail. Meta-analysis can help. Combining all existing information about the test drug, meta-analyses intent to give better estimation of effect size for a new drug that determines required sample. In current years, one of the obstacles was that meta-analysis is not an easy task and special software is usually required to perform it. The goal of this paper is to demonstrate that concept of meta-analyses is apprehensible, and SAS® software can be used to perform meta-analysis on regular basis. User-friendly SAS macro calculates the effect size and determines what sample size is needed to reach the goal of clinical trial. For visual presentation of the result, macro generates accompanying forest plots of effect sizes and plot of anticipated sample size. For the sake of validation, the results from meta-analysis were compared with analysis of subject-level (pooled) data. The conclusions from two approaches came out the same. It demonstrates the validity and strength of meta-analysis. This paper suggests that when data sets have been accumulated with ongoing research, the effect size calculated in meta-analysis can be treated as “best evidence” and should be taking in consideration while designing the next clinical trial that will lead to successful NDA submission. INTRODUCTION “Five clinical trials were conducted in my company for a new drug with one success and four failures...” sounds familiar for everyone who worked long enough in Pharmaceutical industry. “Hmm...” is your answer: “There is something wrong with expectations from a new drug, and sample size was probably too small. Why didn’t you check the effect size of your company’s new drug while accumulating data from study to study?” Let’s do it. It is never too late. Effect size is measured as standardized difference between two groups. Along with Confidence Limit Intervals (CLI), it gives information how different two samples are. In other words, effect size estimates the magnitude of treatment effect for a new drug. Effect size should be used to calculate sample size required to meet a p-value with level of significance α. For this paper, data for ten studies were simulated using RAND function and normal distribution. Assumption was made that there are equal variances in the groups. Analysis for each study was done by PROC MIXED. Metaanalysis, and individual data analysis was performed for overall effect size calculations. The results from two approaches were compared. Sample size was proposed for a new clinical trial using estimated effect size of a new treatment. SAS® 9.2 was used for analyses.