TIGHTENING THE CLINICAL-TRIAL

Randomized clinical trials adhere more closely to pre-agreed-on protoc ols than almost any other type of experiment; yet we can tighten up th eir analysis if we desire. If we convert the analysis into a randomiza tion analysis-where the one set of data is analyzed many times-once as though each acceptable assignment has been employed, we can eliminate any dependence of the analysis on statistical or probabilistic assump tions. To do this effectively when many assignments could be acceptabl e, we can go to double randomization, in which a subset, usefully kept balanced, of acceptable assignments is selected (perhaps randomly) be fore data acquisition. If we have one covariate, adjustment for which answers a question that is at least as appropriate, we can easily buil d on this. Imperfect covariance adjustments can help almost as much as perfect ones. If it is appropriate to work with many covariate(s), it is often desirable to first construct a (few) compound covariate(s) a nd then work with it (them). Often we can base the coefficients in our compound covariate on the univariate regressions of response on singl e covariates. Doing this within each arm of the trial and pooling keep s the fitting of the final adjustment unbiased. Since we can prespecif y how the compounds are to be calculated and fitted, we can do all thi s while retaining rigid prespecification. Prespecification, randomizat ion, and intelligent use of covariates combined to make the resulting significance analysis of platinum standard quality. (If we want confid ence statements, as we ordinarily should, it may make sense, for techn ical reasons, to plan for somewhat less than platinum standard quality .)