Self-designing clinical trials.
I present a method of sequential analysis for randomized clinical trials that allows use of all prior data in a trial to determine the use and weighting of subsequent observations. One continues to assign subjects until one has 'used up' all the variance of the test statistic. There are many strategies to determine the weights including Bayesian methods (though the proposal is a frequentist design). I explore further the self-designing aspect of the randomized trial to note that in some cases it makes good sense (i) to change the weighting on components of a multivariate endpoint, (ii) to add or drop treatment arms (especially in a parallel group dose ranging/efficacy/safety trial), (iii) to select sites to use as the trial goes on, (iv) to change the test statistic and (v) even to rethink the whole drug development paradigm to shorten drug development time while keeping current standards for the level of evidence necessary for approval.
Fisher
L D
LD
Department of Biostatistics, University of Washington, Seattle 98195-7232, USA. lfisher@biostat.washington.edu
D001499:Bayes Theorem; D003625:Data Collection / Q000706:statistics & numerical data; D003627:Data Interpretation, Statistical; D006801:Humans; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D012107:Research Design; D016896:Treatment Outcome