Analyzing longitudinal count data from adaptive clinical trials: a weighted generalized quasi-likelihood approach
In an adaptive clinical trial set up, there exist some adaptive designs to assign an incoming individual to a treatment so that more study subjects are assigned to the better treatment. These designs are however developed under the assumption that an individual patient provides a single response. In practice, there are situations where an individual assigned to a treatment may be required to provide a repeated number of responses over a period of time. Recently, Sutradhar et al. [ Sutradhar, B. C., Biswas, A., and Bari, W., 2005, Marginal regression for binary longitudinal data in adaptive clinical trials. Scandinavian Journal of Statistics, 32, 93 - 113.] have proposed a simple longitudinal play-the-winner (SLPW) design as a generalization of the existing simple play-the-winner (SPW) design, in order to assign an incoming individual to a better treatment, under the binary longitudinal set up. In this paper, we deal with the longitudinal count responses and examine, through a simulation study, the performances of the SLPW design and a new bivariate random walk type design in allocating an individual patient to the better treatment group. As far as the estimation of the parameters is concerned, we examine the performance of a weighted generalized quasi-likelihood approach in estimating the parameters of the longitudinal model including the treatment effects.
clinical trials longitudinal count responses adaptive randomization consistency design weights efficiency generalized linear model