Modeling auxiliary information in clinical trials
Berry, Donald A.
Doctor of Philosophy
During a clinical trial, early endpoints may be available on some patients for whom the primary endpoint has not been observed. To model this situation, we develop a parametric model and a nonparametric model that utilize auxiliary endpoint data to predict the missing primary endpoint data. These predicted primary endpoint data assist researchers in determining whether the conclusions of a clinical trial can be obtained and announced earlier than otherwise. And such modeling may be able to enhance the precision of comparisons of the primary endpoint across treatment arms. The parametric model is developed using a Bayesian paradigm assuming that the data are normally distributed. The nonparametric model is developed using kernel density estimation. In both cases we base the conditional predictive distribution of the missing primary endpoint data on the auxiliary endpoint data for patients with missing data and on the pairs of observations for patients who have achieved both endpoints. The effects of bandwidth on the performance of the nonparametric model are evaluated. We consider a two-treatment clinical trial in which the primary objective is to compare the two treatments on the basis of the primary endpoint. We compare the performances of our two proposed models with those of two conventional methods, Last Observation Carried Forward (LOCF) and Ignoring Missing Values (IMV). Our simulation results demonstrate that both the parametric and the nonparametric model have advantages over conventional methods. The parametric model performs slightly better than the nonparametric model when the distributions of the auxiliary endpoint data and primary endpoint data are jointly normal. The nonparametric model is better than the parametric model when these distributions deviate sufficiently from normality. So the nonparametric model is robust in this sense.