  The amount of data collected from patients involved in clinical trials is
continuously growing. All patient characteristics are potential covariates that
could be used to improve clinical trial analysis and power. However, the
restricted number of patients in phases I and II studies limits the possible
number of covariates included in the analyses. In this paper, we investigate
the cost/benefit ratio of including covariates in the analysis of clinical
trials. Within this context, we address the long-running question "What is the
optimum number of covariates to include in a clinical trial?" To further
improve the cost/benefit ratio of covariates, historical data can be leveraged
to pre-specify the covariate weights, which can be viewed as the definition of
a new composite covariate. We analyze the use of a composite covariate while
estimating the treatment effect in small clinical trials. A composite covariate
limits the loss of degrees of freedom and the risk of overfitting.
