Objective: In addition to comparing drug treatment groups, the wealth of genetic and clinical data collected in the Clinical Antipsychotic Trials of Intervention Effectiveness study offers tremendous opportunities to study individual differences in response to treatment with antipsychotics. A major challenge, however, is how to estimate the individual responses to treatments. For this purpose, we propose a systematic method that condenses all information collected during the trials in an optimal, empirical fashion.
Methods: Our method comprises three steps. First, we test how to best model treatment effects over time. Next, we screen many covariates to select those that will further improve the precision of the individual treatment effect estimates which, for example, improves power to detect predictors of individual treatment response. Third, Best Linear Unbiased Predictors (BLUPs) of the random effects are used to estimate for each individual a treatment effect based on the model empirically indicated to best fit the data. We illustrate our method for the Positive and Negative Syndrome Scale (PANSS).
Results: A model assuming it takes on average about 30 days for a treatment to exert an effect that will then remain about the same for the rest of the trial showed the best fit to the data. Of all screened covariates, only two improved the precision of the individual treatment effect estimates. Finally, correlations between drug effects and PANSS scales suggested that in CATIE it cannot be recommended to simply combine treatment effects across drugs (e.g. to study common drug mechanisms), but it is sensible to study how a given drug affects multiple symptom dimensions.
Conclusions: We demonstrate that treatment effects can be estimated in a way that condenses all information collected in an optimal, empirical fashion. We expect the proposed method to be valuable for other clinical outcomes in CATIE and potentially other clinical trials.