Advancing the analysis of treatment process

To review the role of process research in clinical research, to summarize progress in statistical methods for process analyses, and to describe a dynamic analytical approach that can provide new insights into the processes responsible for the effects of treatments and other variables.

Process research helps us to understand what happens during our interventions, and can yield valuable knowledge regardless of whether an intervention is found to have significant effects. This is a review of recent statistical advances for dealing with missing data, tests for mediation and hierarchical modeling and demonstrate how these advances can help process researchers overcome obstacles that had limited past studies. However, the standard paradigm for process analysis, although conceptually sound, is based upon a static model that does little justice to the dynamics of treatment. Therefore, it is proposed that the paradigm is extended to study the time-course of dynamic processes, using existing statistical methods. Hierarchical linear modeling, structural regression modeling and event history methods are among the most promising tools for more advanced process analyses because of their ability to incorporate time-varying predictors.

The function of process analysis is to probe into the mechanisms of action of treatment to locate both weaknesses and strengths, but methods for process research are still rudimentary. By conceptualizing process analysis as a problem of relating multiple time-series, many new analytical opportunities, and challenges, present themselves. Modern statistical methods can help to lead to broad advances in our understanding of the processes that affect treatment success.