Effectively Selecting a Target Population for a Future Comparative Study

• Published in: Journal of the American Statistical Association Vol. 108; no. 502; pp. 527 - 539
• Main Authors: Zhao, Lihui, Tian, Lu, Cai, Tianxi, Claggett, Brian, Wei, L. J
• Format: Journal Article
• Language: English
• Published: United States Taylor & Francis Group 01.06.2013; Taylor & Francis Group, LLC; Taylor & Francis Ltd
• Subjects: Bias; cardiovascular diseases; Clinical trials; Comparative analysis; Cross-training-evaluation; data collection; Effectiveness; Human immunodeficiency virus; Lasso procedure; patients; Personalized medicine; Prediction; randomized clinical trials; Ridge regression; risk; Statistics; Stratified medicine; Subgroup analysis; Theory and Methods; Training; Variable selection; Ridge regression; Subgroup analysis; Stratified medicine; Cross-training-evaluation; Prediction; Lasso procedure; Personalized medicine; Variable selection
• ISSN: 1537-274X; 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2013.770705

When comparing a new treatment with a control in a randomized clinical study, the treatment effect is generally assessed by evaluating a summary measure over a specific study population. The success of the trial heavily depends on the choice of such a population. In this article, we show a systematic, effective way to identify a promising population, for which the new treatment is expected to have a desired benefit, using the data from a current study involving similar comparator treatments. Specifically, using the existing data, we first create a parametric scoring system as a function of multiple baseline covariates to estimate subject-specific treatment differences. Based on this scoring system, we specify a desired level of treatment difference and obtain a subgroup of patients, defined as those whose estimated scores exceed this threshold. An empirically calibrated threshold-specific treatment difference curve across a range of score values is constructed. The subpopulation of patients satisfying any given level of treatment benefit can then be identified accordingly. To avoid bias due to overoptimism, we use a cross-training-evaluation method for implementing the above two-step procedure. We then show how to select the best scoring system among all competing models. Furthermore, for cases in which only a single prespecified working model is involved, inference procedures are proposed for the average treatment difference over a range of score values using the entire dataset and are justified theoretically and numerically. Finally, the proposals are illustrated with the data from two clinical trials in treating HIV and cardiovascular diseases. Note that if we are not interested in designing a new study for comparing similar treatments, the new procedure can also be quite useful for the management of future patients, so that treatment may be targeted toward those who would receive nontrivial benefits to compensate for the risk or cost of the new treatment. Supplementary materials for this article are available online.