Efficiency of Enrichment Design for Pre–Post Trials with Binary Endpoint
- 36 Downloads
Enrichment design is a common strategy in personalized medicine in which treatments are given only to patients who are tested positive on a genomic biomarker. Such a targeted trial, as compared to the untargeted trials in which all patients receive the treatments, can substantially reduce the sample size needed for a study as demonstrated in the literature for two-arm trials. To fill in the gaps in the existing literature, we consider trials to evaluate a targeted treatment by comparing pre–post trial outcomes to investigate the intervention effect after the treatment. We investigate the relative efficiency in terms of sample size reduction of an enrichment design against the conventional design, focusing on binary endpoints. The effects of misclassification of the genomic classifier on the relative efficiency are also investigated.
KeywordsEnrichment designs Genomic biomarkers Misclassification Personalized medicine Sample sizes and power Targeted treatment
The research was supported by the Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), the National Institutes of Health (NIH). The authors thank the Guest Editor-in-Chief and two referees for their helpful comments that improved the paper.
- 1.Gordon AN, Tonda M, Sun S, Rackoff W, Doxil study 30–49 investigators (2004) Long-term survival advantage for women treated with pegylated liposomal doxorubicin compared with topotecan in a phase 3 randomized study of recurrent and refractory epithelial ovarian cancer. Gynecol Oncol 95:1–8CrossRefGoogle Scholar
- 2.Nebert DW (1997) Polymorphisms in drug-metabolizing enzymes: what is their clinical relevance and why do they exist? Am J Hum Genet 60(2):265–271Google Scholar
- 3.Rosenwald A, Wright G, Wiestner A, Chan WC, Connors JM, Campo E, Gascoyne RD, Grogan TM, Muller- Hermelink HK, Smeland EB, Chiorazzi M et al (2003) The proliferation gene expression signature is a quantitative integrator of oncogenic events that predicts survival in mantle cell lymphoma. Cancer Cell 3(2):185–197CrossRefGoogle Scholar
- 10.Wang SJ (2007) Biomarker as a classier in pharmacogenomics clinical trials: a tribute to 30th anniversary of PSI. Pharm Stat 6:283296Google Scholar
- 13.Simon R, Maitournam A (2004) Evaluating the efficiency of targeted designs for randomized clinical trials. Clin Cancer Res 10:67596763Google Scholar
- 15.Bellomo R, Goldsmith D, Uchino S, Buckmaster J, Hart G, Opdam H, Silvester W, Doolan L, Gutteridge G (2005) A before and after trial of the effect of a high-dependency unit on post-operative morbidity and mortality. Crit Care Resusc 7(1):16–21Google Scholar
- 17.Dimitrov DM, Rumrill PD Jr (2003) Pretest-posttest designs and measurement of change. Work 20(2):159–165Google Scholar