Strategies for power calculations in predictive biomarker studies in survival data
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Dung-Tsa Chen1, Po-Yu Huang2, Hui-Yi Lin3, Eric B. Haura4 , Scott J. Antonia4, W. Douglas Cress5 and Jhanelle E. Gray4
1 Department of Biostatistics and Bioinformatics, Moffitt Cancer Center & Research Institute, Tampa, FL, USA
2 Computational Intelligence Technology Center, Industrial Technology Research Institute, Taichung City, Taiwan
3 Biostatistics program, School of Public Health, Louisiana State University Health Sciences Center , New Orleans, LA, USA
4 Department of Thoracic Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
5 Department of Molecular Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
Dung-Tsa Chen, email:
Keywords: predictive biomarker, precision medicine, prospective study, retrospective study, survival data
Received: August 29, 2016 Accepted: September 02, 2016 Published: September 19, 2016
Purpose: Biomarkers and genomic signatures represent potentially predictive tools for precision medicine. Validation of predictive biomarkers in prospective or retrospective studies requires statistical justification of power and sample size. However, the design of these studies is complex and the statistical methods and associated software are limited, especially in survival data. Herein, we address common statistical design issues relevant to these two types of studies and provide guidance and a general template for analysis.
Methods: A statistical interaction effect in the Cox proportional hazards model is used to describe predictive biomarkers. The analytic form by Peterson et al. and Lachin is utilized to calculate the statistical power for both prospective and retrospective studies.
Results: We demonstrate that the common mistake of using only Hazard Ratio’s Ratio (HRR) or two hazard ratios (HRs) can mislead power calculations. We establish that the appropriate parameter settings for prospective studies require median survival time (MST) in 4 subgroups (treatment and control in positive biomarker, treatment and control in negative biomarker). For the retrospective study which has fixed survival time and censored status, we develop a strategy to harmonize the hypothesized parameters and the study cohort. Moreover, we provide an easily-adapted R software application to generate a template of statistical plan for predictive biomarker validation so investigators can easily incorporate into their study proposals.
Conclusion: Our study provides guidance and software to help biostatisticians and clinicians design sound clinical studies for testing predictive biomarkers.
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