title: Strategies for power calculations in predictive biomarker studies in survival data.
abstract: OBJECTIVE
  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.
  
  
  CONCLUSIONS
  Our study provides guidance and software to help biostatisticians and clinicians design sound clinical studies for testing predictive biomarkers.
authors.affiliation: Department of Biostatistics and Bioinformatics, Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
authors.forename: Dung-Tsa
authors.initials: DT
authors.lastname: Chen
authors.affiliation: Computational Intelligence Technology Center, Industrial Technology Research Institute, Taichung City, Taiwan.
authors.forename: Po-Yu
authors.initials: PY
authors.lastname: Huang
authors.affiliation: Biostatistics program, School of Public Health, Louisiana State University Health Sciences Center , New Orleans, LA, USA.
authors.forename: Hui-Yi
authors.initials: HY
authors.lastname: Lin
authors.affiliation: Department of Thoracic Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
authors.forename: Eric B
authors.initials: EB
authors.lastname: Haura
authors.affiliation: Department of Thoracic Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
authors.forename: Scott J
authors.initials: SJ
authors.lastname: Antonia
authors.affiliation: Department of Molecular Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
authors.forename: W Douglas
authors.initials: WD
authors.lastname: Cress
authors.affiliation: Department of Thoracic Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
authors.forename: Jhanelle E
authors.initials: JE
authors.lastname: Gray
mesh_terms: D014408:Biomarkers, Tumor / Q000032:analysis*; D056808:Biostatistics / Q000379:methods*; D003627:Data Interpretation, Statistical; D023381:Endpoint Determination / Q000706:statistics & numerical data*; D005819:Genetic Markers; D006801:Humans; D015233:Models, Statistical; D009369:Neoplasms / Q000188:drug therapy* / Q000235:genetics / Q000378:metabolism / Q000401:mortality; D016016:Proportional Hazards Models; D011446:Prospective Studies; D015203:Reproducibility of Results; D012107:Research Design / Q000706:statistics & numerical data*; D012189:Retrospective Studies; D013997:Time Factors; D016896:Treatment Outcome
chemical_list: D014408:Biomarkers, Tumor; D005819:Genetic Markers
keywords: precision medicine; predictive biomarker; prospective study; retrospective study; survival data