title: Multistage adaptive biomarker-directed targeted design for randomized clinical trials.
abstract: BACKGROUND
  Precision medicine is changing how patients are treated and how therapies are developed. In recent years, biomarker-directed targeted designs have been developed for pharmaceutical development aimed at patient subpopulation with a specific disease etiology. To integrate multistage testing into the targeted designs enhances flexibility of targeted trials by enabling sequential monitoring and stochastic curtailment.
  
  
  RESULTS
  We studied a multistage adaptive design for targeted trials with either normally distributed endpoint or binary endpoint. The design is based on the fact that distribution of the sequence of test statistics from multistage testing is asymptotically well approximated by a Brownian motion in targeted trials with normally distributed continuous endpoint or binary endpoint. This study has demonstrated that the targeted multistage design improves study efficiency, information accumulation and conditional power as compared with its untargeted counterpart. Furthermore, our study has indicated that biomarker performance plays a crucial role in efficiency and effectiveness of the multistage adaptive design. The sensitivity and specificity of a biomarker used for patient enrichment influence level of heterogeneity of the targeted study population, and subsequently impact overall trial efficiency and statistical power as well as information accruement and conditional/predictive power for stochastic curtailment. When performance of a biomarker is imperfect, conditional/predictive power at an earlier stage may be over-estimated, resulting invalid early stopping decision. Thus, great care is needed to ensure that biomarker performance is considered in statistical planning of the multistage targeted trials.
  
  
  CONCLUSIONS
  In summary, the multistage adaptive design provides targeted trials with flexibility in multistage testing and early stopping while retaining the rigor of the study design.
authors.affiliation: Department of Mathematics and Statistics, University of Maryland, Baltimore County, Baltimore, MD 21250, United States.
authors.forename: Zhong
authors.initials: Z
authors.lastname: Gao
authors.affiliation: Department of Mathematics and Statistics, University of Maryland, Baltimore County, Baltimore, MD 21250, United States.
authors.forename: Anindya
authors.initials: A
authors.lastname: Roy
authors.affiliation: Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University, Washington, DC 20057, United States. Electronic address: mtt34@georgetown.edu.
authors.forename: Ming
authors.initials: M
authors.lastname: Tan
mesh_terms: D015415:Biomarkers; D003627:Data Interpretation, Statistical; D017598:Efficiency, Organizational; D023381:Endpoint Determination / Q000379:methods; D006801:Humans; D057285:Precision Medicine / Q000379:methods*; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D012680:Sensitivity and Specificity
chemical_list: D015415:Biomarkers
keywords: Biomarker; Multistage adaptive design; Targeted clinical trial