Biomarker adaptive designs in clinical trials

Predictive biomarkers are used to develop (binary) classifiers to identify patients as either good or poor candidates for clinical decision to optimize treatment selection. Ideally, these candidate biomarkers have been well studied in the phase II developmental stage, the performance characteristics of the classifier are well established in one or more retrospective validation, and the assay and predictive performance are reproducible and robust experimentally and analytically. However, completely phase II validated biomarkers for uses in phase III trial are often unavailable. Adaptive signature design (ASD) combines the biomarker identification and classifier development to the selection of candidate patients and a statistical test for treatment effect on the selected patient subgroup for phase III clinical trials. Biomarker-adaptive designs identify the most suitable target subpopulations, based on clinical observations or known biomarkers, and evaluate the effectiveness of the treatment on that subpopulation in a statistically valid manner. This review is concerned with statistical aspects in the biomarker adaptive design for randomized clinical trials. Statistical issues include the interaction test to identify predictive biomarkers, subgroup analysis, multiple testing and false discovery rate (FDR), classification of imbalanced class size data, sample size and power, and validation of the classification model.

[1]  S. Pocock,et al.  Statistical problems in the reporting of clinical trials. A survey of three medical journals. , 1987, The New England journal of medicine.

[2]  Michael W Kattan,et al.  Evaluating a New Marker’s Predictive Contribution , 2004, Clinical Cancer Research.

[3]  Giovanni Parmigiani,et al.  A Cross-Study Comparison of Gene Expression Studies for the Molecular Classification of Lung Cancer , 2004, Clinical Cancer Research.

[4]  M. Schumacher,et al.  Consistent Estimation of the Expected Brier Score in General Survival Models with Right‐Censored Event Times , 2006, Biometrical journal. Biometrische Zeitschrift.

[5]  James J. Chen,et al.  Ensemble methods for classification of patients for personalized medicine with high-dimensional data , 2007, Artif. Intell. Medicine.

[6]  R. Kay.  The Analysis of Survival Data , 2012 .

[7]  Sue-Jane Wang,et al.  Adaptive patient enrichment designs in therapeutic trials , 2009, Biometrical journal. Biometrische Zeitschrift.

[8]  Daniel J Sargent,et al.  Clinical trial designs for predictive biomarker validation: theoretical considerations and practical challenges. , 2009, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[9]  Richard Simon,et al.  Adaptive Clinical Trial Designs for Simultaneous Testing of Matched Diagnostics and Therapeutics , 2011, Clinical Cancer Research.

[10]  C. Weir,et al.  Statistical evaluation of biomarkers as surrogate endpoints: a literature review , 2006, Statistics in medicine.

[11]  Jae Won Lee,et al.  An extensive comparison of recent classification tools applied to microarray data , 2004, Comput. Stat. Data Anal..

[12]  J. Wittes,et al.  Analysis and interpretation of treatment effects in subgroups of patients in randomized clinical trials. , 1991, JAMA.

[13]  James J. Chen,et al.  Assessment of performance of survival prediction models for cancer prognosis , 2012, BMC Medical Research Methodology.

[14]  Sue-Jane Wang,et al.  Approaches to evaluation of treatment effect in randomized clinical trials with genomic subset , 2007, Pharmaceutical statistics.

[15]  K. Covinsky,et al.  Assessing the Generalizability of Prognostic Information , 1999, Annals of Internal Medicine.

[16]  Sara T Brookes,et al.  Subgroup analyses in randomized trials: risks of subgroup-specific analyses; power and sample size for the interaction test. , 2004, Journal of clinical epidemiology.

[17]  James J. Chen,et al.  Identification of reproducible gene expression signatures in lung adenocarcinoma , 2013, BMC Bioinformatics.

[18]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[19]  I. Jurisica,et al.  Modeling of lung cancer by an orthotopically growing H460SM variant cell line reveals novel candidate genes for systemic metastasis , 2004, Oncogene.

[20]  E. Lander,et al.  A molecular signature of metastasis in primary solid tumors , 2003, Nature Genetics.

[21]  D. Cox,et al.  Analysis of Survival Data. , 1986 .

[22]  P. Tugwell,et al.  Definitions and validation criteria for biomarkers and surrogate endpoints: development and testing of a quantitative hierarchical levels of evidence schema. , 2007, The Journal of rheumatology.

[23]  H M James Hung,et al.  A Regulatory Perspective on Essential Considerations in Design and Analysis of Subgroups When Correctly Classified , 2014, Journal of biopharmaceutical statistics.

[24]  Lei Shen,et al.  A Statistical Framework for Decision Making in Confirmatory Multipopulation Tailoring Clinical Trials , 2012 .

[25]  B. Freidlin,et al.  Statistical and practical considerations for clinical evaluation of predictive biomarkers. , 2013, Journal of the National Cancer Institute.

[26]  D. DeMets,et al.  Surrogate End Points in Clinical Trials: Are We Being Misled? , 1996, Annals of Internal Medicine.

[27]  Daniel J Sargent,et al.  Clinical trial designs for predictive marker validation in cancer treatment trials. , 2005, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[28]  Daniel J Sargent,et al.  Clinical Trial Designs for Predictive Biomarker Validation: One Size Does Not Fit All , 2009, Journal of biopharmaceutical statistics.

[29]  J. J. Chen,et al.  Classification ensembles for unbalanced class sizes in predictive toxicology , 2005, SAR and QSAR in environmental research.

[30]  Richard Simon,et al.  Gene expression-based prognostic signatures in lung cancer: ready for clinical use? , 2010, Journal of the National Cancer Institute.

[31]  Y. Benjamini,et al.  THE CONTROL OF THE FALSE DISCOVERY RATE IN MULTIPLE TESTING UNDER DEPENDENCY , 2001 .

[32]  C. Harbron,et al.  A statistician's perspective on biomarkers in drug development , 2011, Pharmaceutical statistics.

[33]  James J. Chen,et al.  Development of biomarker classifiers from high-dimensional data , 2009, Briefings Bioinform..

[34]  Aixia Guo,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2014 .

[35]  R. Simon,et al.  Adaptive Signature Design: An Adaptive Clinical Trial Design for Generating and Prospectively Testing A Gene Expression Signature for Sensitive Patients , 2005, Clinical Cancer Research.

[36]  S. Dudoit,et al.  Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data , 2002 .

[37]  K. Gabriel,et al.  On closed testing procedures with special reference to ordered analysis of variance , 1976 .

[38]  T. Lumley,et al.  Time‐Dependent ROC Curves for Censored Survival Data and a Diagnostic Marker , 2000, Biometrics.

[39]  B. Kramer,et al.  A perfect correlate does not a surrogate make , 2003, BMC medical research methodology.

[40]  Gene expression-based pharmacodynamic biomarkers: the beginning of a new era in biomarker-driven anti-tumor drug development. , 2010, Current molecular medicine.

[41]  F. Balis,et al.  Evolution of anticancer drug discovery and the role of cell-based screening. , 2002, Journal of the National Cancer Institute.

[42]  James J. Chen,et al.  Reproducibility of microarray data: a further analysis of microarray quality control (MAQC) data , 2007, BMC Bioinformatics.

[43]  Michael J Schell,et al.  The false discovery rate: a key concept in large-scale genetic studies. , 2010, Cancer control : journal of the Moffitt Cancer Center.

[44]  M. Schemper.  Predictive accuracy and explained variation , 2003, Statistics in medicine.

[45]  Sue-Jane Wang,et al.  Impacts of Predictive Genomic Classifier Performance on Subpopulation-Specific Treatment Effects Assessment , 2016 .

[46]  Chen-An Tsai,et al.  Estimation of False Discovery Rates in Multiple Testing: Application to Gene Microarray Data , 2003, Biometrics.

[47]  Igor Jurisica,et al.  Three-gene prognostic classifier for early-stage non small-cell lung cancer. , 2007, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[48]  James J. Chen,et al.  Assessment of reproducibility of cancer survival risk predictions across medical centers , 2013, BMC Medical Research Methodology.

[49]  S. Assmann,et al.  Subgroup analysis and other (mis)uses of baseline data in clinical trials , 2000, The Lancet.

[50]  R. Simon,et al.  The Cross-Validated Adaptive Signature Design , 2010, Clinical Cancer Research.

[51]  James J. Chen,et al.  Class-imbalanced classifiers for high-dimensional data , 2013, Briefings Bioinform..

[52]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[53]  R. Simon,et al.  Biomarker-adaptive threshold design: a procedure for evaluating treatment with possible biomarker-defined subset effect. , 2007, Journal of the National Cancer Institute.

[54]  George Y H Chi,et al.  A method for testing a prespecified subgroup in clinical trials , 2007, Statistics in medicine.

[55]  R. Newson.  Confidence Intervals for Rank Statistics: Somers’ D and Extensions , 2006 .

[56]  D. DeMets,et al.  Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework , 2001, Clinical pharmacology and therapeutics.

[57]  H. Hansen,et al.  Lung cancer. , 1990, Cancer chemotherapy and biological response modifiers.

[58]  R. Schilsky.  End points in cancer clinical trials and the drug approval process. , 2002, Clinical cancer research : an official journal of the American Association for Cancer Research.

[59]  Maqc Consortium.  The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements , 2006, Nature Biotechnology.

[60]  Daniel J Sargent,et al.  Integrating biomarkers in clinical trials , 2011, Expert review of molecular diagnostics.

[61]  J. Roth,et al.  Survival in early-stage non-small cell lung cancer. , 1995, The Annals of thoracic surgery.

[62]  Sue-Jane Wang,et al.  Biomarker as a classifier in pharmacogenomics clinical trials: a tribute to 30th anniversary of PSI , 2007, Pharmaceutical statistics.

[63]  F. Harrell,et al.  Evaluating the yield of medical tests. , 1982, JAMA.

[64]  R. Tibshirani,et al.  Semi-Supervised Methods to Predict Patient Survival from Gene Expression Data , 2004, PLoS biology.

[65]  Mace L. Rothenberg,et al.  Improving the evaluation of new cancer treatments: challenges and opportunities , 2003, Nature Reviews Cancer.

[66]  John D. Storey.  A direct approach to false discovery rates , 2002 .

[67]  I. Jurisica,et al.  Validating the prognostic value of marker genes derived from a non-small cell lung cancer microarray study. , 2004, Lung cancer.

[68]  Dung-Tsa Chen,et al.  Prognostic and predictive value of a malignancy-risk gene signature in early-stage non-small cell lung cancer. , 2011, Journal of the National Cancer Institute.

[69]  M. Schemper,et al.  The relative importance of prognostic factors in studies of survival. , 1991, Statistics in medicine.