Statistical approaches for evaluating surrogate outcomes in clinical trials: A systematic review

ABSTRACT The use of surrogate outcomes that predict treatment effect on an unobserved true outcome may have substantial economic and ethical advantages, through reducing the length and size of clinical trials. There has been extensive investigation of the best means of evaluating putative surrogates. We present a systematic review on the evolution of statistical methods for validating surrogates starting from the defining paper of Prentice (1989). We highlight the fundamental differences in the current statistical evaluation approaches, their advantages and disadvantages, and examine the understanding and perceptions of investigators in this area.

[1]  Jeremy MG Taylor,et al.  Surrogacy assessment using principal stratification when surrogate and outcome measures are multivariate normal. , 2014, Biostatistics.

[2]  Jeremy MG Taylor,et al.  Accommodating missingness when assessing surrogacy via principal stratification , 2013, Clinical trials.

[3]  B. Kramer,et al.  Surrogate endpoint analysis: an exercise in extrapolation. , 2013, Journal of the National Cancer Institute.

[4]  Corwin M Zigler,et al.  A Bayesian Approach to Improved Estimation of Causal Effect Predictiveness for a Principal Surrogate Endpoint , 2012, Biometrics.

[5]  James Y. Dai,et al.  A unified procedure for meta-analytic evaluation of surrogate end points in randomized clinical trials. , 2012, Biostatistics.

[6]  A. Gangopadhyay,et al.  An entropy‐based nonparametric test for the validation of surrogate endpoints , 2012, Statistics in medicine.

[7]  Evaluating Correlation‐Based Metric for Surrogate Marker Qualification within a Causal Correlation Framework , 2012, Biometrics.

[8]  Alessandra Mattei,et al.  The International Journal of Biostatistics A Refreshing Account of Principal Stratification , 2012 .

[9]  Bradley P Carlin,et al.  Bayesian adjusted R2 for the meta‐analytic evaluation of surrogate time‐to‐event endpoints in clinical trials , 2012, Statistics in medicine.

[10]  G. Izmirlian,et al.  Discussion of the paper of Ghosh, Taylor, and Sargent. , 2012, Biometrics.

[11]  D. Sargent,et al.  Predicting Treatment Effect from Surrogate Endpoints and Historical Trials: An Extrapolation Involving Probabilities of a Binary Outcome or Survival to a Specific Time , 2012, Biometrics.

[12]  G. Molenberghs.  Discussion Contribution to 091037PR4 (Ghosh, Taylor, and Sargent) , 2012, Biometrics.

[13]  Debashis Ghosh,et al.  Rejoinder for “Meta‐analysis for Surrogacy: Accelerated Failure Time Models and Semicompeting Risks Modeling” , 2012, Biometrics.

[14]  Debashis Ghosh,et al.  Meta‐analysis for Surrogacy: Accelerated Failure Time Models and Semicompeting Risks Modeling , 2012, Biometrics.

[15]  Peter B Gilbert,et al.  Comparing Biomarkers as Principal Surrogate Endpoints , 2011, Biometrics.

[16]  M. Hudgens,et al.  The International Journal of Biostatistics Commentary on " Principal Stratification — a Goal or a Tool ? " by Judea Pearl , 2012 .

[17]  M. Joffe.  The International Journal of Biostatistics Principal Stratification and Attribution Prohibition : Good Ideas Taken Too Far , 2012 .

[18]  Qian Shi,et al.  Comparative assessment of trial-level surrogacy measures for candidate time-to-event surrogate endpoints in clinical trials , 2011, Comput. Stat. Data Anal..

[19]  Z. Geng,et al.  Sufficient conditions for concluding surrogacy based on observed data , 2011, Statistics in medicine.

[20]  Jeremy MG Taylor,et al.  Causal assessment of surrogacy in a meta-analysis of colorectal cancer trials. , 2011, Biostatistics.

[21]  G. Molenberghs,et al.  An information-theoretic approach to surrogate-marker evaluation with failure time endpoints , 2011, Lifetime data analysis.

[22]  J. Pearl.  The International Journal of Biostatistics Principal Stratification — a Goal or a Tool ? , 2011 .

[23]  Elias Bareinboim,et al.  Transportability across studies: A formal approach , 2011 .

[24]  R. Katz,et al.  Biomarkers and surrogate markers: An FDA perspective , 2004, NeuroRX.

[25]  Debashis Ghosh,et al.  Links between analysis of surrogate endpoints and endogeneity , 2010, Statistics in medicine.

[26]  Julian Wolfson,et al.  Statistical Identifiability and the Surrogate Endpoint Problem, with Application to Vaccine Trials , 2010, Biometrics.

[27]  Yongming Qu,et al.  Adjustment for the measurement error in evaluating biomarkers , 2010, Statistics in medicine.

[28]  S. Jalaie,et al.  Mistake on quantifying the indirect treatment effect via surrogate markers by Y. Qu and M. Case Statistics in Medicine 2006; 25:223–231 , 2010, Statistics in medicine.

[29]  José Cortiñas Abrahantes,et al.  Simplified modeling strategies for surrogate validation with multivariate failure-time data , 2010, Comput. Stat. Data Anal..

[30]  Michael R Elliott,et al.  A Bayesian Approach to Surrogacy Assessment Using Principal Stratification in Clinical Trials , 2010, Biometrics.

[31]  Richard Emsley,et al.  Ian R White interventions Mediation and moderation of treatment effects in randomised controlled trials of complex , 2010 .

[32]  Bin Huang,et al.  Evaluating the Proportion of Treatment Effect Explained by a Continuous Surrogate Marker in Logistic or Probit Regression Models , 2010, Statistics in biopharmaceutical research.

[33]  Geert Molenberghs,et al.  Using earlier measures in a longitudinal sequence as a potential surrogate for a later one , 2010, Comput. Stat. Data Anal..

[34]  Zhi Geng,et al.  Criteria for surrogate end points based on causal distributions , 2010 .

[35]  M. Taljaard,et al.  Changes in surrogate outcomes can be translated into clinical outcomes using a Monte Carlo model. , 2009, Journal of clinical epidemiology.

[36]  Tom Greene,et al.  Related Causal Frameworks for Surrogate Outcomes , 2009, Biometrics.

[37]  Debashis Ghosh,et al.  On Assessing Surrogacy in a Single Trial Setting Using a Semicompeting Risks Paradigm , 2009, Biometrics.

[38]  I. Schnauder,et al.  Discussions , 2009 .

[39]  Nick Freemantle,et al.  LDL-cholesterol differences predicted survival benefit in statin trials by the surrogate threshold effect (STE). , 2009, Journal of clinical epidemiology.

[40]  G. Molenberghs,et al.  Investigating Association Between Behavior, Corticosterone, Heart Rate, and Blood Pressure in Rats Using Surrogate Marker Evaluation Methodology , 2009, Journal of biopharmaceutical statistics.

[41]  Andrew J Dunning.  Comment on ‘Evaluating a surrogate endpoint at three levels, with application to vaccine development’ by Peter B. Gilbert, Li Qin and Steven G. Self, Statistics in Medicine, DOI: 10.1002/sim.3122 , 2008, Statistics in medicine.

[42]  Michael G Hudgens,et al.  Evaluating Candidate Principal Surrogate Endpoints , 2008, Biometrics.

[43]  Peter B Gilbert,et al.  Evaluating a surrogate endpoint at three levels, with application to vaccine development , 2008, Statistics in medicine.

[44]  Jing Qin,et al.  Improving semiparametric estimation by using surrogate data , 2008 .

[45]  G. Molenberghs,et al.  Alternative methods to evaluate trial level surrogacy , 2008, Clinical trials.

[46]  Marissa N Lassere,et al.  The Biomarker-Surrogacy Evaluation Schema: a review of the biomarker-surrogate literature and a proposal for a criterion-based, quantitative, multidimensional hierarchical levels of evidence schema for evaluating the status of biomarkers as surrogate endpoints , 2008, Statistical methods in medical research.

[47]  Geert Molenberghs,et al.  Information Theory–Based Surrogate Marker Evaluation from Several Randomized Clinical Trials with Binary Endpoints, Using SAS , 2008, Journal of biopharmaceutical statistics.

[48]  Debashis Ghosh,et al.  Semiparametric Inference for Surrogate Endpoints with Bivariate Censored Data , 2008, Biometrics.

[49]  Peter B Gilbert,et al.  ASSESSING SURROGATE ENDPOINTS IN VACCINE TRIALS WITH CASE-COHORT SAMPLING AND THE COX MODEL. , 2008, The annals of applied statistics.

[50]  S. Baker.  Two simple approaches for validating a binary surrogate endpoint using data from multiple trials , 2008, Statistical methods in medical research.

[51]  Geert Molenberghs,et al.  Evaluating time to cancer recurrence as a surrogate marker for survival from an information theory perspective , 2008, Statistical methods in medical research.

[52]  D. Ghosh.  Composite endpoint analysis for assessing surrogacy with censored data , 2008 .

[53]  Sylvie Chevret,et al.  Assessing surrogacy from the joint modelling of multivariate longitudinal data and survival: Application to clinical trial data on chronic lymphocytic leukaemia , 2007, Statistics in medicine.

[54]  Geert Molenberghs,et al.  Information-theory based surrogate marker evaluation from several randomized clinical trials with continuous true and binary surrogate endpoints , 2007, Clinical trials.

[55]  C. Gerlinger,et al.  Sperm count as a surrogate endpoint for male fertility control , 2007, Statistics in medicine.

[56]  Zhi Geng,et al.  Criteria for surrogate end points , 2007 .

[57]  Peter B Gilbert,et al.  A framework for assessing immunological correlates of protection in vaccine trials. , 2007, The Journal of infectious diseases.

[58]  D P MacKinnon,et al.  The intermediate endpoint effect in logistic and probit regression , 2007, Clinical trials.

[59]  G. Molenberghs,et al.  Quantifying the Effect of the Surrogate Marker by Information Gain , 2007, Biometrics.

[60]  Geert Molenberghs,et al.  Flexible surrogate marker evaluation from several randomized clinical trials with continuous endpoints, using R and SAS , 2007, Comput. Stat. Data Anal..

[61]  S. Sarkar,et al.  Quantifying the treatment effect explained by markers in the presence of measurement error , 2007, Statistics in medicine.

[62]  G. Molenberghs,et al.  Surrogate Marker Evaluation from an Information Theory Perspective , 2007, Biometrics.

[63]  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.

[64]  M. Buyse,et al.  Simulation studies of surrogate endpoint validation using single trial and multitrial statistical approaches. , 2007, The Journal of rheumatology.

[65]  Geert Molenberghs,et al.  Evaluation of Surrogate Endpoints , 2006, Handbook of Statistical Methods for Randomized Controlled Trials.

[66]  J. O'Quigley,et al.  Quantification of the Prentice Criteria for Surrogate Endpoints , 2006, Biometrics.

[67]  Geert Molenberghs,et al.  A unifying approach for surrogate marker validation based on Prentice's criteria , 2006, Statistics in medicine.

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

[69]  M. Case,et al.  Quantifying the indirect treatment effect via surrogate markers , 2006, Statistics in medicine.

[70]  M. Buyse,et al.  Surrogate threshold effect: an alternative measure for meta-analytic surrogate endpoint validation. , 2006, Pharmaceutical statistics.

[71]  Stuart G Baker,et al.  A simple meta-analytic approach for using a binary surrogate endpoint to predict the effect of intervention on true endpoint. , 2006, Biostatistics.

[72]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[73]  Peter B. Gilbert,et al.  Evaluating Causal Effect Predictiveness of Candidate Surrogate Endpoints , 2006 .

[74]  Rodolphe Thiébaut,et al.  Counterfactual Links to the Proportion of Treatment Effect Explained by a Surrogate Marker , 2005, Biometrics.

[75]  G. Izmirlian,et al.  Resolving paradoxes involving surrogate end points , 2005 .

[76]  N. Shah,et al.  The true treatment benefit is unpredictable in clinical trials using surrogate outcome measured with diagnostic tests. , 2005, Journal of clinical epidemiology.

[77]  Laurence S. Freedman,et al.  Commentary on Assessing surrogates as trial endpoints using mixed models by E. L. Korn, P. S. Albert and L. M. McShane , 2005 .

[78]  P. Albert,et al.  Rejoinder to commentary by Dr Freedman of ‘assessing surrogates as trial endpoints using mixed models’ , 2005 .

[79]  Paul S Albert,et al.  Assessing surrogates as trial endpoints using mixed models , 2005, Statistics in medicine.

[80]  Niels Keiding,et al.  The Mediation Proportion: A Structural Equation Approach for Estimating the Proportion of Exposure Effect on Outcome Explained by an Intermediate Variable , 2005, Epidemiology.

[81]  N. Keiding,et al.  Mediation proportion. , 2005, Epidemiology.

[82]  L. Freedman.  Comment on: Assessing surrogates as trial endpoints using mixed models. , 2005, Statistics in medicine.

[83]  Geert Molenberghs,et al.  Validation of surrogate markers in multiple randomized clinical trials with repeated measurements: canonical correlation approach. , 2004 .

[84]  N. Wermuth,et al.  Causality: a Statistical View , 2004 .

[85]  Geert Molenberghs,et al.  Choice of units of analysis and modeling strategies in multilevel hierarchical models , 2004, Comput. Stat. Data Anal..

[86]  Geert Molenberghs,et al.  Prentice's Approach and the Meta‐Analytic Paradigm: A Reflection on the Role of Statistics in the Evaluation of Surrogate Endpoints , 2004, Biometrics.

[87]  G. Molenberghs,et al.  Modelling associations between time‐to‐event responses in pilot cancer clinical trials using a Plackett–Dale model , 2004, Statistics in medicine.

[88]  D. Rubin.  Direct and Indirect Causal Effects via Potential Outcomes * , 2004 .

[89]  G. Molenberghs,et al.  A perspective on surrogate endpoints in controlled clinical trials , 2004, Statistical methods in medical research.

[90]  Geert Molenberghs,et al.  The validation of surrogate end points by using data from randomized clinical trials: a case‐study in advanced colorectal cancer , 2004 .

[91]  Geert Molenberghs,et al.  Validation of Surrogate Markers in Multiple Randomized Clinical Trials with Repeated Measurements , 2003, Biometrics.

[92]  Jing Qin,et al.  Information Recovery in a Study With Surrogate Endpoints , 2003 .

[93]  Hongwei Wang,et al.  Proportion of treatment effect (PTE) explained by a surrogate marker , 2003, Statistics in medicine.

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

[95]  Geert Molenberghs,et al.  Simplified hierarchical linear models for the evaluation of surrogate endpoints , 2003 .

[96]  M. Hudgens,et al.  Sensitivity Analysis for the Assessment of Causal Vaccine Effects on Viral Load in HIV Vaccine Trials , 2003, Biometrics.

[97]  Geert Molenberghs,et al.  Validation of a longitudinally measured surrogate marker for a time-to-event endpoint , 2003 .

[98]  Geert Molenberghs,et al.  Validation of Surrogate Endpoints in Multiple Randomized Clinical Trials with Discrete Outcomes , 2002 .

[99]  Jeremy M G Taylor,et al.  A Measure of the Proportion of Treatment Effect Explained by a Surrogate Marker , 2002, Biometrics.

[100]  M. Cowles.  Bayesian estimation of the proportion of treatment effect captured by a surrogate marker , 2002, Statistics in medicine.

[101]  D. Rubin,et al.  Principal Stratification in Causal Inference , 2002, Biometrics.

[102]  Geert Molenberghs,et al.  INVESTIGATING THE CRITERION VALIDITY OF PSYCHIATRIC SYMPTOM SCALES USING SURROGATE MARKER VALIDATION METHODOLOGY , 2002, Journal of biopharmaceutical statistics.

[103]  G Molenberghs,et al.  Evaluation of surrogate endpoints in randomized experiments with mixed discrete and continuous outcomes , 2001, Statistics in medicine.

[104]  G. Molenberghs,et al.  Validation of surrogate end points in multiple randomized clinical trials with failure time end points , 2001 .

[105]  G. Molenberghs,et al.  The validation of surrogate endpoints in meta-analyses of randomized experiments. , 2000, Biostatistics.

[106]  C. Begg,et al.  On the use of surrogate end points in randomized trials , 2000 .

[107]  G. Molenberghs,et al.  Criteria for the validation of surrogate endpoints in randomized experiments. , 1998, Biometrics.

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

[109]  S. Duffy,et al.  Trial Design Based on Surrogate End Points — Application to Comparison of Different Breast Screening Frequencies , 1996 .

[110]  M S Pepe,et al.  Surrogate and auxiliary endpoints in clinical trials, with potential applications in cancer and AIDS research. , 1994, Statistics in medicine.

[111]  J. Robins,et al.  Identifiability and Exchangeability for Direct and Indirect Effects , 1992, Epidemiology.

[112]  B. Graubard,et al.  Statistical validation of intermediate endpoints for chronic diseases. , 1992, Statistics in medicine.

[113]  R. Prentice.  Surrogate endpoints in clinical trials: definition and operational criteria. , 1989, Statistics in medicine.

[114]  R. Plackett.  A Class of Bivariate Distributions , 1965 .

[115]  E. Kaplan,et al.  Nonparametric Estimation from Incomplete Observations , 1958 .