Comparison of effect sizes associated withsurrogate and final primary endpoints in       randomised clinical trials      C...
BackgroundClassic Definition for surrogatesDisease-centered characteristics                       Patient-centered charact...
BackgroundHTA-based Definition of surrogatesDisease-centered characteristics                        Patient-centered chara...
Objectives of the studyI. To study the association between primary endpoint(surrogate vs final) and treatment effect estim...
MethodsStudy selection                                                Initial sample of abstracts (N = 639)               ...
MethodsData extraction Effect Size    Binary endpoints: n/N data for each arm    Continuous endpoints: SS, Mean, SD for...
MethodsData analyses   Primary Analysis      Random-effects meta-analysis           Binary endpoints: TEs expressed as ...
ResultsStudy characteristics                                                     Surrogate            Final              P...
ResultsComparison of TEs – primary analysisMethod of Analysis                                   Surrogate               Fi...
ResultsComparison of TEs – sensitivity analyses Method of Analysis                                                        ...
ResultsRisk of bias                                     Surrogate    Final outcomesRisk of Bias Assessment, N(%)          ...
Discussion and limitations Between-trial comparison of treatment effects Possible role of smaller trial sample size in s...
Thanks for your attention          Q&Aoriana.ciani@pcmd.ac.uk                            13
Main References1. Biomarkers Definitions Working Group. Biomarkers and surrogate endpoints:   preferred definitions and co...
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Comparison of effect sizes associated with surrogate and final primary endpoints in randomised clinical trials. Cianti.

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Comparison of effect sizes associated with surrogate and final primary endpoints in randomised clinical trials.

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Comparison of effect sizes associated with surrogate and final primary endpoints in randomised clinical trials. Cianti.

  1. 1. Comparison of effect sizes associated withsurrogate and final primary endpoints in randomised clinical trials Ciani O., Garside R., Pavey T., Stein K., Taylor R.S. 1
  2. 2. BackgroundClassic Definition for surrogatesDisease-centered characteristics Patient-centered characteristicsBiomarkers Surrogate outcomes Final outcomeA characteristic that is A characteristicobjectively measured A biomarker that is that reflects howand evaluated as an intended to substitute patient feels,indicator of normal, and predict for a final functions orpathogenic or outcome. survives.pharmacologicresponses to atherapeutic intervention. Cardiovascular e.g. LDL-cholesterol Mortality e.g. Intraocular pressure Loss of vision
  3. 3. BackgroundHTA-based Definition of surrogatesDisease-centered characteristics Patient-centered characteristicsBiomarkers Surrogate outcomes Final outcomeA characteristic that is A characteristicobjectively measured A biomarker - or clinical that reflects howand evaluated as an or patient-relevant patient feels,indicator of normal, outcome - that is functions orpathogenic or intended to substitute survives.pharmacologic and predict for a finalresponses to a outcome, namelytherapeutic intervention. survival or HRQoL. e.g. Rate of hip fracture Mortality/HRQoL e.g. Event-free Survival Overall Survival
  4. 4. Objectives of the studyI. To study the association between primary endpoint(surrogate vs final) and treatment effect estimates inRCTsII.To compare the risk of bias in trials reporting asurrogate endpoint vs trials reporting a final primaryendpoint 4
  5. 5. MethodsStudy selection Initial sample of abstracts (N = 639) Initial sample of abstracts (N = 639) Excluded (N = 55) Excluded (N = 55) NotRCTs (N = 17) Not RCTs (N = 17) Economicevaluation studies (N = 11) Economic evaluation studies (N = 11) Noninterventional treatment (N = 25) Non interventional treatment (N = 25) Secondaryanalysis (N = 2) Secondary analysis (N = 2) For outcomes classification (N = 584) For outcomes classification (N = 584) Composite mixed outcomes (N = 73) Composite mixed outcomes (N = 73) Eligible for the study (N = 511) Eligible for the study (N = 511) Matching procedure Matching procedure Surrogate outcomes based (N = 137) Surrogate outcomes based (N = 137) Final outcomes based (N = 137) Final outcomes based (N = 137) Excluded (N = 36) Excluded (N = 36) Excluded (N = 53) Excluded (N = 53) Compositemixed outcomes (N = 9) Composite mixed outcomes (N = 9) Equivalence/Non-inferioritystudy (N = 15) Equivalence/Non-inferiority study (N = 15) Earlytermination (N = 1) Early termination (N = 1) UnpooledMuliti-arm (N = 33) Unpooled Muliti-arm (N = 33) Equivalence/Non-inferioritystudy (N = 11) Equivalence/Non-inferiority study (N = 11) Noanalysable data (N = 5) No analysable data (N = 5) UnpooledMuliti-arm (N = 11) Unpooled Muliti-arm (N = 11) Noanalysable data (N = 4) No analysable data (N = 4) Surrogate outcome trials (N = 84) Surrogate outcome trials (N = 84) Final outcome trials (N = 101) Final outcome trials (N = 101) Binaryendpoint (N = 51) Binaryendpoint (N = 83) 5 Binary endpoint (N = 51) Binary endpoint (N = 83)
  6. 6. MethodsData extraction Effect Size  Binary endpoints: n/N data for each arm  Continuous endpoints: SS, Mean, SD for each arm  TEs(95%CI) as reported by authors Study characteristics: sample size, follow-up, type of intervention, patient population, sponsor (i.e. FP, NFP and mixed), positive outcome in favour of the new treatment Risk of bias: adoption of the intention to treat (ITT) principle, adequate randomized sequence generation and allocation concealment, double- blind/placebo-control Surrogate outcomes: type of surrogate (i.e. imaging, histo/biochemical, instrumental, other), authors’ statement about validation and use of a substitute outcome 6
  7. 7. MethodsData analyses  Primary Analysis  Random-effects meta-analysis  Binary endpoints: TEs expressed as ORs  Meta-regression models  Binary endpoints: Ratio of ORs (95%CI)  ROR > 1 → greater TEs of the surrogate endpoints  Adjustment for key trial characteristics  Sensitivity Analyses  Pooled Relative Risk Ratios estimate (RRR)  Combined continuous and binary endpoints ROR estimation  Within-pair comparison of differences in ln(OR)  Secondary Analysis  Logistic regression model  OR of reporting result in favour of the new treatment  Risk of bias assessment  χ2 - test of methodological quality dimensions across groups 7
  8. 8. ResultsStudy characteristics Surrogate Final P- Characteristics outcomes (N = 84) outcomes (N = 101) value Intervention, N(%) 0.33 Pharmaceuticals 49 (58) 61 (60) Medical Devices 7 (8) 7 (7) Surgical procedures 4 (5) 8 (8) Health promotion activities 7 (8) 2 (2) Other therapeutic technologies 17 (20) 23 (23) Sponsor, N(%) 0.86 Profit 24 (29) 28 (28) Not-for-Profit 49 (58) 57 (56) Mixed 11 (12) 16 (16) Sample size, Median (IQR) 371 (162-787) 741 (300-4731) <0.001 Follow up, [days] Median (IQR) 255 (137-540) 180 (40-730) 0.73 8 *Chi-square test, Fisher exact test, Mann-Whitney U test
  9. 9. ResultsComparison of TEs – primary analysisMethod of Analysis Surrogate Final outcome Adjusted^(Nr of Surrogate trials vs. Nr of outcome Trials Trials ROR (95% CI) ROR (95% CI)Final Outcome trials) OR (95% CI) OR (95% CI)Primary analysisBinary outcomes 0.51 0.76 1.47 1.46(51 vs. 83) (0.42 to 0.60) (0.70 to 0.82) (1.07 to 2.01) (1.05 to 2.04)ORs = Odds ratios pooled using DerSimonian & Laird random effects meta-analyses. ROR: Relative Odds Ratio; ^Adjusted for trial-levelcharacteristics of clinical area of intervention, patient population, type of intervention, sponsor, journal, mean sample size and mean followup time 9
  10. 10. ResultsComparison of TEs – sensitivity analyses Method of Analysis Adjusted^ Surrogate Trials Final Trials ROR or RRR (Nr Surrogate trials vs. ROR or RRR RR (95% CI) RR (95% CI) (95% CI) Nr Final Outcome trials) (95% CI) Inclusion of risk ratios as 0.56 0.80 1.38 1.36 reported by authors (0.48 to 0.65) (0.75 to 0.86) (1.12 to 1.71) (1.08 to 1.70) (57 vs. 86) Inclusion of continuous 0.46 0.68 1.44 1.48 outcomes (0.39 to 0.54) (0.62 to 0.74) (0.83 to 2.49) (0.83 to 2.62) (84 vs. 101) Binary outcomes 0.48 0.68 1.38 matched-pairs - (0.39 to 0.59) (0.61 to 0.77) (1.01 to 1.88) (43 vs. 43)RRR: Relative Risk Ratio; ^Adjusted for trial-level characteristics of clinical area of intervention, patient population, type of intervention,sponsor, journal, mean sample size and mean follow up time 10
  11. 11. ResultsRisk of bias Surrogate Final outcomesRisk of Bias Assessment, N(%) P-value outcomes (N=84) (N=101)ITT adoption 62 (74) 83 (82) 0.17Adequate Randomization 54 (64) 65 (64) 0.99sequence generationAdequate Randomization 61 (73) 74 (73) 0.92allocation concealmentDouble Blinding/Placebo control 42 (50) 43 (43) 0.31 *Chi-square test 11
  12. 12. Discussion and limitations Between-trial comparison of treatment effects Possible role of smaller trial sample size in surrogate outcome trials ~40% ‘overestimation’ of TEs in surrogate outcomes trials Consistent result across sensitivity analyses, confirmed by secondary analyses Findings not explained by methodological quality or other key trial characteristics 12
  13. 13. Thanks for your attention Q&Aoriana.ciani@pcmd.ac.uk 13
  14. 14. Main References1. Biomarkers Definitions Working Group. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clinical Pharmacology and Therapeutics 2001; 69: 89–95.2. Bucher, H. C. et al. Users guides to the medical literature: XIX. Applying clinical trial results. A. How to use an article measuring the effect of an intervention on surrogate end points. Evidence-Based Medicine Working Group. JAMA, 1999: 282, 771-8.3. Fleming TR, DeMets DL. Surrogate endpoints in clinical trials: Are we being misled? Annals of Internal Medicine 1996; 125: 605–13.4. Lassere M. 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 .Statistical Methods in Medical Research 2007; 17: 303–340.5. Taylor RS, Elston J. The use of surrogate outcomes in model-based cost- effectiveness analyses: a survey of UK Health Technology Assessment reports. Health Technol Assess 2009; 13(8).6. Weir CJ, Walley RJ. Statistical evaluation of biomarkers as surrogate endpoints: a literature review. Stat Med 2006; 25: 183-203. 14

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