Challenges and Solutions to Pre- and Post-Randomization Subgroup Analyses

Current Cardiology Reports

Volumn 16, Issue 10, 2014, Pages 1-8

  Desai, Manisha ^a   Pieper, Karen S ^b   Mahaffey, Ken ^c  

^a STANFORD UNIVERSITY   (United States)

^b DUKE CLINICAL RESEARCH INSTITUTE   (United States)

^c STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

A priori hypotheses; Bias; Causal inference; Multiplicity; Post randomization; Subgroup analyses; Tests of interaction

Indexed keywords

HUMAN; METHODOLOGY; PRACTICE GUIDELINE; PROCEDURES; RANDOMIZED CONTROLLED TRIAL (TOPIC); STATISTICAL ANALYSIS; STATISTICAL BIAS;

BIAS (EPIDEMIOLOGY); DATA INTERPRETATION, STATISTICAL; GUIDELINES AS TOPIC; HUMANS; RANDOMIZED CONTROLLED TRIALS AS TOPIC; RESEARCH DESIGN;

EID: 84905876541     PISSN: 15233782     EISSN: 15343170     Source Type: Journal    
DOI: 10.1007/s11886-014-0531-2     Document Type: Review

References (35)

1. Hirji KF, Fagerland MW. Outcome based subgroup analysis: a neglected concern. Trials. 2009;10:33.
2. Pocock SJ, Hughs MD, Lee RJ. Statistical problems in the reporting of clinical trials. N Engl J Med. 1987;317(7):426–32.
3. Parker AB, Naylor CD. Subgroups, treatment effects, and baseline risks: some lessons from major cardiovascular trials. Am Heart J. 2000;139(6):953–961.
4. Pocock SJ, Travison TG, Wruck LM. Figures in clinical trial reports: current practice & scope for improvement. Trials. 2007;8:36.
5. Yusuf S, Wittes J, Probstfield J, Tyroler H. Analysis and interpretation of treatment effects in subgroups of patients in randomized clinical trials. JAMA. 1991;266(1):93.
6. Assmann SF, Pocock SJ, Enos LE, Kasten LE. Subgroup analysis and other misuses of baseline data in clinical trials. Lancet. 2000;355:1064–9.
7. Wang R, Lagakos SW, Ware JH, Hunter DJ, Drazen JM. Statistics in medicine—reporting of subgroup analyses in clinical trials. N Engl J Med. 2007;357(21):2189–94.
8. Alosh M, Huque MF. Multiplicity considerations for subgroup analysis subject to consistency constraint. Biom J. 2013;55(3):444–62.
9. Stallones RA. The use and abuse of subgroup analysis in epidemiological research. Prev Med. 1987;16:183–94.
10. Feinstein AR. The problem of cogent subgroups: a clinicostatistical tragedy. J Clin Epidemiol. 1998;51(4):297–9.
11. Bulpitt, CJ. Medical statistics. The Lancet 1988; 31–34.
12. Buyse ME. Analysis of clinical trial outcomes: some comments of subgroup analyses. Control Clin Trials. 1989;10:187S–94.
13. Schneider B. Analysis of clinical trial outcomes: alternative approaches to subgroup analysis. Control Clin Trials. 1989;10:176S–86.
14. Angrist JD, Imbens GW, Rubin DB. Identification of causal effects using instrumental variables. J Am Stat Assoc. 1996;91(434):444–55.
15. Imbens, Rubin. Estimating outcome distributions for compliers in instrumental variable models. Rev Econ Stud. 1994;64:555–74.
16. Altman D. Within trial variation—a false trail? J Clin Epidemiol. 1998;51(4):301–3.
17. Adams Jr KF. Post hoc subgroup analysis and the truth of a clinical trial. Am Heart J. 1998;136:753–8.
18. Julian DG. Debate: a subversive view of subsets—a dissident clinician’s opinion. Curr Control Trials Cardiovasc Med. 2000;1:28–30.
19. Sleight P. Debate: subgroup analyses in clinical trials—fun to look at, but don’t believe them! Curr Control Trials Cardiovasc Med. 2000;1:25–7.
20. Pieper KS, Anastasios AT, Davidian M, Hasselblad V, Kleiman NS, Boersma E, et al. Differential treatment benefit of platelet glycoprotein IIb/IIa inhibition with percutaneous coronary intervention versus medical therapy for acute coronary syndromes: exploration of methods. Circulation. 2004;109:641–6.
21. Guillemin F. Primer: the fallacy of subgroup analysis. Nat Clin Pract Rheumatol. 2007;3(7):407–13.
22. Altstein L, Li G. Latent subgroup analysis of a randomized clinical trial through a semiparametric accelerated failure time mixture model. Biometrics. 2013;69:52–61. This paper illustrates a novel statistical approach for drawing causal inference when performing post-randomization subgroup analyses with the goal of estimating the biological effect of an experimental therapy.
23. Furberg CD, Byington RP. What do subgroup analyses reveal about differential response to beta-blocker therapy? The Beta-Blocker Heart Attack Trial experience. Circulation. 1983;67(6):98–101.
24. Hochberg Y, Tamhane A. Multiple comparison procedures. New York: Wiley; 1987.
25. Benjamini, Hochberg. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B. 1995;57(1):289–300.
26. Agresti A. An introduction to categorical data analysis, 2nd edition. Wiley-Interscience; 2007.
27. Piantadosi S. Clinical trials: a methodological perspective, 2nd edition. Wiley Series in Probability and Statistics; 2005.
28. Rosenbaum P, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70(1):41–55.
29. Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivar Behav Res. 2011;46:399–424. This paper gives a nice introduction to propensity scores and describes four approaches that can be applied to estimate causal effects for post-randomization subgroup analyses.
30. Frangakis CE, Rubin DB. Principal stratification in causal inference. Biometrics. 2002;58(1):21–9.
31. Robins J, Hernan M, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology. 2000;11(5):550–60.
32. Allison P. Missing data. Series: quantitative applications in the social sciences. A SAGE University Paper; 2001.
33. Little, Rubin. Statistical analysis with missing data. New York: J. Wiley & Sons; 1987.
34. Thomas L, Peterson ED. The value of statistical analysis plans in observational research: defining high-quality research from the start. J Am Med Assoc. 2012;308(8):773–4.
35. Borenstein M, Hedges L, Higgins J, Rothstein HR. Introduction to meta-analysis. Wiley; 2009.