Assessing the applicability of trial evidence to a target sample in the presence of heterogeneity of treatment effect

Pharmacoepidemiology and Drug Safety

Volumn 21, Issue SUPPL.2, 2012, Pages 121-129

  Weiss, Carlos O ^a,b,c   Segal, Jodi B ^c,d   Varadhan, Ravi ^c,d,e  

^a MICHIGAN STATE UNIVERSITY   (United States)

^b Saint Mary's Health Care   (United States)

^c JOHNS HOPKINS UNIVERSITY SCHOOL OF MEDICINE   (United States)

^d JOHNS HOPKINS BLOOMBERG SCHOOL OF PUBLIC HEALTH   (United States)

^e JOHNS HOPKINS UNIVERSITY   (United States)

Author keywords

Cross design synthesis; Generalizability; Multivariate interaction

Indexed keywords

ENALAPRIL;

AGED; ARTICLE; DISEASE REGISTRY; EVIDENCE BASED MEDICINE; FEMALE; HEART FAILURE; HETEROGENEITY OF TREATMENT EFFECT; HUMAN; MAJOR CLINICAL STUDY; MALE; MORTALITY; OUTCOME ASSESSMENT; PREDICTIVE VALUE; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; RISK BENEFIT ANALYSIS; RISK REDUCTION; THERAPY EFFECT;

AGED; ANGIOTENSIN-CONVERTING ENZYME INHIBITORS; FEMALE; HEART FAILURE; HUMANS; MALE; OUTCOME AND PROCESS ASSESSMENT (HEALTH CARE); PATIENT SELECTION; PHARMACOEPIDEMIOLOGY; PROPORTIONAL HAZARDS MODELS; RANDOMIZED CONTROLLED TRIALS AS TOPIC;

EID: 84860595866     PISSN: 10538569     EISSN: 10991557     Source Type: Journal    
DOI: 10.1002/pds.3242     Document Type: Article

References (51)

1. Kaptchuk TJ. The double-blind, randomized, placebo-controlled trial: gold standard or golden calf? J Clin Epidemiol 2001; 54: 541-549.
2. Weiss CO, Segal JB, Boyd CM, et al. A Framework to Identify and Address Heterogeneity of Treatment Effect in Comparative Effectiveness Research. Agency for Healthcare Research and Quality: Rockville, MD, 2010.
3. Kravitz RL, Duan N, Braslow J. Evidence-based medicine, heterogeneity of treatment effects, and the trouble with averages. Milbank Q 2004; 82: 661-687.
4. Weisberg HI, Hayden VC, Pontes VP. Selection criteria and generalizability within the counterfactual framework: explaining the paradox of antidepressant-induced suicidality? Clin Trials 2009; 6: 109-118. DOI: 10.1177/1740774509102563
5. Frangakis C. The calibration of treatment effects from clinical trials to target populations. Clin Trials 2009; 6: 136-140.
6. Stevens J, Kelleher K, Greenhouse J, et al. Empirical evaluation of the generalizability of the sample from the multimodal treatment study for ADHD. Adm Policy Ment Health 2007; 34: 221-232. DOI:10.1007/s10488-006-0097-4
7. Greenhouse JB, Kaizar EE, Kelleher K, et al. Generalizing from clinical trial data: a case study. The risk of suicidality among pediatric antidepressant users. Stat Med 2008; 27: 1801-1813. DOI:10.1002/sim.3218
8. Cole SR, Stuart EA. Generalizing evidence from randomized clinical trials to target populations: The ACTG 320 trial. Am J Epidemiol 2010; 172: 107-115. DOI:10.1093/aje/kwq084
9. Studies of left ventricular dysfunction (SOLVD)--rationale, design and methods: two trials that evaluate the effect of enalapril in patients with reduced ejection fraction. Am J Cardiol 1990; 66: 315-322.
10. Effect of enalapril on survival in patients with reduced left ventricular ejection fractions and congestive heart failure. The SOLVD Investigators. N Engl J Med 1991; 325: 293-302. DOI:10.1056/NEJM199108013250501
11. Bangdiwala SI, Weiner DH, Bourassa MG, et al. Studies of Left Ventricular Dysfunction (SOLVD) Registry: rationale, design, methods and description of baseline characteristics. Am J Cardiol 1992; 70: 347-353.
12. Varadhan R, Weiss C, Segal J, et al. Evaluating Health Outcomes in the Presence of Competing Risks: A Review of Statistical Methods and Clinical Applications. Med Care 2010; 48: S96-S105.
13. Fried LP, Kronmal RA, Newman AB, et al. Risk factors for 5-year mortality in older adults: the Cardiovascular Health Study. JAMA 1998; 279: 585-592.
14. Schonberg MA, Davis RB, Mccarthy EP, et al. Index to predict 5-year mortality of community-dwelling adults aged 65 and older using data from the national health interview survey. J Gen Intern Med 2009; 24: 1115-1122. DOI:10.1007/s11606-009-1073-y
15. Lee SJ, Lindquist K, Segal MR, et al. Development and validation of a prognostic index for 4-year mortality in older adults. JAMA 2006; 295: 801-808.
16. Kearney MT, Nolan J, Lee AJ, et al. A prognostic index to predict long-term mortality in patients with mild to moderate chronic heart failure stabilised on angiotensin converting enzyme inhibitors. Eur J Heart Fail 2003; 5: 489-497. DOI:10.1016/S1388-9842(03)00053-9
17. Levy WC, Mozaffarian D, Linker DT, et al. The Seattle Heart Failure Model: prediction of survival in heart failure. Circulation 2006; 113: 1424-1433. DOI:10.1161/CIRCULATIONAHA.105.584102
18. Pocock SJ, Wang D, Pfeffer MA, et al. Predictors of mortality and morbidity in patients with chronic heart failure. Eur Heart J 2006; 27: 65-75. DOI:10.1093/eurheartj/ehi555
19. Vazquez R, Bayes-Genis A, Cygankiewicz I, et al. The MUSIC Risk score: a simple method for predicting mortality in ambulatory patients with chronic heart failure. Eur Heart J 2009; 30: 1088-1096. DOI:10.1093/eurheartj/ehp032
20. Effect of enalapril on mortality and the development of heart failure in asymptomatic patients with reduced left ventricular ejection fractions. The SOLVD Investigattors. N Engl J Med 1992; 327: 685-691.
21. Wermuth N, Cochran WG. Detecting Systematic Errors in Multi-Clinic Observational Data. Biometrics 1979; 35: 683-686.
22. Gnanadesikan R. Methods for Statistical Data Analysis of Multivariate Observations. New York: Wiley; 1977.
23. Gabriel KR, Odoroff CL. Biplots in biomedical research. Stat Med 1990; 9: 469-485.
24. White IR, Royston P, Wood AM. Multiple imputation using chained equations: Issues and guidance for practice. Stat Med 2011; 30: 377-399. DOI:10.1002/sim.4067
25. Stuart EA. Recent Advances in Missing Data Methods: Multiple Imputation by Chained Equations. Paper presented at: Academy Health Annual Conference; November 12, 2010, Boston, MA, 2010.
26. Brand R. Kragt H. Importance of trends in the interpretation of an overall odds ratio in the meta-analysis of clinical trials. Stat Med 1992; 11: 2077-2082.
27. Mcintosh MW. The population risk as an explanatory variable in research synthesis of clinical trials. Stat Med 1996; 15: 1713-1728.
28. Kent DM, Hayward RA. Limitations of applying summary results of clinical trials to individual patients: the need for risk stratification. JAMA 2007; 298: 1209-1212.
29. Follmann DA, Proschan MA. A multivariate test of interaction for use in clinical trials. Biometrics 1999; 55: 1151-1155.
30. Austin PC. Absolute risk reductions and numbers needed to treat can be obtained from adjusted survival models for time-to-event outcomes. J Clin Epidemiol 2010; 63: 46-55.
31. Katki HA, Mark SD. Survival analysis for cohorts with missing covariate information. R news 2008; 8: 14-19.
32. R: A language and environment ofr statistical computing [computer program]. Version index version 2.2.1. Vienna, Austria: Foundation for Statistical Computing, 2005.
33. Harrell FE. rms: Regression Modeling Strategies. 2011. (accessed 12 November 2010)
34. Therneau T. Survival: Survival analysis, including penalised likelihood. 2011. (accessed 12 November 12 2010)
35. Stata MP-Parallel Edition Statistical Software [computer program]. Version 11. College Station, TX, 2010.
36. Longford NT. Selection bias and treatment heterogeneity in clinical trials. Stat Med 1999; 18: 1467-1474.
37. Piantadosi S. Clinical Trials: A Methodologic Perspective. Hoboken: John Wiley & Sons, Inc., 2005.
38. Droitcour J, Silberman G, Chelimsky E. A new form of meta-analysis for combining results from randomized clinical trials and medical-practice databases. Int J Technol Assess Health Care 1993; 9: 440-449.
39. Eddy DM. The confidence profile method: a Bayesian method for assessing health technologies. Oper Res 1989; 37: 210-228. DOI:10.1287/opre.37.2.210
40. Kraemer HC, Frank E. Evaluation of comparative treatment trials: assessing clinical benefits and risks for patients, rather than statistical effects on measures. JAMA 2010; 304: 683-684. DOI:10.1001/jama.2010.1133
41. Politi MC, Han PK, Col NF. Communicating the uncertainty of harms and benefits of medical interventions. Med Dec Making: Int J Soc Med Dec Making 2007; 27: 681-695. DOI:10.1177/0272989X07307270
42. Devereaux PJ, Anderson DR, Gardner MJ, et al. Differences between perspectives of physicians and patients on anticoagulation in patients with atrial fibrillation: observational study. BMJ 2001; 323: 1218-1222.
43. Guo JJ, Pandey S, Doyle J, et al. A review of quantitative risk-benefit methodologies for assessing drug safety and efficacy-report of the ISPOR risk-benefit management working group. Value in health: J Int So Pharmacoeconomics and Outcomes Research 2010; 13: 657-666. DOI:10.1111/j.1524-4733.2010.00725.x
44. Weiss CO, Segal JB, Boyd CM, et al. Assessment of the Clinical Exchangeability of a Trial with a Target Sample Using Multivariate Methods Agency for Healthcare Research and Quality: Rockville, MD, 2010.
45. Hansen BB. The prognostic analogue of the propensity score. Biometrika 2008; 95: 481-488.
46. Schmid CH, Lau J, Mcintosh MW, et al. An empirical study of the effect of the control rate as a predictor of treatment efficacy in meta-analysis of clinical trials. Stat Med 1998; 17: 1923-1942.
47. Eberly LE. Consequences of event rate heterogeneity across non-randomized study sub-groups. Stat Med 2004; 23: 2023-2036. DOI:10.1002/sim.1795
48. Ioannidis JP, Lau J. Heterogeneity of the baseline risk within patient populations of clinical trials: a proposed evaluation algorithm. Am J Epidemiol 1998; 148: 1117-1126.
49. Walter SD. Variation in baseline risk as an explanation of heterogeneity in meta-analysis. Stat Med 1997; 16: 2883-2900. DOI:10.1002/(SICI)1097-0258(19971230)16:24<2883::AID-SIM825>3.0.CO;2-B
50. Sharp SJ, Thompson SG. Analysing the relationship between treatment effect and underlying risk in meta-analysis: comparison and development of approaches. Stat Med 2000; 19: 3251-3274. DOI:10.1002/1097-0258(20001215)19:23<3251::AID-SIM625>3.0.CO;2-2
51. Thorpe KE, Zwarenstein M, Oxman AD, et al. A pragmatic-explanatory continuum indicator summary (PRECIS): a tool to help trial designers. J Clin Epidemiol 2009; 62: 464-475. DOI:10.1016/j.jclinepi.2008.12.011