Estimating effects from randomized trials with discontinuations: the need for intent-to-treat design and G-estimation

Abstract

Background Randomized trials provide pivotal evidence for evaluation and approval of therapies. Nonetheless, such trials are often plagued by noncompliance, especially in the form of premature discontinuation of treatment. While intent-to-treat (ITT) analysis can provide valid tests of no-effect hypotheses, some trials may make ITT analysis impossible by ceasing follow-up when patients go off assigned treatment. Furthermore, estimates based on ITT, on-treatment, or per-protocol comparisons can seriously understate harm or benefit.

Purpose To show how g-estimation based on randomization status is a natural generalization of ITT null testing to estimating efficacy from trials with important discontinuation or noncompliance.

Methods We contrast with an analysis of the effect of a tiotropium inhaler on the occurrence of chronic obstructive pulmonary disease (COPD) events in a six-month double-blind placebo-controlled trial of 1829 patients with good but imperfect compliance.

Results The covariate-adjusted point estimates, 95% confidence limits (CL), and null P-values comparing expected COPD event times in placebo versus tiotropium patients were: ITT, 1.21, CL = 1.02, 1.43, P = 0.027; on-treatment, 1.27, CL = 1.06, 1.52, P = 0.009; per-protocol, 1.36, CL = 1.13, 1.63, P = 0.001; and g-estimation, 1.31, CL = 1.03,1.72, P = 0.027. Thus g-estimation preserved the ITT test of the null, but exhibited more uncertainty about the size of the tiotropium effect than the other methods. In particular, it allowed for a much larger potential effect than did ITT analysis, but produced a much larger null P than exhibited by per-protocol analysis. Limitations Like ITT analysis, g-estimation requires all patients be followed to the end of the trial protocol, regardless of whether they comply with the protocol. Like on-treatment and per-protocol analyses, it also requires accurate compliance information be recorded.

Conclusion G-estimation should become a standard procedure for the analysis of trials with noncompliance. Software to do so is available in major packages, and the procedure is easily coded for other packages. Clinical Trials 2008; 5: 5—13. http://ctj.sagepub.com

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References

D'Agostino RB Sr , Massaro JM , Sullivan LM Non-inferiority trials: design concepts and issues—the encounters of academic consultants in statistics. Stat Med 2003; 22: 169—86.

Robins JM , Tsiatis AA Correcting for non-compliance in randomized trials using rank-preserving structural failure-time models. Commun Stat 1991; 20: 2609—31.

Scheiner LB , Rubin DB Intent-to-treat analysis and the goals of clinical trials. Clin Pharm Therap 1995; 57: 6—15.

Pearl J. Causality: Models, Reasoning, and Inference Ch. 8, Cambridge University Press, Cambridge, New York, 2000.

Dunn G. , Goetgebheur E. Analysing compliance in clinical trials. Stat Methods Med Res 2005; 14: 325—6.

Sommer A. , Zeger S. On estimating efficacy from clinical trials. Stat Med 1991; 10: 47—52.

Angrist JD , Imbens GW , Rubin DB Identification of causal effects using instrumental variables (with discussion) . J Am Stat Assoc 1996; 91: 444—55.

Greenland S. An introduction to instrumental variables for epidemiologists. Int J Epidemiol 2000; 29: 722—9.

Dunn G. , Maracy M. , Tomenson B. Estimating treatment effects from randomized clinical trials with noncompliance and loss to follow-up: the role of instrumental variable methods. Stat Methods Med Res 2005; 14: 369—95.

10.

Mark SD , Robins JM A method for the analysis of randomized trials with compliance information: an application to the Multiple Risk Factor Intervention Trial. Control Clin Trials 1993; 14: 79—97.

11.

Robins JM Correcting for noncompliance in randomized trials using structural nested mean models. Commun Stat Theory Meth 1994; 23: 2379—412.

12.

Korhonen PA , Laird NM , Palmgren J. Correcting for non-compliance in randomized trials: an application to the ATBC study. Stat Med 1999; 18: 2879—97.

13.

Cole SR , Chu H. Effect of acyclovir on herpetic ocular recurrence using a structural nested model. Contemp Clin Trials 2005; 26: 300—10.

14.

Niewoehner DE , Rice K. , Cote C. , et al. Prevention of exacerbations of chronic obstructive pulmonary disease with tiotropium, a once-daily inhaled anticholinergic bronchodilator. Ann Intern Med 2005; 143: 317—26.

15.

Kesten S. , Plautz M. , Piquette C. , et al. Premature discontinuation of patients most afflicted from the disease under study: a potential bias in COPD clinical trials. European Respiratory Journal 2007; 30: 898—906.

16.

Kalbfleisch JD , Prentice RL The Statistical Analysis of Failure Time Data (2nd ed.) Wiley, New York, 2002.

17.

Robins JM , Blevins D. , Ritter G. , Wulfsohn M. G-estimation of the effect of prophylaxis therapy for pneumocystis carinii pneumonia on the survival of AIDS patients. Epidemiology 1992; 3: 319—36 [errata: Epidemiology 1993; 4: 189].

18.

Hernán MA , Brumback B. , Robins JM Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. Epidemiology 2000; 11: 561—70.

19.

Loeys T. , Goetghebeur E. , Vandebosch A. Causal proportional hazards models and time-constant exposure in randomized clinical trials. Lifetime Data Analysis 2005 ; 11: 435—49.

20.

Witteman Jcm , D Agostino RB Sr , Stijnen T. , et al. G-estimation of causal effects: isolated systolic hypertension and cardiovascular death in the Framingham Study. Am J Epidemiol 1998 ; 148: 390—401.

21.

Sterne JAC , Tilling K. G-estimation of causal effects, allowing for time-varying confounding. The Stata Journal 2002; 2: 164—82.

22.

White IR , Walker S. , Babiker A. Strbee: Randomization-based efficacy estimator. The Stata Journal 2002; 2: 140—50.

23.

Robins JM , Greenland S. Adjusting for differential rates of PCP prophylaxis in high- versus low-dose AZT treatment arms in an AIDS randomized trial. J Am Stat Assoc 1994; 89: 737—49.

24.

Hernán MA , Cole SR , Margolick J. , et al. Structural accelerated failure time models for survival analysis in studies with time-varying treatments. Pharmacoepidemiology Drug Safety 2005; 14: 477—91.

25.

Robins JM , Rotnitzky A. , Zhao LP Analysis of semiparametric regression models for repeated outcomes in the presence of missing data. Journal of the American Statistical Association 1995; 90: 106—21.

26.

Robins JM , Finkelstein D. Correcting for noncompliance and dependent censoring in an AIDS clinical trial with inverse probability of censoring weighted (IPCW) log-rank tests. Biometrics 2000; 56: 779—88.

27.

Joffe MM Administrative and artificial censoring in censored regression models. Stat Med 2001; 20: 2287—304.