Specifying a target trial prevents immortal time bias and other self-inflicted injuries in observational analyses

Journal of Clinical Epidemiology

Volumn 79, Issue , 2016, Pages 70-75

  Hernán, Miguel A ^a,b   Sauer, Brian C ^c   Hernández Díaz, Sonia ^a   Platt, Robert ^d,e,f   Shrier, Ian ^f  

^a HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

^b HARVARD MIT DIVISION OF HEALTH SCIENCES AND TECHNOLOGY   (United States)

^c UNIVERSITY OF UTAH SCHOOL OF MEDICINE   (United States)

^d MCGILL UNIVERSITY   (Canada)

^e RESEARCH INSTITUTE OF THE MCGILL UNIVERSITY HEALTH CENTRE   (Canada)

^f MCGILL UNIVERSITY   (Canada)

Author keywords

Comparative effectiveness research; Immortal time bias; Observational; Selection bias; studies; Target trial; Time zero

Indexed keywords

ARTICLE; AUTOMUTILATION; COMPARATIVE EFFECTIVENESS; FOLLOW UP; PATIENT SAFETY; PRIORITY JOURNAL; SELECTION BIAS; TIME; EPIDEMIOLOGY; HUMAN; OBSERVATIONAL STUDY; STATISTICAL BIAS; STATISTICS AND NUMERICAL DATA; TIME FACTOR;

BIAS (EPIDEMIOLOGY); COMPARATIVE EFFECTIVENESS RESEARCH; EPIDEMIOLOGIC RESEARCH DESIGN; HUMANS; OBSERVATIONAL STUDIES AS TOPIC; SELECTION BIAS; TIME FACTORS;

EID: 85006299163     PISSN: 08954356     EISSN: 18785921     Source Type: Journal    
DOI: 10.1016/j.jclinepi.2016.04.014     Document Type: Article

References (26)

1. [1] Hernán, M.A., Robins, J.M., Using big data to emulate a target trial when a randomized trial is not available. Am J Epidemiol 183 (2016), 758–764.
2. [2] Cochran, W., Observational studies. Bancroft, T.A., (eds.) Statistical Papers in Honor of George W. Snedecor, 1972, Iowa State University Press, Ames, Iowa, 77–90.
3. [3] Walker, A.M., Observation and inference: an introduction to the methods of epidemiology. 1991, Epidemiology Resources Inc, Newton Lower Falls.
4. [4] Suissa, S., Immortal time bias in pharmaco-epidemiology. Am J Epidemiol 167 (2008), 492–499.
5. [5] Giobbie-Hurder, A., Gelber, R.D., Regan, M.M., Challenges of guarantee-time bias. J Clin Oncol 31 (2013), 2963–2969.
6. [6] Little, R.J., D'Agostino, R., Cohen, M.L., Dickersin, K., Emerson, S.S., Farrar, J.T., et al. The prevention and treatment of missing data in clinical trials. N Engl J Med 367 (2012), 1355–1360.
7. [7] Hernán, M.A., Robins, J.M., Causal inference. 2016, Chapman & Hall, Boca Raton, Florida.
8. [8] Hernán, M.A., Counterpoint: epidemiology to guide decision-making: moving away from practice-free research. Am J Epidemiol 182 (2015), 834–839.
9. [9] Ray, W.A., Evaluating medication effects outside of clinical trials: new-user designs. Am J Epidemiol 158 (2003), 915–920.
10. [10] Robins, J.M., Hernan, M.A., Rotnitzky, A., Effect modification by time-varying covariates. Am J Epidemiol 166 (2007), 994–1002 discussion 1003-4.
11. [11] Hernán, M.A., Hernández-Díaz, S., Robins, J.M., A structural approach to selection bias. Epidemiology 15 (2004), 615–625.
12. [12] Gail, M.H., Does cardiac transplantation prolong life? A reassessment. Ann Intern Med 76 (1972), 815–817.
13. [13] Anderson, J.R., Cain, K.C., Gelber, R.D., Analysis of survival by tumor response. J Clin Oncol 1 (1983), 710–719.
14. [14] Suissa, S., Immortal time bias in observational studies of drug effects. Pharmacoepidemiol Drug Saf 16 (2007), 241–249.
15. [15] Suissa, S., Dell'aniello, S., Vahey, S., Renoux, C., Time-window bias in case-control studies: statins and lung cancer. Epidemiology 22 (2011), 228–231.
16. [16] Hernandez-Diaz, S., Name of the bias and sex of the angels. Epidemiology 22 (2011), 232–233.
17. [17] Hernán, M.A., Alonso, A., Logan, R., Grodstein, F., Michels, K.B., Willett, W.C., et al. Observational studies analyzed like randomized experiments: an application to postmenopausal hormone therapy and coronary heart disease. Epidemiology 19 (2008), 766–779.
18. [18] Danaei, G., Rodríguez, L.A., Cantero, O.F., Logan, R., Hernán, M.A., Observational data for comparative effectiveness research: an emulation of randomised trials of statins and primary prevention of coronary heart disease. Stat Methods Med Res 22 (2013), 70–96.
19. [19] Robins, J.M., A new approach to causal inference in mortality studies with a sustained exposure period—application to the healthy worker survivor effect. [published errata appear in Computers and Mathematics with Applications 1987;14:917-21] Math Model 7 (1986), 1393–1512.
20. [20] Robins, J.M., Finkelstein, D., Correcting for non-compliance and dependent censoring in an AIDS clinical trial with inverse probability of censoring weighted (IPCW) Log-rank tests. Biometrics 56 (2000), 779–788.
21. [21] Robins, J.M., Rotnitzky, A., Recovery of information and adjustment for dependent censoring using surrogate markers. Jewell, N., Dietz, K., Farewell, V., (eds.) AIDS Epidemiology – Methodological Issues, 1992, Birkhäuser, Boston, MA, 297–331.
22. [22] Cain, L.E., Robins, J.M., Lanoy, E., Logan, R., Costagliola, D., Hernán, M.A., When to start treatment? a systematic approach to the comparison of dynamic regimes using observational data. Int J Biostat, 6(2), 2010 Article 18.
23. [23] Orellana, L., Rotnitzky, A., Robins, J.M., Dynamic regime marginal structural mean models for estimation of optimal dynamic treatment regimes, Part II: proofs of results. Int J Biostat, 6(2), 2010 Article 9.
24. [24] Orellana, L., Rotnitzky, A., Robins, J.M., Dynamic regime marginal structural mean models for estimation of optimal dynamic treatment regimes, Part I: main content. Int J Biostat, 6(2), 2010 Article 8.
25. [25] van der Laan, M.J., Petersen, M.L., Causal effect models for realistic individualized treatment and intention to treat rules. Int J Biostat, 3(1), 2007 Article 3.
26. [26] Huitfeldt, A., Kalager, M., Robins, J.M., Hoff, G., Hernán, M.A., Methods to estimate the comparative effectiveness of clinical strategies that administer the same intervention at different times. Curr Epidemiol Rep 2:3 (2015), 149–161.