1.
Cochran, WG . The planning of observational studies of human populations. J Roy Stat Soc Ser A 1965; 128(2): 234–265.
Google Scholar |
Crossref2.
Mostazir, M, Taylor, RS, Henley, W, et al. An overview of statistical methods for handling nonadherence to intervention protocol in randomized control trials: a methodological review. J Clin Epidemiol 2019; 108: 121–131.
Google Scholar |
Crossref |
Medline3.
The ICH E9 Addendum on “Estimands and sensitivity analysis in clinical trials ,” 2020,
https://isctm.org/public_access/Feb2020/Presentation/Butlen-Ducuing-Presentation.pdf Google Scholar4.
Lipkovich, I, Ratitch, B, Mallinckrodt, CH. Causal inference and estimands in clinical trials. Stat Biopharmaceut Res 2020; 12(1): 54–67.
Google Scholar |
Crossref5.
Grant, AM, Wileman, SM, Ramsay, CR, et al. The effectiveness and cost-effectiveness of minimal access surgery amongst people with gastro-oesophageal reflux disease—a UK collaborative study. Health Technol Assess 2008; 12(31): 1–181.
Google Scholar |
Crossref |
Medline6.
Grant, A, Boachie, C, Cotton, S, et al. Clinical and economic evaluation of laparoscopic surgery compared with medical management for gastro-oesophageal reflux disease: 5-year follow-up of multicentre randomised trial (the reflux trial). Health Technol Assess 2013; 17(22): 1–167.
Google Scholar |
Crossref |
Medline7.
Rabin, R, de Charro, F. EQ-SD: a measure of health status from the EuroQol Group. Ann Med 2001; 33(5): 337–343.
Google Scholar |
Crossref |
Medline |
ISI8.
Pearl, J . Causality: models, reasoning, and inference. 2nd ed. New York: Cambridge University Press, 2018.
Google Scholar9.
Rubin, DB . Which ifs have causal answers. J Am Stat Assoc 1986; 81(396): 961–962.
Google Scholar |
ISI10.
Hernán, MA, Hernández-Díaz, S. Beyond the intention-to-treat in comparative effectiveness research. Clin Trials 2012; 9(1): 48–55.
Google Scholar |
SAGE Journals |
ISI11.
Hernán, MA, Robins, JM. Per-protocol analyses of pragmatic trials. New Engl J Med 2017; 377(14): 1391–1398.
Google Scholar |
Crossref |
Medline12.
Little, RJ, Rubin, DB. Causal effects in clinical and epidemiological studies via potential outcomes: concepts and analytical approaches. Ann Rev Publ Health 2000; 21: 121–145.
Google Scholar |
Crossref |
Medline |
ISI13.
Imbens, GW, Rubin, DB. Causal inference for statistics, social, and biomedical sciences: an introduction. Cambridge: Cambridge University Press, 2015.
Google Scholar |
Crossref14.
Murray, EJ, Hernán, MA. Improved adherence adjustment in the coronary drug project. Trials 2018; 19(1): 158.
Google Scholar |
Crossref |
Medline15.
Murray, EJ, Hernán, MA. Adherence adjustment in the coronary drug project: a call for better per-protocol effect estimates in randomized trials. Clin Trials 2016; 13(4): 372–378.
Google Scholar |
SAGE Journals |
ISI16.
Robins, JM, Hernán, MA, Brumback, B. Marginal structural models and causal inference in epidemiology. Epidemiology 2000; 11(5): 550–560.
Google Scholar |
Crossref |
Medline |
ISI17.
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(5): 561–570.
Google Scholar |
Crossref |
Medline |
ISI18.
Murray, EJ, Claggett, BL, Granger, B, et al. Adherence-adjustment in placebo-controlled randomized trials: an application to the candesartan in heart failure randomized trial. Contemp Clin Trials 2020; 90: 105937.
Google Scholar |
Crossref |
Medline19.
Imbens, GW, Angrist, JD. Identification and estimation of local average treatment effects. Econometrica 1994; 62(2): 467–476.
Google Scholar |
Crossref |
ISI20.
Frangakis, CA, Rubin, DB. Principal stratification in causal inference. Biometrics 2002; 58(1): 21–29.
Google Scholar |
Crossref |
Medline |
ISI21.
Angrist, JD, Imbens, GW, Rubin, DB. Identification of causal effects using instrumental variables. J Am Stat Assoc 1996; 91(434): 444–455.
Google Scholar |
Crossref |
ISI22.
Swanson, SA, Hernán, MA. The challenging interpretation of instrumental variable estimates under monotonicity. Int J Epidemiol 2018; 47(4): 1289–1297.
Google Scholar |
Crossref |
Medline23.
Imbens, GW . Better LATE than nothing: some comments on Deaton (2009) and Heckman and Urzua (2009). J Econ Lit 2010; 48(2): 399–423.
Google Scholar |
Crossref24.
Imbens, G . Instrumental variables: an econometrician’s perspective. Stat Sci 2014; 29(3): 323–358.
Google Scholar |
Crossref25.
Baiocchi, M, Cheng, J, Small, DS. Instrumental variable methods for causal inference. Stat Med 2014; 33(13): 2297–2340.
Google Scholar |
Crossref |
Medline26.
Angrist, JD, Pischke, S. Mostly harmless econometrics. Princeton, NJ: Princeton University Press, 2009.
Google Scholar |
Crossref27.
Marbach, M, Hangartner, D. Profiling compliers and noncompliers for instrumental-variable analysis. Polit Anal 2020; 28(3): 435–444.
Google Scholar |
Crossref28.
Kang, H, Peck, L, Keele, L. Inference for instrumental variables: a randomization inference approach. J Roy Stat Soc Ser A 2018,
https://arxiv.org/pdf/1606.04146.pdf Google Scholar29.
Soumerai, SB, Koppel, R. The reliability of instrumental variables in health care effectiveness research: less is more. Health Serv Res 2017; 52(1): 9–15.
Google Scholar |
Crossref |
Medline30.
Soumerai, SB, Starr, D, Majumdar, SR. How do you know which health care effectiveness research you can trust? A guide to study design for the perplexed. Prevent Chron Dis 2015; 12: 150–187.
Google Scholar31.
Karanicolas, PJ, Farrokhyar, F, Bhandari, M. Blinding: who, what, when, why, how? Can J Surg 2010; 53(5): 345–348.
Google Scholar |
Medline32.
Wald, A . The fitting of straight lines if both variables are subject to error. Ann Math Stat 1940; 11: 284–300.
Google Scholar |
Crossref33.
Wooldridge, JM . Econometric analysis of cross section and panel data. Boston, MA: MIT Press, 2010.
Google Scholar34.
Qu, Y, Fu, H, Luo, J, et al. A general framework for treatment effect estimators considering patient adherence. Stat Biopharmaceut Res 2020; 12(1): 1–18.
Google Scholar |
Crossref35.
Hernán, MA, Robins, JM. Causal inference: what if. Boca Raton, FL: Chapman & Hall/CRC, 2020.
Google Scholar36.
Bartlett, JW, Carpenter, JR, Tilling, K, et al. Improving upon the efficiency of complete case analysis when covariates are MNAR. Biostatistics 2014; 15(4): 719–730.
Google Scholar |
Crossref |
Medline37.
Mealli, F, Rubin, DB. Assumptions when analyzing randomized experiments with noncompliance and missing outcomes. Health Serv Outcome Res Methodol 2002; 3(3–4): 225–232.
Google Scholar |
Crossref38.
Mason, AJ, Gomes, M, Carpenter, J, et al. Flexible Bayesian longitudinal models for cost-effectiveness analyses with informative missing data. Health Econ. Epub ahead of print 25 September 2021. DOI:
10.1002/hec.4408.
Google Scholar |
Crossref39.
Akacha, M, Bretz, F, Ruberg, S. Estimands in clinical trials—broadening the perspective. Stat Med 2017; 36(1): 5–19.
Google Scholar |
Crossref |
Medline
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