Observational studies analyzed like randomized experiments: an application to postmenopausal hormone therapy and coronary heart disease

By Miguel A. Hern'an, Á. Alonso, R. Logan, F. Grodstein, Karin B. Michels, MR Stampfer, W. Willett, JoAnn E. Manson, James M. Robins. Epidemiology, 2008

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Background: The Women's Health Initiative randomized trial found greater coronary heart disease (CHD) risk in women assigned to estrogen/progestin therapy than in those assigned to placebo. Observational studies had previously suggested reduced CHD risk in hormone users. Methods: Using data from the observational Nurses’ Health Study, we emulated the design and intention-to-treat (ITT) analysis of the randomized trial. The observational study was conceptualized as a sequence of “trials,” in which eligible women were classified as initiators or noninitiators of estrogen/progestin therapy. Results: The ITT hazard ratios (HRs) (95% confidence intervals) of CHD for initiators versus noninitiators were 1.42 (0.92–2.20) for the first 2 years, and 0.96 (0.78–1.18) for the entire follow-up. The ITT HRs were 0.84 (0.61–1.14) in women within 10 years of menopause, and 1.12 (0.84–1.48) in the others (P value for interaction = 0.08). These ITT estimates are similar to those from the Women's Health Initiative. Because the ITT approach causes severe treatment misclassification, we also estimated adherence-adjusted effects by inverse probability weighting. The HRs were 1.61 (0.97–2.66) for the first 2 years, and 0.98 (0.66–1.49) for the entire follow-up. The HRs were 0.54 (0.19–1.51) in women within 10 years after menopause, and 1.20 (0.78–1.84) in others (P value for interaction = 0.01). We also present comparisons between these estimates and previously reported Nurses’ Health Study estimates. Conclusions: Our findings suggest that the discrepancies between the Women's Health Initiative and Nurses’ Health Study ITT estimates could be largely explained by differences in the distribution of time since menopause and length of follow-up.

Subfields: Endocrinology, Diabetes and Metabolism, Genetics, Public Health, Environmental and Occupational Health

View Paper: https://www.semanticscholar.org/paper/5752083c8e18d6beb512f7f95ead83098a1090d6

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Citations in the corpus

ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions (2016, background): “18 However, fear that evidence from NRSI may be biased, based on misleading results of some NRSI, 19 20 has led to caution in their use in making judgements about efficacy.”
Can Nonrandomized Experiments Yield Accurate Answers? A Randomized Experiment Comparing Random and Nonrandom Assignments (2008, background): “(For a more detailed description, see Hernan et al. 2008.)”
The calibration of treatment effects from clinical trials to target populations (2009, result): “Recent literature [1,2] suggests that differences between the results seen in an RCT and in an observational study can be larger than expected not because of confounding, but because of differences between their respective patient populations.”
Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available. (2016, background): “, every 2 years, like in the Nurses’Health Study), then we can emulate a new trial starting at each prespecified time (13).”
A Review of Generalizability and Transportability (2021, background): “That is, change the estimand of interest by aligning inclusion and exclusion criteria, outcome timepoints, or treatment doses (Hernán et al., 2008; Weisberg, Hayden and Pontes, 2009).”
Observational data for comparative effectiveness research: an emulation of randomised trials to estimate the effect of statins on primary prevention of coronary heart disease (2011, methodology): “One approach to solve the latter problem is using the observational data to emulate a hypothetical randomised trial.(6,7) Here, we describe this approach along with a real-world application to estimate the effect of statin therapy on the risk of coronary heart disease (CHD) using electronic medical ”