Background Weight gain after starting antihypertensive medications is a frequent concern for patients, but there is limited data on expected weight change after initiation of these medications. A comparative effectiveness trial to evaluate this outcome would not be feasible. Objective To estimate and compare average weight change under initiating and adhering to commonly prescribed, first-line antihypertensive medications as monotherapy by emulating a target trial. Design Retrospective observational cohort study over 24 months of follow-up using electronic health records (EHR). Participants 141,260 patients prescribed one of seven antihypertensives between 2010 and 2019 across 8 US health systems. Main Outcome and Measures We examined mean weight change associated with initiation of and adherence to amlodipine, atenolol, hydrochlorothiazide, losartan, metoprolol, or propranolol, relative to lisinopril, at 6, 12, and 24 months after initiation. To adjust for baseline confounding and informative outcome measurement, we used inverse probability weighting with repeated outcome marginal structural models. Key Results After baseline and time-varying covariate adjustment, initiation of and adherence to lisinopril were associated with mean weight loss at 6 months (− 0.69 kg, 95% CI − 0.92, − 0.47), 12 months (− 0.58 kg, 95% CI − 1.05, − 0.30), and 24 months (− 1.121 kg, 95% CI − 2.013, − 0.46). Compared to lisinopril, the estimated 6-month weight change was higher for patients prescribed hydrochlorothiazide (0.68 kg, 95% CI 0.31, 1.04), losartan (0.54 kg, 95% CI 0.17, 0.93), metoprolol (1.38 kg, 95% CI 0.95, 1.76), and propranolol (1.03 kg, 95% CI 0.346, 1.62). At 12 months, metoprolol (1.74 kg, 95% CI 1.03, 2.41) and propranolol (1.72 kg, 95% CI 0.06, 3.235) continued to show higher weight change compared to lisinopril. Conclusion We observed small differences in weight change across antihypertensive medications, with lisinopril leading to weight loss and metoprolol and propranolol to modest weight gain. Clinicians should consider potential weight gain when selecting antihypertensive medications.

Epidemiological researchers often examine associations between risk factors and health outcomes in non-experimental designs. Observed associations may be causal or confounded by unmeasured factors. Sibling and co-twin control studies account for familial confounding by comparing exposure levels among siblings (or twins). If the exposure-outcome association is causal, the siblings should also differ regarding the outcome. However, such studies may sometimes introduce more bias than they alleviate. Measurement error in the exposure may bias results and lead to erroneous conclusions that truly causal exposure-outcome associations are confounded by familial factors. The current study used Monte Carlo simulations to examine bias due to measurement error in sibling control models when the observed exposure-outcome association is truly causal. The results showed that decreasing exposure reliability and increasing sibling-correlations in the exposure led to deflated exposure-outcome associations and inflated associations between the family mean of the exposure and the outcome. The risk of falsely concluding that causal associations were confounded was high in many situations. For example, when exposure reliability was 0.7 and the observed sibling-correlation was r = 0.4, about 30–90% of the samples (n = 2,000) provided results supporting a false conclusion of confounding, depending on how p-values were interpreted as evidence for a family effect on the outcome. The current results have practical importance for epidemiological researchers conducting or reviewing sibling and co-twin control studies and may improve our understanding of observed associations between risk factors and health outcomes. We have developed an app (SibSim) providing simulations of many situations not presented in this paper.

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This is an in-person two-day course for quantitative researchers wanting to perform longitudinal data analyses with the programming language R. The course