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Petimar J, Young JG, Yu H, Rifas-Shiman SL, Daley MF, Heerman WJ, Janicke DM, Jones WS, Lewis KH, Lin PID, Prentice C, Merriman JW, Toh S, Block JP. Medication-Induced Weight Change Across Common Antidepressant Treatments : A Target Trial Emulation Study. Ann Intern Med 2024. [PMID: 38950403 DOI: 10.7326/m23-2742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/03/2024] Open
Abstract
BACKGROUND Antidepressants are among the most commonly prescribed medications, but evidence on comparative weight change for specific first-line treatments is limited. OBJECTIVE To compare weight change across common first-line antidepressant treatments by emulating a target trial. DESIGN Observational cohort study over 24 months. SETTING Electronic health record (EHR) data from 2010 to 2019 across 8 U.S. health systems. PARTICIPANTS 183 118 patients. MEASUREMENTS Prescription data determined initiation of treatment with sertraline, citalopram, escitalopram, fluoxetine, paroxetine, bupropion, duloxetine, or venlafaxine. The investigators estimated the population-level effects of initiating each treatment, relative to sertraline, on mean weight change (primary) and the probability of gaining at least 5% of baseline weight (secondary) 6 months after initiation. Inverse probability weighting of repeated outcome marginal structural models was used to account for baseline confounding and informative outcome measurement. In secondary analyses, the effects of initiating and adhering to each treatment protocol were estimated. RESULTS Compared with that for sertraline, estimated 6-month weight gain was higher for escitalopram (difference, 0.41 kg [95% CI, 0.31 to 0.52 kg]), paroxetine (difference, 0.37 kg [CI, 0.20 to 0.54 kg]), duloxetine (difference, 0.34 kg [CI, 0.22 to 0.44 kg]), venlafaxine (difference, 0.17 kg [CI, 0.03 to 0.31 kg]), and citalopram (difference, 0.12 kg [CI, 0.02 to 0.23 kg]); similar for fluoxetine (difference, -0.07 kg [CI, -0.19 to 0.04 kg]); and lower for bupropion (difference, -0.22 kg [CI, -0.33 to -0.12 kg]). Escitalopram, paroxetine, and duloxetine were associated with 10% to 15% higher risk for gaining at least 5% of baseline weight, whereas bupropion was associated with 15% reduced risk. When the effects of initiation and adherence were estimated, associations were stronger but had wider CIs. Six-month adherence ranged from 28% (duloxetine) to 41% (bupropion). LIMITATION No data on medication dispensing, low medication adherence, incomplete data on adherence, and incomplete data on weight measures across time points. CONCLUSION Small differences in mean weight change were found between 8 first-line antidepressants, with bupropion consistently showing the least weight gain, although adherence to medications over follow-up was low. Clinicians could consider potential weight gain when initiating antidepressant treatment. PRIMARY FUNDING SOURCE National Institutes of Health.
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Affiliation(s)
- Joshua Petimar
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, and Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts (J.P., J.G.Y.)
| | - Jessica G Young
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, and Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts (J.P., J.G.Y.)
| | - Han Yu
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts (H.Y., S.L.R.-S., P.-I.D.L., S.T., J.P.B.)
| | - Sheryl L Rifas-Shiman
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts (H.Y., S.L.R.-S., P.-I.D.L., S.T., J.P.B.)
| | - Matthew F Daley
- Institute for Health Research, Kaiser Permanente Colorado, Denver, Colorado (M.F.D.)
| | - William J Heerman
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee (W.J.H.)
| | - David M Janicke
- Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida, Gainesville, Florida (D.M.J.)
| | - W Schuyler Jones
- Division of Cardiology, Duke University Department of Medicine, and Duke University Medical Center, Duke Clinical Research Institute, Durham, North Carolina (W.S.J.)
| | - Kristina H Lewis
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, North Carolina (K.H.L.)
| | - Pi-I D Lin
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts (H.Y., S.L.R.-S., P.-I.D.L., S.T., J.P.B.)
| | - Carly Prentice
- Faith Family Medical Center, Nashville, Tennessee (C.P.)
| | - John W Merriman
- Department of Medicine, College of Medicine, University of Florida, Gainesville, Florida (J.W.M.)
| | - Sengwee Toh
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts (H.Y., S.L.R.-S., P.-I.D.L., S.T., J.P.B.)
| | - Jason P Block
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts (H.Y., S.L.R.-S., P.-I.D.L., S.T., J.P.B.)
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Rojas-Saunero LP, Glymour MM, Mayeda ER. Selection Bias in Health Research: Quantifying, Eliminating, or Exacerbating Health Disparities? CURR EPIDEMIOL REP 2024; 11:63-72. [PMID: 38912229 PMCID: PMC11192540 DOI: 10.1007/s40471-023-00325-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/02/2023] [Indexed: 06/25/2024]
Abstract
Purpose of review To summarize recent literature on selection bias in disparities research addressing either descriptive or causal questions, with examples from dementia research. Recent findings Defining a clear estimand, including the target population, is essential to assess whether generalizability bias or collider-stratification bias are threats to inferences. Selection bias in disparities research can result from sampling strategies, differential inclusion pipelines, loss to follow-up, and competing events. If competing events occur, several potentially relevant estimands can be estimated under different assumptions, with different interpretations. The apparent magnitude of a disparity can differ substantially based on the chosen estimand. Both randomized and observational studies may misrepresent health disparities or heterogeneity in treatment effects if they are not based on a known sampling scheme. Conclusion Researchers have recently made substantial progress in conceptualization and methods related to selection bias. This progress will improve the relevance of both descriptive and causal health disparities research.
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Affiliation(s)
- L. Paloma Rojas-Saunero
- Department of Epidemiology, University of California, Los Angeles Fielding School of Public Health, Los Angeles, California, USA
| | - M. Maria Glymour
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Elizabeth Rose Mayeda
- Department of Epidemiology, University of California, Los Angeles Fielding School of Public Health, Los Angeles, California, USA
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Janvin M, Young JG, Ryalen PC, Stensrud MJ. Causal inference with recurrent and competing events. LIFETIME DATA ANALYSIS 2024; 30:59-118. [PMID: 37173588 PMCID: PMC10764453 DOI: 10.1007/s10985-023-09594-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Accepted: 02/14/2023] [Indexed: 05/15/2023]
Abstract
Many research questions concern treatment effects on outcomes that can recur several times in the same individual. For example, medical researchers are interested in treatment effects on hospitalizations in heart failure patients and sports injuries in athletes. Competing events, such as death, complicate causal inference in studies of recurrent events because once a competing event occurs, an individual cannot have more recurrent events. Several statistical estimands have been studied in recurrent event settings, with and without competing events. However, the causal interpretations of these estimands, and the conditions that are required to identify these estimands from observed data, have yet to be formalized. Here we use a formal framework for causal inference to formulate several causal estimands in recurrent event settings, with and without competing events. When competing events exist, we clarify when commonly used classical statistical estimands can be interpreted as causal quantities from the causal mediation literature, such as (controlled) direct effects and total effects. Furthermore, we show that recent results on interventionist mediation estimands allow us to define new causal estimands with recurrent and competing events that may be of particular clinical relevance in many subject matter settings. We use causal directed acyclic graphs and single world intervention graphs to illustrate how to reason about identification conditions for the various causal estimands based on subject matter knowledge. Furthermore, using results on counting processes, we show that our causal estimands and their identification conditions, which are articulated in discrete time, converge to classical continuous time counterparts in the limit of fine discretizations of time. We propose estimators and establish their consistency for the various identifying functionals. Finally, we use the proposed estimators to compute the effect of blood pressure lowering treatment on the recurrence of acute kidney injury using data from the Systolic Blood Pressure Intervention Trial.
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Affiliation(s)
- Matias Janvin
- Department of Mathematics, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
| | - Jessica G Young
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, USA
- CAUSALab, Harvard T.H. Chan School of Public Health, Boston, USA
| | - Pål C Ryalen
- Department of Biostatistics, University of Oslo, Oslo, Norway
| | - Mats J Stensrud
- Department of Mathematics, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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Yland JJ, Wesselink AK, Hernandez-Diaz S, Huybrechts K, Hatch EE, Wang TR, Savitz D, Kuohung W, Rothman KJ, Wise LA. Preconception contraceptive use and miscarriage: prospective cohort study. BMJ MEDICINE 2023; 2:e000569. [PMID: 37705685 PMCID: PMC10496668 DOI: 10.1136/bmjmed-2023-000569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 07/14/2023] [Indexed: 09/15/2023]
Abstract
Objectives To evaluate the association between preconception contraceptive use and miscarriage. Design Prospective cohort study. Setting Residents of the United States of America or Canada, recruited from 2013 until the end of 2022. Participants 13 460 female identified participants aged 21-45 years who were planning a pregnancy were included, of whom 8899 conceived. Participants reported data for contraceptive history, early pregnancy, miscarriage, and potential confounders during preconception and pregnancy. Main outcome measure Miscarriage, defined as pregnancy loss before 20 weeks of gestation. Results Preconception use of combined and progestin-only oral contraceptives, hormonal intrauterine devices, copper intrauterine devices, rings, implants, or natural methods was not associated with miscarriage compared with use of barrier methods. Participants who most recently used patch (incidence rate ratios 1.34 (95% confidence interval 0.81 to 2.21)) or injectable contraceptives (1.44 (0.99 to 2.12)) had higher rates of miscarriage compared with recent users of barrier methods, although results were imprecise due to the small numbers of participants who used patch and injectable contraceptives. Conclusions Use of most contraceptives before conception was not appreciably associated with miscarriage rate. Individuals who used patch and injectable contraceptives had higher rates of miscarriage relative to users of barrier methods, although these results were imprecise and residual confounding was possible.
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Affiliation(s)
- Jennifer J Yland
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Amelia K Wesselink
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Sonia Hernandez-Diaz
- Department of Epidemiology and CAUSALab, Harvard University T H Chan School of Public Health, Boston, MA, USA
| | - Krista Huybrechts
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Elizabeth E Hatch
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Tanran R Wang
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - David Savitz
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
| | - Wendy Kuohung
- Department of Obstetrics and Gynecology, Boston University School of Medicine, Boston, MA, USA
| | - Kenneth J Rothman
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Lauren A Wise
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
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Stensrud MJ, Dukes O. Translating questions to estimands in randomized clinical trials with intercurrent events. Stat Med 2022; 41:3211-3228. [PMID: 35578779 PMCID: PMC9321763 DOI: 10.1002/sim.9398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 03/02/2022] [Accepted: 03/14/2022] [Indexed: 11/08/2022]
Abstract
Intercurrent (post‐treatment) events occur frequently in randomized trials, and investigators often express interest in treatment effects that suitably take account of these events. Contrasts that naively condition on intercurrent events do not have a straight‐forward causal interpretation, and the practical relevance of other commonly used approaches is debated. In this work, we discuss how to formulate and choose an estimand, beyond the marginal intention‐to‐treat effect, from the point of view of a decision maker and drug developer. In particular, we argue that careful articulation of a practically useful research question should either reflect decision making at this point in time or future drug development. Indeed, a substantially interesting estimand is simply a formalization of the (plain English) description of a research question. A common feature of estimands that are practically useful is that they correspond to possibly hypothetical but well‐defined interventions in identifiable (sub)populations. To illustrate our points, we consider five examples that were recently used to motivate consideration of principal stratum estimands in clinical trials. In all of these examples, we propose alternative causal estimands, such as conditional effects, sequential regime effects, and separable effects, that correspond to explicit research questions of substantial interest.
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Affiliation(s)
- Mats J Stensrud
- Department of Mathematics, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Oliver Dukes
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Applied Mathematics, Statistics and Computer Science, Ghent University, Ghent, Belgium
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