title: Methodological comparison of marginal structural model, time-varying Cox regression, and propensity score methods: the example of antidepressant use and the risk of hip fracture.
abstract: BACKGROUND
  Observational studies including time-varying treatments are prone to confounding. We compared time-varying Cox regression analysis, propensity score (PS) methods, and marginal structural models (MSMs) in a study of antidepressant [selective serotonin reuptake inhibitors (SSRIs)] use and the risk of hip fracture.
  
  
  METHODS
  A cohort of patients with a first prescription for antidepressants (SSRI or tricyclic antidepressants) was extracted from the Dutch Mondriaan and Spanish Base de datos para la Investigación Farmacoepidemiológica en Atención Primaria (BIFAP) general practice databases for the period 2001-2009. The net (total) effect of SSRI versus no SSRI on the risk of hip fracture was estimated using time-varying Cox regression, stratification and covariate adjustment using the PS, and MSM. In MSM, censoring was accounted for by inverse probability of censoring weights.
  
  
  RESULTS
  The crude hazard ratio (HR) of SSRI use versus no SSRI use on hip fracture was 1.75 (95%CI: 1.12, 2.72) in Mondriaan and 2.09 (1.89, 2.32) in BIFAP. After confounding adjustment using time-varying Cox regression, stratification, and covariate adjustment using the PS, HRs increased in Mondriaan [2.59 (1.63, 4.12), 2.64 (1.63, 4.25), and 2.82 (1.63, 4.25), respectively] and decreased in BIFAP [1.56 (1.40, 1.73), 1.54 (1.39, 1.71), and 1.61 (1.45, 1.78), respectively]. MSMs with stabilized weights yielded HR 2.15 (1.30, 3.55) in Mondriaan and 1.63 (1.28, 2.07) in BIFAP when accounting for censoring and 2.13 (1.32, 3.45) in Mondriaan and 1.66 (1.30, 2.12) in BIFAP without accounting for censoring.
  
  
  CONCLUSIONS
  In this empirical study, differences between the different methods to control for time-dependent confounding were small. The observed differences in treatment effect estimates between the databases are likely attributable to different confounding information in the datasets, illustrating that adequate information on (time-varying) confounding is crucial to prevent bias.
authors.affiliation: Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, the Netherlands.|Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands.
authors.forename: M Sanni
authors.initials: MS
authors.lastname: Ali
authors.affiliation: Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, the Netherlands.|Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands.
authors.forename: Rolf H H
authors.initials: RH
authors.lastname: Groenwold
authors.affiliation: Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, the Netherlands.
authors.forename: Svetlana V
authors.initials: SV
authors.lastname: Belitser
authors.affiliation: Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, the Netherlands.
authors.forename: Patrick C
authors.initials: PC
authors.lastname: Souverein
authors.affiliation: BIFAP Research Unit, Division of Pharmacoepidemiology and Pharmacovigilance, Agencia Española de Medicamentos y Productos Sanitarios (AEMPS), Madrid, Spain.
authors.forename: Elisa
authors.initials: E
authors.lastname: Martín
authors.affiliation: Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA.|Epidemiology, Worldwide Safety and Regulatory, Pfizer Inc., New York, NY, USA.
authors.forename: Nicolle M
authors.initials: NM
authors.lastname: Gatto
authors.affiliation: BIFAP Research Unit, Division of Pharmacoepidemiology and Pharmacovigilance, Agencia Española de Medicamentos y Productos Sanitarios (AEMPS), Madrid, Spain.
authors.forename: Consuelo
authors.initials: C
authors.lastname: Huerta
authors.affiliation: Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, the Netherlands.|Department of Clinical Pharmacy, Division of Laboratory and Pharmacy, University Medical Center Utrecht, Utrecht, the Netherlands.
authors.forename: Helga
authors.initials: H
authors.lastname: Gardarsdottir
authors.affiliation: Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands.
authors.forename: Kit C B
authors.initials: KC
authors.lastname: Roes
authors.affiliation: Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands.
authors.forename: Arno W
authors.initials: AW
authors.lastname: Hoes
authors.affiliation: Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, the Netherlands.
authors.forename: Antonius
authors.initials: A
authors.lastname: de Boer
authors.affiliation: Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, the Netherlands.|Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands.
authors.forename: Olaf H
authors.initials: OH
authors.lastname: Klungel
mesh_terms: D000928:Antidepressive Agents / Q000009:adverse effects*; D005260:Female; D006620:Hip Fractures / Q000209:etiology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D017891:Pharmacoepidemiology / Q000379:methods*; D057216:Propensity Score; D016016:Proportional Hazards Models; D012044:Regression Analysis; D012307:Risk Factors; D017367:Selective Serotonin Reuptake Inhibitors / Q000009:adverse effects
chemical_list: D000928:Antidepressive Agents; D017367:Selective Serotonin Reuptake Inhibitors
keywords: Cox model; bias; collider stratification; confounding; inverse probability of treatment weighting; pharmacoepidemiology; time-dependent propensity score; time-varying treatment