title: Marginal structural models for the estimation of the risk of Diabetes Mellitus in the presence of elevated depressive symptoms and antidepressant medication use in the Women's Health Initiative observational and clinical trial cohorts.
abstract: BACKGROUND
  We evaluate the combined effect of the presence of elevated depressive symptoms and antidepressant medication use with respect to risk of type 2 diabetes among approximately 120,000 women enrolled in the Women's Health Initiative (WHI), and compare several different statistical models appropriate for causal inference in non-randomized settings.
  
  
  METHODS
  Data were analyzed for 52,326 women in the Women's Health Initiative Clinical Trials (CT) Cohort and 68,169 women in the Observational Study (OS) Cohort after exclusions. We included follow-up to 2005, resulting in a median duration of 7.6 years of follow up after enrollment. Results from three multivariable Cox models were compared to those from marginal structural models that included time varying measures of antidepressant medication use, presence of elevated depressive symptoms and BMI, while adjusting for potential confounders including age, ethnicity, education, minutes of recreational physical activity per week, total energy intake, hormone therapy use, family history of diabetes and smoking status.
  
  
  RESULTS
  Our results are consistent with previous studies examining the relationship of antidepressant medication use and risk of type 2 diabetes. All models showed a significant increase in diabetes risk for those taking antidepressants. The Cox Proportional Hazards models using baseline covariates showed the lowest increase in risk , with hazard ratios of 1.19 (95 % CI 1.06 - 1.35) and 1.14 (95 % CI 1.01 - 1.30) in the OS and CT, respectively. Hazard ratios from marginal structural models comparing antidepressant users to non-users were 1.35 (95 % CI 1.21 - 1.51) and 1.27 (95 % CI 1.13 - 1.43) in the WHI OS and CT, respectively - however, differences among estimates from traditional Cox models and marginal structural models were not statistically significant in both cohorts. One explanation suggests that time-dependent confounding was not a substantial factor in these data, however other explanations exist. Unadjusted Cox Proportional Hazards models showed that women with elevated depressive symptoms had a significant increase in diabetes risk that remained after adjustment for confounders. However, this association missed the threshold for statistical significance in propensity score adjusted and marginal structural models.
  
  
  CONCLUSIONS
  Results from the multiple approaches provide further evidence of an increase in risk of type 2 diabetes for those on antidepressants.
authors.affiliation: Division of Preventive and Behavioral Medicine, Department of Medicine, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA, 01655, USA. Christine.Frisard@umassmed.edu.
authors.forename: Christine
authors.initials: C
authors.lastname: Frisard
authors.affiliation: Division of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, 715 North Pleasant Street, Amherst, MA, 01003, USA. ustcgxd@gmail.com.
authors.forename: Xiangdong
authors.initials: X
authors.lastname: Gu
authors.affiliation: Division of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, 715 North Pleasant Street, Amherst, MA, 01003, USA. bwhitcomb@schoolph.umass.edu.
authors.forename: Brian
authors.initials: B
authors.lastname: Whitcomb
authors.affiliation: Division of Preventive and Behavioral Medicine, Department of Medicine, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA, 01655, USA. Yunsheng.Ma@umassmed.edu.
authors.forename: Yunsheng
authors.initials: Y
authors.lastname: Ma
authors.affiliation: Division of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, 715 North Pleasant Street, Amherst, MA, 01003, USA. ppekow@schoolph.umass.edu.
authors.forename: Penelope
authors.initials: P
authors.lastname: Pekow
authors.affiliation: Division of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, 715 North Pleasant Street, Amherst, MA, 01003, USA. mzorn@schoolph.umass.edu.
authors.forename: Martha
authors.initials: M
authors.lastname: Zorn
authors.affiliation: Division of Preventive and Behavioral Medicine, Department of Medicine, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA, 01655, USA. Deidre.Sepavich@umassmed.edu.
authors.forename: Deidre
authors.initials: D
authors.lastname: Sepavich
authors.affiliation: Division of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, 715 North Pleasant Street, Amherst, MA, 01003, USA. rbalasub@schoolph.umass.edu.
authors.forename: Raji
authors.initials: R
authors.lastname: Balasubramanian
mesh_terms: D000368:Aged; D000928:Antidepressive Agents / Q000009:adverse effects*; D003863:Depression / Q000150:complications* / Q000188:drug therapy* / Q000523:psychology; D003924:Diabetes Mellitus, Type 2 / Q000139:chemically induced / Q000453:epidemiology*; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D015994:Incidence; D008404:Massachusetts / Q000453:epidemiology; D008875:Middle Aged; D015233:Models, Statistical; D011379:Prognosis; D012307:Risk Factors; D016387:Women's Health
chemical_list: D000928:Antidepressive Agents