Antidepressant Use and Cognitive Deficits in Older Men: Addressing Confounding by Indications with Different Methods
Introduction
The authors of observational studies of adverse drug effects face a challenge in ascertaining the causation of an observed association, i.e., confounding by indications or reasons for prescribing. When an adverse health outcome is observed, the drugs prescribed to treat a disease may be claimed as the cause, whereas in fact the underlying disease that triggered the drug treatment is responsible 1, 2, 3. The source of this bias can be traced to a study design defect, i.e., the lack of randomization in treatment assignment. As a result, an observed “treatment effect” may reflect the differences in key population characteristics that affect both the probability of receiving the treatment and risk of the outcome, rather than the treatment under study. Because of the inability to pre-balance these characteristics at the design stage, observational studies must address such a potential bias analytically 1, 2.
The most common analytical method to control confounding by indications is to use a multivariable method, such as multiple linear regression (MLR), in which the identified “indications” or their surrogates are included as covariates. The adjusted effect estimate for drug use is then conditional on the observed values of these covariates or as a weighted average of them 1, 2 and hence, free of indication bias. Although such a direct modeling adjustment is simple and intuitive, treating the indication as regular confounders has several limitations. First and foremost, the temporal precedence of the indications to the drug use can not be realized in a conventional regression context, where both are treated as equally important independent variables. In addition, because of a lack of specific clinical assessments, observational studies often have to rely on multiple nonspecific measures as surrogates for indications for drug prescribing, which makes regression adjustment scientifically dubious and statistically inefficient 2, 3, 4.
To move towards causality, a two-step propensity score (PS) method was developed 2, 4, 5. First, a PS, or conditional probability, of receiving a specific drug given a set of predictors is estimated. Then in the hypothesis testing stage, the estimated PS is used as a covariate, or a stratification or matching variable, to control for confounding in the multivariable model of the relationship between drug exposure and study outcome. Conceptually the PS is expected to increase the validity (by pre-balancing observed confounders between exposure groups) and efficiency (by reducing number of parameters) of regression models. However, studies have not always found it superior to traditional multivariable adjustments (5).
Path analysis is a type of structural equation modeling (SEM) that tests statistical models among observed variables by the use of covariance structures. Although sharing many properties with traditional MLR, path analysis has several unique features. First, it allows for explicit specification of the directionality and hierarchy of several hypothesized “causal” relationships 6, 7, 8. Second, it recognizes the imperfect nature of measurements by directly modeling, rather than ignoring, measurement errors, and by providing multiple model fit statistics, including model χ2, root mean squared error of approximation (RMSEA) and comparative fit index (CFI). Finally, it typically uses a graphical path diagram as a starting point to delineate theoretically complex (causal or noncausal) relationships. Estimates for the specified relationships and model fit are then derived by solving a set of simultaneous equations. Unlike the MLR and PS modeling, the use of SEM in the medical field is relatively limited 7, 9.
In this study, we illustrate how conventional MLR, PS, and SEM can be used to address potential confounding by indication surrounding the relationship between antidepressant use and cognitive impairment by using data from a cohort of community-dwelling older persons. Antidepressant medications, especially the tricyclic agents, have been associated with cognitive impairment in the elderly 10, 11. Although recent randomized trials demonstrated potential cognitive benefits of antidepressant medications in treating major depressive disorders 12, 13, little is known about their long-term cognitive outcome in older persons living in the community, who often suffer from mild-to-moderate depressive symptoms, but multiple other chronic conditions 14, 15. Our hypothesis was that the apparent relationship between antidepressant use and cognitive impairment observed in the elderly may reflect potential confounding by underlying depression or the severity of depressive symptoms and would therefore diminish after appropriate adjustment for such covariates.
Section snippets
Participants
The Connecticut Veterans Longitudinal Cohort consisted of 767 community-dwelling older persons aged ≥65 years. The cohort was originally assembled at a Veterans Administration (VA) primary care clinic between July 2000 and August 2001. Data on demographic profiles, health-behaviors, comorbidities, blood pressure readings, and cognitive and physical functioning were collected at baseline and at 1-year follow-up. The study protocol was approved by the Yale University School of Medicine
Baseline Characteristics and Antidepressant Use During Follow-Up
At baseline, the study cohort had mild-to-moderate hypertension severity, a high level of functional autonomy, and a lower level of depressive symptoms (Table 1). One hundred twenty (22.1%) participants had a depression-related diagnosis, and 85 (15.6%) were prescribed antidepressants. The most frequent antidepressants are sertraline (5.2%), amitriptyline (2.8%), nortriptyline (1.8%), trazodone (1.5%), and fluoxetine (1.3%). During the 1-year follow-up period, 107 (19.7%) participants were
Discussion
In this cohort of community-dwelling older men with hypertension, we demonstrated that the apparent association between antidepressant use and memory deficit diminished substantially and became nonsignificant after adjusting for potential indications for antidepressant prescribing, or a PS for it, but not by using the global comorbidity measures and other risk factors alone. In addition, the best fitting empirical SEM with greatest clinical interpretability seems the one under our a priori
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This study was supported in part by the Claude D. Pepper Older Americans Independence Center at Yale University School of Medicine (#P30AG21342) from National Institute on Aging. The funding source had no involvement in the study design, data collection, analysis and interpretation.
The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs.