Background: Inverse-probability-of-treatment-weighted estimation (IPTW) of marginal structural models was proposed to adjust for time-varying confounders that are influenced by prior treatment use. It is unknown whether pharmacoepidemiologic studies that applied IPTW conformed to the recommendations proposed by methodological studies. In addition, no previous study has compared the performance of different analytic strategies adopted in IPTW analyses. Objectives: This project aims 1) to review the reporting practice of pharmacoepidemiologic studies that applied IPTW, 2) to compare the validity and precision of several approaches to constructing weight, 3) to use IPTW to estimate the effectiveness of glucosamine and chondroitin in treating osteoarthritis. Methods: We systematically retrieved pharmacoepidemiologic studies that were published in 2012 and applied IPTW to estimate the effect of a time-varying treatment. Under a variety of simulated scenarios, we assessed the performance of four analytic approaches what were commonly used in studies conducting IPTW analyses. Finally, using data from Osteoarthritis Initiative, we applied IPTW to estimate the long-term effectiveness of glucosamine and chondroitin on treating knee osteoarthritis. Results: The practice of reporting use of IPTW in pharmacoepidemiologic studies was suboptimal. The majority of reviewed studies did not report that the positivity assumption was assessed, and several studies used unstablized weights or did not report that the stabilized weights were used. With data simulation, we found that intention-to-treat analyses underestimated the actual treatment effect when there was non-null treatment effect and treatment non-adherence. This underestimation was linearly correlated with adherence levels. As-treated analyses that took into account the complex mechanism of treatment use generated approximately unbiased estimates without sacrificing the estimate precision when the treatment effect was non-null. Finally, after adjustment for potential confounders with marginal structural models, we found no clinically meaningful benefits of glucosamine/chondroitin in relieving knee pain, stiffness and physical function or slowing joint space narrowing. Conclusions: It may be prudent to develop best practices of reporting the use of IPTW. Studies performing intention-to-treat analyses should report the levels of adherence after treatment initiation, and studies performing as-treated analyses should take into the complex mechanism of treatment use in weight construction.

Objective: The project aimed to compare marginal structural models, and propensity score adjusted models with Cox Proportional Hazards models to address confounding by indication due to time-dependent confounders. These methods were applied to data from approximately 120,000 women in the Women’s Health Initiative to evaluate the causal effect of antidepressant medication with respect to diabetes risk. Methods: Four approaches were compared. Three Cox Models were used. The first used baseline covariates. The second used time-varying antidepressant medication use, BMI and presence of elevated depressive symptoms and adjusted for other baseline covariates. The third used time-varying antidepressant medication use, BMI and presence of elevated depressive symptoms and adjusted for other baseline covariates and propensity to taking antidepressants at baseline. Our fourth method used a Marginal Structural Cox Model with Inverse Probability of Treatment Weighting that included time-varying antidepressant medication, BMI and presence of elevated depressive symptoms and adjusted for other baseline covariates. Results: All approaches showed an increase in diabetes risk for those taking antidepressants. Diabetes risk increased with adjustment for time-dependent confounding and results for these three approaches were similar. All models were statistically significant. Ninety-five percent confidence intervals overlapped for all approaches showing they were not different from one another. Conclusions: Our analyses did not find a difference between Cox Proportional Hazards Models and Marginal Structural Cox Models in the WHI cohorts. Estimates of the Inverse Probability of Treatment Weights were very close to 1 which explains why we observed similar results.

Depression increases the risk of suicide death and non-fatal suicide attempt. Between 2 - 6% of persons with depression will die by suicide1 and 25 - 31% of persons with depression will make a non-fatal suicide attempt during their lifetime.2,3 Despite the strong association between depression and suicidal behavior, the vast majority of persons with depression will not engage in suicidal behavior, making it difficult to accurately predict who is at risk for suicide and non-fatal suicide attempt. Identifying high risk persons who should be connected to suicide prevention interventions is an important public health goal. Furthermore, depression often co-occurs with other mental disorders, which may exert an interactive influence on the risk of suicide and suicide attempt. Understanding the joint influence of depression and other mental disorders on suicide outcomes may inform prevention strategies. The goals of this dissertation were to predict suicide and non-fatal suicide attempt among persons with depression and to quantify the causal joint effect of depression and comorbid psychiatric disorders on suicide and suicide attempt. For all three studies, we used data from Danish registries, which routinely collect high-quality data in a setting of universal health care with long-term follow-up and registration of most health and life events.4 In Study 1, we predicted suicide deaths among men and women diagnosed with depression using a case-cohort design (n = 14,737). Approximately 800 predictors were included in the machine learning models (classification trees and random forests), spanning demographic characteristics, income, employment, immigrant status, citizenship, family suicidal history (parent or spouse), previous suicide attempts, mental disorders, physical health disorders, surgeries, prescription drugs, and psychotherapy. In depressed men, we found interactions between hypnotics and sedatives, analgesics and antipyretics, and previous poisonings that were associated with a high risk of suicide. In depressed women, there were interactions between poisoning and anxiolytics and between anxiolytics and hypnotics and sedatives that were associated with suicide risk. The variables in the random forests that contributed the most to prediction accuracy in depressed men were previous poisoning diagnoses and prescriptions of hypnotics and sedatives and anxiolytics. In depressed women, the most important predictors of suicide were receipt of state pension, prescriptions for psychiatric medications (anxiolytics and antipsychotics) and diagnoses of poisoning, alcohol related disorders, and reaction to severe stress and adjustment disorders. Prescriptions of analgesics and antipyretics (e.g., acetaminophen) and antithrombotic agents (e.g., aspirin) emerged as important predictors for both depressed men and women. Study 2 predicted non-fatal suicide attempts among men and women diagnosed with depression using a case-cohort design (n = 17,995). Among depressed men, there was a high risk of suicide attempt among those who received a state pension and were diagnosed with toxic effects of substances. There was also an interaction between reaction to severe stress and adjustment disorder and not receiving psychological help that was associated with suicide attempt risk among depressed men. In depressed women, suicide attempt risk was high in those who were prescribed antipsychotics, diagnosed with specific personality disorders, did not have a poisoning diagnosis, and were not receiving a state pension. For both men and women, the random forest results showed that the strongest contributors to prediction accuracy of suicide attempts were poisonings, alcohol related disorders, reaction to severe stress and adjustment disorders, drugs used to treat psychiatric disorders (e.g., drugs used in addictive disorders, anxiolytics, hypnotics and sedatives), anti-inflammatory medications, receipt of state pension, and remaining single. Study 3 examined the joint effect of depression and other mental disorders on suicide and non-fatal suicide attempts using a case-cohort design (suicide death analysis n = 279,286; suicide attempt analysis n = 288,157). We examined pairwise combinations of depression with: 1) organic disorders, 2) substance use disorders, 3) schizophrenia, 4) bipolar disorder, 5) neurotic disorders, 6) eating disorders, 7) personality disorders, 8) intellectual disabilities, 9) developmental disorders, and 10) behavioral disorders. We fit sex-stratified joint marginal structural Cox models to account for time-varying confounding. We observed large hazard ratios for the joint effect of depression and comorbid mental disorders on suicide and suicide attempts, the effect of depression in the absence of comorbid mental disorders, and for the effect of comorbid mental disorders in the absence of depression. We observed positive and negative interdependence between different combinations of depression and comorbid mental disorders on the rate of suicide and suicide attempt, with variation by sex. Overall, depression and comorbid mental disorders are harmful exposures, both independently and jointly. All of the studies in this dissertation highlight the important role of interactions between risk factors in suicidal behavior among persons with depression. Depression is one of the most commonly assessed risk factors for suicide,5,6 and our findings underscore the value of considering additional risk factors such as other psychiatric disorders, psychiatric medications, and social factors in combination with depression. The results of this dissertation may help inform potential risk identification strategies which may facilitate the targeting of suicide prevention interventions to those most vulnerable.