Application Of Marginal Structural Models Research Articles

Antidepressant medication and risk of cardiovascular disease in posttraumatic stress disorder patients: application of marginal structural models in Korean National Health Insurance Database

Abstract Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): - National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT Introduction Previous evidence on antidepressant medication and cardiovascular disease (CVD) in posttraumatic stress disorder (PTSD) patients is mixed. We evaluated associations between antidepressant medication and CVD by applying marginal structural model. Methods We gathered medical utilization records of 27,170 patients diagnosed with PTSD in years 2004 - 2018 without prior CVD diagnosis from Korean National Health Insurance Database (NHID). PTSD and CVD were characterized in correspondence of ICD-10 diagnostic codes. We acquired information on antidepressant use from medical utilization records and categorized by medication category: selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors (SNRIs) and tricyclic antidepressants (TCAs). Discrete-time survival analysis with marginal structural inverse probability weighting was used to test association between antidepressants and CVDs and were compared to results from traditional time-fixed model and simple time-varying model. We calculated cumulative daily defined doses (DDDs) of each antidepressant categories and conducted polynomial splining to test dose response. Results SSRI use was associated with acute coronary syndrome (hazard ratio [HR] 1.53, 95% confidence interval [CI] 1.26 - 1.86) and ischemic stroke (HR 1.33, 95% CI 1.08 - 1.63). SNRI use was associated with acute coronary syndrome (HR 1.52, 95% CI 1.09 - 2.12). TCA use was associated with acute coronary syndrome (HR 1.54, 1.21 - 1.96) and ischemic stroke (HR 1.34, 1.06 - 1.70). Polynomial spline showed positive dose-response in association between antidepressant use and CVD. Conclusion Antidepressant use is causally associated with CVD in patients with PTSD. Higher cumulative DDD of antidepressant increases the risk of CVD.

The causal effect of dynamic fertility treatment strategies on the probability of pregnancy: a novel application of marginal structural models (MSMs)

The causal effect of dynamic fertility treatment strategies on the probability of pregnancy: a novel application of marginal structural models (MSMs)

Clustering and Residual Confounding in the Application of Marginal Structural Models: Dialysis Modality, Vascular Access, and Mortality.

In the application of marginal structural models to compare time-varying treatments, it is rare that the hierarchical structure of a data set is accounted for or that the impact of unmeasured confounding on estimates is assessed. These issues often arise when analyzing data sets drawn from clinical registries, where patients may be clustered within health-care providers, and the amount of data collected from each patient may be limited by design (e.g., to reduce costs or encourage provider participation). We compared the survival of patients undergoing treatment with various dialysis types, where some patients switched dialysis modality during the course of their treatment, by estimating a marginal structural model using data from the Australia and New Zealand Dialysis and Transplant Registry, 2003-2011. The number of variables recorded by the registry is limited, and patients are clustered within the dialysis centers responsible for their treatment, so we assessed the impact of accounting for unmeasured confounding or clustering on estimated treatment effects. Accounting for clustering had limited impact, and only unreasonable levels of unmeasured confounding would have changed conclusions about treatment comparisons. Our analysis serves as a case study in assessing the impact of unmeasured confounding and clustering in the application of marginal structural models.

Application of marginal structural models in pharmacoepidemiologic studies: a systematic review

We systematically reviewed pharmacoepidemiologic studies published in 2012 that used inverse probability weighted (IPW) estimation of marginal structural models (MSM) to estimate the effect from a time-varying treatment. Potential studies were retrieved through a citation search within Web of Science and a keyword search within PubMed. Eligibility of retrieved studies was independently assessed by at least two reviewers. One reviewer performed data extraction, and a senior epidemiologist confirmed the extracted information for all eligible studies. Twenty pharmacoepidemiologic studies were eligible for data extraction. The majority of reviewed studies did not report whether the positivity assumption was checked. Six studies performed intention-to-treat analyses, but none of them reported adherence levels after treatment initiation. Eight studies chose an as-treated analytic strategy, but only one of them reported modeling the multiphase of treatment use. Almost all studies performing as-treated analyses chose the most recent treatment status as the functional form of exposure in the outcome model. Nearly half of the studies reported that the IPW estimate was substantially different from the estimate derived from a standard regression model. The use of IPW method to control for time-varying confounding is increasing in medical literature. However, reporting of the application of the technique is variable and suboptimal. It may be prudent to develop best practices in reporting complex methods in epidemiologic research.

The potential role of Marginal Structural Models (MSMs) in testing the effectiveness of antidepressants in the treatment of patients with major depression in everyday clinical practice

Objectives. To better evaluate the effectiveness of antidepressant drugs in the treatment of major depression in clinical practice. Methods. A simulation experiment was used to illustrate an application of marginal structural models (MSMs) via inverse probability of treatment weighting (IPTW) approach in the context of non-randomized data on N = 1,000 depressed subjects, initially subjected to “watchful waiting“. In simulation we assumed that subjects with worse intermediate outcome have a higher probability of being subsequently assigned to antidepressant treatment while those who receive antidepressant treatment are more likely to reach remission and less likely to reach relapse state. The outcomes from multiple (500) simulated data sets are analyzed using simple unadjusted analysis based on logistic regression and using MSM. Results. In contrast to unadjusted analysis, but consistent with the treatment assumptions, using MSM via IPTW results in strong evidence of the effectiveness of the antidepressant treatment. Furthermore MSM via IPTW substantially reduces the probability of wrongly rejecting the null hypothesis. However, the instability of weights due to the sparse data and incorrectly specified MSM may still result in inflation of Type I error rates. Conclusions. MSMs may allow evaluating the causal effects associated with antidepressant treatment from the data observed in clinical practice.

Estimating the Effect of Medication Adherence on Health Outcomes among Patients with Type 2 Diabetes—An Application of Marginal Structural Models

Objective We applied marginal structural models (MSMs) to estimate the effects of medication adherence with hypoglycemics on reducing the risk of microvascular complications in type 2 diabetic patients. Methods A retrospective longitudinal cohort study for type 2 diabetes patients was conducted using the California Medicaid claims database (1995–2002). Medication adherence and multiple time-varying confounders were measured quarterly over a maximum of 7.5 years follow-up. Cox regression models and MSMs results on the effect of compliance were compared. Results Of 4708 eligible patients, 2644 (56.2%) experienced microvascular complications during the follow-up period. After controlling for baseline covariates, standard Cox models estimated that adherence was associated with increased risk of complication with hazard ratio (HR) of 1.09 (95% confidence interval (CI): 1.00, 1.18). With adjustment of time-varying confounders as exogenous variables, the HR was 0.96 (0.88, 1.04). Using the MSM technique, the HR was 0.76 (95% bootstrap CI: 0.60, 0.92), indicating a significant benefit of medication adherence with hypoglycemics on the reduction of microvascular complications. This result contrasts with the negative results obtained in the hazard model, and is more consistent with prior clinical trial results Conclusion Unlike conventional models, MSMs estimated that higher medication adherence may result in reduced risk of microvascular complications among patients with type 2 diabetes.

Estimating the Causal Effect of Some Exposure From Nonrandomized Studies: The Example of Reduced Intensity Conditioning (RIC) in Hematological Diseases.

Abstract 3365Poster Board III-253 Introduction:Although allogeneic SCT with RIC has now gained wide acceptance, its eventual benefit again non-transplant approach is largely unknown (outside the setting of large randomized trials). When evaluating the impact on survival of reduced intensity conditioning in malignant hematological diseases, standard estimations based on Cox regression from observational databases could be biased because they ignore covariates that confound treatment decision. In this setting, we applied and compared two different statistical methods that were developed to control for confounding in estimating exposure (or treatment) effect from epidemiological studies. Patients and Methods:The statistical challenge was that allograft tended to be given when a patient was in advanced phase of his/her hematological malignancy, so that treatment was confounded by performance indicators, which in turn lie on the causal pathway between treatment and outcome. Thus, comparison of outcome first used propensity score (PS) analyses that attempt to create a comparison group of non-treated patients that closely resembles the group of treated patients by matching for the likelihood that a given patient has received the treatment. Then, we used marginal structural models (MSMs) that consist in creating, by using inverse probability of treatment weights, a pseudo-population in which the probability of treatment does no longer depend on covariates, and the effect of treatment on outcome is the same as in the original population. Result:Reduced intensity conditioning allograft was performed in 82 patients with chemotherapy-sensitive patients relapsing after autologous transplantation. Patients with myeloma (MM, 23 pts), follicular lymphoma (FL, 28 pts) or Hodgkin disease (HD, 31 pts), were compared to 276 patients who relapsed after autologous transplantation but did not underwent allogeneic stem cell transplantation (142 MM, 115 FL and 19 HD). From original datasets, 21 (91%) matched pairs could be constituted from MM patients, as compared to 19 (68%) of the FL patients, down to 15 (48%) of the HD patients. Based on these PS-matched samples, a significant benefit of reduced intensity conditioning as compared with non allografted patients was observed in MM, with estimated hazard ratio (HR) of death at 0.34 (95% confidence interval, CI: 0.14-0.88), as well as in FL (HR= 0.78, 95%CI: 0.27;2.30) and in HD (HR= 0.24; 95%CI: 0.09-0.62). MSM-based analyses that applied to the reweighted populations confirmed these trends towards survival benefits in FL, though partially erased in MM and HD. Conclusions:We reported the application of marginal structural models, a new class of causal models to estimate the effect of nonrandomized treatments as an alternative to PS based approaches in small samples. We expect that an increasing number of physicians involved in clinical cohorts become familiar with these novel and appealing quantitative methods when assessing innovative treatment effects. Disclosures:No relevant conflicts of interest to declare.