ASSOCIATION BETWEEN CONCOMITANT USE OF BISPHOSPHONATES AND SEROTONIN REUPTAKE INHIBITORS AND INCREASED RISK OF OSTEOPOROTIC-RELATED FRACTURES: AMONG COMMUNITY-DWELLING POSTMENOPAUSAL WOMEN

Osteoporosis and depression are prevalent among older postmenopausal women 65 years or older. Bisphosphonates (BPs) and selective serotonin reuptake inhibitors (SSRIs) or serotonin norepinephrine reuptake inhibitors (SNRIs) are commonly used medications to treat these conditions. Inhibitory effects of BPs on osteoclasts are responsible for the reduction in fracture risk. SSRIs, however, are associated with increased fracture risk through decreasing osteoblasts and increasing osteoclastic activity. These effects of SSRIs could attenuate the beneficial effects of BPs. This dissertation describes the concomitant use of BPs and SSRIs among postmeopausa women and reports findings from examining the association between concomitant use of BPs and SSRIs and fracture risk. Separate cross-sectional analyses were performed using data from the 2004-2008 Medical Expenditure Panel Survey (MEPS) and Medicare Part D prescriptions claims data (2008-2010) to examine usage patterns of BPs and SSRIs/SNRIs for women aged ≥45 years and ≥65 years, respectively. For our second objective, a nested-case control was conducted using Medicare claims data (2008-2010). Data from Medicare inpatient claims were linked to Medicare Part D data for all female BP users 65 years or older. We used Cox proportional hazards model to assess the increased risk of osteoporotic-related fractures among propensity score matched (1:1 ratio) cohorts of concomitant users of BPs and SSRIs and BP alone users. Concomitant use of BPs and SSRIs was prevalent and increased with age for each timeframe examined. Findings showed that approximately 12% (using MEPS) and 28% (using Medicare data) of women on BPs were also on SSRIs. For the second objective, 4,214 propensity score matched pairs (average age=80.4 years) of subjects were analyzed. Findings showed that concomitant use of BPs and SSRIs was associated with statistically significant increased risk for any fracture (HR=1.29, 95% CI, 1.07-1.57), but statistically non-significant increased risk for hip (HR=1.16, 95% CI, 0.92-1.47) and vertebral fractures (HR=1.55, 95% CI, 0.97-2.48). Current findings indicate that concomitant use of BPs and SSRIs is not uncommon among postmenopausal women and suggest potential attenuation of antifracture efficacy of BPs by SSRIs. Further studies are needed to understand the clinical impact of concomitant use of these medications among older postmenopausal women

Estimating causal effects : considering three alternatives to difference-in-differences estimation

Difference-in-differences (DiD) estimators provide unbiased treatment effect estimates when, in the absence of treatment, the average outcomes for the treated and control groups would have followed parallel trends over time. This assumption is implausible in many settings. An alternative assumption is that the potential outcomes are independent of treatment status, conditional on past outcomes. This paper considers three methods that share this assumption: the synthetic control method, a lagged dependent variable (LDV) regression approach, and matching on past outcomes. Our motivating empirical study is an evaluation of a hospital pay-for-performance scheme in England, the best practice tariffs programme. The conclusions of the original DiD analysis are sensitive to the choice of approach. We conduct a Monte Carlo simulation study that investigates these methods’ performance. While DiD produces unbiased estimates when the parallel trends assumption holds, the alternative approaches provide less biased estimates of treatment effects when it is violated. In these cases, the LDV approach produces the most efficient and least biased estimates

Causal Effect Random Forest Of Interaction Trees For Learning Individualized Treatment Regimes In Observational Studies: With Applications To Education Study Data

Learning individualized treatment regimes (ITR) using observational data holds great interest in various fields, as treatment recommendations based on individual characteristics may improve individual treatment benefits with a reduced cost. It has long been observed that different individuals may respond to a certain treatment with significant heterogeneity. ITR can be defined as a mapping between individual characteristics to a treatment assignment. The optimal ITR is the treatment assignment that maximizes expected individual treatment effects. Rooted from personalized medicine, many studies and applications of ITR are in medical fields and clinical practice. Heterogeneous responses are also well documented in educational interventions. However, unlike the efficacy study in medical studies, educational interventions are often not randomized. Study results often suffer greatly from self-selection bias. Besides the intervention itself, the efficacy and effectiveness of interventions usually interact with a wide range of confounders. In this study, we propose a novel algorithm to extend random forest of interaction trees to Casual Effect Random Forest of Interaction Trees (CERFIT) for learning individualized treatment effects and regimes. We first consider the study under a binary treatment setting. Each interaction tree recursively partitions the data into two subgroups with greatest heterogeneity of treatment effect. By integrating propensity score into the tree growing process, subgroups from the proposed CERFIT not only have maximized treatment effect differences, but also similar baseline covariates. Thus it allows for the estimation of the individualized treatment effects using observational data. In addition, we also propose to use residuals from linear models instead of the original responses in the algorithm. By doing so, the numerical stability of the algorithm is greatly improved, which leads to an improved prediction accuracy. We then consider the learning problem under non-binary treatment settings. For multiple treatments, through recursively partitioning data into two subgroups with greatest treatment effects heterogeneity with respect to two randomly selected treatment groups, the algorithm transforms the multiple learning ITR into a binary task. Similarly, continuous treatment can be handled through recursively partitioning the data into subgroups with greatest homogeneity in terms of the association between the response and the treatment within a child node. For all treatment settings, the CERFIT provides variable importance ranking in terms of treatment effects. Extensive simulation studies for assessing estimation accuracy and variable importance ranking are presented. CERFIT demonstrates competitive performance among all competing methods in simulation studies. The methods are also illustrated through an assessment of a voluntary education intervention for binary treatment setting and learning optimal ITR among multiple interventions for non-binary treatments using data from a large public university

MPs for Sale? Estimating Returns to Office in Post-War British Politics

While the role of money in policymaking is a central question in political economy research, surprisingly little attention has been given to the rents politicians actually derive from politics. We use both matching and a regression discontinuity design to analyze an original dataset on the estates of recently deceased British politicians. We find that serving in Parliament roughly doubled the wealth at death of Conservative MPs but had no discernible effect on the wealth of Labour MPs. We argue that Conservative MPs profited from office in a lax regulatory environment by using their political positions to obtain outside work as directors, consultants, and lobbyists, both while in office and after retirement. Our results are consistent with anecdotal evidence on MPs' outside financial dealings but suggest that the magnitude of Conservatives' financial gains from office was larger than has been appreciated.British Politics, returns to office, rents from office, political economy, money and politics, regression discontinuity

Effectiveness of denosumab for fracture prevention in real-world postmenopausal women with osteoporosis: a retrospective cohort study

Summary: To determine denosumab’s effectiveness for fracture prevention among postmenopausal women with osteoporosis in East Asia, the risk of fracture was compared between patients continuing denosumab therapy versus patients discontinuing denosumab after one dose. The real-world effectiveness was observed to be consistent with the efficacy demonstrated in the phase III trial. Introduction: After therapeutic efficacy is demonstrated for subjects in global clinical trials, real-world evidence may provide complementary knowledge of therapeutic effectiveness in a heterogeneous mix of patients seen in clinical practice. This retrospective cohort study was conducted to compare the fracture risk in real-world clinical care received in Taiwan and Hong Kong between a treatment cohort (patients receiving denosumab 60 mg subcutaneously every 6 months) versus an off-treatment cohort (patients discontinuing after 1 dose of denosumab, which has no known clinical benefit) among real-world postmenopausal women. Methods: This study included 38,906 and 2,835 postmenopausal women receiving denosumab in Taiwan and Hong Kong, respectively. The primary endpoint was hip fracture, and secondary endpoints were clinical vertebral and nonvertebral fractures. Propensity-score-matched analysis, adjusting for known covariates, was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). The robustness of findings was evaluated with a series of sensitivity and quantitative bias analyses. Results: In this study, 554 hip fractures were included in the primary Taiwan population analysis. The crude incidence rate was 0.9 per 100 person-years in the treatment cohort (n = 25,059) and 1.7 per 100 person-years in the off-treatment cohort (n = 13,847). After adjusting for prognostic differences between cohorts, denosumab reduced the risk of hip fractures by 38% (HR = 0.62, CI:0.52–0.75). Risk reductions of similar magnitude were observed for the secondary endpoints and for the analysis of the smaller Hong Kong population. Conclusion: The effectiveness of denosumab for fracture reduction among real-world postmenopausal women with osteoporosis was consistent with the efficacy demonstrated in a global clinical trial

On the Effectiveness of Sexual Offender Treatment in Prisons: A Comparison of Two Different Evaluation Designs in Routine Practice

Although there is less continuity of sexual offending in the life course than stereotypes suggest, treatment should lead to a further reduction of reoffending. Contrary to this aim, a recent large British study using propensity score matching (PSM) showed some negative effects of the core sex offender treatment program (SOTP) in prisons. International meta-analyses on the effects of sex offender treatment revealed that there is considerable variety in the results, and methodological aspects and the context play a significant role. Therefore, this study compared different designs in the evaluation of sex offender treatment in German prisons. PSM was compared with an exact matching (EM) by the Static-99 in a sample of 693 sex offenders from Bavarian prisons. Most results were similar for both methods and not significant due to low base rates. There was a treatment effect at p < .05 on general recidivism in the EM and at p = .06 on serious reoffending in the PSM. For sexual recidivism, EM showed a negative trend, whereas PSM suggested the opposite. Overall, the study underlines the need for more replications of evaluations of routine practice, methodological comparisons, sensitive outcome criteria, and differentiated policy information

Tailored therapy in type 2 diabetes:unintended effects of glucose-lowering agents

The clinical treatment landscape of type 2 diabetes has rapidly evolved over the last decades. With the arrival of several new treatment options, the possibility of tailored therapy for patients with type 2 diabetes has become an option. This thesis investigated several advantages and disadvantages of the treatment options. Some of the results contradict with the Dutch treatment guideline, e.g. gliclazide does not seem to better than other sulphonylureas with regard to some important outcomes and there might be role for thiazolidinedionen in the treatment of selected patients. This underlines the importance of tailored therapy. However, this thesis also showed that implementation of tailored therapy is difficult and that there is room for improvement in guidelines and among prescribers

Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression

Propensity scores for the analysis of observational data are typically estimated using logistic regression. Our objective in this Review was to assess machine learning alternatives to logistic regression which may accomplish the same goals but with fewer assumptions or greater accuracy