Using High-Dimensional Disease Risk Scores in Comparative Effectiveness Research of New Treatments

Nonexperimental research using automated healthcare databases can supplement randomized trials to provide both clinicians and patients with timely information to optimize treatment decisions. These studies, however, are susceptible to confounding and require design and statistical methods to control for large numbers of confounding variables. The propensity score (PS), defined as the conditional probability of treatment given a set of covariates, has become increasingly popular for controlling large numbers of covariates in pharmacoepidemiologic studies. During early periods after the introduction of a new treatment, however, accurately modeling the PS can be difficult because of rapid change over time in drug prescribing patterns and few exposed individuals. A historically estimated disease risk score (DRS), which summarizes covariate associations with the outcome absent of exposure, has been proposed as an alternative to PSs for controlling large numbers of covariates during these periods. Little is known about the performance and potential benefits of using DRSs for confounding control when evaluating the comparative effectiveness of newly marketed drugs. In this study, we examined the benefits and challenges of using historically estimated DRSs compared to PSs when controlling for large numbers of covariates during early periods of drug approval. We further evaluated novel strategies for determining the validity of fitted DRS models in their ability to control confounding. We investigated these methodological questions using Monte Carlo simulations and empirical data. The empirical analyses included 20% and 1% samples of Medicare claims data to compare the new oral anticoagulant dabigatran with warfarin in reducing the risk of combined ischemic stroke and all-cause mortality in older populations. When PS distributions are separated, DRS matching can improve the precision of effect estimates and allow researchers to evaluate the treatment effect in a larger proportion of the treated population. However, accurately modeling the DRS can be challenging compared to the PS. When evaluating the validity of DRS models, measures of predictive performance do not always correspond well with reduced bias in treatment effect estimates. Calculating the pseudo bias within a "dry run" analysis can provide a more direct measure for assessing the ability of fitted DRS models to control confounding.Doctor of Philosoph

Comparative Effectiveness and Safety of Prasugrel versus Ticagrelor following Percutaneous Coronary Intervention: An Observational Study

Background: Observational studies comparing ticagrelor and prasugrel in patients with acute coronary syndrome (ACS) undergoing percutaneous coronary intervention (PCI) have yielded contradictory results, but these studies often did not consider differential censoring (e.g., for treatment switching or insurance disenrollment) or confounding by time‐dependent factors. Objective: Our objective was to conduct a comparative effectiveness and safety analysis of ticagrelor and prasugrel in patients who underwent PCI after being hospitalized for an ACS. Methods: This study used the Optum’s de‐identified Clinformatics® Data Mart Database and included patients aged 18 years or older with an index hospital admission between May 2012 and September 2015, a diagnosis of ACS managed with PCI, and treatment with either ticagrelor or prasugrel. The primary composite outcome was defined as the first occurrence of all‐cause death, myocardial infarction (MI), or ischemic stroke. The secondary composite outcome included the first occurrence of gastrointestinal (GI) bleed, intracranial hemorrhage (ICH), or other major bleeds requiring hospitalization. Weighted Cox proportional hazard models and robust variance estimation were implemented to adjust for baseline comorbidities, time‐varying exposure, time‐dependent confounders, and differential censoring. Results: Included in the analysis were 2,559 patients initiated on ticagrelor and 4,456 patients initiated on prasugrel following PCI. Patients initiated on ticagrelor were 10% more likely to have eligibility disenrollment (Ticagrelor: 57%, Prasugrel: 47%, P\u3c.01) and 7 percentage‐points more likely to switch medication (Ticagrelor: 35%, Prasugrel: 28%, P\u3c.01). After adjusting for multiple factors, including time‐varying exposure and censoring imbalance, ticagrelor was associated with a higher risk of all‐cause death, MI, or stroke when compared to prasugrel (Hazard ratio (HR): 1.33; 95% CI: 1.04‐1.68). Similarly, ticagrelor was associated with a higher risk in bleeding events when compared with prasugrel (HR: 1.61; 95% CI: 1.19‐2.17). Conclusion: When compared with ticagrelor, prasugrel use following PCI for ACS was associated with a lower risk of death, MI, or stroke, as well as a reduced risk of major bleeding

Optical Properties of Human Uterus at 630 nm

The optical properties of normal and fibriotic human uteri were determined using frequency-domain and steady-state techniques

Propensity score prediction for electronic healthcare databases using Super Learner and High-dimensional Propensity Score Methods

The optimal learner for prediction modeling varies depending on the underlying data-generating distribution. Super Learner (SL) is a generic ensemble learning algorithm that uses cross-validation to select among a library of candidate prediction models. The SL is not restricted to a single prediction model, but uses the strengths of a variety of learning algorithms to adapt to different databases. While the SL has been shown to perform well in a number of settings, it has not been thoroughly evaluated in large electronic healthcare databases that are common in pharmacoepidemiology and comparative effectiveness research. In this study, we applied and evaluated the performance of the SL in its ability to predict treatment assignment using three electronic healthcare databases. We considered a library of algorithms that consisted of both nonparametric and parametric models. We also considered a novel strategy for prediction modeling that combines the SL with the high-dimensional propensity score (hdPS) variable selection algorithm. Predictive performance was assessed using three metrics: the negative log-likelihood, area under the curve (AUC), and time complexity. Results showed that the best individual algorithm, in terms of predictive performance, varied across datasets. The SL was able to adapt to the given dataset and optimize predictive performance relative to any individual learner. Combining the SL with the hdPS was the most consistent prediction method and may be promising for PS estimation and prediction modeling in electronic healthcare databases

The “Dry-Run” Analysis: A Method for Evaluating Risk Scores for Confounding Control

A propensity score (PS) model's ability to control confounding can be assessed by evaluating covariate balance across exposure groups after PS adjustment. The optimal strategy for evaluating a disease risk score (DRS) model's ability to control confounding is less clear. DRS models cannot be evaluated through balance checks within the full population, and they are usually assessed through prediction diagnostics and goodness-of-fit tests. A proposed alternative is the "dry-run" analysis, which divides the unexposed population into "pseudo-exposed" and "pseudo-unexposed" groups so that differences on observed covariates resemble differences between the actual exposed and unexposed populations. With no exposure effect separating the pseudo-exposed and pseudo-unexposed groups, a DRS model is evaluated by its ability to retrieve an unconfounded null estimate after adjustment in this pseudo-population. We used simulations and an empirical example to compare traditional DRS performance metrics with the dry-run validation. In simulations, the dry run often improved assessment of confounding control, compared with the C statistic and goodness-of-fit tests. In the empirical example, PS and DRS matching gave similar results and showed good performance in terms of covariate balance (PS matching) and controlling confounding in the dry-run analysis (DRS matching). The dry-run analysis may prove useful in evaluating confounding control through DRS models

Propensity Score Methods for Confounding Control in Nonexperimental Research

Nonexperimental studies are increasingly used to investigate the safety and effectiveness of medical products as they are used in routine care. One of the primary challenges of such studies is confounding, systematic differences in prognosis between patients exposed to an intervention of interest and the selected comparator group. In the presence of uncontrolled confounding, any observed difference in outcome risk between the groups cannot be attributed solely to a causal effect of the exposure on the outcome. Confounding in studies of medical products can arise from a variety of different sociomedical processes.1 The most common form of confounding arises from good medical practice, physicians prescribing medications and performing procedures on patients who are most likely to benefit from them. This leads to a bias known as confounding by indication, which can cause medical interventions to appear to cause events that they prevent.2,3 Conversely, patients who are perceived by a physician to be near the end of life may be less likely to receive preventive medications, leading to confounding by frailty or comorbidity.4–6 Additional sources of confounding bias can result from patients’ health-related behaviors. For example, patients who initiate a preventive medication may be more likely than other patients to engage in other healthy, prevention-oriented behaviors leading to bias known as the healthy user/adherer effect.7–9 Many statistical approaches can be used to remove the confounding effects of such factors if they are captured in the data. The most common statistical approaches for confounding control are based on multivariable regression models of the outcome. To yield unbiased estimates of treatment effects, these approaches require that the researcher correctly models the effect of the treatment and covariates on the outcome. However, correct specification of an outcome model can be challenging, particularly in studies

Variable selection for propensity score models when estimating treatment effects on multiple outcomes: a simulation study: PS VARIABLE SELECTION FOR MULTIPLE OUTCOMES

It is often preferable to simplify the estimation of treatment effects on multiple outcomes by using a single propensity score (PS) model. Variable selection in PS models impacts the efficiency and validity of treatment effects. However, the impact of different variable selection strategies on the estimated treatment effects in settings involving multiple outcomes is not well understood. The authors use simulations to evaluate the impact of different variable selection strategies on the bias and precision of effect estimates to provide insight into the performance of various PS models in settings with multiple outcomes

Health of mobile pastoralists in the Sahel - assessment of 15 years of research and development

In the Sahel, between Mauritania and Somalia including Northern Kenya, about 20-30 million people live as mobile pastoralists. The rhythm of their migration follows the seasons and the availability of resources such as water, pasture and salt. Despite their high exposure to zoonoses and problems caused by extreme climatic conditions, mobile pastoralists are virtually excluded from health services because the provision of social services adapted to their way of life is challenging. In cooperation with various partners in the region, the Swiss Tropical and Public Health Institute has been active in research and development in the Sahel for 15 years. Based on the perceived needs of mobile pastoralists and the necessities of development, interdisciplinary research has considerably contributed to better understanding of their situation and their problems. Close contact between humans and livestock necessitates close cooperation between human and animal health specialists. Such useful approaches should be continued and extended

Assessing the Impact of Propensity Score Estimation and Implementation on Covariate Balance and Confounding Control Within and Across Important Subgroups in Comparative Effectiveness Research

Researchers are often interested in estimating treatment effects in subgroups controlling for confounding based on a propensity score (PS) estimated in the overall study population