- Browse by Subject
Browsing by Subject "propensity score"
Now showing 1 - 3 of 3
Results Per Page
Sort Options
Item Doubly Robust Estimation of Causal Effect: Upping the Odds of Getting the Right Answers(AHA, 2020) Li, Xiaochun; Shen, Changyu; Biostatistics, School of Public HealthPropensity score–based methods or multiple regressions of the outcome are often used for confounding adjustment in analysis of observational studies. In either approach, a model is needed: A model describing the relationship between the treatment assignment and covariates in the propensity score–based method or a model for the outcome and covariates in the multiple regressions. The 2 models are usually unknown to the investigators and must be estimated. The correct model specification, therefore, is essential for the validity of the final causal estimate. We describe in this article a doubly robust estimator which combines both models propitiously to offer analysts 2 chances for obtaining a valid causal estimate and demonstrate its use through a data set from the Lindner Center Study.Item Impact of Level of Effort on the Effects of Compliance with the 3-Hour Rule(Elsevier, 2019) Beaulieu, Cynthia L.; Peng, Juan; Hade, Erinn M.; Corrigan, John D.; Seel, Ronald T.; Dijkers, Marcel P.; Hammond, Flora M.; Horn, Susan D.; Timpson, Misti L.; Swan, Melanie; Bogner, Jennifer; Physical Medicine and Rehabilitation, School of MedicineObjective To determine if patients’ level of effort (LOE) in therapy sessions during traumatic brain injury (TBI) rehabilitation modifies the effect of compliance with the 3-Hour Rule of the Centers for Medicare & Medicaid Services. Design Propensity score methodology applied to the TBI-Practice-Based Evidence (TBI-PBE) database, consisting of multi-site, prospective, longitudinal observational data. Setting Acute inpatient rehabilitation facilities (IRF). Participants Patients (n=1820) who received their first IRF admission for TBI in the US and were enrolled for 3 and 9 month follow-up. Main Outcome Measures Participation Assessment with Recombined Tools-Objective-17, FIMTM Motor and Cognitive scores, Satisfaction with Life Scale, and Patient Health Questionnaire-9. Results When the full cohort was examined, no strong main effect of compliance with the 3-Hour Rule was identified and LOE did not modify the effect of compliance with the 3-Hour Rule. In contrast, LOE had a strong positive main effect on all outcomes, except depression. When the sample was stratified by level of disability, LOE modified the effect of compliance, particularly on the outcomes of participants with less severe disability. For these patients, providing 3 hours of therapy for 50%+ of therapy days in the context of low effort resulted in poorer performance on select outcome measures at discharge and up to 9 months post discharge compared to patients with <50% of 3-hr therapy days. Conclusions LOE is an active ingredient in inpatient TBI rehabilitation, while compliance with the 3-Hour Rule was not found to have a substantive impact on the outcomes. The results support matching time in therapy during acute TBI rehabilitation to patients’ LOE in order to optimize long-term benefits on outcomes.Item Propensity score‐adjusted three‐component mixture model for drug‐drug interaction data mining in FDA Adverse Event Reporting System(Wiley, 2020-03) Wang, Xueying; Li, Lang; Wang, Lei; Feng, Weixing; Zhang, Pengyue; BioHealth Informatics, School of Informatics and ComputingWith increasing trend of polypharmacy, drug-drug interaction (DDI)-induced adverse drug events (ADEs) are considered as a major challenge for clinical practice. As premarketing clinical trials usually have stringent inclusion/exclusion criteria, limited comedication data capture and often times small sample size have limited values in study DDIs. On the other hand, ADE reports collected by spontaneous reporting system (SRS) become an important source for DDI studies. There are two major challenges in detecting DDI signals from SRS: confounding bias and false positive rate. In this article, we propose a novel approach, propensity score-adjusted three-component mixture model (PS-3CMM). This model can simultaneously adjust for confounding bias and estimate false discovery rate for all drug-drug-ADE combinations in FDA Adverse Event Reporting System (FAERS), which is a preeminent SRS database. In simulation studies, PS-3CMM performs better in detecting true DDIs comparing to the existing approach. It is more sensitive in selecting the DDI signals that have nonpositive individual drug relative ADE risk (NPIRR). The application of PS-3CMM is illustrated in analyzing the FAERS database. Compared to the existing approaches, PS-3CMM prioritizes DDI signals differently. PS-3CMM gives high priorities to DDI signals that have NPIRR. Both simulation studies and FAERS data analysis conclude that our new PS-3CMM is a new method that is complement to the existing DDI signal detection methods.