Covariate balance in a Bayesian propensity score analysis of beta blocker therapy in heart failure patients.
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ABSTRACT:
SUBMITTER: McCandless LC
PROVIDER: S-EPMC2758880 | biostudies-other | 2009
REPOSITORIES: biostudies-other
Publications
Covariate balance in a Bayesian propensity score analysis of beta blocker therapy in heart failure patients.
McCandless Lawrence C LC Gustafson Paul P Austin Peter C PC Levy Adrian R AR
Epidemiologic perspectives & innovations : EP+I 20090910
Regression adjustment for the propensity score is a statistical method that reduces confounding from measured variables in observational data. A Bayesian propensity score analysis extends this idea by using simultaneous estimation of the propensity scores and the treatment effect. In this article, we conduct an empirical investigation of the performance of Bayesian propensity scores in the context of an observational study of the effectiveness of beta-blocker therapy in heart failure patients. W ...[more]
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Project description:Propensity score methods are an important tool to help reduce confounding in non-experimental studies and produce more accurate causal effect estimates. Most propensity score methods assume that covariates are measured without error. However, covariates are often measured with error. Recent work has shown that ignoring such error could lead to bias in treatment effect estimates. In this paper, we consider an additional complication: that of differential measurement error across treatment groups, such as can occur if a covariate is measured differently in the treatment and control groups. We propose two flexible Bayesian approaches for handling differential measurement error when estimating average causal effects using propensity score methods. We consider three scenarios: systematic (i.e., a location shift), heteroscedastic (i.e., different variances), and mixed (both systematic and heteroscedastic) measurement errors. We also explore various prior choices (i.e., weakly informative or point mass) on the sensitivity parameters related to the differential measurement error. We present results from simulation studies evaluating the performance of the proposed methods and apply these approaches to an example estimating the effect of neighborhood disadvantage on adolescent drug use disorders.
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Project description:Beta-blockers (metoprolol, bisoprolol, and carvedilol) are a cornerstone of heart failure (HF) treatment. However, it is well recognized that responses to a beta-blocker are variable among patients with HF. Numerous studies now suggest that genetic polymorphisms may contribute to variability in responses to a beta-blocker, including left ventricular ejection fraction improvement, survival, and hospitalization due to HF exacerbation. This review summarizes the pharmacogenetic data for beta-blockers in patients with HF and discusses the potential implications of beta-blocker pharmacogenetics for HF patients.
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Project description:BackgroundWhen data is distributed across multiple sites, sharing information at the individual level among sites may be difficult. In these multi-site studies, propensity score model can be fitted with data within each site or data from all sites when using inverse probability-weighted Cox regression to estimate overall hazard ratio. However, when there is unknown heterogeneity of covariates in different sites, either approach may lead to potential bias or reduced efficiency. In this study, we proposed a method to estimate propensity score based on covariate balance-related criterion and estimate the overall hazard ratio while overcoming data sharing constraints across sites.MethodsThe proposed propensity score was generated by choosing between global and local propensity score based on covariate balance-related criterion, combining the global propensity score fitted in the entire population and the local propensity score fitted within each site. We used this proposed propensity score to estimate overall hazard ratio of distributed survival data with multiple sites, while requiring only the summary-level information across sites. We conducted simulation studies to evaluate the performance of the proposed method. Besides, we applied the proposed method to real-world data to examine the effect of radiation therapy on time to death among breast cancer patients.ResultsThe simulation studies showed that the proposed method improved the performance in estimating overall hazard ratio comparing with global and local propensity score method, regardless of the number of sites and sample size in each site. Similar results were observed under both homogeneous and heterogeneous settings. Besides, the proposed method yielded identical results to the pooled individual-level data analysis. The real-world data analysis indicated that the proposed method was more likely to find a significant effect of radiation therapy on mortality compared to the global propensity score method and local propensity score method.ConclusionsThe proposed covariate balance-related propensity score in multi-site distributed survival data outperformed the global propensity score estimated using data from the entire population or the local propensity score estimated within each site in estimating the overall hazard ratio. The proposed approach can be performed without individual-level data transfer between sites and would yield the same results as the corresponding pooled individual-level data analysis.
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Project description:BackgroundFluid resuscitation is necessary to correct the sepsis-induced hypoperfusion, which is contradictory to the treatment of heart failure. This study explored the association between fluid balance (FB) of the first 24 h after ICU admission and mortality in critically ill patients with heart failure and sepsis.MethodsData were extracted from the Medical Information Mart for Intensive Care database. The locally weighted scatterplot smoothing (Lowess) method was used to demonstrate the relationship between FB and in-hospital mortality. Groups were divided into high FB (≥ 55.85 ml/kg) and low FB (< 55.85 ml/kg) according to the cut-off value of FB using Receiver operating characteristic analysis and Youden index method. The primary outcome was in-hospital mortality. Subgroup analyses, multivariable logistic regression analyses, and Kaplan-Meier curves were used to detect the association and survival difference between groups. Inverse probability treatment weighting (IPTW) and propensity score matching (PSM) were performed to minimize the bias of confounding factors and facilitate the comparability between groups.ResultsA total of 936 patients were included. The Lowess curve showed an approximate positive linear relationship for FB and in-hospital mortality. In the multivariable logistic regression adjusted model, high FB showed strong associations with in-hospital mortality (OR 2.53, 95% CI 1.60-3.99, p < 0.001) as compared to the low FB group. In IPTW and PSM models, high FB consistently showed higher in-hospital mortality (IPTW model: OR 1.94, 95% CI 1.52-2.49, p < 0.001; PSM model: OR 2.93, 95% CI 1.75-4.90, p < 0.001) and 30-day mortality (IPTW model: OR 1.65, 95% CI 1.29-2.10, p < 0.001; PSM model: OR 2.50, 95% CI 1.51-4.15, p < 0.001), compared with the low FB group.ConclusionFor critically ill patients with heart failure and sepsis, high FB within the first 24 h after ICU admission could serve as an independent risk factor for in-hospital mortality and 30-day mortality. The avoidance of fluid overload exerts important effects on reducing mortality in such patients.
| S-EPMC9587660 | biostudies-literature
Project description:To assess the covariate balancing properties of propensity score-based algorithms in which covariates affecting treatment choice are both measured and unmeasured.A simulation model of treatment choice and outcome.Simulation.Eight simulation scenarios varied with the values placed on measured and unmeasured covariates and the strength of the relationships between the measured and unmeasured covariates. The balance of both measured and unmeasured covariates was compared across patients either grouped or reweighted by propensity scores methods.Propensity score algorithms require unmeasured covariate variation that is unrelated to measured covariates, and they exacerbate the imbalance in this variation between treated and untreated patients relative to the full unweighted sample.The balance of measured covariates between treated and untreated patients has opposite implications for unmeasured covariates in randomized and observational studies. Measured covariate balance between treated and untreated patients in randomized studies reinforces the notion that all covariates are balanced. In contrast, forced balance of measured covariates using propensity score methods in observational studies exacerbates the imbalance in the independent portion of the variation in the unmeasured covariates, which can be likened to squeezing a balloon. If the unmeasured covariates affecting treatment choice are confounders, propensity score methods can exacerbate the bias in treatment effect estimates.
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Project description:Heart failure is becoming an increasingly common and significant problem in the field of pediatric cardiology. The numerous types of cardiomyopathies, and more recently, long-term survival of patients with congenital heart disease, have added to a growing patient population. Over the last several decades, our knowledge base regarding mechanisms of disease and therapeutic intervention in adult patients with heart failure has drastically changed. The most recent and important breakthrough in the pharmacologic treatment of heart failure has been the particular role of ?-blocker therapy. This medication has led to significant improvements in survival and symptoms in adults, with less convincing findings in limited studies in pediatrics. The ability to study the benefits of this therapy in patients has been challenging owing to the heterogeneity of the patient population and lack of large sample sizes. However, as we investigate the mechanisms behind the disease process, the differences that exist between disease conditions and ages, and the significant alterations that may exist at the molecular and genetic level, our understanding of ?-blocker therapy in pediatric heart failure will improve, and ultimately may lead to patient-specific therapy.
| S-EPMC3143493 | biostudies-other
Project description:Backgroundβ-blockers (BBs) are considered primary therapy in stable heart failure (HF) with reduced ejection fraction (HFrEF) without atrial fibrillation (AF); evidence-based benefits of BB on outcome have been documented. However, BBs have not been shown to improve mortality or reduce hospital admissions in HF patients with AF. This study assessed the relationship between BBs at discharge and relevant clinical outcomes in acute heart failure (AHF) patients with AF.MethodsFrom the Korean Acute Heart Failure Registry, 936 HFrEF and 639 HF patients with preserved ejection fraction (HFpEF) and AF were selected. Propensity score (PS) matching accounted for BB selection bias when assessing associations.ResultsBB-untreated patients in the overall cohort of HFrEF and HFpEF had greater deteriorated clinical and laboratory characteristics. In the 670 PS-matched cohort of HFrEF patients, incidences of all clinical events at 60 days and 1 year were not different according to use of BBs. In the 470 PS-matched cohort of HFpEF, rehospitalization and composite outcome at 6 months and 1 year more frequently occurred in non-users of BBs. After adjusting for covariates in the multivariable Cox model of matched cohorts, BB was not associated with clinical outcomes at 60 days and 1 year in HFrEF with AF patients. In HFpEF patients with AF, BB use was associated with reduced 6-month (hazard ratio [HR], 0.38; 95% confidence interval [CI], 0.20-0.74) and 1-year rehospitalization (HR, 0.53; 95% CI, 0.34-0.82).ConclusionIn the HFrEF with AF PS-matched cohort, the use of BBs at discharge was not associated with clinical outcome. However, in HFpEF with AF, the use of BB was associated with reduced rehospitalization during the 6-month and 1-year follow up.
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Project description:BackgroundThe clinical efficiency of routine oxygen therapy is uncertain in patients with acute heart failure (AHF) who do not have hypoxemia. The aim of this study was to investigate the association between oxygen therapy and clinical outcomes in normoxemic patients hospitalized with AHF using real-world data.MethodsNormoxemic patients diagnosed with AHF on ICU admission from the electronic ICU (eICU) Collaborative Research Database were included in the current study, in which the study population was divided into the oxygen therapy group and the ambient-air group. Propensity score matching (PSM) was applied to create a balanced covariate distribution between patients receiving supplemental oxygen and those exposed to ambient air. Linear regression and logistic regression models were performed to assess the associations between oxygen therapy and length of stay (LOS), and all-cause in-hospital as well as ICU mortality rates, respectively. A series of sensitivity and subgroup analyses were conducted to further validate the robustness of our findings.ResultsA total of 2922 normoxemic patients with AHF were finally included in the analysis. Overall, 42.1% (1230/2922) patients were exposed to oxygen therapy, and 57.9% (1692/2922) patients did not receive oxygen therapy (defined as the ambient-air group). After PSM analysis, 1122 pairs of patients were matched: each patient receiving oxygen therapy was matched with a patient without receiving supplemental oxygen. The multivariable logistic model showed that there was no significant interaction between the ambient air and oxygen group for all-cause in-hospital mortality [odds ratio (OR) 1.30; 95% confidence interval (CI) 0.92-1.82; P = 0.138] or ICU mortality (OR 1.39; 95% CI 0.83-2.32; P = 0.206) in the post-PSM cohorts. In addition, linear regression analysis revealed that oxygen therapy was associated with prolonged ICU LOS (OR 1.11; 95% CI 1.06-1.15; P < 0.001) and hospital LOS (OR 1.06; 95% CI 1.01-1.10; P = 0.009) after PSM. Furthermore, the absence of an effect of supplemental oxygen on mortality was consistent in all subgroups.ConclusionRoutine use of supplemental oxygen in AHF patients without hypoxemia was not found to reduce all-cause in-hospital mortality or ICU mortality.
| S-EPMC8268364 | biostudies-literature
Project description:Sex hormones influence the prevalence and the outcome of heart diseases. The conversion of testosterone to its more active metabolite dihydrotestosterone drives cardiac growth and dysfunction, while inhibition of this step by the anti-androgenic drug finasteride counteracts these pathological processes in preclinical models. In this retrospective, observational study, we aim to investigate whether finasteride, which is in clinical use mainly for prostate disease, might ameliorate cardiac hypertrophy and heart failure in patients. Retrospective chart review of 1041 medical cases with heart failure between 1995 and 2015 was conducted. Stratification was performed by concomitant prostate treatment status (tamsulosin versus finasteride). A propensity score analysis yielded a total of 328 matched medical cases without residual differences in the baseline patient characteristics. In this propensity score matched samples, anti-androgenic therapy with finasteride was associated with significantly reduced left ventricular hypertrophy (interventricular septal thickness 13.3 ± 2.4 mm control vs. 12.6 ± 2.1 mm finasteride group (p = 0.029); estimated average treatment effects on the treated: -0.7 mm, 95% CI mean difference -1.3 to -0.1). In this retrospective analysis anti-androgenic therapy with finasteride for prostate disease was associated with attenuated cardiac hypertrophy in patients with heart failure. Therefore, our data encourage further analysis of this approach in larger heart failure patient cohorts.
| S-EPMC6626053 | biostudies-literature