Bayesian Hierarchical Models Combining Different Study Types and Adjusting for Covariate Imbalances: A Simulation Study to Assess Model Performance

BACKGROUND: Bayesian hierarchical models have been proposed to combine evidence from different types of study designs. However, when combining evidence from randomised and non-randomised controlled studies, imbalances in patient characteristics between study arms may bias the results. The objective of this study was to assess the performance of a proposed Bayesian approach to adjust for imbalances in patient level covariates when combining evidence from both types of study designs. METHODOLOGY/PRINCIPAL FINDINGS: Simulation techniques, in which the truth is known, were used to generate sets of data for randomised and non-randomised studies. Covariate imbalances between study arms were introduced in the non-randomised studies. The performance of the Bayesian hierarchical model adjusted for imbalances was assessed in terms of bias. The data were also modelled using three other Bayesian approaches for synthesising evidence from randomised and non-randomised studies. The simulations considered six scenarios aimed at assessing the sensitivity of the results to changes in the impact of the imbalances and the relative number and size of studies of each type. For all six scenarios considered, the Bayesian hierarchical model adjusted for differences within studies gave results that were unbiased and closest to the true value compared to the other models. CONCLUSIONS/SIGNIFICANCE: Where informed health care decision making requires the synthesis of evidence from randomised and non-randomised study designs, the proposed hierarchical Bayesian method adjusted for differences in patient characteristics between study arms may facilitate the optimal use of all available evidence leading to unbiased results compared to unadjusted analyses

The importance of adjusting for potential confounders in Bayesian hierarchical models synthesising evidence from randomised and non-randomised studies: an application comparing treatments for abdominal aortic aneurysms

<p>Abstract</p> <p>Background</p> <p>Informing health care decision making may necessitate the synthesis of evidence from different study designs (e.g., randomised controlled trials, non-randomised/observational studies). Methods for synthesising different types of studies have been proposed, but their routine use requires development of approaches to adjust for potential biases, especially among non-randomised studies. The objective of this study was to extend a published Bayesian hierarchical model to adjust for bias due to confounding in synthesising evidence from studies with different designs.</p> <p>Methods</p> <p>In this new methodological approach, study estimates were adjusted for potential confounders using differences in patient characteristics (e.g., age) between study arms. The new model was applied to synthesise evidence from randomised and non-randomised studies from a published review comparing treatments for abdominal aortic aneurysms. We compared the results of the Bayesian hierarchical model adjusted for differences in study arms with: 1) unadjusted results, 2) results adjusted using aggregate study values and 3) two methods for downweighting the potentially biased non-randomised studies. Sensitivity of the results to alternative prior distributions and the inclusion of additional covariates were also assessed.</p> <p>Results</p> <p>In the base case analysis, the estimated odds ratio was 0.32 (0.13,0.76) for the randomised studies alone and 0.57 (0.41,0.82) for the non-randomised studies alone. The unadjusted result for the two types combined was 0.49 (0.21,0.98). Adjusted for differences between study arms, the estimated odds ratio was 0.37 (0.17,0.77), representing a shift towards the estimate for the randomised studies alone. Adjustment for aggregate values resulted in an estimate of 0.60 (0.28,1.20). The two methods used for downweighting gave odd ratios of 0.43 (0.18,0.89) and 0.35 (0.16,0.76), respectively. Point estimates were robust but credible intervals were wider when using vaguer priors.</p> <p>Conclusions</p> <p>Covariate adjustment using aggregate study values does not account for covariate imbalances between treatment arms and downweighting may not eliminate bias. Adjustment using differences in patient characteristics between arms provides a systematic way of adjusting for bias due to confounding. Within the context of a Bayesian hierarchical model, such an approach could facilitate the use of all available evidence to inform health policy decisions.</p

Magnetic resonance imaging of abnormal ventricular septal motion in heart diseases: a pictorial review

The purpose of this article is to illustrate the usefulness of MR imaging in the clinical evaluation of congenital and acquired cardiac diseases characterised by ventricular septal wall motion abnormality. Recognition of the features of abnormal ventricular septal motion in MR images is important to evaluate the haemodynamic status in patients with congenital and acquired heart diseases in routine clinical practice

Transcriptional Profiling Uncovers a Network of Cholesterol-Responsive Atherosclerosis Target Genes

Despite the well-documented effects of plasma lipid lowering regimes halting atherosclerosis lesion development and reducing morbidity and mortality of coronary artery disease and stroke, the transcriptional response in the atherosclerotic lesion mediating these beneficial effects has not yet been carefully investigated. We performed transcriptional profiling at 10-week intervals in atherosclerosis-prone mice with human-like hypercholesterolemia and a genetic switch to lower plasma lipoproteins (Ldlr−/−Apo100/100 Mttpflox/flox Mx1-Cre). Atherosclerotic lesions progressed slowly at first, then expanded rapidly, and plateaued after advanced lesions formed. Analysis of lesion expression profiles indicated that accumulation of lipid-poor macrophages reached a point that led to the rapid expansion phase with accelerated foam-cell formation and inflammation, an interpretation supported by lesion histology. Genetic lowering of plasma cholesterol (e.g., lipoproteins) at this point all together prevented the formation of advanced plaques and parallel transcriptional profiling of the atherosclerotic arterial wall identified 37 cholesterol-responsive genes mediating this effect. Validation by siRNA-inhibition in macrophages incubated with acetylated-LDL revealed a network of eight cholesterol-responsive atherosclerosis genes regulating cholesterol-ester accumulation. Taken together, we have identified a network of atherosclerosis genes that in response to plasma cholesterol-lowering prevents the formation of advanced plaques. This network should be of interest for the development of novel atherosclerosis therapies

Relationship between treatment delay and final infarct size in STEMI patients treated with abciximab and primary PCI

Background Studies on the impact of time to treatment on myocardial infarct size have yielded   conflicting results. In this study of ST-Elevation Myocardial Infarction (STEMI) treated   with primary percutaneous coronary intervention (PCI), we set out to investigate the   relationship between the time from First Medical Contact (FMC) to the demonstration   of an open infarct related artery (IRA) and final scar size. Between February 2006 and September 2007, 89 STEMI patients treated with primary PCI   were studied with contrast enhanced magnetic resonance imaging (ceMRI) 4 to 8 weeks   after the infarction. Spearman correlation was computed for health care delay time   (defined as time from FMC to PCI) and myocardial injury. Multiple linear regression   was used to determine covariates independently associated with infarct size. Results An occluded artery (Thrombolysis In Myocardial Infarction, TIMI flow 0-1 at initial   angiogram) was seen in 56 patients (63%). The median FMC-to-patent artery was 89 minutes.   There was a weak correlation between time from FMC-to-patent IRA and infarct size,   r = 0.27, p = 0.01. In multiple regression analyses, LAD as the IRA, smoking and an occluded vessel   at the first angiogram, but not delay time, correlated with infarct size. Conclusions In patients with STEMI treated with primary PCI we found a weak correlation between   health care delay time and infarct size. Other factors like anterior infarction, a   patent artery pre-PCI and effects of reperfusion injury may have had greater influence   on infarct size than time-to-treatment per se