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

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

The "smoker's paradox" in patients with acute coronary syndrome: a systematic review

<p>Abstract</p> <p>Background</p> <p>Smokers have been shown to have lower mortality after acute coronary syndrome than non-smokers. This has been attributed to the younger age, lower co-morbidity, more aggressive treatment and lower risk profile of the smoker. Some studies, however, have used multivariate analyses to show a residual survival benefit for smokers; that is, the "smoker's paradox". The aim of this study was, therefore, to perform a systematic review of the literature and evidence surrounding the existence of the "smoker's paradox".</p> <p>Methods</p> <p>Relevant studies published by September 2010 were identified through literature searches using EMBASE (from 1980), MEDLINE (from 1963) and the Cochrane Central Register of Controlled Trials, with a combination of text words and subject headings used. English-language original articles were included if they presented data on hospitalised patients with defined acute coronary syndrome, reported at least in-hospital mortality, had a clear definition of smoking status (including ex-smokers), presented crude and adjusted mortality data with effect estimates, and had a study sample of > 100 smokers and > 100 non-smokers. Two investigators independently reviewed all titles and abstracts in order to identify potentially relevant articles, with any discrepancies resolved by repeated review and discussion.</p> <p>Results</p> <p>A total of 978 citations were identified, with 18 citations from 17 studies included thereafter. Six studies (one observational study, three registries and two randomised controlled trials on thrombolytic treatment) observed a "smoker's paradox". Between the 1980s and 1990s these studies enrolled patients with acute myocardial infarction (AMI) according to criteria similar to the World Health Organisation criteria from 1979. Among the remaining 11 studies not supporting the existence of the paradox, five studies represented patients undergoing contemporary management.</p> <p>Conclusion</p> <p>The "smoker's paradox" was observed in some studies of AMI patients in the pre-thrombolytic and thrombolytic era, whereas no studies of a contemporary population with acute coronary syndrome have found evidence for such a paradox.</p