Long-Term Outcomes of Resynchronization-Defibrillation for Heart Failure.

BACKGROUND: The Resynchronization-Defibrillation for Ambulatory Heart Failure Trial (RAFT) showed a greater benefit with respect to mortality at 5 years among patients who received cardiac-resynchronization therapy (CRT) than among those who received implantable cardioverter-defibrillators (ICDs). However, the effect of CRT on long-term survival is not known. METHODS: We randomly assigned patients with New York Heart Association (NYHA) class II or III heart failure, a left ventricular ejection fraction of 30% or less, and an intrinsic QRS duration of 120 msec or more (or a paced QRS duration of 200 msec or more) to receive either an ICD alone or a CRT defibrillator (CRT-D). We assessed long-term outcomes among patients at the eight highest-enrolling participating sites. The primary outcome was death from any cause; the secondary outcome was a composite of death from any cause, heart transplantation, or implantation of a ventricular assist device. RESULTS: The trial enrolled 1798 patients, of whom 1050 were included in the long-term survival trial; the median duration of follow-up for the 1050 patients was 7.7 years (interquartile range, 3.9 to 12.8), and the median duration of follow-up for those who survived was 13.9 years (interquartile range, 12.8 to 15.7). Death occurred in 405 of 530 patients (76.4%) assigned to the ICD group and in 370 of 520 patients (71.2%) assigned to the CRT-D group. The time until death appeared to be longer for those assigned to receive a CRT-D than for those assigned to receive an ICD (acceleration factor, 0.80; 95% confidence interval, 0.69 to 0.92; P = 0.002). A secondary-outcome event occurred in 412 patients (77.7%) in the ICD group and in 392 (75.4%) in the CRT-D group. CONCLUSIONS: Among patients with a reduced ejection fraction, a widened QRS complex, and NYHA class II or III heart failure, the survival benefit associated with receipt of a CRT-D as compared with ICD appeared to be sustained during a median of nearly 14 years of follow-up. (RAFT ClinicalTrials.gov number, NCT00251251.).

Impact of the 1997 Canadian guidelines on the conduct of Canadian-based economic evaluations in the published literature.

OBJECTIVE: To assess the impact of the 1997 Canadian guidelines on the methods and presentation of economic evaluations conducted from a Canadian perspective in the published literature. METHODS: A systematic literature review was conducted to identify health technology economic evaluations conducted from a Canadian perspective published in peer-reviewed journals between 2001 and 2006. To investigate the impact of the 1997 Canadian Coordinating Office of Health Technology Assessment guidelines, each included study was assessed against 17 of the 25 recommendations. RESULTS: Of the 153 included studies, a base set of 9 methodological standards, as outlined by the 1997 guidelines, were followed by over 50% of the studies including: indications, outcomes for cost utility analysis, outcomes for cost benefit analysis, discounting future cost and outcomes, cost identification and valuation, evaluating uncertainty and disclosing funding relationships. Main divergences from the guidelines were found for analytic technique (38%), study perspective (23%), source of preferences (8%), equity (7%), and cost measurement (24%). CONCLUSION: The current assessment has shown that the 1997 Canadian guidelines have set a minimum methodological standard within the community of "doers" conducting economic analyses from a Canadian perspective. Although there was divergence from some of the recommendations, the majority were reflected as changes in the 2006 Canadian guidelines.

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.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Handling uncertainty in economic evaluations of patient level data: a review of the use of Bayesian methods to inform health technology assessments.

OBJECTIVES: Due to potential advantages (e.g., using all available evidence), Bayesian methods have been proposed to assist healthcare decision making. This review provides a detailed description of how Bayesian methods have been applied to economic evaluations of patient level data. The results serve both as a reference and as a means by which to examine the appropriate application of Bayesian methods to inform decision making. METHODS: MEDLINE, EMBASE, and Cochrane Economic Evaluation databases were searched to identify studies, published up to November 2007, meeting three inclusion criteria: (i) the study conducted an economic evaluation, (ii) sampling uncertainty was incorporated using Bayesian methods, (iii) the likelihood function was informed by patient level data from a single source. Data were collected on key study characteristics (e.g., prior distribution, likelihood function, presentation of uncertainty). RESULTS: The search identified 366 potentially relevant studies, from which 103 studies underwent full-text review. Sixteen studies met the inclusion criteria. Half of the studies used uninformative priors; most studies incorporated the potential dependence between costs and effects, and presented cost-effectiveness acceptability curves. Results were sensitive to changes in the priors and likelihoods. CONCLUSIONS: Limited use of informative priors, among the included studies, gives policy makers little guidance on one of the main benefits of Bayesian methods, the ability to integrate all available evidence to capture the uncertainty inherent in decision making.

The impact of using informative priors in a Bayesian cost-effectiveness analysis: an application of endovascular versus open surgical repair for abdominal aortic aneurysms in high-risk patients.

BACKGROUND: Bayesian methods have been proposed as a way of synthesizing all available evidence to inform decision making. However, few practical applications of the use of Bayesian methods for combining patient-level data (i.e., trial) with additional evidence (e.g., literature) exist in the cost-effectiveness literature. The objective of this study was to compare a Bayesian cost-effectiveness analysis using informative priors to a standard non-Bayesian nonparametric method to assess the impact of incorporating additional information into a cost-effectiveness analysis. METHODS: Patient-level data from a previously published nonrandomized study were analyzed using traditional nonparametric bootstrap techniques and bivariate normal Bayesian models with vague and informative priors. Two different types of informative priors were considered to reflect different valuations of the additional evidence relative to the patient-level data (i.e., "face value" and "skeptical"). The impact of using different distributions and valuations was assessed in a sensitivity analysis. Models were compared in terms of incremental net monetary benefit (INMB) and cost-effectiveness acceptability frontiers (CEAFs). RESULTS: The bootstrapping and Bayesian analyses using vague priors provided similar results. The most pronounced impact of incorporating the informative priors was the increase in estimated life years in the control arm relative to what was observed in the patient-level data alone. Consequently, the incremental difference in life years originally observed in the patient-level data was reduced, and the INMB and CEAF changed accordingly. CONCLUSIONS: The results of this study demonstrate the potential impact and importance of incorporating additional information into an analysis of patient-level data, suggesting this could alter decisions as to whether a treatment should be adopted and whether more information should be acquired.

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.