Bayesian heterogeneity in a meta-analysis with two studies and binary data.

The meta-analysis of two trials is valuable in many practical situations, such as studies of rare and/or orphan diseases focussed on a single intervention. In this context, additional concerns, like small sample size and/or heterogeneity in the results obtained, might make standard frequentist and Bayesian techniques inappropriate. In a meta-analysis, moreover, the presence of between-sample heterogeneity adds model uncertainty, which must be taken into consideration when drawing inferences. We suggest that the most appropriate way to measure this heterogeneity is by clustering the samples and then determining the posterior probability of the cluster models. The meta-inference is obtained as a mixture of all the meta-inferences for the cluster models, where the mixing distribution is the posterior model probability. We present a simple two-component form of Bayesian model averaging that is unaffected by characteristics such as small study size or zero-cell counts, and which is capable of incorporating uncertainties into the estimation process. Illustrative examples are given and analysed, using real sparse binomial data.

Role of primary care in the follow-up of patients with obstructive sleep apnoea undergoing CPAP treatment: a randomised controlled trial.

OBJECTIVE: To evaluate whether follow-up of patients with obstructive sleep apnoea (OSA) undergoing CPAP treatment could be performed in primary care (PC) settings. DESIGN: Non-inferiority, randomised, prospective controlled study. SETTINGS: Sleep unit (SU) at the University Hospital and in 8 PC units in Lleida, Spain. PARTICIPANTS: Patients with OSA were randomised to be followed up at the SU or PC units over a 6-month period. MAIN OUTCOMES MEASURED: The primary outcome was CPAP compliance at 6 months. The secondary outcomes were Epworth Sleep Scale (ESS) score, EuroQoL, patient satisfaction, body mass index (BMI), blood pressure and cost-effectiveness. RESULTS: We included 101 patients in PC ((mean±SD) apnoea-hypopnoea index (AHI) 50.8±22.9/h, age 56.2±11 years, 74% male) and 109 in the SU (AHI 51.4±24.4/h, age 55.8±11 years, 77% male)). The CPAP compliance was (mean (95% CI) 4.94 (4.47 to 5.5) vs 5.23 (4.79 to 5.66) h, p=0.18) in PC and SU groups, respectively. In the SU group, there were greater improvements in ESS scores (mean change 1.79, 95% CI +0.05 to +3.53, p=0.04) and patient satisfaction (-1.49, 95% CI -2.22 to -0.76); there was a significant mean difference in BMI between the groups (0.57, 95% CI +0.01 to +1.13, p=0.04). In the PC setting, there was a cost saving of 60%, with similar effectiveness, as well as a decrease in systolic blood pressure (-5.32; 95% CI -10.91 to +0.28, p=0.06). CONCLUSIONS: For patients with OSA, treatment provided in a PC setting did not result in worse CPAP compliance compared with a specialist model and was shown to be a cost-effective alternative. TRIAL REGISTRATION NUMBER: Clinical Trials NCT01918449.

Objective Bayesian meta-analysis for sparse discrete data.

This paper presents a Bayesian model for meta-analysis of sparse discrete binomial data, which are out of the scope of the usual hierarchical normal random-effect models. Treatment effectiveness data are often of this type. The crucial linking distribution between the effectiveness conditional on the healthcare center and the unconditional effectiveness is constructed from specific bivariate classes of distributions with given marginals. This assures coherency between the marginal and conditional prior distributions utilized in the analysis. Further, we impose a bivariate class of priors that is able to accommodate a wide range of heterogeneity degrees between the multicenter clinical trials involved. Applications to real multicenter data are given and compared with previous meta-analysis.

Using covariates to reduce uncertainty in the economic evaluation of clinical trial data.

As part of their practice, policymakers have to make economic evaluations using clinical trial data. Recent interest has been expressed in determining how cost-effectiveness analysis can be undertaken in a regression framework. In this respect, published research basically provides a general method for prognostic factor adjustment in the presence of imbalance, emphasizing sub-group analysis. In this paper, we present an alternative method from a Bayesian approach. We propose the use of covariates in Bayesian health technology assessment in order to reduce uncertainty about the effect of treatments. We show its advantages by comparison with another published method that do not adjust for covariates using simulated data.