Costs of Treatment of Chronic Obstructive Pulmonary Disease

The aim of this study was to analyze direct costs of COPD therapy in relation with clinical course and stage of the disease. Sixty patients with moderate to severe COPD were included into the study. The average cost was taken from institutional data file and was also assessed from a social perspective. Results were presented as average costs per patient per year. Forty two percent of patients was classified as GOLD D category, while categories A, B, and C accounted for 8 %, 27 %, and 23 %, respectively. Approximately 65 % of patients had 2-3 degrees of dyspnea according to the Modified Medical Research Council Dyspnea Scale. About 60 % of patients underwent two or three exacerbations per year and those patients had one or two co-morbidities diagnosed. Treatment costs almost doubled with disease progression, mainly due to exacerbations. In patients in Group C and Group D with exacerbations the direct costs were several times higher than in group A or B and the difference increased with progression of the disease. In Groups A and B, the costs of treatment of stable disease or with exacerbation were comparable. We conclude that costs of treatment of COPD patients were highest in advanced disease and were strongly related to COPD exacerbations.

A Bayesian sensitivity study of risk difference in the meta-analysis of binary outcomes from sparse data

In most cases, including those of discrete random variables, statistical meta-analysis is carried out using the normal random effect model. The authors argue that normal approximation does not always properly reflect the underlying uncertainty of the original discrete data. Furthermore, in the presence of rare events the results from this approximation can be very poor. This review proposes a Bayesian meta-analysis to address binary outcomes from sparse data and also introduces a simple way to examine the sensitivity of the quantities of interest in the meta-analysis with respect to the structure dependence selected. The findings suggest that for binary outcomes data it is possible to develop a Bayesian procedure, which can be directly applied to sparse data without ad hoc corrections. By choosing a suitable class of linking distributions, the authors found that a Bayesian robustness study can be easily implemented. For illustrative purposes, an example with real data is analyzed using the proposed Bayesian meta-analysis for binomial sparse data.