Full Bayesian Methods to Handle Missing Data in Health Economic Evaluation

Trial-based economic evaluations are performed on individual-level data, which almost invariably contain missing values. Missingness represents a threat for the analysis because any statistical method makes assumptions about the unobserved values that cannot be verified from the data at hand; when these assumptions are not realistic, they could lead to biased inferences and mislead the cost-effectiveness assessment. We start by investigating the current missing data handling in economic evaluations and provide recommendations about how information about missingness and related methods should be reported in the analysis. We illustrate the pitfalls and issues that affect the methods used in routine analyses, which typically do not account for the intrinsic complexities of the data and rarely include sensitivity analysis to the missingness assumptions. We propose to overcome these problems using a full Bayesian approach. We use two case studies to demonstrate the benefits of our approach, which allows for a flexible specification of the model to jointly handle the complexities of the data and the uncertainty around the missing values. Finally, we present a longitudinal bivariate model to handle nonignorable missingness. The model extends the standard approach by accounting for all observed data, for which a flexible parametric model is specified. Missing data are handled through a combination of identifying restrictions and sensitivity parameters. First, a benchmark scenario is specified and then plausible nonignorable departures are assessed using alternative prior distributions on the sensitivity parameters. The model is applied to and motivated by one of the two case studies considered

Erratum to: Handling Missing Data in Within-Trial Cost-Effectiveness Analysis: A Review with Future Recommendations.

Reference 5, which reads: 5. Manca P, Palmer S. Handling missing values in cost effectiveness analyses that use data from cluster randomized trials. Appl Health Econ Health Policy. 2006;4:65–75. Should read: 5. Manca A, Palmer S. Handling missing data in patientlevel cost-effectiveness analysis alongside randomised clinical trials. Appl Health Econ Health Policy. 2005;4:65–75

Handling Missing Data in Within-Trial Cost-Effectiveness Analysis: A Review with Future Recommendations.

Cost-effectiveness analyses (CEAs) alongside randomised controlled trials (RCTs) are increasingly designed to collect resource use and preference-based health status data for the purpose of healthcare technology assessment. However, because of the way these measures are collected, they are prone to missing data, which can ultimately affect the decision of whether an intervention is good value for money. We examine how missing cost and effect outcome data are handled in RCT-based CEAs, complementing a previous review (covering 2003-2009, 88 articles) with a new systematic review (2009-2015, 81 articles) focussing on two different perspectives. First, we provide guidelines on how the information about missingness and related methods should be presented to improve the reporting and handling of missing data. We propose to address this issue by means of a quality evaluation scheme, providing a structured approach that can be used to guide the collection of information, elicitation of the assumptions, choice of methods and considerations of possible limitations of the given missingness problem. Second, we review the description of the missing data, the statistical methods used to deal with them and the quality of the judgement underpinning the choice of these methods. Our review shows that missing data in within-RCT CEAs are still often inadequately handled and the overall level of information provided to support the chosen methods is rarely satisfactory

Linear mixed models to handle missing at random data in trial-based economic evaluations

Trial-based cost-effectiveness analyses (CEAs) are an important source of evidence in the assessment of health interventions. In these studies, cost and effectiveness outcomes are commonly measured at multiple time points, but some observations may be missing. Restricting the analysis to the participants with complete data can lead to biased and inefficient estimates. Methods, such as multiple imputation, have been recommended as they make better use of the data available and are valid under less restrictive Missing At Random (MAR) assumption. Linear mixed effects models (LMMs) offer a simple alternative to handle missing data under MAR without requiring imputations, and have not been very well explored in the CEA context. In this manuscript, we aim to familiarize readers with LMMs and demonstrate their implementation in CEA. We illustrate the approach on a randomized trial of antidepressants, and provide the implementation code in R and Stata. We hope that the more familiar statistical framework associated with LMMs, compared to other missing data approaches, will encourage their implementation and move practitioners away from inadequate methods

Blue-green endoscopy in a dog presenting chronic vomiting-regurgitation

A 2-year-old male Maremma sheepdog presenting with chronic vomiting-regurgitation was examined at the University Veterinary Teaching Hospital, Camerino University. An oesophagogastroscopy with a single blue + green (BG) filter restricting wavelengths from 400 to 550 nm was carried out. A conventional white light endoscopy showed a dilated oesophagus with mildly diffuse erythematous mucosa (more accentuated proximal to the cardia); some portions of the gastric mucosa were covered with fluids and appeared only slightly erythematous. A blue green endoscopy highlighted the oesophageal lesions in dark blue, which made them appear more clearly defined from the remaining mucosa. In the gastric antrum, a small, slightly darker blue roundish area was visible. This area did not show up under the white light endoscopy. A histopathological assessment of biopsy specimens from the distal oesophagus, antrum (including the area highlighted only by BG endoscopy) and gastric body showed chronic-active hyperplastic esophagitis and superficial squamous epithelial dysplasia, while gastric samples showed severe diffuse hyperaemic gastritis of the antrum and superficial diffuse atrophy of the gastric body. The authors believe that the use of a BG endoscopy could be useful in veterinary medicine to increase the diagnostic potential of endoscopic assessment in animals