Incorporation of genuine prior information in cost-effectiveness analysis of clinical trial data

The Bayesian approach to statistics has been growing rapidly in popularity as an alternative to the frequentist approach in the appraisal of heathcare technologies in clinical trials. Bayesian methods have significant advantages over classical frequentist statistical methods and the presentation of evidence to decision makers. A fundamental feature of a Bayesian analysis is the use of prior information as well as the clinical trial data in the final analysis. However, the incorporation of prior information remains a controversial subject that provides a potential barrier to the acceptance of practical uses of Bayesian methods. The pur pose of this paper is to stimulate a debate on the use of prior information in evidence submitted to decision makers. We discuss the advantages of incorporating genuine prior information in cost-effectiveness analyses of clinical trial data and explore mechanisms to safeguard scientific rigor in the use of such prior information

A uniform Time Trade Off method for states better and worse than dead: feasibility study of the ‘lead time’ approach

The way Time Trade Off (TTO) values are elicited for states of health considered ‘worse than being dead’ has important implications for the mean values used in economic evaluation. Conventional approaches to TTO, as used in the UK’s ‘MVH’ value set, are problematic because they require fundamentally different tradeoffs tasks for the valuation of states better and worse than dead. This study aims to refine and test the feasibility of a new approach described by Robinson and Spencer (2006), and to explore the characteristics of the valuation data it generates. The approach introduces a ‘lead time’ into the TTO, producing a uniform procedure for generating values either >0 or <0. We used this lead time TTO to value 10 moderate to severe EQ-5D states using a sample of the general public (n=109). We conclude that the approach is feasible for use in valuation studies, and appears to overcome the discontinuity in values around 0 evident in conventional methods. However, further research is required to resolve the issue of how to handle participants who ‘use up’ all lead time; to develop ways of controlling for individual time preferences; and to better understand the implications for valuations of states better than dead

What Do You Think? Using Expert Opinion to Improve Predictions of Response Propensity Under a Bayesian Framework

Responsive survey designs introduce protocol changes to survey operations based on accumulating paradata. Case-level predictions, including response propensity, can be used to tailor data collection features in pursuit of cost or quality goals. Unfortunately, predictions based only on partial data from the current round of data collection can be biased, leading to ineffective tailoring. Bayesian approaches can provide protection against this bias. Prior beliefs, which are generated from data external to the current survey implementation, contribute information that may be lacking from the partial current data. Those priors are then updated with the accumulating paradata. The elicitation of the prior beliefs, then, is an important characteristic of these approaches. While historical data for the same or a similar survey may be the most natural source for generating priors, eliciting prior beliefs from experienced survey managers may be a reasonable choice for new surveys, or when historical data are not available. Here, we fielded a questionnaire to survey managers, asking about expected attempt-level response rates for different subgroups of cases, and developed prior distributions for attempt-level response propensity model coefficients based on the mean and standard error of their responses. Then, using respondent data from a real survey, we compared the predictions of response propensity when the expert knowledge is incorporated into a prior to those based on a standard method that considers accumulating paradata only, as well as a method that incorporates historical survey data

Study Protocol for Investigating Physician Communication Behaviours that Link Physician Implicit Racial Bias and Patient Outcomes in Black Patients with Type 2 Diabetes Using an Exploratory Sequential Mixed Methods Design

Introduction Patient-physician racial discordance is associated with Black patient reports of dissatisfaction and mistrust, which in turn are associated with poor adherence to treatment recommendations and underutilisation of healthcare. Research further has shown that patient dissatisfaction and mistrust are magnified particularly when physicians hold high levels of implicit racial bias. This suggests that physician implicit racial bias manifests in their communication behaviours during medical interactions. The overall goal of this research is to identify physician communication behaviours that link physician implicit racial bias and Black patient immediate (patient-reported satisfaction and trust) and long-term outcomes (eg, medication adherence, self-management and healthcare utilisation) as well as clinical indicators of diabetes control (eg, blood pressure, HbA1c and history of diabetes complication). Methods and analysis Using an exploratory sequential mixed methods research design, we will collect data from approximately 30 family medicine physicians and 300 Black patients with type 2 diabetes mellitus. The data sources will include one physician survey, three patient surveys, medical interaction videos, video elicitation interviews and medical chart reviews. Physician implicit racial bias will be assessed with the physician survey, and patient outcomes will be assessed with the patient surveys and medical chart reviews. In video elicitation interviews, a subset of patients (approximately 20–40) will watch their own interactions while being monitored physiologically to identify evocative physician behaviours. Information from the interview will determine which physician communication behaviours will be coded from medical interactions videos. Coding will be done independently by two trained coders. A series of statistical analyses (zero-order correlations, partial correlations, regressions) will be conducted to identify physician behaviours that are associated significantly with both physician implicit racial bias and patient outcomes