The NICE Decision Support Unit has just published A review of the use of statistical regression models to inform cost effectiveness analyses within the NICE Technology Appraisals programme. By Ben Kearns, Roberta Ara and Allan Wailoo.
From the Executive Summary:
“Of the 79 technology appraisals examined, 47 included at least one regression analysis and a total of 91 separate regression analyses were reported. 56 were de novo analyses provided by the manufacturer/sponsor of the technology (34 from Single Technology Appraisals and 22 from Multiple Technology Appraisals), while the remaining 35 were sourced from existing published literature. Over 50% involved health state utility values with the balance involving health care costs (11%) or probabilities of clinical events (35%).
For the de novo analyses, reporting was poorest around the sample size used, the justification of the type of model estimated, the selection of covariates used, the strategy for identifying the preferred final model and any validation used. Across all the analyses, there was potential for improvement in the reporting of: the description of the dataset, the model type, the rationale for inclusion of model covariates, the validity of the final model and the uncertainty in the model.
Statistical regression models are in widespread use in NICE TAs yet reporting standards relating to basic information are poor. Whilst some of this may be due to the word limit imposed on TAs, there is still scope for improvement. This is important as increasing levels of reporting transparency enable policy decision makers to have increasing levels of confidence in the resulting estimates of cost-effectiveness. We suggest a series of recommendations that could be used for the minimum reporting requirements for any statistical regression analyses used in a DAM.”
