ObjectivesMany UK primary care trusts have recently introduced eligibility criteria restricting total knee replacement (TKR) to patients with low pre-operative Oxford Knee Scores (OKS) to cut expenditure. We evaluate these criteria by assessing the cost-effectiveness of TKR compared with no knee replacement for patients with different baseline characteristics from an NHS perspective.DesignThe cost-effectiveness of TKR in different patient subgroups was assessed using regression analyses of patient-level data from the Knee Arthroplasty Trial, a large, pragmatic randomised trial comparing knee prostheses.Setting34 UK hospitals.Participants2131 osteoarthritis patients undergoing TKR.Interventions and outcome measuresCosts and quality-adjusted life years (QALYs) observed in the Knee Arthroplasty Trial within 5 years of TKR were compared with conservative assumptions about the costs and outcomes that would have been accrued had TKR not been performed.ResultsOn average, primary TKR and 5 years of subsequent care cost £7458 per patient (SD: £4058), and patients gained an average of 1.33 (SD: 1.43) QALYs. As a result, TKR cost £5623/QALY gained. Although costs and health outcomes varied with age and sex, TKR cost <£20 000/QALY gained for patients with American Society of Anaesthesiologists grades 1–2 who had baseline OKS <40 and for American Society of Anaesthesiologists grade 3 patients with OKS <35, even with highly conservative assumptions about costs and outcomes without TKR. Body mass index had no significant effect on costs or outcomes. Restricting TKR to patients with pre-operative OKS <27 would inappropriately deny a highly cost-effective treatment to >10 000 patients annually.ConclusionsTKR is highly cost-effective for most current patients if the NHS is willing to pay £20 000–£30 000/QALY gained. At least 97% of TKR patients in England have more severe symptoms than the thresholds we have identified, suggesting that further rationing by OKS is probably unjustified.Trial registration numberISRCTN 45837371.

Systematic literature searches were conducted to identify studies that conducted statistical mapping to predict EQ-5D utilities or responses from any source instrument and reported the estimated algorithms in sufficient detail to allow other researchers to use them to predict EQ-5D in other studies. Ninety studies reporting 121 mapping algorithms met the inclusion criteria. The studies estimated EQ-5D utilities from 80 source instruments. All but two studies included direct utility mapping to predict EQ-5D utilities, while 20 studies (22%) conducted response mapping to predict responses to each EQ-5D domain. Seventy-two studies (80%) explored ordinary least squares regression and 16 (18%) used censored least absolute deviations (CLAD) models. The details of the studies identified are made available in an online database, which will be updated regularly to enable researchers to easily identify studies that can help them to estimate utilities for economic evaluation.

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Increasing numbers of economic evaluations are conducted alongside randomised controlled trials. Such studies include factorial trials, which randomise patients to different levels of two or more factors and can therefore evaluate the effect of multiple treatments alone and in combination. Factorial trials can provide increased statistical power or assess interactions between treatments, but raise additional challenges for trial‐based economic evaluations: interactions may occur more commonly for costs and quality‐adjusted life‐years (QALYs) than for clinical endpoints; economic endpoints raise challenges for transformation and regression analysis; and both factors must be considered simultaneously to assess which treatment combination represents best value for money. This article aims to examine issues associated with factorial trials that include assessment of costs and/or cost‐effectiveness, describe the methods that can be used to analyse such studies and make recommendations for health economists, statisticians and trialists. A hypothetical worked example is used to illustrate the challenges and demonstrate ways in which economic evaluations of factorial trials may be conducted, and how these methods affect the results and conclusions. Ignoring interactions introduces bias that could result in adopting a treatment that does not make best use of healthcare resources, while considering all interactions avoids bias but reduces statistical power. We also introduce the concept of the opportunity cost of ignoring interactions as a measure of the bias introduced by not taking account of all interactions. We conclude by offering recommendations for planning, analysing and reporting economic evaluations based on factorial trials, taking increased analysis costs into account. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.