Economic Evaluation of Factorial Trials: Cost-Utility Analysis of the Atorvastatin in Factorial With Omega EE90 Risk Reduction in Diabetes 2 × 2 × 2 Factorial Trial of Atorvastatin, Omega-3 Fish Oil, and Action Planning.

OBJECTIVES: We applied principles for conducting economic evaluations of factorial trials to a trial-based economic evaluation of a cluster-randomized 2 × 2 × 2 factorial trial. We assessed the cost-effectiveness of atorvastatin, omega-3 fish oil, and an action-planning leaflet, alone and in combination, from a UK National Health Service perspective. METHODS: The Atorvastatin in Factorial With Omega EE90 Risk Reduction in Diabetes (AFORRD) Trial randomized 800 patients with type 2 diabetes to atorvastatin, omega-3, or their respective placebos and randomized general practices to receive a leaflet-based action-planning intervention designed to improve compliance or standard care. The trial was conducted at 59 UK general practices. Sixteen-week outcomes for each trial participant were extrapolated for 70 years using the United Kingdom Prospective Diabetes Study Outcomes Model v2.01. We analyzed the trial as a 2 × 2 factorial trial (ignoring interactions between action-planning leaflet and medication), as a 2 × 2 × 2 factorial trial (considering all interactions), and ignoring all interactions. RESULTS: We observed several qualitative interactions for costs and quality-adjusted life-years (QALYs) that changed treatment rankings. However, different approaches to analyzing the factorial design did not change the conclusions. There was a ≥99% chance that atorvastatin is cost-effective and omega-3 is not, at a £20 000/QALY threshold. CONCLUSIONS: Atorvastatin monotherapy was the most cost-effective combination of the 3 trial interventions at a £20 000/QALY threshold. Omega-3 fish oil was not cost-effective, while there was insufficient evidence to draw firm conclusions about action planning. Recently-developed methods for analyzing factorial trials and combining parameter and sampling uncertainty were extended to estimate cost-effectiveness acceptability curves within a 2x2x2 factorial design with model-based extrapolation.

Partial factorial trials: comparing methods for statistical analysis and economic evaluation.

BACKGROUND: Partial factorial trials compare two or more pairs of treatments on overlapping patient groups, randomising some (but not all) patients to more than one comparison. The aims of this research were to compare different methods for conducting and analysing economic evaluations on partial factorial trials and assess the implications of considering factors simultaneously rather than drawing independent conclusions about each comparison. METHODS: We estimated total costs and quality-adjusted life years (QALYs) within 10 years of surgery for 2252 patients in the Knee Arthroplasty Trial who were randomised to one or more comparisons of different surgical types. We compared three analytical methods: an "at-the-margins" analysis including all patients randomised to each comparison (assuming no interaction); an "inside-the-table" analysis that included interactions but focused on those patients randomised to two comparisons; and a Bayesian vetted bootstrap, which used results from patients randomised to one comparison as priors when estimating outcomes for patients randomised to two comparisons. Outcomes comprised incremental costs, QALYs and net benefits. RESULTS: Qualitative interactions were observed for costs, QALYs and net benefits. Bayesian bootstrapping generally produced smaller standard errors than inside-the-table analysis and gave conclusions that were consistent with at-the-margins analysis, while allowing for these interactions. By contrast, inside-the-table gave different conclusions about which intervention had the highest net benefits compared with other analyses. CONCLUSIONS: All analyses of partial factorial trials should explore interactions and assess whether results are sensitive to assumptions about interactions, either as a primary analysis or as a sensitivity analysis. For partial factorial trials closely mirroring routine clinical practice, at-the-margins analysis may provide a reasonable estimate of average costs and benefits for the whole trial population, even in the presence of interactions. However, such conclusions will be misleading if there are large interactions or if the proportion of patients allocated to different treatments differs markedly from what occurs in clinical practice. The Bayesian bootstrap provides an alternative to at-the-margins analysis for analysing clinical or economic endpoints from partial factorial trials, which allows for interactions while making use of the whole sample. The same techniques could be applied to analyses of clinical endpoints. TRIAL REGISTRATION: ISRCTN, ISRCTN45837371 . Registered on 25 April 2003.

Population-based study of determinants of initial secondary care costs of acute stroke in the United Kingdom.

BACKGROUND AND PURPOSE: To determine the cost-effectiveness of specific interventions to prevent or treat acute stroke, it is necessary to know the costs of stroke according to patient characteristics and stroke subtype and etiology. However, very few such data are available and none from population-based studies. We determined the predictors of resource use and acute care costs of stroke using data from a population-based study. METHODS: Data were obtained from the Oxford Vascular study, a population-based cohort of all individuals in nine general practices in Oxfordshire, UK, which identified 346 patients with a first or recurrent stroke during April 1, 2002, to March 31, 2004. Univariate and multivariate analyses were performed to identify the main predictors of resource use and costs. RESULTS: Acute care costs ranged from 326 pounds sterling (lower decile) to 19,901 pounds sterling (upper decile). There were multiple important univariate interrelations of patient characteristics, stroke subtype, and stroke etiology with hospital admission, length of stay, and 30-day case-fatality. For example, patients with primary intracerebral hemorrhage were more likely to be admitted than patients with partial anterior circulation ischemic stroke and less likely to survive without disability, but length of stay was reduced as a result of high early case-fatality such that cost was substantially less. However, the majority of univariate predictors of resource use, cost, and outcome were confounded by initial stroke severity as measured by the National Institutes of Health Stroke Scale score, which accounted for approximately half of the predicted variance in cost. Cost increased approximately linearly up to an National Institutes of Health Stroke Scale score of 18 and then fell steeply at higher scores as a result of rising early case-fatality. CONCLUSIONS: Several patient and event-related characteristics explained the wide range of initial secondary care costs of acute stroke, but stroke severity was by far the most important independent predictor.