Manuel Gomes

Endovascular or open repair strategy for ruptured abdominal aortic aneurysm: 30 day outcomes from IMPROVE randomised trial.

Objective To assess whether a strategy of endovascular repair (if aortic morphology is suitable, open repair if not) versus open repair reduces early mortality for patients with suspected ruptured abdominal aortic aneurysm. Design Randomised controlled trial. Setting 30 vascular centres (29 UK, 1 Canadian), 2009-13. Participants 613 eligible patients (480 men) with a clinical diagnosis of ruptured aneurysm. Interventions 316 patients were randomised to the endovascular strategy (275 confirmed ruptures, 174 anatomically suitable for endovascular repair) and 297 to open repair (261 confirmed ruptures). Main outcome measures 30 day mortality, with 24 hour and in-hospital mortality, costs, and time and place of discharge as secondary outcomes. Results 30 day mortality was 35.4% (112/316) in the endovascular strategy group and 37.4% (111/297) in the open repair group: odds ratio 0.92 (95% confidence interval 0.66 to 1.28; P=0.62); odds ratio after adjustment for age, sex, and Hardman index 0.94 (0.67 to 1.33). Women may benefit more than men (interaction test P=0.02) from the endovascular strategy: odds ratio 0.44 (0.22 to 0.91) versus 1.18 (0.80 to 1.75). 30 day mortality for patients with confirmed rupture was 36.4% (100/275) in the endovascular strategy group and 40.6% (106/261) in the open repair group (P=0.31). More patients in the endovascular strategy than in the open repair group were discharged directly to home (189/201 (94%) v 141/183 (77%); P<0.001). Average 30 day costs were similar between the randomised groups, with an incremental cost saving for the endovascular strategy versus open repair of £1186 (€1420;

A guide to handling missing data in cost-effectiveness analysis conducted within randomised controlled trials.

1939) (95% confidence interval −£625 to £2997). Conclusions A strategy of endovascular repair was not associated with significant reduction in either 30 day mortality or cost. Longer term cost effectiveness evaluations are needed to assess the full effects of the endovascular strategy in both men and women. Trial registration Current Controlled Trials ISRCTN48334791.OBJECTIVE To assess whether a strategy of endovascular repair (if aortic morphology is suitable, open repair if not) versus open repair reduces early mortality for patients with suspected ruptured abdominal aortic aneurysm. DESIGN Randomised controlled trial. SETTING 30 vascular centres (29 UK, 1 Canadian), 2009-13. PARTICIPANTS 613 eligible patients (480 men) with a clinical diagnosis of ruptured aneurysm. INTERVENTIONS 316 patients were randomised to the endovascular strategy (275 confirmed ruptures, 174 anatomically suitable for endovascular repair) and 297 to open repair (261 confirmed ruptures). MAIN OUTCOME MEASURES 30 day mortality, with 24 hour and in-hospital mortality, costs, and time and place of discharge as secondary outcomes. RESULTS 30 day mortality was 35.4% (112/316) in the endovascular strategy group and 37.4% (111/297) in the open repair group: odds ratio 0.92 (95% confidence interval 0.66 to 1.28; P=0.62); odds ratio after adjustment for age, sex, and Hardman index 0.94 (0.67 to 1.33). Women may benefit more than men (interaction test P=0.02) from the endovascular strategy: odds ratio 0.44 (0.22 to 0.91) versus 1.18 (0.80 to 1.75). 30 day mortality for patients with confirmed rupture was 36.4% (100/275) in the endovascular strategy group and 40.6% (106/261) in the open repair group (P=0.31). More patients in the endovascular strategy than in the open repair group were discharged directly to home (189/201 (94%) v 141/183 (77%); P<0.001). Average 30 day costs were similar between the randomised groups, with an incremental cost saving for the endovascular strategy versus open repair of £1186 (€1420;

Observations from the IMPROVE trial concerning the clinical care of patients with ruptured abdominal aortic aneurysm

1939) (95% confidence interval -£625 to £2997). CONCLUSIONS A strategy of endovascular repair was not associated with significant reduction in either 30 day mortality or cost. Longer term cost effectiveness evaluations are needed to assess the full effects of the endovascular strategy in both men and women. TRIAL REGISTRATION Current Controlled Trials ISRCTN48334791.

Developing Appropriate Methods for Cost-Effectiveness Analysis of Cluster Randomized Trials

Missing data are a frequent problem in cost-effectiveness analysis (CEA) within a randomised controlled trial. Inappropriate methods to handle missing data can lead to misleading results and ultimately can affect the decision of whether an intervention is good value for money. This article provides practical guidance on how to handle missing data in within-trial CEAs following a principled approach: (i) the analysis should be based on a plausible assumption for the missing data mechanism, i.e. whether the probability that data are missing is independent of or dependent on the observed and/or unobserved values; (ii) the method chosen for the base-case should fit with the assumed mechanism; and (iii) sensitivity analysis should be conducted to explore to what extent the results change with the assumption made. This approach is implemented in three stages, which are described in detail: (1) descriptive analysis to inform the assumption on the missing data mechanism; (2) how to choose between alternative methods given their underlying assumptions; and (3) methods for sensitivity analysis. The case study illustrates how to apply this approach in practice, including software code. The article concludes with recommendations for practice and suggestions for future research.

Statistical Methods for Cost-Effectiveness Analyses That Use Data from Cluster Randomized Trials A Systematic Review and Checklist for Critical Appraisal

Single‐centre series of the management of patients with ruptured abdominal aortic aneurysm (AAA) are usually too small to identify clinical factors that could improve patient outcomes.

Addressing Missing Data in Patient‐Reported Outcome Measures (PROMS): Implications for the Use of PROMS for Comparing Provider Performance

Aim. Cost-effectiveness analyses (CEAs) may use data from cluster randomized trials (CRTs), where the unit of randomization is the cluster, not the individual. However, most studies use analytical methods that ignore clustering. This article compares alternative statistical methods for accommodating clustering in CEAs of CRTs. Methods. Our simulation study compared the performance of statistical methods for CEAs of CRTs with 2 treatment arms. The study considered a method that ignored clustering—seemingly unrelated regression (SUR) without a robust standard error (SE)—and 4 methods that recognized clustering—SUR and generalized estimating equations (GEEs), both with robust SE, a “2-stage” nonparametric bootstrap (TSB) with shrinkage correction, and a multilevel model (MLM). The base case assumed CRTs with moderate numbers of balanced clusters (20 per arm) and normally distributed costs. Other scenarios included CRTs with few clusters, imbalanced cluster sizes, and skewed costs. Performance was reported as bias, root mean squared error (rMSE), and confidence interval (CI) coverage for estimating incremental net benefits (INBs). We also compared the methods in a case study. Results. Each method reported low levels of bias. Without the robust SE, SUR gave poor CI coverage (base case: 0.89 v. nominal level: 0.95). The MLM and TSB performed well in each scenario (CI coverage, 0.92–0.95). With few clusters, the GEE and SUR (with robust SE) had coverage below 0.90. In the case study, the mean INBs were similar across all methods, but ignoring clustering underestimated statistical uncertainty and the value of further research. Conclusions. MLMs and the TSB are appropriate analytical methods for CEAs of CRTs with the characteristics described. SUR and GEE are not recommended for studies with few clusters.

Multiple Imputation Methods for Handling Missing Data in Cost-effectiveness Analyses That Use Data from Hierarchical Studies An Application to Cluster Randomized Trials

Introduction. The best data for cost-effectiveness analyses (CEAs) of group-level interventions often come from cluster randomized trials (CRTs), where randomization is by cluster (e.g., the hospital attended), not by individual. Methods for these CEAs need to recognize both the correlation between costs and outcomes and that these data may be dependent on the cluster. General checklists and methodological guidance for critically appraising CEA ignore these issues. This article develops a new checklist and applies it in a systematic review of CEAs that use CRTs. Methods. The authors developed a checklist for CEAs that use CRTs, informed by a conceptual review of statistical methods. This checklist included criteria such as whether the analysis allowed for both clustering and the correlation between individuals’ costs and outcomes. The authors undertook a systematic literature review of full economic evaluations that used CRTs. The quality of studies was assessed with the new checklist and by the “Drummond checklist.” Results. The authors identified 62 papers that met the inclusion criteria. On average, studies satisfied 9 of the 10 criteria for the checklist but only 20% of criteria for the new checklist. More than 40% of studies adopted statistical methods that completely ignored clustering, and 75% disregarded any correlation between costs and outcomes. Only 4 studies employed appropriate statistical methods that allowed for both clustering and correlation. Conclusions. Most economic evaluations that use data from CRTs ignored clustering or correlation. Statistical methods that address these issues are available, and their use should be encouraged. The new checklist can supplement generic CEA guidelines and highlight where research practice can be improved.

EQ-5D-5L versus EQ-5D-3L: The Impact on Cost Effectiveness in the United Kingdom

Abstract Patient‐reported outcome measures (PROMs) are now routinely collected in the English National Health Service and used to compare and reward hospital performance within a high‐powered pay‐for‐performance scheme. However, PROMs are prone to missing data. For example, hospitals often fail to administer the pre‐operative questionnaire at hospital admission, or patients may refuse to participate or fail to return their post‐operative questionnaire. A key concern with missing PROMs is that the individuals with complete information tend to be an unrepresentative sample of patients within each provider and inferences based on the complete cases will be misleading. This study proposes a strategy for addressing missing data in the English PROM survey using multiple imputation techniques and investigates its impact on assessing provider performance. We find that inferences about relative provider performance are sensitive to the assumptions made about the reasons for the missing data.

Should English healthcare providers be penalised for failing to collect patient-reported outcome measures? A retrospective analysis

Purpose. Multiple imputation (MI) has been proposed for handling missing data in cost-effectiveness analyses (CEAs). In CEAs that use cluster randomized trials (CRTs), the imputation model, like the analysis model, should recognize the hierarchical structure of the data. This paper contrasts a multilevel MI approach that recognizes clustering, with single-level MI and complete case analysis (CCA) in CEAs that use CRTs. Methods. We consider a multilevel MI approach compatible with multilevel analytical models for CEAs that use CRTs. We took fully observed data from a CEA that evaluated an intervention to improve diagnosis of active labor in primiparous women using a CRT (2078 patients, 14 clusters). We generated scenarios with missing costs and outcomes that differed, for example, according to the proportion with missing data (10%–50%), the covariates that predicted missing data (individual, cluster-level), and the missingness mechanism: missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR). We estimated incremental net benefits (INBs) for each approach and compared them with the estimates from the fully observed data, the “true” INBs. Results. When costs and outcomes were assumed to be MCAR, the INBs for each approach were similar to the true estimates. When data were MAR, the point estimates from the CCA differed from the true estimates. Multilevel MI provided point estimates and standard errors closer to the true values than did single-level MI across all settings, including those in which a high proportion of observations had cost and outcome data MAR and when data were MNAR. Conclusions. Multilevel MI accommodates the multilevel structure of the data in CEAs that use cluster trials and provides accurate cost-effectiveness estimates across the range of circumstances considered.

Development of a practical approach to expert elicitation for randomised controlled trials with missing health outcomes: Application to the IMPROVE trial.

OBJECTIVES To model the relationship between the three-level (3L) and the five-level (5L) EuroQol five-dimensional questionnaire and examine how differences have an impact on cost effectiveness in case studies. METHODS We used two data sets that included the 3L and 5L versions from the same respondents. The EuroQol Group data set (n = 3551) included patients with different diseases and a healthy cohort. The National Data Bank data set included patients with rheumatoid disease (n = 5205). We estimated a system of ordinal regressions in each data set using copula models to link responses of the 3L instrument to those of the 5L instrument and its UK tariff, and vice versa. Results were applied to nine cost-effectiveness studies. RESULTS Best-fitting models differed between the EuroQol Group and the National Data Bank data sets in terms of the explanatory variables, copulas, and coefficients. In both cases, the coefficients of the covariates and latent factors between the 3L and the 5L instruments were significantly different, indicating that moving between instruments is not simply a uniform re-alignment of the response levels for most dimensions. In the case studies, moving from the 3L to the 5L caused a decrease of up to 87% in incremental quality-adjusted life-years gained from effective technologies in almost all cases. Incremental cost-effectiveness ratios increased, often substantially. Conversely, one technology with a significant mortality gain saw increased incremental quality-adjusted life-years. CONCLUSIONS The 5L shifts mean utility scores up the utility scale toward full health and compresses them into a smaller range, compared with the 3L. Improvements in quality of life are valued less using the 5L than using the 3L. The 3L and the 5L can produce substantially different estimates of cost effectiveness. There is no simple proportional adjustment that can be made to reconcile these differences.