Data For Cost-effectiveness Analysis Research Articles

Integrating real-world data in cost-effectiveness analysis of universal HLA-B*15:02 screening in Malaysia.

Despite the availability of newer antiseizure medications, carbamazepine (CBZ) remains the gold standard. However, patients of Asian ancestry are susceptible to CBZ-related severe cutaneous adverse reactions. Universal HLA-B*15:02 screening is a promising intervention to address this. With the increasing recognition of integrating real-world evidence in economic evaluations, the cost-effectiveness of universal HLA-B*15:02 screening was assessed using available real-world data in Malaysia. A hybrid model of a decision tree and Markov model was developed to evaluate 3 strategies for treating newly diagnosed epilepsy among adults: (i) CBZ initiation without HLA-B*15:02 screening (current practice); (ii) universal HLA-B*15:02 screening prior to CBZ initiation; and (iii) alternative prescribing without HLA-B*15:02 screening. The model was populated with real-world inputs derived from the Malaysian population. From a societal perspective, base-case analysis and sensitivity analyses estimated the costs and outcomes over a lifetime. Incremental cost-effectiveness ratios were calculated. In the base-cases analysis, universal HLA-B*15:02 screening yielded the lowest total costs and the highest total quality-adjusted life years (QALYs) gained. Compared with current practice, universal screening was less costly by USD100 and more effective by QALYs increase of 0.1306, while alternative prescribing resulted in 0.1383 QALYs loss at additional costs of USD332. The highest seizure remission rate (56%) was estimated for universal HLA-B*15:02 screening vs. current practice (54%) and alternative prescribing (48%). Our study suggests that universal HLA-B*15:02 screening is a cost-effective intervention in Malaysia. With the demonstrated value of real-world evidence in economic evaluations, more relevant standardization efforts should be emphasized to better inform decision-making.

P45 The Use of Historical Clinical Trial Data to Inform Survival Extrapolation

Standard parametric distributions are commonly used for the extrapolation of survival data in cost-effectiveness analyses. However, survival data is often immature and uncertainty remains around the survival extrapolations. Mature historical data can be used to better predict survival beyond trial data. This study assessed two methods to incorporate historical data in the extrapolation of immature survival data.

RM1 - SENSITIVITY ANALYSIS FOR NOT-AT-RANDOM MISSING DATA IN TRIAL-BASED COST-EFFECTIVENESS ANALYSIS USING MULTIPLE IMPUTATION

Multiple imputation is being increasingly used to address missing data in cost-effectiveness analysis (CEA) of randomised trials, assuming data are missing-at-random (MAR). However, in many CEA settings the missing data is related to unobserved values, i.e. data are missing-not-at-random (MNAR). For example, if patients in poorer health are less likely to complete health questionnaires, given the observed data. Guidelines recommend sensitivity analyses under plausible MNAR assumptions, but this is rarely done in practice. We aim to illustrate an accessible framework to conduct sensitivity analysis for departure from MAR in trial-based CEA, using the Ten Top Tips (10TT) trial evaluating a weight management intervention. We illustrate the implementation of pattern-mixture models using multiple imputation, modifying the imputed data to reflect a plausible departure from the MAR assumption. Sensitivity analyses are conducted under a range of plausible values (deviations from MAR), to assess the robustness of the study conclusions. We applied this framework to the 10TT CEA, to address the concern that participants who dropped out of the study (about 42%) could be those less successful at losing weight (MNAR). Under the base-case MAR assumption using multiple imputation, 10TT resulted in 0.004 fewer quality-adjusted life years (95%CI -0.074 to 0.066) and £35 lower costs (-504 to 434) compared to the control arm, with a 48% probability of being cost-effective at £20,000 per QALY. The sensitivity analysis illustrated that the study conclusions were sensitive to small departures from MAR (e.g. assuming missing quality-of-life was 10% lower than observed), with the probability of 10TT being cost-effective ranging from 16% to 75%. MNAR is an important concern in trial-based CEA and sensitivity analyses are recommended to assess whether the study conclusions are robust to departures from the missing-at-random assumption. We illustrated an accessible framework to conduct and report these sensitivity analyses.

Value of Primary Data in Cost-effectiveness Analyses

Value of Primary Data in Cost-effectiveness Analyses

A guide to handling missing data in cost-effectiveness analysis conducted within randomised controlled 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.Electronic supplementary materialThe online version of this article (doi:10.1007/s40273-014-0193-3) contains supplementary material, which is available to authorized users.

Multiple Imputation Methods for Handling Missing Data in Cost-effectiveness Analyses That Use Data from Hierarchical Studies

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. 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. 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. 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.

Extrapolation of Survival Data in Cost-effectiveness Analyses

In many countries, reimbursement decisions are informed by estimates of the relative cost-effectiveness of alternative technologies, with costs and health outcomes projected over the lifetime of patients. Most cost-effectiveness analyses (CEAs) use decision models populated with evidence from randomized controlled trials (RCTs) that do not follow all patients until death. In the presence of such censored data, analyses of clinical effectiveness tend to use Cox proportional hazards regression models and avoid specifying a baseline hazard. This approach is insufficient for CEA, which must predict expected survival over time, a process that often requires extrapolation beyond the RCT follow-up data. There are many options for conducting such extrapolations and great uncertainty as to the best choice of approach; consequently, gaming by manufacturers is a potential concern. A standard approach is to apply parametric survival functions to the individual patient data (IPD) from RCTs to predict mean survival by treatment arm, over the lifetime. Davies and others illustrate just how important and uncertain model selection for extrapolation can be, even in the simple setting where IPD are available to estimate relative effectiveness for the 2 comparators of interest. Davies and others updated a CEA of 2 alternative prostheses for total hip replacement using 16 years of follow-up data, rather than the 8 years that were available at the time the previous study was conducted, and found that the original conclusion was overturned. How critical should we be of the previous CEAs, which may have provided the best recommendation given the evidence available at that time? Who is to say that the current results, which still do not follow all patients for their lifetime, represent the truth? Rational decision making requires extrapolation approaches that are grounded in objective criteria and make efficient use of all evidence available at the time of decision. Recent reviews of technology appraisals for the National Institute for Health and Care Excellence (NICE) have shown that studies used extrapolation approaches that were inadequate or inadequately described. Latimer’s paper, which summarizes a longer report, proposes an algorithm to help future analysts improve their approach to extrapolation. The algorithm is limited to CEAs that compare 2 alternatives in which IPD are available from the pivotal RCT and there is no treatment switching. Within this context, there are 3 main suggestions: first, select the range of candidate models from a description of the occurrence of events over time, specifically by plotting the log of the cumulative hazards against the log of time; second, fit the chosen range of models to the entire RCT dataset and assess their relative fit to the observed data; and third, consider the relative plausibility of the predictions in the unobserved period. The development of the algorithm is to be welcomed, given the current state of both applied and methodological work in this area. We believe that the algorithm can improve the accuracy of extrapolations that are undertaken, not least by encouraging analysts to report a broader range of approaches than is current practice (in effect, report a structural sensitivity analysis). That said, we think further consideration From Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK (RG, MP); and Oxford Outcomes, Oxford, UK (NH).

The Impact of Outliers on Net-Benefit Regression Model in Cost-Effectiveness Analysis

Ordinary least square (OLS) in regression has been widely used to analyze patient-level data in cost-effectiveness analysis (CEA). However, the estimates, inference and decision making in the economic evaluation based on OLS estimation may be biased by the presence of outliers. Instead, robust estimation can remain unaffected and provide result which is resistant to outliers. The objective of this study is to explore the impact of outliers on net-benefit regression (NBR) in CEA using OLS and to propose a potential solution by using robust estimations, i.e. Huber M-estimation, Hampel M-estimation, Tukey's bisquare M-estimation, MM-estimation and least trimming square estimation. Simulations under different outlier-generating scenarios and an empirical example were used to obtain the regression estimates of NBR by OLS and five robust estimations. Empirical size and empirical power of both OLS and robust estimations were then compared in the context of hypothesis testing.Simulations showed that the five robust approaches compared with OLS estimation led to lower empirical sizes and achieved higher empirical powers in testing cost-effectiveness. Using real example of antiplatelet therapy, the estimated incremental net-benefit by OLS estimation was lower than those by robust approaches because of outliers in cost data. Robust estimations demonstrated higher probability of cost-effectiveness compared to OLS estimation. The presence of outliers can bias the results of NBR and its interpretations. It is recommended that the use of robust estimation in NBR can be an appropriate method to avoid such biased decision making.

Confounding and missing data in cost-effectiveness analysis: comparing different methods

IntroductionCommon approaches in cost-effectiveness analyses do not adjust for confounders. In nonrandomized studies this can result in biased results. Parametric models such as regression models are commonly applied to adjust for confounding, but there are several issues which need to be accounted for. The distribution of costs is often skewed and there can be a considerable proportion of observations of zero costs, which cannot be well handled using simple linear models. Associations between costs and effectiveness cannot usually be explained using observed background information alone, which also requires special attention in parametric modeling. Furthermore, in longitudinal panel data, missing observations are a growing problem also with nonparametric methods when cumulative outcome measures are used.MethodsWe compare two methods, which can handle the aforementioned issues, in addition to the standard unadjusted bootstrap techniques for assessing cost-effectiveness in the Helsinki Psychotherapy Study based on five repeated measurements of the Global Severity Index (SCL-90-GSI) and direct costs during one year of follow-up in two groups defined by the Defence Style Questionnaire (DSQ) at baseline. The first method models cumulative costs and effectiveness using generalized linear models, multiple imputation and bootstrap techniques. The second method deals with repeated measurement data directly using a hierarchical two-part logistic and gamma regression model for costs, a hierarchical linear model for effectiveness, and Bayesian inference.ResultsThe adjustment for confounders mitigated the differences of the DSQ groups. Our method, based on Bayesian inference, revealed the unexplained association of costs and effectiveness. Furthermore, the method also demonstrated strong heteroscedasticity in positive costs.ConclusionsConfounders should be accounted for in cost-effectiveness analyses, if the comparison groups are not randomized.JEL classificationC1; C3; I1

Missing data in trial‐based cost‐effectiveness analysis: the current state of play

Randomised controlled trial (RCT)-based cost-effectiveness analyses, which are prone to missing data, are increasingly used in healthcare technology assessment. This has highlighted the need for appropriate methodological approaches to the handling of missing data. This paper reviews missing data methodology used in RCT-based cost-effectiveness analyses since 2003. Complete case analysis, which may lead to inappropriate conclusions, is still the most popular approach and its use has increased with time. The degree of missing data in cost-effectiveness analyses was often poorly reported and the methodology was often unclear. Reporting of missing data sensitivity analyses would improve article transparency.

Can we afford to ignore missing data in cost-effectiveness analyses?

Can we afford to ignore missing data in cost-effectiveness analyses?