Noémi Kreif

Health-related quality-of-life of people with HIV in the era of combination antiretroviral treatment: a cross-sectional comparison with the general population

BACKGROUNDnCombination antiretroviral therapy has substantially increased life-expectancy in people living with HIV, but the effects of chronic infection on health-related quality of life (HRQoL) are unclear. We aimed to compare HRQoL in people with HIV and the general population.nnnMETHODSnWe merged two UK cross-sectional surveys: the ASTRA study, which recruited participants aged 18 years or older with HIV from eight outpatient clinics in the UK between Feb 1, 2011, and Dec 31, 2012; and the Health Survey for England (HSE) 2011, which measures health and health-related behaviours in individuals living in a random sample of private households in England. The ASTRA study has data for 3258 people (response rate 64%) and HSE for 8503 people aged 18 years or older (response rate 66%). HRQoL was assessed with the Euroqol 5D questionnaire 3 level (EQ-5D-3L) instrument that measures health on five domains, each with three levels. The responses are scored on a scale where a value of 1 represents perfect health and a value of 0 represents death, known as the utility score. We used multivariable models to compare utility scores between the HIV and general population samples with adjustment for several sociodemographic factors.nnnFINDINGSn3151 (97%) of 3258 of participants in ASTRA and 7424 (87%) of 8503 participants in HSE had complete EQ-5D-3L data. The EQ-5D-3L utility score was lower for people with HIV compared with that in the general population (marginal effect in utility score adjusted for age, and sex/sexuality -0·11; 95% CI -0·13 to -0·10; p < 0·0001). HRQoL was lower for people with HIV for all EQ-5D-3L domains, particularly for anxiety/depression. The difference in utility score was significant after adjustment for several additional sociodemographic variables (ethnic origin, education, having children, and smoking status) and was apparent across all CD4 cell count, antiretroviral therapy, and viral load strata, but was greatest for those people diagnosed with HIV in earlier calendar periods. Reduction in HRQoL with age was not greater in people with HIV than in the general population (pinteraction > 0·05).nnnINTERPRETATIONnPeople living with HIV have significantly lower HRQoL than do the general population, despite most HIV positive individuals in this study being virologically and immunologically stable. Although this difference could in part be due to factors other than HIV, this study provides additional evidence of the loss of health that can be avoided through prevention of further HIV infections.nnnFUNDINGnUK National Institute for Health Research.

EXAMINATION OF THE SYNTHETIC CONTROL METHOD FOR EVALUATING HEALTH POLICIES WITH MULTIPLE TREATED UNITS

Abstract This paper examines the synthetic control method in contrast to commonly used difference‐in‐differences (DiD) estimation, in the context of a re‐evaluation of a pay‐for‐performance (P4P) initiative, the Advancing Quality scheme. The synthetic control method aims to estimate treatment effects by constructing a weighted combination of control units, which represents what the treated group would have experienced in the absence of receiving the treatment. While DiD estimation assumes that the effects of unobserved confounders are constant over time, the synthetic control method allows for these effects to change over time, by re‐weighting the control group so that it has similar pre‐intervention characteristics to the treated group. We extend the synthetic control approach to a setting of evaluation of a health policy where there are multiple treated units. We re‐analyse a recent study evaluating the effects of a hospital P4P scheme on risk‐adjusted hospital mortality. In contrast to the original DiD analysis, the synthetic control method reports that, for the incentivised conditions, the P4P scheme did not significantly reduce mortality and that there is a statistically significant increase in mortality for non‐incentivised conditions. This result was robust to alternative specifications of the synthetic control method.

Overview of Parametric Survival Analysis for Health-Economic Applications

Health economic models rely on data from trials to project the risk of events (e.g., death) over time beyond the span of the available data. Parametric survival analysis methods can be applied to identify an appropriate statistical model for the observed data, which can then be extrapolated to derive a complete time-to-event curve. This paper describes the properties of the most commonly used statistical distributions as a basis for these models and describes an objective process of identifying the most suitable parametric distribution in a given dataset. The approach can be applied with both individual-patient data as well as with survival probabilities derived from published Kaplan–Meier curves. Both are illustrated with analyses of overall survival from the Sorafenib Hepatocellular Carcinoma Assessment Randomised Protocol trial.

Validation of the SF-36 in patients with endometriosis

ObjectivesEndometriosis presents with significant pain as the most common symptom. Generic health measures can allow comparisons across diseases or populations. However, the Medical Outcomes Study Short Form 36 (SF-36) has not been validated for this disease. The goal of this study was to validate the SF-36 (version 2) for endometriosis.MethodsUsing data from two clinical trials (Nxa0=xa0252 and 198) of treatment for endometriosis, a full complement of psychometric analyses was performed. Additional instruments included a pain visual analog scale (VAS); a physician-completed questionnaire based on patient interview (modified Biberoglu and Behrman—B&B); clinical global impression of change (CGI-C); and patient satisfaction with treatment.ResultsBodily pain (BP) and the Physical Component Summary Score (PCS) were correlated with the pain VAS at baseline and over time and the B&B at baseline and end of study. In addition, those who had the greatest change in BP and PCS also reported the greatest change on CGI-C and patient satisfaction with treatment. Other subscales showed smaller, but significant, correlations with change in the pain VAS, CGI-C, and patient satisfaction with treatment.ConclusionsThe SF-36—particularly BP and the PCS—appears to be a valid and responsive measure for endometriosis and its treatment.

Evaluating treatment effectiveness in patient subgroups: a comparison of propensity score methods with an automated matching approach

Abstract Propensity score (Pscore) matching and inverse probability of treatment weighting (IPTW) can remove bias due to observed confounders, if the Pscore is correctly specified. Genetic Matching (GenMatch) matches on the Pscore and individual covariates using an automated search algorithm to balance covariates. This paper compares common ways of implementing Pscore matching and IPTW, with Genmatch for balancing time-constant baseline covariates}. The methods are considered when estimates of treatment effectiveness are required for patient subgroups, and the treatment allocation process differs by subgroup. We apply these methods in a prospective cohort study that estimates the effectiveness of Drotrecogin alfa activated, for subgroups of patients with severe sepsis. In a simulation study we compare the methods when the Pscore is correctly specified, and then misspecified by ignoring the subgroup-specific treatment allocation. The simulations also consider poor overlap in baseline covariates, and different sample sizes. In the case study, GenMatch reports better covariate balance than IPTW or Pscore matching. In the simulations with correctly specified Pscores, good overlap and reasonable sample sizes, all methods report minimal bias. When the Pscore is misspecified, GenMatch reports the least imbalance and bias. With small sample sizes, IPTW is the most efficient approach, but all methods report relatively high bias of treatment effects. This study shows that overall GenMatch achieves the best covariate balance for each subgroup, and is more robust to Pscore misspecification than common alternative Pscore approaches.

STATISTICAL METHODS FOR COST‐EFFECTIVENESS ANALYSES THAT USE OBSERVATIONAL DATA: A CRITICAL APPRAISAL TOOL AND REVIEW OF CURRENT PRACTICE

Many cost-effectiveness analyses (CEAs) use data from observational studies. Statistical methods can only address selection bias if they make plausible assumptions. No quality assessment tool is available for appraising CEAs that use observational studies. We developed a new checklist to assess statistical methods for addressing selection bias in CEAs that use observational data. The checklist criteria were informed by a conceptual review and applied in a systematic review of economic evaluations. Criteria included whether the study assessed the no unobserved confounding assumption, overlap of baseline covariates between the treatment groups and the specification of the regression models. The checklist also considered structural uncertainty from the choice of statistical approach. We found 81 studies that met the inclusion criteria: studies tended to use regression (51%), matching on individual covariates (25%) or matching on the propensity score (22%). Most studies (77%) did not assess the no observed confounding assumption, and few studies (16%) fully considered structural uncertainty from the choice of statistical approach. We conclude that published CEAs do not assess the main assumptions behind statistical methods for addressing selection bias. This checklist can raise awareness about the assumptions behind statistical methods for addressing selection bias and can complement existing method guidelines for CEAs.

Cost-Effectiveness Analysis of Anastrozole versus Tamoxifen in Adjuvant Therapy for Early-Stage Breast Cancer - a Health-Economic Analysis Based on the 100-Month Analysis of the ATAC Trial and the German Health System

Background: In the ‘Arimidex’, Tamoxifen Alone or in Combination (ATAC) trial, the aromatase inhibitor (AI) anastrozole had a significantly better efficacy and safety profile than tamoxifen as initial adjuvant therapy for hormone receptor-positive (HR+) early breast cancer (EBC) in postmenopausal patients. To compare the combined long-term clinical and economic benefits, we carried out a cost-effectiveness analysis (CEA) of anastrozole versus tamoxifen based on the data of the 100month analysis of the ATAC trial from the perspective of the German public health insurance. Patients and Methods: A Markov model with a 25-year time horizon was developed using the 100-month analysis of the ATAC trial as well as data obtained from published literature and expert opinion. Results: Adjuvant treatment of EBC with anastrozole achieved an additional 0.32 quality-adjusted life-years (QALYs) gained per patient compared with tamoxifen, at an additional cost of D 6819 per patient. Thus, the incremental cost effectiveness of anastrozole versus tamoxifen at 25 years was D 21,069 (

Evaluating treatment effectiveness under model misspecification: A comparison of targeted maximum likelihood estimation with bias-corrected matching

30,717) per QALY gained. Conclusions: This is the first CEA of an AI that is based on extended follow-up data, taking into account the carryover effect of anastrozole, which maintains the efficacy benefits beyond therapy completion after 5 years. Adjuvant treatment with anastrozole for postmenopausal women with HR+ EBC is a cost-effective alternative to tamoxifen.

Methods for estimating subgroup effects in cost-effectiveness analyses that use observational data

Statistical approaches for estimating treatment effectiveness commonly model the endpoint, or the propensity score, using parametric regressions such as generalised linear models. Misspecification of these models can lead to biased parameter estimates. We compare two approaches that combine the propensity score and the endpoint regression, and can make weaker modelling assumptions, by using machine learning approaches to estimate the regression function and the propensity score. Targeted maximum likelihood estimation is a double-robust method designed to reduce bias in the estimate of the parameter of interest. Bias-corrected matching reduces bias due to covariate imbalance between matched pairs by using regression predictions. We illustrate the methods in an evaluation of different types of hip prosthesis on the health-related quality of life of patients with osteoarthritis. We undertake a simulation study, grounded in the case study, to compare the relative bias, efficiency and confidence interval coverage of the methods. We consider data generating processes with non-linear functional form relationships, normal and non-normal endpoints. We find that across the circumstances considered, bias-corrected matching generally reported less bias, but higher variance than targeted maximum likelihood estimation. When either targeted maximum likelihood estimation or bias-corrected matching incorporated machine learning, bias was much reduced, compared to using misspecified parametric models.

Quality of life and drug costs associated with switching antipsychotic medication to once-daily extended release quetiapine fumarate in patients with schizophrenia

Decision makers require cost-effectiveness estimates for patient subgroups. In nonrandomized studies, propensity score (PS) matching and inverse probability of treatment weighting (IPTW) can address overt selection bias, but only if they balance observed covariates between treatment groups. Genetic matching (GM) matches on the PS and individual covariates using an automated search algorithm to directly balance baseline covariates. This article compares these methods for estimating subgroup effects in cost-effectiveness analyses (CEA). The motivating case study is a CEA of a pharmaceutical intervention, drotrecogin alfa (DrotAA), for patient subgroups with severe sepsis (n = 2726). Here, GM reported better covariate balance than PS matching and IPTW. For the subgroup at a high level of baseline risk, the probability that DrotAA was cost-effective ranged from 30% (IPTW) to 90% (PS matching and GM), at a threshold of £20 000 per quality-adjusted life-year. We then compared the methods in a simulation study, in which initially the PS was correctly specified and then misspecified, for example, by ignoring the subgroup-specific treatment assignment. Relative performance was assessed as bias and root mean squared error (RMSE) in the estimated incremental net benefits. When the PS was correctly specified and inverse probability weights were stable, each method performed well; IPTW reported the lowest RMSE. When the subgroup-specific treatment assignment was ignored, PS matching and IPTW reported covariate imbalance and bias; GM reported better balance, less bias, and more precise estimates. We conclude that if the PS is correctly specified and the weights for IPTW are stable, each method can provide unbiased cost-effectiveness estimates. However, unlike IPTW and PS matching, GM is relatively robust to PS misspecification.