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Directed acyclic graphs (DAGs) are a useful tool to represent, in a graphical format, researchers’ assumptions about the causal structure among variables while providing a rationale for the choice of confounding variables to adjust for. With origins in the field of probabilistic graphical modelling, DAGs are yet to be widely adopted in applied health research, where causal assumptions are frequently made for the purpose of evaluating health services initiatives. In this context, there is still limited practical guidance on how to construct and use DAGs. Some progress has recently been made in terms of building DAGs based on studies from the literature, but an area that has received less attention is how to create DAGs from information provided by domain experts, an approach of particular importance when there is limited published information about the intervention under study. This approach offers the opportunity for findings to be more robust and relevant to patients, carers and the public, and more likely to inform policy and clinical practice. This article draws lessons from a stakeholder workshop involving patients, health care professionals, researchers, commissioners and representatives from industry, whose objective was to draw DAGs for a complex intervention—online consultation, i.e. written exchange between the patient and health care professional using an online system—in the context of the English National Health Service. We provide some initial, practical guidance to those interested in engaging with domain experts to develop DAGs.

In studies based on electronic health records (EHR), the frequency of covariate monitoring can vary by covariate type, across patients, and over time, which can limit the generalizability of inferences about the effects of adaptive treatment strategies. In addition, monitoring is a health intervention in itself with costs and benefits, and stakeholders may be interested in the effect of monitoring when adopting adaptive treatment strategies. This paper demonstrates how to exploit non‐systematic covariate monitoring in EHR‐based studies to both improve the generalizability of causal inferences and to evaluate the health impact of monitoring when evaluating adaptive treatment strategies. Using a real world, EHR‐based, comparative effectiveness research (CER) study of patients with type II diabetes mellitus, we illustrate how the evaluation of joint dynamic treatment and static monitoring interventions can improve CER evidence and describe two alternate estimation approaches based on inverse probability weighting (IPW). First, we demonstrate the poor performance of the standard estimator of the effects of joint treatment‐monitoring interventions, due to a large decrease in data support and concerns over finite‐sample bias from near‐violations of the positivity assumption (PA) for the monitoring process. Second, we detail an alternate IPW estimator using a no direct effect (NDE) assumption. We demonstrate that this estimator can improve efficiency but at the potential cost of increase in bias from violations of the PA for the treatment process.

Policymakers seeking to target health policies efficiently towards specific population groups need to know which individuals stand to benefit the most from each of these policies. While traditional approaches for subgroup analyses are constrained to only consider a small number of pre-defined subgroups, recently proposed causal machine learning (CML) approaches help explore treatment-effect heterogeneity in a more flexible yet principled way. Causal forests use a generalisation of the random forest algorithm to estimate heterogenous treatment effects both at the individual and the subgroup level. Our paper aims to explore this approach in the setting of health policy evaluation with strong observed confounding, applied specifically to the context of mothers’ health insurance enrolment in Indonesia. Comparing two health insurance schemes (subsidised and contributory) against no insurance, we find beneficial average impacts of enrolment in contributory health insurance on maternal health care utilisation and infant mortality, but no impact of subsidised health insurance. The causal forest algorithm identified significant heterogeneity in the impacts of contributory insurance, not just along socioeconomic variables that we pre-specified (indicating higher benefits for poorer, less educated, and rural women), but also according to some other characteristics not foreseen prior to the analysis, suggesting in particular important geographical impact heterogeneity. Our study demonstrates the power of CML approaches to uncover unexpected heterogeneity in policy impacts. The findings from our evaluation of past health insurance expansions can potentially guide the re-design of the eligibility criteria for subsidised health insurance in Indonesia.

Background The present study seeks to evaluate the change in mental health inequalities in the department of Meta after the signing of Colombia’s Peace Agreement in 2016 with the FARC guerrilla group. Using a validated survey instrument composed of 20 questions (‘SRQ-20’), we measure changes in mental health inequalities from 2014, before the signing of the agreement, to 2018, after the signing. We then decompose the changes in inequalities to establish which socioeconomic factors explain differences in mental health inequalities over time. Methods Our study uses information from the Conflicto, Salud y Paz (CONPAS) survey conducted in the department of Meta, Colombia, in 1309 households in 2018, with retrospective information for 2014. To measure inequalities, we calculate the concentration indices for both years. Through the Oaxaca change decomposition method, we disaggregate changes in mental health inequalities into its underlying factors. This method allows us to explain the relationship between changes in mental health inequalities and changes in inequalities in several sociodemographic factors. It also identifies the extent to which these factors help explain the changes in mental health inequalities. Results Mental health inequalities in Meta were reduced almost by half from 2014 to 2018. In 2018, the population at the lower and middle socioeconomic levels had fewer chances of experiencing mental health disorders in comparison to 2014. The reduction in mental health differences is mostly attributed to reductions in the influence of certain sociodemographic variables, such as residence in rural zones and conflict-affected territories, working in the informal sector, or experiencing internal displacement. However, even though mental health inequalities have diminished, overall mental health outcomes have worsened in these years. Conclusions The reduction in the contribution of conflict-related variables for explaining mental health inequalities could mean that the negative consequences of conflict on mental health have started to diminish in the short run after the peace agreement. Nevertheless, conflict and the presence of other socioeconomic inequalities still contribute to persistent adverse mental health outcomes in the overall population. Thus, public policy should be oriented towards improving mental health care services in these territories, given the post-accord context.

Public payers around the world are increasingly using cost-effectiveness thresholds (CETs) to assess the value-for-money of an intervention and make coverage decisions. However, there is still much confusion about the meaning and uses of the CET, how it should be calculated, and what constitutes an adequate evidence base for its formulation. One widely referenced and used threshold in the last decade has been the 1-3 GDP per capita, which is often attributed to the Commission on Macroeconomics and WHO guidelines on Choosing Interventions that are Cost Effective (WHO-CHOICE). For many reasons, however, this threshold has been widely criticised; which has led experts across the world, including the WHO, to discourage its use. This has left a vacuum for policy-makers and technical staff at a time when countries are wanting to move towards Universal Health Coverage. This article seeks to address this gap by offering five practical options for decision-makers in low- and middle-income countries that can be used instead of the 1-3 GDP rule, to combine existing evidence with fair decision-rules or develop locally relevant CETs. It builds on existing literature as well as an engagement with a group of experts and decision-makers working in low, middle and high income countries.

Background: The present study seeks to evaluate the evolution of mental health inequalities in the department of Meta after the signing of Colombia's Peace Agreement in 2016 with the FARC guerrilla group. Using a validated survey instrument composed of 20 questions (‘SRQ-20’), we measure changes in mental health inequalities from 2014, before the signing of the agreement, to 2018, after the signing of the agreement. We then decompose the changes in inequalities to establish which socioeconomic factors explain differences over time.Methods: Our study uses information from the Conflicto, Salud y Paz (CONPAS) survey conducted in the department of Meta, Colombia, in 1,309 households in 2018, with retrospective information for 2014. To measure inequalities, we calculate the concentration indices for both years. Through the Oaxaca change decomposition method, we disaggregate changes in mental health inequalities into its underlying factors. This method allows us to explain the relationship between changes in mental health inequalities and reduced inequality in several sociodemographic factors. It also identifies the extent to which these factors help explain the changes in mental health inequalities. Results: Mental health inequalities in Meta were reduced almost by half from 2014 to 2018. In 2018, the population at the lower and middle socioeconomic levels had fewer chances of experiencing mental health disorders in comparison to 2014. The reduction in mental health differences is mostly attributed to reductions in the influence of certain sociodemographic variables, such as residence in rural zones and conflict-affected territories, working in the informal sector, or experiencing internal displacement. However, even though mental health inequalities have diminished, overall mental health outcomes have worsened in these years. Conclusions: The reduction in the contribution of conflict-related variables for explaining mental health inequalities could mean that the negative consequences of conflict on mental health have started to diminish in the short run after the peace agreement. Nevertheless, conflict and the presence of other socioeconomic inequalities still contribute to persistent adverse mental health outcomes in the overall population. Thus, public policy should be oriented towards improving mental health care services in these territories, given the post-accord context.

Internal armed conflicts have become more common and more physically destructive since the mid-20th century, with devastating consequences for health and development in low- and middle-income countries. This paper investigates the causal impacts of the long-term internal conflict on child health in Colombia, following an identification strategy based on the temporal and geographic variation of conflict intensity. We estimate the effect of different levels of conflict intensity on height-for-age (HAZ), weight-for-age (WAZ), and weight-for-height z-scores among children under five years old, and explore the underlying potential mechanisms, through maternal health behavior and health care utilization. We find a harmful effect of exposure to conflict violence in utero and in early childhood for HAZ and WAZ, in the full sample and even more strongly in the rural sample, yet these estimates are smaller than those found for shorter term conflicts. The underlying pathways appear to operate around the time of the pregnancy and birth (in the form of maternal alcohol use, use of antenatal care and skilled birth attendance), rather than during the post-birth period (via breastfeeding or vaccination), and the impacts accumulate over the childhood. The most adverse impacts of conflict violence on child health and utilization of maternal healthcare were observed in municipalities which suffered from intermittent presence of armed groups.

Objective: To compare interactive fixed effects (IFE) and generalised synthetic control (GSC) methods to methods prevalent in health policy evaluation and re-evaluate the impact of the hip fracture Best Practice Tariffs introduced for hospitals in England in 2010. Data sources: Simulations and Hospital Episode Statistics.Study design: Best Practice Tariffs aimed to incentivise providers to deliver care in line with guidelines. Under the scheme, 62 providers received an additional payment for each hip fracture admission, while 49 providers did not. We estimate the impact using Difference-in-Differences (DiD), Synthetic Controls (SC), IFE and GSC methods. We contrast the estimation methods’ performance in a Monte Carlo simulation study.Principal findings: Unlike DiD, SC and IFE methods, the GSC method provided reliable estimates across a range of simulation scenarios and was preferred for this case study. The introduction of Best Practice Tariffs led to a 5.9 percentage point increase in the proportion of patients having surgery within 48 hours, and a statistically insignificant 0.6 percentage point reduction in 30 day mortality. Conclusions: The GSC approach is an attractive method for health policy evaluation. We cannot be confident that Best Practice Tariffs were effective.Objective: To compare interactive fixed effects (IFE) and generalised synthetic control (GSC) methods to methods prevalent in health policy evaluation and re-evaluate the impact of the hip fracture Best Practice Tariffs introduced for hospitals in England in 2010. Data sources: Simulations and Hospital Episode Statistics.Study design: Best Practice Tariffs aimed to incentivise providers to deliver care in line with guidelines. Under the scheme, 62 providers received an additional payment for each hip fracture admission, while 49 providers did not. We estimate the impact using Difference-in-Differences (DiD), Synthetic Controls (SC), IFE and GSC methods. We contrast the estimation methods’ performance in a Monte Carlo simulation study.Principal findings: Unlike DiD, SC and IFE methods, the GSC method provided reliable estimates across a range of simulation scenarios and was preferred for this case study. The introduction of Best Practice Tariffs led to a 5.9 percentage point increase in the proportion of patients having surgery within 48 hours, and a statistically insignificant 0.6 percentage point reduction in 30 day mortality. Conclusions: The GSC approach is an attractive method for health policy evaluation. We cannot be confident that Best Practice Tariffs were effective.Objective: To compare interactive fixed effects (IFE) and generalised synthetic control (GSC) methods to methods prevalent in health policy evaluation and re-evaluate the impact of the hip fracture Best Practice Tariffs introduced for hospitals in England in 2010. Data sources: Simulations and Hospital Episode Statistics.Study design: Best Practice Tariffs aimed to incentivise providers to deliver care in line with guidelines. Under the scheme, 62 providers received an additional payment for each hip fracture admission, while 49 providers did not. We estimate the impact using Difference-in-Differences (DiD), Synthetic Controls (SC), IFE and GSC methods. We contrast the estimation methods’ performance in a Monte Carlo simulation study.Principal findings: Unlike DiD, SC and IFE methods, the GSC method provided reliable estimates across a range of simulation scenarios and was preferred for this case study. The introduction of Best Practice Tariffs led to a 5.9 percentage point increase in the proportion of patients having surgery within 48 hours, and a statistically insignificant 0.6 percentage point reduction in 30 day mortality. Conclusions: The GSC approach is an attractive method for health policy evaluation. We cannot be confident that Best Practice Tariffs were effective.Objective: To compare interactive fixed effects (IFE) and generalised synthetic control (GSC) methods to methods prevalent in health policy evaluation and re-evaluate the impact of the hip fracture Best Practice Tariffs introduced for hospitals in England in 2010. Data sources: Simulations and Hospital Episode Statistics.Study design: Best Practice Tariffs aimed to incentivise providers to deliver care in line with guidelines. Under the scheme, 62 providers received an additional payment for each hip fracture admission, while 49 providers did not. We estimate the impact using Difference-in-Differences (DiD), Synthetic Controls (SC), IFE and GSC methods. We contrast the estimation methods’ performance in a Monte Carlo simulation study.Principal findings: Unlike DiD, SC and IFE methods, the GSC method provided reliable estimates across a range of simulation scenarios and was preferred for this case study. The introduction of Best Practice Tariffs led to a 5.9 percentage point increase in the proportion of patients having surgery within 48 hours, and a statistically insignificant 0.6 percentage point reduction in 30 day mortality. Conclusions: The GSC approach is an attractive method for health policy evaluation. We cannot be confident that Best Practice Tariffs were effective.Objective: To compare interactive fixed effects (IFE) and generalised synthetic control (GSC) methods to methods prevalent in health policy evaluation and re-evaluate the impact of the hip fracture Best Practice Tariffs introduced for hospitals in England in 2010. Data sources: Simulations and Hospital Episode Statistics.Study design: Best Practice Tariffs aimed to incentivise providers to deliver care in line with guidelines. Under the scheme, 62 providers received an additional payment for each hip fracture admission, while 49 providers did not. We estimate the impact using Difference-in-Differences (DiD), Synthetic Controls (SC), IFE and GSC methods. We contrast the estimation methods’ performance in a Monte Carlo simulation study.Principal findings: Unlike DiD, SC and IFE methods, the GSC method provided reliable estimates across a range of simulation scenarios and was preferred for this case study. The introduction of Best Practice Tariffs led to a 5.9 percentage point increase in the proportion of patients having surgery within 48 hours, and a statistically insignificant 0.6 percentage point reduction in 30 day mortality. Conclusions: The GSC approach is an attractive method for health policy evaluation. We cannot be confident that Best Practice Tariffs were effective.

To identify the pattern of the risk of death over long-term in unresectable hepatocellular carcinoma by determining the appropriate distribution to extrapolate overall survival and to assess the role of the Weibull distribution as the standard survival model in oncology. To select the appropriate distribution, three types of data sources have been analysed. Patient level data from two randomized controlled trials and published Kaplan–Meier curves from a systematic literature review provided short term follow-up data. They were supplemented with patient level data, with long-term follow-up from the Cancer Institute New South Wales, Australia. Published Kaplan–Meier curves were read in and a time-to-event dataset was created. Distributions were fitted to the data from the different sources separately. Their fit was assessed visually and compared using statistical criteria based on log-likelihood, the Akaike information criterion (AIC), and the Bayesian information criterion (BIC). Based on both published and patient-level, and both short- and long-term follow-up data, the Weibull distribution, used very often in cost-effectiveness models in oncology, does not seem to offer a good fit in hepatocellular carcinoma among the different survival models. The best fitting distribution appears to be the lognormal, with loglogistic as the second-best fitting function. Results were consistent between the different sources of data. In unresectable hepatocellular carcinoma, the Weibull model, which is often treated at the gold standard, does not appear to be appropriate based on different sources of data (two clinical trials, a retrospective database and published Kaplan–Meier curves). Lognormal distribution seems to be the most appropriate distribution for extrapolating overall survival.

While machine learning (ML) methods have received a lot of attention in recent years, these methods are primarily for prediction. Empirical researchers conducting policy evaluations are, on the other hand, preoccupied with causal problems, trying to answer counterfactual questions: what would have happened in the absence of a policy? Because these counterfactuals can never be directly observed (described as the “fundamental problem of causal inference”) prediction tools from the ML literature cannot be readily used for causal inference. In the last decade, major innovations have taken place incorporating supervised ML tools into estimators for causal parameters such as the average treatment effect (ATE). This holds the promise of attenuating model misspecification issues, and increasing of transparency in model selection. One particularly mature strand of the literature include approaches that incorporate supervised ML approaches in the estimation of the ATE of a binary treatment, under the unconfoundedness and positivity assumptions (also known as exchangeability and overlap assumptions). This article begins by reviewing popular supervised machine learning algorithms, including trees-based methods and the lasso, as well as ensembles, with a focus on the Super Learner. Then, some specific uses of machine learning for treatment effect estimation are introduced and illustrated, namely (1) to create balance among treated and control groups, (2) to estimate so-called nuisance models (e.g., the propensity score, or conditional expectations of the outcome) in semi-parametric estimators that target causal parameters (e.g., targeted maximum likelihood estimation or the double ML estimator), and (3) the use of machine learning for variable selection in situations with a high number of covariates. Since there is no universal best estimator, whether parametric or data-adaptive, it is best practice to incorporate a semi-automated approach than can select the models best supported by the observed data, thus attenuating the reliance on subjective choices.

Background: Health impact evaluations (HIEs) are currently the main way of assessing policy changes in low-and middle-income countries (LMICs). However, evidence on effectiveness alone cannot reliably inform decisions over the allocation of limited resources. Health economic evaluation provides a suitable framework for ‘value for money’ assessments. Methods: In this article we explore to what extent economic evaluations have been conducted alongside published health impact evaluations, then we assess the quality of these, using criteria from an economic evaluation reference case developed for use in LMICs. Results: Among the 2419 HIEs stored in the International Initiative for Impact Evaluations (3ie) database, and among the 8155 studies identified by the Ovid Medline database search, only 70 studies included an economic evaluation. When measured against the quality assessment criteria, study quality showed great variation. Many studies did not fulfil the basic requirements for economic evaluation, such as stating the perspective of the budget holder, using generic health measures that can be compared across diseases, or suitably reflecting uncertainty. Conclusions: Greater effort should be directed towards bringing the fields of impact evaluation and economic evaluation together to better inform resource allocation decisions in global health.

We study a pay-for-efficiency scheme that encourages hospitals to admit and discharge patients on the same calendar day when clinically appropriate. Since 2010, hospitals in the English NHS are incentivised by a higher price for patients treated as same-day discharge than for overnight stays, despite the former being less costly. We analyse administrative data for patients treated during 2006–2014 for 191 conditions for which same-day discharge is clinically appropriate – of which 32 are incentivised. Using difference-in-difference and synthetic control methods, we find that the policy had generally a positive impact with a statistically significant effect in 14 out of the 32 conditions. The median elasticity is 0.24 for planned and 0.01 for emergency conditions. Condition-specific design features explain some, but not all, of the differential responses.

Summary For a continuous treatment, the generalised propensity score (GPS) is defined as the conditional density of the treatment, given covariates. GPS adjustment may be implemented by including it as a covariate in an outcome regression. Here, the unbiased estimation of the dose–response function assumes correct specification of both the GPS and the outcome‐treatment relationship. This paper introduces a machine learning method, the ‘Super Learner’, to address model selection in this context. In the two‐stage estimation approach proposed, the Super Learner selects a GPS and then a dose–response function conditional on the GPS, as the convex combination of candidate prediction algorithms. We compare this approach with parametric implementations of the GPS and to regression methods. We contrast the methods in the Risk Adjustment in Neurocritical care cohort study, in which we estimate the marginal effects of increasing transfer time from emergency departments to specialised neuroscience centres, for patients with acute traumatic brain injury. With parametric models for the outcome, we find that dose–response curves differ according to choice of specification. With the Super Learner approach to both regression and the GPS, we find that transfer time does not have a statistically significant marginal effect on the outcomes. © 2015 The Authors. Health Economics Published by John Wiley & Sons Ltd.

Internationally, policy makers are increasingly focussed on reducing the detrimental consequences and rising costs associated with unhealthy diets, inactivity, smoking, alcohol and other risk factors on the health of their populations. This has led to an increase in the demand for evidence-based, cost-effective Population Health Interventions (PHIs) to reverse this trend. Given that research designs such as randomised controlled trials (RCTs) are often not suited to the evaluation of PHIs, Natural Experiments (NEs) are now frequently being used as a design to evaluate such complex, preventive PHIs. However, current guidance for economic evaluation focusses on RCT designs and therefore does not address the specific challenges of NE designs. Using such guidance can lead to sub-optimal design, data collection and analysis for NEs, leading to bias in the estimated effectiveness and cost-effectiveness of the PHI. As a consequence, there is a growing recognition of the need to identify a robust methodological framework for the design and conducting of economic evaluations alongside such NEs. This paper outlines the challenges inherent to the design and conduct of economic evaluations of PHIs alongside NEs, providing a comprehensive framework and outlining a research agenda in this area.

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.

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.

Sunitinib, an oral, multitargeted receptor tyrosine kinase inhibitor, delays disease progression, with a median overall survival (OS) of more than 2 years, improves quality of life and is becoming the first-line standard of care for metastatic renal carcinoma (mRCC). To assess the economic value of sunitinib as first-line therapy in mRCC within the Spanish healthcare system. An adapted Markov model with a 10-year time horizon was used to analyse the cost effectiveness of sunitinib vs. sorafenib (SFN) and bevacizumab/interferon-alpha (BEV/IFN) as first-line mRCC therapy from the Spanish third-party payer perspective. Progression-free survival (PFS) and OS data from sunitinib, SFN and BEV/IFN pivotal trials were extrapolated to project survival and costs in 6-week cycles. Results, in progression-free life-years (PFLY), life years (LY) and quality-adjusted life-years (QALY) gained, expressed as incremental cost-effectiveness ratios (ICER) with costs and benefits discounted annually at 3%, were obtained using deterministic and probabilistic analyses. Sunitinib was more effective and less costly than both SFN (gains of 0.52 PFLY, 0.16 LY, 0.17 QALY) and BEV/IFN (gains of 0.19 PFLY, 0.23 LY, 0.16 QALY) with average cost savings/patients of a,not sign,124 and a,not sign23,218, respectively. Using a willingness-to-pay (WTP) threshold of a,not sign50,000/QALY, sunitinib achieved an incremental net benefit (INB) of a,not sign9,717 and a,not sign31,211 compared with SFN and BEV/IFN, respectively. At this WTP, the probability of sunitinib providing the highest INB was 75%. Our analysis suggests that sunitinib is a cost-effective alternative to other targeted therapies as first-line mRCC therapy in the Spanish healthcare setting.

Background: In the ‘Arimidex’, Tamoxifen Alone or in Combination (ATAC) trial, the aromatase inhibitor (AI) anastrozole had a ignificantly 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 100- month 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 ($ 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. Hintergrund: Bei der adjuvanten Therapie von postmenopausalen Patientinnen mit Hormonrezeptor-positivem (HR+) Mammakarzinom belegen die ATAC-100-Monatsdaten (ATAC-Studie: ‘Arimidex’, Tamoxifen Alone or in Combination) einen signifikanten Vorteil von Anastrozol gegenüber Tamoxifen in Bezug auf Rezidivrisiko und Verträglichkeit. Es wurde eine Kosten-Nutzwert-Analyse von Anastrozol im Vergleich zu Tamoxifen aus der Sicht des deutschen Gesundheitssystems durchgeführt. Material und Methoden: Als Berechnungsbasis wurde ein Markov- Modell zur Abschätzung der Kosteneffektivität entwickelt. Der Modellierungszeitraum umfasste 25 Jahre. Die Daten wurden anhand der ATAC-100-Monatsdaten, vorliegender Literatur und durch ein interdisziplinäres Expertenteam ermittelt. Ergebnisse: Eine adjuvante Therapie mit Anastrozol erzielte 0,32 quality-adjusted life-years (QALYs) pro Patientin mehr, verglichen mit einer adjuvanten Tamoxifentherapie. Die zusätzlichen Kosten der Therapie mit Anastrozol lagen bei 6819 D pro Patientin. Im Vergleich mit Tamoxifen erzielte Anastrozol einen ICER (Incremental Cost-Effectiveness Ratio) von 21 069 D (30 717 $)/QALY über den gesamten Modellierungszeitraum. Schlussfolgerung: Diese Kosten- Nutzwert-Analyse eines Aromatasehemmers basiert erstmals auf einer Datenanalyse, die auch das Follow-Up und den sogenannten Carryover- Effekt nach einer abgeschlossenen 5-Jahres-Therapie beinhaltet. Anastrozol ist auch nach dieser Analyse aus der Sicht des deutschen Gesundheitssystems eine kosteneffektive Therapieoption für postmenopausale Patientinnen mit einem HR+ frühen Mammakarzinom. Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich.

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. © 2015 The Authors. Health Economics published by John Wiley & Sons Ltd.

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.

OBJECTIVES: Endometriosis 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. METHODS: Using data from two clinical trials (N = 252 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. RESULTS: Bodily 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. CONCLUSIONS: The SF-36--particularly BP and the PCS--appears to be a valid and responsive measure for endometriosis and its treatment.

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.

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.

The objective of this study was to assess the quality of life and drug costs associated with switching from any ongoing antipsychotic treatment to once-daily extended release quetiapine fumarate (quetiapine XR) in patients with schizophrenia.This assessment was based on data collected during a 12-week study in patients with schizophrenia (n = 477) who switched from their current antipsychotic due to insufficient efficacy or poor tolerability to a flexible dose of quetiapine XR. Patients were assigned utilities based on their Positive and Negative Syndrome Scale (PANSS) scores and the presence of adverse events by applying the methods of Lenert et al.1. Quality adjusted life year (QALY) gains were calculated assuming a linear change of utility between two consecutive visits. Incremental costs were calculated by comparing the hypothetical mean drug cost (assuming patients stay on previous treatment) with the actual mean cost of quetiapine XR based on European prices.Patients who completed the study (n = 279) increased their average utility by 0.116, corresponding to a QALY gain of 0.0207. For the total sample, the mean utility increased by 0.09, reflecting a QALY gain of 0.0170. The additional costs for quetiapine XR per QALY gained varied from approximately 16,000 euro to 24,000 euro. Notably, this is a non-comparative study; therefore, no conclusions can be reached regarding the relative impact of switching to quetiapine XR compared with other antipsychotics. Further limitations included the short trial duration on which the utility estimates are based, and the restriction of cost data to drug costs alone. Furthermore, in a 'real world' scenario, it is to be expected that other drug regimens might be introduced during periods of treatment failure.This analysis demonstrates that patients with schizophrenia who switch their antipsychotic medication to quetiapine XR because of insufficient efficacy or poor tolerability benefit from significant QALY gains at a reasonable increase in drug cost.

e17556 Background: RCC, the most prevalent kidney cancer, is a relatively rare malignancy that carries a poor prognosis. New targeted therapies, such as sunitinib, sorafenib, temsirolimus, and bevacizumab + interferon-alfa (IFN-α), are now available in the US for the treatment of mRCC. In the absence of head-to-head trials, the aim of this analysis was to assess the economic value of these therapies as first-line treatment of mRCC from a U.S. third-party payer perspective, using an indirect comparison based on reported survival data. Methods: An economic model was built to simulate progression-free and overall survival based on each treatment's hazard ratio against IFN-α as reported from phase II and III clinical trials. Clinical model parameters were also derived from these trials and complemented with clinical experts’ opinions. Costs of drugs, routine follow-up, treatment-related adverse events, disease progression, and best supportive care of terminally-ill patients were included in the model. Results, expressed as life-years (LY), progression-free LY (PFLY), and quality adjusted LY (QALY) gained, treatment costs (applied in 2008 USD), and incremental cost-effectiveness ratios (ICER), were obtained through probabilistic analysis over a 10-year time horizon. Since the phase III clinical trial of temsirolimus included the MSKCC (modified) poor risk group patients only, two separate evaluations were carried out: (1) comparison of sunitinib, sorafenib, and bevacizumab + IFN-α in all patients and (2) a similar comparison of sunitinib and temsirolimus in the poor-risk group patients only. Results: In the first comparison model, sunitinib was both more effective (with gains of 0.52 and 0.19 PFLY, and 0.17 and 0.03 QALY) and less costly (by $13,675 and $84,260) than sorafenib and bevacizumab + IFN-α, respectively, over 10 years. Similarly, sunitinib was both more effective (with gains of 0.12 PFLY and 0.07 QALY) and less costly (saving $9,605 over ten years) than temsirolimus in patients in the poor risk group. Conclusions: These model results suggest that sunitinib is a cost-effective alternative to sorafenib, bevacizumab + IFN-α, and temsirolimus as a first-line treatment of mRCC. [Table: see text]

SummaryBackgroundCombination 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.MethodsWe 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.Findings3151 (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; p0·05).InterpretationPeople 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.FundingUK National Institute for Health Research.

Difference-in-differences (DiD) estimators provide unbiased treatment effect estimates when, in the absence of treatment, the average outcomes for the treated and control groups would have followed parallel trends over time. This assumption is implausible in many settings. An alternative assumption is that the potential outcomes are independent of treatment status, conditional on past outcomes. This paper considers three methods that share this assumption: the synthetic control method, a lagged dependent variable (LDV) regression approach, and matching on past outcomes. Our motivating empirical study is an evaluation of a hospital pay-for-performance scheme in England, the best practice tariffs programme. The conclusions of the original DiD analysis are sensitive to the choice of approach. We conduct a Monte Carlo simulation study that investigates these methods’ performance. While DiD produces unbiased estimates when the parallel trends assumption holds, the alternative approaches provide less biased estimates of treatment effects when it is violated. In these cases, the LDV approach produces the most efficient and least biased estimates. Electronic supplementary material The online version of this article (doi:10.1007/s10742-016-0146-8) contains supplementary material, which is available to authorized users.