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Abstract Objectives: 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. Research design and methods: 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). Results: 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. Conclusions: 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. [ABSTRACT FROM AUTHOR]

Objective: 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.Methods: 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.Results: 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.Conclusion: 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. [ABSTRACT FROM AUTHOR]

There is increasing interest in moving away from "one size fits all (OSFA)" approaches toward stratifying treatment decisions. Understanding how expected effectiveness and cost-effectiveness varies with patient covariates is a key aspect of stratified decision making. Recently proposed machine learning (ML) methods can learn heterogeneity in outcomes without pre-specifying subgroups or functional forms, enabling the construction of decision rules ('policies') that map individual covariates into a treatment decision. However, these methods do not yet integrate ML estimates into a decision modeling framework in order to reflect long-term policy-relevant outcomes and synthesize information from multiple sources. In this paper, we propose a method to integrate ML and decision modeling, when individual patient data is available to estimate treatment-specific survival time. We also propose a novel implementation of policy tree algorithms to define subgroups using decision model output. We demonstrate these methods using the SPRINT (Systolic Blood Pressure Intervention Trial), comparing outcomes for "standard" and "intensive" blood pressure targets. We find that including ML into a decision model can impact the estimate of incremental net health benefit (INHB) for OSFA policies. We also find evidence that stratifying treatment using subgroups defined by a tree-based algorithm can increase the estimates of the INHB., (© 2024 The Authors. Health Economics published by John Wiley & Sons Ltd.)

Background: Recent developments in causal inference and machine learning (ML) allow for the estimation of individualized treatment effects (ITEs), which reveal whether treatment effectiveness varies according to patients' observed covariates. ITEs can be used to stratify health policy decisions according to individual characteristics and potentially achieve greater population health. Little is known about the appropriateness of available ML methods for use in health technology assessment., Methods: In this scoping review, we evaluate ML methods available for estimating ITEs, aiming to help practitioners assess their suitability in health technology assessment. We present a taxonomy of ML approaches, categorized by key challenges in health technology assessment using observational data, including handling time-varying confounding and time-to event data and quantifying uncertainty., Results: We found a wide range of algorithms for simpler settings with baseline confounding and continuous or binary outcomes. Not many ML algorithms can handle time-varying or unobserved confounding, and at the time of writing, no ML algorithm was capable of estimating ITEs for time-to-event outcomes while accounting for time-varying confounding. Many of the ML algorithms that estimate ITEs in longitudinal settings do not formally quantify uncertainty around the point estimates., Limitations: This scoping review may not cover all relevant ML methods and algorithms as they are continuously evolving., Conclusions: Existing ML methods available for ITE estimation are limited in handling important challenges posed by observational data when used for cost-effectiveness analysis, such as time-to-event outcomes, time-varying and hidden confounding, or the need to estimate sampling uncertainty around the estimates., Implications: ML methods are promising but need further development before they can be used to estimate ITEs for health technology assessments., Highlights: Estimating individualized treatment effects (ITEs) using observational data and machine learning (ML) can support personalized treatment advice and help deliver more customized information on the effectiveness and cost-effectiveness of health technologies.ML methods for ITE estimation are mostly designed for handling confounding at baseline but not time-varying or unobserved confounding. The few models that account for time-varying confounding are designed for continuous or binary outcomes, not time-to-event outcomes.Not all ML methods for estimating ITEs can quantify the uncertainty of their predictions.Future work on developing ML that addresses the concerns summarized in this review is needed before these methods can be widely used in clinical and health technology assessment-like decision making., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Financial support for this study was provided entirely by The HTx project. The funding agreement ensured the authors’ independence in designing the study, interpreting the data, writing, and publishing the report. The HTx project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 825162 for 5 years from January 2019. This dissemination reflects only the authors’ view, and the Commission is not responsible for any use that may be made of the information it contains. The main aim of HTx is to create a framework for the Next Generation Health Technology Assessment (HTA) to support patient-centered, societally oriented, real-time decision making on access to and reimbursement for health technologies throughout Europe.

We investigated the causal impact of conflict-related violence on individual mental health and its potential pathways in Colombia. Using data from before and after the 2016 peace accord between the Colombian government and the Revolutionary Armed Forces of Colombia (FARC), we adopted a difference-in-differences empirical design combined with instrumental variables estimation. We also used formal mediation analysis to investigate a possible mediating role of alcohol consumption in the relationship between conflict exposure and mental health. Our results did not support the hypothesis that changes in exposure to conflict violence after the peace accord causally led to any changes in individual mental health. We were unable to identify a statistically significant mediating effect of alcohol consumption in the relationship between exposure to conflict violence and mental health., (© 2024 The Authors.)

Background: The marginal productivity of a country's healthcare system refers to the health gains produced per unit change in the level of spending. In budget-constrained settings, this metric reflects the opportunity cost, in terms of health gains forgone, of committing additional or existing resources to alternative uses within the healthcare system. It can therefore assist in evidence-based decisions on whether different interventions represent good value for money., Objective: The aim of this paper was to estimate the marginal productivity of the Indonesian healthcare system using subnational data, and to use this to inform health opportunity costs in the country., Methods: We define a dynamic health production function to model the stream of effects of current and prior public health spending decisions on population under-five mortality. To estimate the model, we use data from the 33 Indonesian provinces for the 2004-2012 period. The estimated elasticity is then translated into gains in terms of cost per DALY (disability-adjusted life-year) averted. We use dynamic panel data methods to address potential endogeneity issues in the model., Results: Our base-case estimates suggest that a 1% expansion in the level of health spending reduces under-five mortality by 0.38% (95% CI 0.00-0.76), which translates into a cost of averting one DALY of $235 (2019 US$)., Conclusion: With Indonesia aiming for universal health coverage, our results support these efforts by highlighting the associated benefits resulting from increases in public health expenditure and have the potential to inform the decision-making process about a suitable locally relevant cost-effectiveness threshold., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

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., (© The Author(s) 2022. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Advances in machine learning (ML) and artificial intelligence offer tremendous potential benefits to patients. Predictive analytics using ML are already widely used in healthcare operations and care delivery, but how can ML be used for health economics and outcomes research (HEOR)? To answer this question, ISPOR established an emerging good practices task force for the application of ML in HEOR. The task force identified 5 methodological areas where ML could enhance HEOR: (1) cohort selection, identifying samples with greater specificity with respect to inclusion criteria; (2) identification of independent predictors and covariates of health outcomes; (3) predictive analytics of health outcomes, including those that are high cost or life threatening; (4) causal inference through methods, such as targeted maximum likelihood estimation or double-debiased estimation-helping to produce reliable evidence more quickly; and (5) application of ML to the development of economic models to reduce structural, parameter, and sampling uncertainty in cost-effectiveness analysis. Overall, ML facilitates HEOR through the meaningful and efficient analysis of big data. Nevertheless, a lack of transparency on how ML methods deliver solutions to feature selection and predictive analytics, especially in unsupervised circumstances, increases risk to providers and other decision makers in using ML results. To examine whether ML offers a useful and transparent solution to healthcare analytics, the task force developed the PALISADE Checklist. It is a guide for balancing the many potential applications of ML with the need for transparency in methods development and findings., (Copyright © 2022. Published by Elsevier Inc.)

Diagnosis Related Group (DRG) payment systems are a common means of paying for hospital services. They reward greater activity and therefore potentially encourage more rapid treatment. This paper uses 15 years of administrative data to examine the impact of a DRG system introduced in England on hospital lengths of stay. We utilize different econometric models, exploiting within and cross jurisdiction variation, to identify policy effects, finding that the reduction of lengths of stay was greater than previously estimated and grew over time. This constitutes new and important evidence of the ability of financing reform to generate substantial and persistent change in healthcare delivery., (© 2022 The Authors. Health Economics published by John Wiley & Sons Ltd.)

Competing Interests: Competing interests: We have read and understood BMJ policy on declaration of interests and have no relevant interests to declare.

Objective: To explore methodological challenges when using real-world evidence (RWE) to estimate comparative-effectiveness in the context of Health Technology Assessment of direct oral anticoagulants (DOACs) in Scotland., Methods: We used linkage data from the Prescribing Information System (PIS), Scottish Morbidity Records (SMR) and mortality records for newly anticoagulated patients to explore methodological challenges in the use of Propensity score (PS) matching, Inverse Probability Weighting (IPW) and covariate adjustment with PS. Model performance was assessed by standardised difference. Clinical outcomes (stroke and major bleeding) and mortality were compared for all DOACs (including apixaban, dabigatran and rivaroxaban) versus warfarin. Patients were followed for 2 years from first oral anticoagulant prescription to first clinical event or death. Censoring was applied for treatment switching or discontinuation., Results: Overall, a good balance of patients' covariates was obtained with every PS model tested. IPW was found to be the best performing method in assessing covariate balance when applied to subgroups with relatively large sample sizes (combined-DOACs versus warfarin). With the IPTW-IPCW approach, the treatment effect tends to be larger, but still in line with the treatment effect estimated using other PS methods. Covariate adjustment with PS in the outcome model performed well when applied to subgroups with smaller sample sizes (dabigatran versus warfarin), as this method does not require further reduction of sample size, and trimming or truncation of extreme weights., Conclusion: The choice of adequate PS methods may vary according to the characteristics of the data. If assumptions of unobserved confounding hold, multiple approaches should be identified and tested. PS based methods can be implemented using routinely collected linked data, thus supporting Health Technology decision-making., Competing Interests: GC and CG have received research grants from Bristol-Myers Squibb UK and Pfizer UK outside the submitted work. OW has received consulting fee from Bayer UK outside the submitted work. MD, NK and PL declare no conflict of interest.

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., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Competing Interests: Dr. Kreif has disclosed that she does not have any potential conflicts of interest.

Background: The present study analyzes inequalities in catastrophic health expenditures in conflict-affected regions of Meta, Colombia and socioeconomic factors contributing to the existence and changes in catastrophic expenditures before and after the sign of Colombian Peace Agreement with FARC-EP guerilla group in 2016., Methods: The study uses the results of the survey Conflicto, Paz y Salud (CONPAS) conducted in 1309 households of Meta, Colombia, a territory historically impacted by armed conflict, for the years 2014 and 2018. We define catastrophic expenditures as health expenditures above 20% of the capacity to pay of a household. We disaggregate the changes in inequalities in catastrophic expenditures through the Oaxaca-Blinder change decomposition method., Results: The incidence of catastrophic expenditures slightly increased between 2014 to 2018, from 29.3 to 30.7%. Inequalities in catastrophic expenditures, measured through concentration indexes (CI), also increased from 2014 (CI: -0.152) to 2018 (CI: -0.232). Results show that differences in catastrophic expenditures between socioeconomic groups are mostly attributed to an increased influence of specific sociodemographic variables such as living in rural zones, being a middle-aged person, living in conflict-affected territories, or presenting any type of mental and physical disability., Conclusions: Conflict-deescalation and the peace agreement may have facilitated lower-income groups to have access to health services, especially in territories highly impacted by conflict. This, consequently, may have led to higher levels of out-of-pocket expenditures and, therefore, to higher chances of experiencing catastrophic expenditures for lower-income groups in comparison to higher-income groups. Therefore, results indicate the importance of designing policies that guarantee access to health services for people in conflict -affected regions but also, that minimize health care inequalities in out-of-pocket payments that may arouse between people at different socioeconomic groups., (© 2021. The Author(s).)

In many low- and middle-income countries, geographical accessibility continues to be a barrier to health care utilization. In this paper, we aim to better understand the full relationship between distance to providers and utilization of maternal delivery services. We address three methodological challenges: non-linear effects between distance and utilization; unobserved heterogeneity through non-random distance "assignment"; and heterogeneous effects of distance. Linking Malawi Demographic Health Survey household data to Service Provision Assessment facility data, we consider distance as a continuous treatment variable, estimating a Dose-Response Function based on generalized propensity scores, allowing exploration of non-linearities in the effect of an increment in distance at different distance exposures. Using an instrumental variables approach, we examine the potential for unobserved differences between women residing at different distances to health facilities. Our results suggest distance significantly reduces the probability of having a facility delivery, with evidence of non-linearities in the effect. The negative relationship is shown to be particularly strong for women with poor health knowledge and lower socio-economic status, with important implications for equity. We also find evidence of potential unobserved confounding, suggesting that methods that ignore such confounding may underestimate the effect of distance on the utilization of health services., (© 2021 The Authors. Health Economics published by John Wiley & Sons Ltd.)

Primary healthcare systems are central to achieving universal healthcare coverage. However, in many low- and middle-income country settings, primary care quality is challenged by inadequate facility infrastructure and equipment, limited human resources, and poor provider process. We study the effects of a recent large-scale quality improvement policy in South Africa, the Ideal Clinics Realization and Maintenance Program (ICRMP). The ICRMP introduced a set of standards for facilities and a quality improvement process involving manuals, district-based support, and external assessment. Exploiting differential prioritization of facilities for the ICRMP's quality improvement process, we apply differences-in-differences methods to identify the effects of the program's efforts on standards scores and primary care quality indicators over the first 12 months of implementation. We find large and statistically significant increases in standards scores, but mixed effects on care outcomes-a small magnitude improvement in early antenatal care usage, null effects on childhood immunization and cervical cancer screening, and small negative effect of human immunodeficiency virus (HIV) care. While the ICRMP process has led to significant improvements in facilities' satisfaction of the program's standards, we were unable to detect meaningful change in care quality indicators., (© 2021 The Authors. Health Economics published by John Wiley & Sons Ltd.)

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 nonsystematic 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 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., (© 2020 The International Biometric Society.)

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., Competing Interests: No competing interests were disclosed., (Copyright: © 2020 Chi YL et al.)

Objective: To compare interactive fixed effects (IFE) and generalized 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 incentivize 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 control (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 (confidence interval: 2.0 to 9.9) percentage point increase in the proportion of patients having surgery within 48 hours and a statistically insignificant 0.6 (confidence interval: -1.4 to 0.4) 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., (© 2020 The Authors. Health Services Research published by Wiley Periodicals, Inc. on behalf of Health Research and Educational Trust.)

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., (Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.)

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., (Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Longitudinal data sources offer new opportunities for the evaluation of sequential interventions. To adjust for time-dependent confounding in these settings, longitudinal targeted maximum likelihood based estimation (TMLE), a doubly robust method that can be coupled with machine learning, has been proposed. This paper provides a tutorial in applying longitudinal TMLE, in contrast to inverse probability of treatment weighting and g-computation based on iterative conditional expectations. We apply these methods to estimate the causal effect of nutritional interventions on clinical outcomes among critically ill children in a United Kingdom study (Control of Hyperglycemia in Paediatric Intensive Care, 2008-2011). We estimate the probability of a child's being discharged alive from the pediatric intensive care unit by a given day, under a range of static and dynamic feeding regimes. We find that before adjustment, patients who follow the static regime "never feed" are discharged by the end of the fifth day with a probability of 0.88 (95% confidence interval: 0.87, 0.90), while for the patients who follow the regime "feed from day 3," the probability of discharge is 0.64 (95% confidence interval: 0.62, 0.66). After adjustment for time-dependent confounding, most of this difference disappears, and the statistical methods produce similar results. TMLE offers a flexible estimation approach; hence, we provide practical guidance on implementation to encourage its wider use., (© The Author(s) 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.)

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., (© 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., (© The Author(s) 2014.)

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.

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.)

Background: Combination 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., Methods: We 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., Findings: 3151 (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)., Interpretation: People 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., Funding: UK National Institute for Health Research., (Copyright © 2014 Miners et al. Open Access article distributed under the terms of CC BY. Published by Elsevier Ltd.. All rights reserved.)

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.

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., (Copyright © 2012 John Wiley & Sons, Ltd.)

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.

INTRODUCTION 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). PURPOSE To assess the economic value of sunitinib as fi rst-line therapy in mRCC within the Spanish healthcare system. METHODS 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-α (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. RESULTS 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 €1,124 and €23,218, respectively. Using a willingness-to-pay (WTP) threshold of €50,000/QALY, sunitinib achieved an incremental net benefit (INB) of €9,717 and €31,211 compared with SFN and BEV/IFN, respectively. At this WTP, the probability of sunitinib providing the highest INB was 75%. CONCLUSION Our analysis suggests that sunitinib is a costeffective 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 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 ($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., (Copyright 2010 S. Karger AG, Basel.)