Medical Decision Making最新文献

Individual-level data are routinely used in trial-based economic evaluations to assess the effectiveness and costs of a given intervention. While effectiveness measures are often expressed via utility scores derived from health-related quality-of-life instruments (e.g., EQ-5D questionnaires), information on different types of health care resource use (HRU) measures (e.g., number and types of services) are collected to compute the costs. Partially complete HRU data, particularly for self-reported questionnaires, are handled via ad hoc methods that rely on some assumptions (fill in a zero) that are typically hard to justify. Although methods have been proposed to account for the uncertainty surrounding missing data, particularly in the form of multiple imputation or Bayesian methods, these have mostly been implemented at the level of costs at different times or over the entire study period, while little attention has been given to how missing values at the level of HRUs should be addressed and their implications on the final analysis. We present a general Bayesian framework for the analysis of partially observed HRUs in trial-based economic evaluations, which can accommodate the typical complexities of the data (e.g., excess zeros, skewness, missingness) and quantify the impact of missingness uncertainty on the results. We show the benefits of our approach with a motivating example and compare the results to those from more standard analyses fitted at the level of cost variables after adopting some ad hoc imputation. This article highlights the importance of adopting a comprehensive modeling approach to handle partially observed HRU data in economic evaluations and the strategic advantages of building these models within a Bayesian framework.HighlightsMissing health care service data in trial-based economic evaluations are often removed or imputed using quite restrictive assumptions (e.g., no use of service).We propose a flexible Bayesian approach to account for missing health care service uncertainty and compare the results with models fitted at more aggregated levels (e.g., total costs) using a real case study.Our results show that, depending on the (assumed) missingness assumptions and the level of data aggregation at which analyses are performed, results may be considerably changed.When feasible, analyses should be conducted at the most disaggregated level to ensure that all available information collected in the trial is used in the analysis without relying on (often) restrictive ad hoc imputation approaches.

Health state values, often in the form of value sets that list values applied to particular health states, are used in cost-effectiveness analyses of health care to calculate gains in quality-adjusted life-years. These values are subject to several sources of uncertainty, arising from the fact that values are not constants but variables and are of different types including variability, heterogeneity, statistical uncertainty, and methodological variation. Currently, these sources are not fully documented and are not fully accounted for when creating and analyzing economic evaluation models. This may provide to users of such models a false sense of the precision of quality-adjusted life-year gain estimates and therefore of cost-effectiveness. This article provides a comprehensive account of such sources of uncertainty and how they interact. It also provides a more detailed account of how uncertainty arises in studies that elicit and model value sets. Its aim is to encourage research to measure and report uncertainty around health state values so it can be better accounted for in cost-effectiveness analyses.HighlightsHealth state values (HSVs) used in cost-effectiveness analysis are subject to multiple types of uncertainty, including variability, heterogeneity, statistical uncertainty, and methodological variation.Current reporting and guidelines often fail to fully document or address all sources of uncertainty in HSVs, which can mislead users about the precision of QALY and cost-effectiveness estimates.Valuation studies should report measures of uncertainty (such as standard errors or variance/covariance matrices) for HSVs, not just point estimates.Researchers, decision modellers, and guideline developers should recognise, measure, and report HSV uncertainty more thoroughly to improve the reliability of cost-effectiveness analyses.

JEL classification: I30, J17.

BackgroundEconomic evaluations in health typically assume a nonwelfarist framework, arguably better served by preferences elicited from a social perspective than a personal one. However, most health state valuation studies elicit personal preferences, leading to a methodological inconsistency. No studies have directly compared social and personal preferences for outcomes using otherwise identical scenarios, leaving their empirical relationship unclear.AimThis unique study examines whether the choice of eliciting preferences from a social or personal perspective influences valuations of health and well-being outcomes.MethodsUsing discrete choice experiments, social and personal preferences for health and well-being attributes were elicited from the UK general public recruited from an internet panel (n = 1,020 personal, n = 3,009 social surveys). Mixed logit models were estimated, and willingness-to-pay (WTP) values for each attribute were calculated to compare differences between the 2 perspectives.ResultsWhile no significant differences were observed in the effects of physical and mental health, loneliness, and neighborhood safety across the 2 perspectives, significant differences emerged in WTP values for employment and housing quality. For instance, other things being the same, personal preferences rate being retired as more preferable than being an informal caregiver, but the social preferences rate them in the reverse order.ConclusionOur findings demonstrate that the perspective matters, particularly for valuing outcomes such as employment and housing. These findings indicate that the exclusive use of personal preferences to value states such as employment and housing quality may potentially lead to suboptimal resource allocation, given that such valuations reflect individual rather than societal benefit. This highlights the importance of considering perspective especially in the resource allocation of public health interventions.HighlightsPersonal preferences were not aligned with social preferences for employment and housing quality outcomes.Respondents valued health outcomes the same in both social and personal perspectives.Using personal preferences in public health resource allocation decisions may not reflect societal priorities.

BackgroundCorrectional facilities can act as amplifiers of infectious disease outbreaks. Small community outbreaks can cause larger prison outbreaks, which can in turn exacerbate the community outbreaks. However, strategies for epidemic control in communities and correctional facilities are generally not closely coordinated. We sought to evaluate different strategies for coordinated control.MethodsWe developed a stochastic simulation model of an epidemic spreading across a network of communities and correctional facilities. We parameterized it for the initial phases of the COVID-19 epidemic for 1) California communities and prisons based on community data from covidestim, prison data from the California Department of Corrections and Rehabilitation, and mobility data from SafeGraph, and 2) a small, illustrative network of communities and prisons. For each community or prison, control measures were defined by the intensity of 2 activities: 1) screening to detect and isolate cases and 2) nonpharmaceutical interventions (e.g., masking and social distancing) to reduce transmission. We compared the performance of different control strategies including heuristic and reinforcement learning (RL) strategies using a reward function, which accounted for both the benefit of averted infections and nonlinear cost of the control measures. Finally, we performed analyses to interpret the optimal strategy and examine its robustness.ResultsThe RL control strategy robustly outperformed other strategies including heuristic approaches such as those that were largely used during the COVID-19 epidemic. The RL strategy prioritized different characteristics of communities versus prisons when allocating control resources and exhibited geo-temporal patterns consistent with mitigating prison amplification dynamics.ConclusionRL is a promising method to find efficient policies for controlling epidemic spread on networks of communities and correctional facilities, providing insights that can help guide policy.HighlightsFor modelers, we developed a stochastic simulation model of an epidemic spreading across a network of communities and correctional facilities, and we parameterized it for the initial phases of the COVID-19 epidemic for California communities and prisons in addition to an illustrative network.We compared different control strategies using a reward function that accounted for both the benefit of averted infections and cost of the control measures; we found that reinforcement learning robustly outperformed the other strategies including heuristic approaches such as those that were largely used during the COVID-19 epidemic.For policy makers, our work suggests that they should consider investing in the further development of such methods and using them for the control of future epidemics.We offer qualitative insights into different factors that might inform resource allocation to communities versus prisons during future epidemics.

IntroductionSubgroup analyses are vital components of health technology assessments, but randomized controlled trials (RCTs) do not commonly report survival distributions for subgroups. This study developed an analytical framework to elicit unreported subgroup-specific survival curves from aggregate RCT data.MethodsAssuming exponentially distributed subgroup survival durations, we developed an optimization model that approximates the restricted mean survival time (RMST) for the overall population via the weighted average of the RMSTs of 2 subgroups in each arm. Reported hazard ratios from the forest plots between the arms were used to enforce the relationship among subgroups' hazard rates in the model. The performance of the model was tested in a real-life test set of 8 RCTs in advanced-stage gastrointestinal tumors, which also reported KM curves for overall survival (OS) for 40 subgroups as well as in 42 synthetic test cases with 168 subgroups as a benchmark. For each subgroup, predicted median survival, OS rates, and the RMSTs were compared against their actual counterparts as well as their 95% confidence intervals (CIs).ResultsPredicted median survivals and RMSTs were within the 95% CIs of the reported values in 32 (80%) and 34 (85%) of 40 subgroups in real-life test cases and in 163 (97%) and 146 (87%) of 168 subgroups in synthetic test cases, respectively. Across all cases, on average, the predicted survival curves laid within the 95% CIs of reported KM curves 71% and 97% of the time in real-life and synthetic test cases, respectively.DiscussionOur study offers a useful and scalable method for extracting subgroup-specific survival from aggregate RCT data to enable subgroup-specific indirect comparisons, and cost-utility and meta-analyses.HiglightsMost randomized controlled trials report survival curves for the overall patient population but do not provide subgroup-specific survival curves, which are crucial for cost-effectiveness analyses and meta-analyses focusing on these subgroups.This study developed an optimization modeling approach to elicit unreported subgroup-specific survival curves from aggregate trial data.The proposed modeling approach accurately predicted the reported subgroup-specific survival curves in 42 simulated test cases with 168 subgroups overall, in which each subgroup-specific survival curve was assumed to followed an exponential distribution.The performance of the proposed modeling approach was sensitive to the assumptions when it was tested using a real-life test set of 8 oncology trials, which also reported survival curves for a total of 40 subgroups.

PurposeWe conducted a distributional cost-effectiveness analysis (DCEA) using routinely collected data to estimate the population health and health inequality impacts of the National Abdominal Aortic Aneurysm Screening Programme (NAAASP) in England.MethodsAn existing discrete event simulation model of AAA screening was adapted to examine differences between socioeconomic groups defined by Index of Multiple Deprivation, obtained from an analysis of secondary data sources. We examined the distributional cost-effectiveness of being invited versus not invited at age 65 y to screen using a National Health Service perspective. Changes in inequality were valued using a measure of equally distributed equivalent health.ResultsThe net health benefits of population screening (317 quality-adjusted life-years [QALYs] gained) were disproportionately accounted for by the effects on those living in more advantaged areas. The NAAASP improved health on average compared with no screening, but the health opportunity cost of the programme exceeded the QALY gains for people living in the most deprived areas, resulting in a negative net health impact for this group (106 QALYs lost) that was driven by differences in the need for screening. Consequently, the NAAASP increased health inequality at the population level. Given current estimates for inequality aversion in England, screening for AAA remains the optimal strategy.ConclusionExamination of the distributional cost-effectiveness of the NAAASP in England using routinely collected data revealed a tradeoff between total population health and health inequality. Study findings suggest that the NAAASP provides value for money despite health impacts being disseminated to those who are more advantaged.HighlightsThis study examines the population health and health inequality effects of the National Abdominal Aortic Aneurysm Screening Programme (NAAASP) between socioeconomic groups defined by Index of Multiple Deprivation.Findings suggest a tradeoff between total population health and health inequality.Given current estimates for inequality aversion in England, screening remains the optimal strategy relative to not screening.Opportunities remain to reduce inequality effects for those most vulnerable through targeted approaches.

BackgroundUpdated estimates of the productivity losses per HIV infection due to premature HIV mortality are needed to help quantify the economic burden of HIV and inform cost-effectiveness analyses.MethodsWe used the human capital approach to estimate the productivity loss due to HIV mortality per HIV infection in the United States, discounted to the time of HIV infection. We incorporated published data on age-specific annual productivity, life expectancy at HIV diagnosis, life-years lost from premature death among persons with HIV (PWH), the number of years from HIV infection to diagnosis, and the percentage of deaths in PWH attributable to HIV. For the base case, we used 2018 life expectancy data for all PWH in the United States. We also examined scenarios using life expectancy in 2010 and life expectancy for cohorts on antiretroviral therapy (ART). We conducted sensitivity analyses to understand the impact of key input parameters.ResultsWe estimated the base-case overall average productivity loss due to HIV mortality per HIV infection at $65,300 in 2022 US dollars. The base-case results showed a 45% decrease in the estimated productivity loss compared with the results when applying life expectancy data from 2010. Productivity loss was 83% lower for cohorts of PWH on ART compared with the base-case scenario. Results were sensitive to assumptions about percentage of deaths attributable to HIV and heterogeneity in age at death.ConclusionThis study provides valuable insights into the economic impact of HIV mortality, illustrating reductions in productivity losses over time due to advancements in treatments.HighlightsUpdated estimates of productivity losses per HIV infection due to premature HIV mortality can help assess the total economic burden of HIV in the United States.This study estimates productivity losses per HIV infection for overall, by sex, and by varying ages of HIV infection.Advancement in treatment has contributed to a significant reduction in productivity losses due to premature HIV mortality in the United States over the past decade.

ObjectivesBayesian multiparameter evidence synthesis (B-MPES) can improve the reliability of long-term survival extrapolations by leveraging registry data. We extended the B-MPES framework to also incorporate historical trial data and examined the impact of alternative external information sources on predictions from early data cuts for a trial in metastatic non-small-cell lung cancer (mNSCLC).MethodsB-MPES models were fitted to survival data from the phase III CheckMate 9LA study of nivolumab plus ipilimumab plus 2 cycles of chemotherapy (NIVO+IPI+CHEMO, v. 4 cycles of CHEMO) in first-line mNSCLC, with 1 y of minimum follow-up. Trial observations were supplemented by registry data from the Surveillance, Epidemiology, and End Results program, general population data, and, optionally, historical trial data with extended follow-up for first-line NIVO+IPI (v. CHEMO) and/or second-line NIVO monotherapy in advanced NSCLC, via estimated 1-y conditional survival. Predictions from the 3 alternative B-MPES models were compared with those from standard parametric models (SPMs).ResultsB-MPES models better anticipated the emergent survival plateau with NIVO+IPI+CHEMO that was apparent in the 4-y data cut compared with SPMs, for which short-term extrapolations in both treatment arms were overly conservative. However, the B-MPES model incorporating NIVO+IPI data slightly overestimated 4-y NIVO+IPI+CHEMO survival owing to a confounding effect on estimated hazards that could not be accounted for a priori until later data cuts of CheckMate 9LA. Extrapolations were relatively robust to the choice of external data sources provided that the prior data had been adjusted to attenuate confounding.ConclusionsIncorporating historical trial data into survival models can improve the plausibility and interpretability of lifetime extrapolations for studies of novel therapies in metastatic cancers when data are immature, and B-MPES provides an appealing method for this purpose.HighlightsLeveraging historical trial data with extended follow-up to extrapolate survival from early study data cuts in a Bayesian evidence synthesis framework can realize anticipated longer-term effects that are characteristic of a novel therapy or class thereof.Using moderately confounded external data sources can improve the reliability of survival extrapolations from B-MPES models provided that the prior information is adjusted and rescaled appropriately, but it is essential to rationalize the implicit assumptions surrounding longer-term treatment effects in the current study.B-MPES models are an attractive option to conduct informed lifetime survival extrapolations based on transparent clinical assumptions via leveraging multiple external data sources, but model flexibility and a priori confidence in external data must be specified carefully to avoid overfitting.