Cost-effectiveness of lung MRI in lung cancer screening.

OBJECTIVES: Recent studies with lung MRI (MRI) have shown high sensitivity (Sn) and specificity (Sp) for lung nodule detection and characterization relative to low-dose CT (LDCT). Using this background data, we sought to compare the potential screening performance of MRI vs. LDCT using a Markov model of lung cancer screening. METHODS: We created a Markov cohort model of lung cancer screening which incorporated lung cancer incidence, progression, and mortality based on gender, age, and smoking burden. Sensitivity (Sn) and Sp for LDCT were taken from the MISCAN Lung Microsimulation and Sn/Sp for MRI was estimated from a published substudy of the German Lung Cancer Screening and Intervention Trial. Screening, work-up, and treatment costs were estimated from published data. Screening with MRI and LDCT was simulated for a cohort of male and female smokers (2 packs per day; 36 pack/years of smoking history) starting at age 60. We calculated the screening performance and cost-effectiveness of MRI screening and performed a sensitivity analysis on MRI Sn/Sp and cost. RESULTS: There was no difference in life expectancy between MRI and LDCT screening (males 13.28 vs. 13.29 life-years; females 14.22 vs. 14.22 life-years). MRI had a favorable cost-effectiveness ratio of $258,169 in men and $403,888 in women driven by fewer false-positive screens. On sensitivity analysis, MRI remained cost effective at screening costs < $396 dollars and Sp > 81%. CONCLUSIONS: In this Markov model of lung cancer screening, MRI has a near-equivalent life expectancy benefit and has superior cost-effectiveness relative to LDCT. KEY POINTS: • In this Markov model of lung cancer screening, there is no difference in mortality between yearly screening with MRI and low-dose CT. • Compared to low-dose CT, screening with MRI led to a reduction in false-positive studies from 26 to 2.8% in men and 26 to 2.6% in women. • Due to similar life-expectancy and reduced false-positive rate, we found a favorable cost-effectiveness ratio of $258,169 in men and $403,888 in women of MRI relative to low-dose CT.

Evaluation of Accepting Kidneys of Varying Quality for Transplantation or Expedited Placement With Decision Trees.

BACKGROUND: Underutilization of marginal-quality kidneys for transplantation produced ideas of expediting kidney placement for populations with decreased opportunities of receiving transplants. Such policies can be less efficacious for specific individuals and should be scrutinized until the decision-making for accepting marginal-quality organs, which has relied on experiential judgment, is better understood at the individual level. There exist rigorous tools promoting personalized decisions with useful and objective information. METHODS: This article introduces a decision-tree methodology that analyzes a patient's dilemma: to accept a kidney offer now or reject it. The methodology calculates the survival benefit of accepting a kidney given a certain quality now and the survival benefit of rejecting it. Survival benefit calculation accounts for patients' and donors' characteristics and transplant centers' and organ procurement organizations' performances and incorporates patients' perceived transplant and dialysis utilities. Valuations of rejecting an offer are contingent on future opportunities and subject to uncertainty in the timing of successive kidney offers and their quality and donor characteristics. RESULTS: The decision tree was applied to a realistic patient profile as a demonstration. The tool was tested on 1000 deceased-donor kidney offers in 2016. Evaluating up to 1 year of future offers, the tool attains 61% accuracy, with transplant utility of 1.0 and dialysis utility of 0.5. The accuracy reveals potential bias in kidney offer acceptance/rejection at transplant centers. CONCLUSIONS: The decision-tree tool presented could aid personalized transplant decision-making in the future by providing patients with calculated, individualized survival benefits between accepting and rejecting a kidney offer.

Cost-effectiveness of Prophylactic Moisturization for Atopic Dermatitis.

Importance: Emerging evidence suggests that the use of moisturizers on newborns and infants (ie, from birth to 6 months of age) is potentially helpful in preventing the development of atopic dermatitis. Objective: To investigate the cost-effectiveness of using a daily moisturizer as prevention against atopic dermatitis among high-risk newborns. Design, Setting, and Participants: In a cost-effectiveness analysis, the average cost of total-body moisturization using 7 common moisturizers from birth to 6 months of age was determined for male and female infants. We assumed the same unit of weight per moisturizer used for a given body surface area. Based on previously reported data (relative risk reduction of 50%), the incremental gain in quality-adjusted life-years (QALYs) was determined using a 6-month time window. The cost-effectiveness of each moisturizer was determined by assuming equal efficacy. A sensitivity analysis was conducted by varying the relative risk from 0.28 to 0.90. Interventions: Use of prophylactic moisturizing compounds. Main Outcomes and Measures: The primary outcomes were the incremental cost-effectiveness values ($/QALY) for each moisturizer in preventing atopic dermatitis during a 6-month time window. Results: The calculated amount of daily all-body moisturizer needed at birth was 3.6 g (0.12 oz) per application, which increased to 6.6 g (0.22 oz) at 6 months of age. Of the 7 products evaluated, the average price was $1.07/oz (range, $0.13/oz-$2.96/oz). For a 6-month time window, the average incremental QALY benefit was 0.021. The sensitivity analysis showed that the incremental gain of QALY ranged from 0.0041 to 0.030. Petrolatum was the most cost-effective ($353/QALY [95% CI, $244-$1769/QALY) moisturizer in the cohort. Even assuming the lowest incremental QALYs for the most expensive moisturizer, the intervention was still less than $45 000/QALY. Conclusions and Relevance: Overall, atopic dermatitis represents a major health expenditure and has been associated with multiple comorbidities. Daily moisturization may represent a cost-effective, preventative strategy to reduce the burden of atopic dermatitis.

A Common Rationale for Global Sensitivity Measures and Their Estimation.

Measures of sensitivity and uncertainty have become an integral part of risk analysis. Many such measures have a conditional probabilistic structure, for which a straightforward Monte Carlo estimation procedure has a double-loop form. Recently, a more efficient single-loop procedure has been introduced, and consistency of this procedure has been demonstrated separately for particular measures, such as those based on variance, density, and information value. In this work, we give a unified proof of single-loop consistency that applies to any measure satisfying a common rationale. This proof is not only more general but invokes less restrictive assumptions than heretofore in the literature, allowing for the presence of correlations among model inputs and of categorical variables. We examine numerical convergence of such an estimator under a variety of sensitivity measures. We also examine its application to a published medical case study.

Simulation modeling of the impact of proposed new simultaneous liver and kidney transplantation policies.

BACKGROUND: Increasing use of kidney grafts for simultaneous liver and kidney (SLK) transplants is causing concern about the most effective utilization of scarce kidney graft resources. This study evaluated the impact of implementing the proposed United Network for Organ Sharing SLK transplant policy on outcomes for end-stage liver disease (ESLD) and end-stage renal disease (ESRD) patients awaiting transplant. METHODS: A Markov model was constructed to simulate a hypothetical cohort of ESLD patients over a 30-year time horizon starting from age 50. The model applies the different criteria being considered in the United Network for Organ Sharing policy and tallies outcomes, including numbers of procedures and life years after liver transplant alone (LTA) or SLK transplant. RESULTS: When 1-week pretransplant dialysis duration is required, the numbers of SLK transplants and LTAs would be 648 and 9,065, respectively. If the pretransplant dialysis duration is extended to 12 weeks, there would be 240 SLK transplants and 9,426 LTAs. This change results in a decrease of 6,483 life years among SLK transplant recipients and an increase of 4,971 life years among LTA recipients. However, by increasing the dialysis duration to 12 weeks from 1 week, 408 kidney grafts would be released to the kidney waitlist because of the decline in SLK transplants; this yields 796 additional life years gained among ESRD patients. CONCLUSION: Implementation of the proposed SLK transplant policy could restore access to kidney transplants for patients with ESRD albeit at the detriment of patients with ESLD and renal impairment.

Comparative effectiveness of liver transplant strategies for end-stage liver disease patients on renal replacement therapy.

There are complex risk-benefit tradeoffs with different transplantation strategies for end-stage liver disease patients on renal support. Using a Markov discrete-time state transition model, we compared survival for this group with 3 strategies: simultaneous liver-kidney (SLK) transplantation, liver transplantation alone (LTA) followed by immediate kidney transplantation if renal function did not recover, and LTA followed by placement on the kidney transplant wait list. Patients were followed for 30 years from the age of 50 years. The probabilities of events were synthesized from population data and clinical trials according to Model for End-Stage Liver Disease (MELD) scores (21-30 and >30) to estimate input parameters. Sensitivity analyses tested the impact of uncertainty on survival. Overall, the highest survival rates were seen with SLK transplantation for both MELD score groups (82.8% for MELD scores of 21-30 and 82.5% for MELD scores > 30 at 1 year), albeit at the cost of using kidneys that might not be needed. Liver transplantation followed by kidney transplantation led to higher survival rates (77.3% and 76.4%, respectively, at 1 year) than placement on the kidney transplant wait list (75.1% and 74.3%, respectively, at 1 year). When uncertainty was considered, the results indicated that the waiting time and renal recovery affected conclusions about survival after SLK transplantation and liver transplantation, respectively. The subgroups with the longest durations of pretransplant renal replacement therapy and highest MELD scores had the largest absolute increases in survival with SLK transplantation versus sequential transplantation. In conclusion, the findings demonstrate the inherent tension in choices about the use of available kidneys and suggest that performing liver transplantation and using renal transplantation only for those who fail to recover their native renal function could free up available donor kidneys. These results could inform discussions about transplantation policy.

Comparative effectiveness of donation after cardiac death versus donation after brain death liver transplantation: Recognizing who can benefit.

Due to organ scarcity and wait-list mortality, transplantation of donation after cardiac death (DCD) livers has increased. However, the group of patients benefiting from DCD liver transplantation is unknown. We studied the comparative effectiveness of DCD versus donation after brain death (DBD) liver transplantation. A Markov model was constructed to compare undergoing DCD transplantation with remaining on the wait-list until death or DBD liver transplantation. Differences in life years, quality-adjusted life years (QALYs), and costs according to candidate Model for End-Stage Liver Disease (MELD) score were considered. A separate model for hepatocellular carcinoma (HCC) patients with and without MELD exception points was constructed. For patients with a MELD score <15, DCD transplantation resulted in greater costs and reduced effectiveness. Patients with a MELD score of 15 to 20 experienced an improvement in effectiveness (0.07 QALYs) with DCD liver transplantation, but the incremental cost-effectiveness ratio (ICER) was >$2,000,000/QALY. Patients with MELD scores of 21 to 30 (0.25 QALYs) and >30 (0.83 QALYs) also benefited from DCD transplantation with ICERs of $478,222/QALY and $120,144/QALY, respectively. Sensitivity analyses demonstrated stable results for MELD scores <15 and >20, but the preferred strategy for the MELD 15 to 20 category was uncertain. DCD transplantation was associated with increased costs and reduced survival for HCC patients with exception points but led to improved survival (0.26 QALYs) at a cost of $392,067/QALY for patients without exception points. In conclusion, DCD liver transplantation results in inferior survival for patients with a MELD score <15 and HCC patients receiving MELD exception points, but provides a survival benefit to patients with a MELD score >20 and to HCC patients without MELD exception points.

The value of environmental information without control of subsequent decisions.

The standard value of information approach of decision analysis assumes that the individual or agency that collects the information is also in control of the subsequent decisions based on the information. We refer to this situation as the "value of information with control (VOI-C)." This paradigm leads to powerful results, for example, that the value of information cannot be negative and that it is zero, when the information cannot change subsequent decisions. In many real world situations, however, the agency collecting the information is different from the one that makes the decision on the basis of that information. For example, an environmental research group may contemplate to fund a study that can affect an environmental policy decision that is made by a regulatory organization. In this two-agency formulation, the information-acquiring agency has to decide, whether an investment in research is worthwhile, while not being in control of the subsequent decision. We refer to this situation as "value of information without control (VOI-NC)." In this article, we present a framework for the VOI-NC and illustrate it with an example of a specific problem of determining the value of a research program on the health effects of power-frequency electromagnetic fields. We first compare the VOI-C approach with the VOI-NC approach. We show that the VOI-NC can be negative, but that with high-quality research (low probabilities of errors of type I and II) it is positive. We also demonstrate, both in the example and in more general mathematical terms, that the VOI-NC for environmental studies breaks down into a sum of the VOI-NC due to the possible reduction of environmental impacts and the VOI-NC due to the reduction of policy costs, with each component being positive for low environmental impacts and high-quality research. Interesting results include that the environmental and cost components of the VOI-NC move in opposite directions as a function of the probability of environmental impacts and that VOI-NC can be positive, even though the probability of environmental impacts is zero or one.

NUTORC-a transdisciplinary health services and outcomes research team in transplantation.

The field of solid organ transplantation has historically concentrated research efforts on basic science and translational studies. However, there has been increasing interest in health services and outcomes research. The aim was to build an effective and sustainable, inter- and transdisciplinary health services and outcomes research team (NUTORC), that leveraged institutional strengths in social science, engineering, and management disciplines, coupled with an international recognized transplant program. In 2008, leading methodological experts across the university were identified and intramural funding was obtained for the NUTORC initiative. Inter- and transdisciplinary collaborative teams were created across departments and schools within the university. Within 3 years, NUTORC became fiscally sustainable, yielding more than tenfold return of the initial investment. Academic productivity included funding for 39 grants, publication of 60 manuscripts, and 166 national presentations. Sustainable educational opportunities for students were created. Inter- and transdisciplinary health services and outcomes research in transplant can be innovative and sustainable.

Cohort decomposition for Markov cost-effectiveness models.

Cohort analysis is a widespread tool for computing expected costs and quality-adjusted life years (QALYs) in Markov models for medical cost-effectiveness analyses. Although not always explicitly identified, such models commonly have multiple simple factors, or components. In these, a health state consists of a multiple component vector, one component for each factor, and arbitrary combinations of components are possible. The authors show here that when the model does not assume any probabilistic dependence among these factors, then a standard cohort analysis may be decomposed into several independent cohort analyses, one for each factor, and the results may be combined to produce desired expected costs and QALYs. These single-factor cohort analyses are not only simpler but also computationally more efficient. The authors derive the appropriate formulas for this cohort decomposition in discrete time and give several examples of their use based on published cost-effectiveness analyses. Explicitly identifying the simple factors of which a model is composed allows these factors to be portrayed graphically. Graphical depiction of the simple factors that comprise a model reduces model complexity, makes model formulation easier and more transparent, and thereby facilitates peer inspection and critique.

Development of goal-sensitive health-related utility assessment procedures.

PURPOSE: To measure the degree to which people express willingness to trade life or health for nonmedical goals. METHOD: In 3 studies, outpatients provided important life goals. In study 1, patients performed time-tradeoff between life-years and goal achievement and chose between states that varied in goal achievement, life expectancy, and disability; in study 2, patients made choices that traded off health state and goal achievement; in study 3, patients performed time-tradeoff assessments in 3 different goal achievement contexts. RESULTS: In study 1 (n = 58), participants were eager to trade life-years for goal achievement, trading, on average, 71% of their remaining life for certain achievement v. certain nonachievement or 54% of their remaining life for their expected likelihood of achievement v. nonachievement. Life expectancy, disability status, and goal achievement each had a significant main effect on utility. In study 2 (n = 54), participants equally preferred a moderately impaired health state with goal achievement to perfect health without goal achievement and more strongly preferred the moderately impaired state with goal achievement than other less impaired states without goal achievement. Study 3 (n = 62) demonstrated that the mere discussion of goals and goal achievement or nonachievement in the context of a standard time-tradeoff assessment (without trading off goals) did not impact the assessment. CONCLUSIONS: Nonmedical life goals are important determinants of quality of life. People express willingness to trade off life and health in pursuit of these goals, which are extrinsic to the standard quality-adjusted life-year model.

Incorporating extrinsic goals into decision and cost-effectiveness analyses.

It has not been widely recognized that medical patients as individuals may have goals that are not easily expressed in terms of quality-adjusted life years (QALYs). The QALY model deals with ongoing goals such as reducing pain or maintaining mobility, but goals such as completing an important project or seeing a child graduate from college occur at unique points in time and do not lend themselves to easy expression in terms of QALYs. Such extrinsic goals have been posited as explanations for preferences inconsistent with the QALY model, such as unwillingness to trade away time or accept gambles. In this article, the authors examine methods for including extrinsic goals in medical decision and cost-effectiveness analyses. As illustrations, they revisit 2 previously published analyses, the management of unruptured intracranial arteriovenous malformations (AVMs) and the evaluation of preventive strategies for BRCA + women.

Life goals and health decisions: what will people live (or die) for?

OBJECTIVE: Quality of life may represent not just quality of health but also the degree to which an individual achieves personally meaningful extrinsic goals unrelated to life duration that are not incorporated in the standard quality-adjusted life year model. The objectives of this study are to develop a typology of life goals and explore whether goal type is related to willingness to consider trading life years or health for goals. DESIGN: . Surveys of 50 Chicago-area residents and 101 inpatients. Outcomes. Participants provided up to 5 goals. For each, they reported 1) how long the goal might take to achieve, 2) whether they would prefer a shorter lifetime with certain goal achievement to their full lifetime without goal achievement, and 3) whether they would prefer lower quality of health with certain goal achievement to their full health without goal achievement. RESULTS: Participant goals were classified by 2 investigators into 7 broad categories: family, wealth, job, education, health/fitness, travel, and personal fulfillment. Respondents in both samples were more likely to be willing to trade life years (community odds ratio [OR] = 7.39, P=0.0004; patient OR=1.82, P=0.008) or health (community OR= 5.11, P = 0.0042; patient OR = 1.83, P = 0.0498) to achieve family goals than other types of goals. CONCLUSIONS: The authors derive a manageable typology of goals that may affect medical decisions and demonstrate interrater reliability. Because willingness to trade life years varies by type of goal, typical time-tradeoff assessments may be systematically influenced by respondents' goals.

Large-sample Bayesian posterior distributions for probabilistic sensitivity analysis.

In probabilistic sensitivity analyses, analysts assign probability distributions to uncertain model parameters and use Monte Carlo simulation to estimate the sensitivity of model results to parameter uncertainty. The authors present Bayesian methods for constructing large-sample approximate posterior distributions for probabilities, rates, and relative effect parameters, for both controlled and uncontrolled studies, and discuss how to use these posterior distributions in a probabilistic sensitivity analysis. These results draw on and extend procedures from the literature on large-sample Bayesian posterior distributions and Bayesian random effects meta-analysis. They improve on standard approaches to probabilistic sensitivity analysis by allowing a proper accounting for heterogeneity across studies as well as dependence between control and treatment parameters, while still being simple enough to be carried out on a spreadsheet. The authors apply these methods to conduct a probabilistic sensitivity analysis for a recently published analysis of zidovudine prophylaxis following rapid HIV testing in labor to prevent vertical HIV transmission in pregnant women.

Stochastic trees and the StoTree modeling environment: models and software for medical decision analysis.

In this paper we present a review of stochastic trees, a convenient modeling approach for medical treatment decision analyses. Stochastic trees are a generalization of decision trees that incorporate useful features from continuous-time Markov chains. We also discuss StoTree, a freely available software tool for the formulation and solution of stochastic trees, implemented in the Excel spreadsheet environment.