Reasonable patient care under uncertainty

Integrating decision modeling and machine learning to inform treatment stratification

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.

The cost-effectiveness of metformin in the US diabetes prevention program trial: Simple interpretations need not apply

Based on previously published US Diabetes Prevention Program (DPP) cost-effectiveness analyses (CEAs) metformin continues to be promoted as "cost-effective." We review the DPP within-trial CEA to assess this claim. Treatment alternatives included placebo (plus standard lifestyle advice), branded metformin and individual lifestyle modification. We added generic metformin as an alternative. Original published CEA data were taken as given and re-analyzed according to accepted principles for calculating incremental cost-effectiveness ratios (ICERs) in the economic evaluation field. With more than two treatments as in the DPP, these require attention to the rankings of interventions according to cost or effect prior to stipulating appropriate ICERs to calculate. With proper ICERs neither branded nor generic metformin was cost-effective, regardless of the value assumed for the willingness to pay for the quality-adjusted life year outcome assessed. Metformin alternatives were technically inefficient compared to placebo or the lifestyle modification alternative. Net loss calculations indicated substantial costs/health losses to using metformin instead of the optimal lifestyle alternative in response to metformin having been inaccurately labelled "cost-effective" in the original CEA. That CEA and subsequent analyses and citations of such analyses continue to claim that both metformin and lifestyle modification are cost-effective in diabetes prevention based on DPP data. Using metformin implies substantial costs and health losses compared to the cost-effective lifestyle modification. It may be that metformin has a role in cost-effective diabetes prevention, but this has yet to be shown based on DPP data.

Patient and peer: Guideline design and expert response

We examine how patients' medical expertise influences adherence to clinical guidelines for a treatment that is common, costly, and rationed by the clinical guidelines. Using administrative data on prenatal diagnostic testing (PDT), we compare the testing rates of medically trained patients (experts) and non-medically trained patients (non-experts) on the margin of eligibility thresholds in clinical guidelines. We find that experts are 9 percentage points more likely to receive PDT than non-experts when they are not eligible for testing and that more than 80% of the difference can be attributed to medical expertise. Our results suggest that the design of clinical guidelines is important for adherence and that having medical expertise as a patient affects treatment, when there is room for a deviation from the guideline.

Identification of Vaccine Effects When Exposure Status Is Unknown

Results from randomized controlled trials (RCTs) help determine vaccination strategies and related public health policies. However, defining and identifying estimands that can guide policies in infectious disease settings is difficult, even in an RCT. The effects of vaccination critically depend on characteristics of the population of interest, such as the prevalence of infection, the number of vaccinated, and social behaviors. To mitigate the dependence on such characteristics, estimands, and study designs, that require conditioning or intervening on exposure to the infectious agent have been advocated. But a fundamental problem for both RCTs and observational studies is that exposure status is often unavailable or difficult to measure, which has made it impossible to apply existing methodology to study vaccine effects that account for exposure status. In this study, we present new results on this type of vaccine effects. Under plausible conditions, we show that point identification of certain relative effects is possible even when the exposure status is unknown. Furthermore, we derive sharp bounds on the corresponding absolute effects. We apply these results to estimate the effects of the ChAdOx1 nCoV-19 vaccine on SARS-CoV-2 disease (COVID-19) conditional on postvaccine exposure to the virus, using data from a large RCT.

Patient-centered appraisal of race-free clinical risk assessment

Until recently, there has been a consensus that clinicians seeking to assess patient risks of illness should condition risk assessments on all observed patient covariates with predictive power. The broad idea is that knowing more about patients enables more accurate predictions of their health risks and, hence, better clinical decisions. This consensus has recently unraveled with respect to a specific covariate, namely race. There have been increasing calls for race-free risk assessment, arguing that using race to predict health risks contributes to racial disparities and inequities in health care. In some medical fields, leading institutions have recommended race-free risk assessment. An important open question is how race-free risk assessment would affect the quality of clinical decisions. Considering the matter from the patient-centered perspective of medical economics yields a disturbing conclusion: Race-free risk assessment would harm patients of all races.

Small differences in EQ-5D-5L health utility scores were interpreted differently between and within respondents

OBJECTIVE

This study aims to determine how population-based health-utility score (HUS) differences reflect individuals' health preferences using responses from the Canadian EQ-5D-5L Valuation Study, including time trade-off (TTO) and discrete-choice experiment (DCE) tasks (n=1073).

STUDY DESIGN AND SETTING

Cardinal TTO responses were transformed into pairwise comparisons to yield ordinal TTO responses. We investigated how EQ-5D-5L HUS differences differ from participants' stated cardinal preferences, and determined the smallest HUS difference that may be expected to represent participants' ordinal preferences.

RESULTS

HUS differences near 0 have 30.6% (95% confidence interval: 29.1 to 31.9%) probability of representing a tie in individuals' TTO values. Differences in EQ-5D-5L HUS of -0.054 (-0.071 to -0.029) and 0.047 (0.026 to 0.076) maximized the sensitivity and specificity of discriminating transitions to worse/better health states. For small HUS differences of +/-0.03 to +/-0.07, the magnitude of respondents' average TTO difference on the cardinal scale was 0.17 and 0.35 whether ties were included or excluded, respectively. Absolute HUS differences between 0.043 and 0.064 had a 50% probability of representing respondents' ordinal preferences.

CONCLUSION

A HUS needs to be large enough to reflect individuals' stated health preferences, which may lend support to the application of a minimally important difference for decision-making.

Embracing Uncertainty: The Value of Partial Identification in Public Health and Clinical Research

INTRODUCTION

This paper describes the methodology of partial identification and its applicability to empirical research in preventive medicine and public health.

METHODS

The authors summarize findings from the methodologic literature on partial identification. The analysis was conducted in 2020-2021.

RESULTS

The applicability of partial identification methods is demonstrated using 3 empirical examples drawn from published literature.

CONCLUSIONS

Partial identification methods are likely to be of considerable interest to clinicians and others engaged in preventive medicine and public health research.

Achievement in active agent structures as a power tools in tumor angiogenesis imaging

According to World Health Organization (WHO) cancer is the second most important cause of death globally. Because angiogenesis is considered as an essential process of growth, proliferation and tumor progression, within this review we decided to shade light on recent development of chemical compounds which play a significant role in its imaging and monitoring. Indeed, the review gives insight about the current achievements of active agents structures involved in imaging techniques such as: positron emission computed tomography (PET), magnetic resonance imaging (MRI) and single photon emission computed tomography (SPECT), as well as combination PET/MRI and PET/CT. The review aims to provide the journal audience with a comprehensive and in-deep understanding of chemistry policy in tumor angiogenesis imaging.

Statistical Decision Properties of Imprecise Trials Assessing Coronavirus Disease 2019 (COVID-19) Drugs

OBJECTIVES

Researchers studying treatment of coronavirus disease 2019 (COVID-19) have reported findings of randomized trials comparing standard care with care augmented by experimental drugs. Many trials have small sample sizes, so estimates of treatment effects are imprecise. Hence, clinicians may find it difficult to decide when to treat patients with experimental drugs. A conventional practice when comparing standard care and an innovation is to choose the innovation only if the estimated treatment effect is positive and statistically significant. This practice defers to standard care as the status quo. We study treatment choice from the perspective of statistical decision theory, which considers treatment options symmetrically when assessing trial findings.

METHODS

We use the concept of near-optimality to evaluate criteria for treatment choice. This concept jointly considers the probability and magnitude of decision errors. An appealing criterion from this perspective is the empirical success rule, which chooses the treatment with the highest observed average patient outcome in the trial.

RESULTS

Considering the design of some COVID-19 trials, we show that the empirical success rule yields treatment choices that are much closer to optimal than those generated by prevailing decision criteria based on hypothesis tests.

CONCLUSION

Using trial findings to make near-optimal treatment choices rather than perform hypothesis tests should improve clinical decision making.

Discovering treatment effectiveness via median treatment effects-Applications to COVID-19 clinical trials

Comparing median outcomes to gauge treatment effectiveness is widespread practice in clinical and other investigations. While common, such difference-in-median characterizations of effectiveness are but one way to summarize how outcome distributions compare. This paper explores properties of median treatment effects (TEs) as indicators of treatment effectiveness. The paper's main focus is on decisionmaking based on median TEs and it proceeds by considering two paths a decisionmaker might follow. Along one, decisions are based on point-identified differences in medians alongside partially identified median differences; along the other decisions are based on point-identified differences in medians in conjunction with other point-identified parameters. On both paths familiar difference-in-median measures play some role yet in both the traditional standards are augmented with information that will often be relevant in assessing treatments' effectiveness. Implementing either approach is straightforward. In addition to its analytical results the paper considers several policy contexts in which such considerations arise. While the paper is framed by recently reported findings on treatments for COVID-19 and uses several such studies to explore empirically some properties of median-treatment-effect measures of effectiveness, its results should be broadly applicable.

Predicting polytomous career choices in healthcare using probabilistic expectations data

This paper compares career expectations and career outcomes of Swiss healthcare assistants (HCA), an occupation created to increase the supply of nurses. We investigate whether HCAs can predict their own professional careers two years ahead by eliciting their expectations for a range of career alternatives, including nursing and other studies. Polytomous choice situations have rarely been analyzed using numerical probabilities in the expectations literature. Our results show that almost all respondents give informative answers to the probabilistic online survey question. Individuals express considerable uncertainty about their future careers, with over 60% attaching positive probabilities to more than one career alternative. The analyses reveal that individuals' numerical expectations have substantial predictive value for their future careers, even after controlling for many variables. This finding confirms that individuals have private information not directly available to researchers, and that eliciting choice probabilities for polytomous choice situations is a viable approach in surveys. However, the mean shares for career alternatives implied by individual probabilities do not fully coincide with actual shares and are more accurate over 4 than over 2 years. The information conveyed in expectations and their deviations from outcomes enables us to derive policy recommendations to increase transitions to nursing.

Practicing Integrated Care Pathways in Norwegian Hospitals: Coordination through Industrialized Standardization, Value Chains, and Quality Management or an Organizational Equivalent to Improvised Jazz Standards

The goal of coordinating pathways for cancer patients through their diagnostic and treatment journey is often approached by borrowing strategies from traditional industries, including standardization, process redesign, and variation reduction. However, the usefulness of these strategies is sometimes limited in the face of the complexity and uncertainty that characterize these processes over time and the situation at both patient and institutional levels. We found this to be the case when we did an in-depth qualitative study of coordination processes in patient pathways for three diagnoses in four Norwegian hospitals. What allows these hospitals to accomplish coordination is supplementing standardization with improvisation. This improvisation is embedded in four types of emerging semi-formal structures: collegial communities, networks, boundary spanners, and physical proximity. The hierarchical higher administrative levels appear to have a limited ability to manage and support coordination of these emerging structures when needed. We claim that this can be explained by viewing line management as representative of an economic-administrative institutional logic while these emerging structures represent a medical-professional logic that privileges proximity to the variation and complexity in the situations. The challenge is then to find a way for emergent and formal structures to coexist.

Sensitization in transplantation: Assessment of risk (STAR) 2019 Working Group Meeting Report

The purpose of the STAR 2019 Working Group was to build on findings from the initial STAR report to further clarify the expectations, limitations, perceptions, and utility of alloimmune assays that are currently in use or in development for risk assessment in the setting of organ transplantation. The goal was to determine the precision and clinical feasibility/utility of such assays in evaluating both memory and primary alloimmune risks. The process included a critical review of biologically driven, state-of-the-art, clinical diagnostics literature by experts in the field and an open public forum in a face-to-face meeting to promote broader engagement of the American Society of Transplantation and American Society of Histocompatibility and Immunogenetics membership. This report summarizes the literature review and the workshop discussions. Specifically, it highlights (1) available assays to evaluate the attributes of HLA antibodies and their utility both as clinical diagnostics and as research tools to evaluate the effector mechanisms driving rejection; (2) potential assays to assess the presence of alloimmune T and B cell memory; and (3) progress in the development of HLA molecular mismatch computational scores as a potential prognostic biomarker for primary alloimmunity and its application in research trial design.

When ignorance is bliss: Intentional agnosticism in drug approval

In developed nations, public health agencies typically use data from randomized controlled trials to evaluate new drugs. However, these trials routinely exclude populations to which clinicians prescribe approved drugs, meaning some patients are treated with drugs, which were approved on the basis of another group's treatment response. Despite having opportunities to change, some health agencies have not mandated greater inclusion in drug trials and appear to prefer remaining ignorant of some populations' treatment effects when approving a drug. To explore this decision, I introduce a novel mechanism by which a health agency would choose to be intentionally agnostic regarding a population's treatment response. The main contribution of this paper is in showing how ambiguity about on-label and off-label prescription rates could possibly encourage population exclusion in drug approval trials even in the absence of concerns about trial necessity or cost.

Beyond the cost-effectiveness acceptability curve: The appropriateness of rank probabilities for presenting the results of economic evaluation in multiple technology appraisal

The cost-effectiveness acceptability curve (CEAC) shows the probability that an option ranks first for net benefit. Where more than two options are under consideration, the CEAC offers only a partial picture of the decision uncertainty. This paper discusses the appropriateness of showing the full set of rank probabilities for reporting the results of economic evaluation in multiple technology appraisal (MTA). A case study is used to illustrate the calculation of rank probabilities and associated metrics, based on Monte Carlo simulations from a decision model. Rank probabilities are often used to show uncertainty in the results of network meta-analysis, but until now have not been used for economic evaluation. They may be useful decision-making tools to complement the CEAC in specific MTA contexts.