Comparing the cost of cirrhosis to other common chronic diseases: A longitudinal study in a large national insurance database.

Background Aims: Cirrhosis prevalence is increasing, yet costs associated with its chronic, complex care are poorly understood. The aim was to characterize the costs of care for patients with cirrhosis and compare them to other chronic diseases such as heart failure (HF) and chronic obstructive pulmonary disease (COPD), for which the public health burden is better recognized.

Approach: Patients enrolled in Medicare Advantage plans from a large national insurer between 2011-2020 with cirrhosis, HF, and COPD were identified by ICD-9/-10 codes.

Using Limited Trial Evidence to Credibly Choose Treatment Dosage when Efficacy and Adverse Effects Weakly Increase with Dose.

It has become standard in medical treatment to base dosage on evidence in randomized trials. Yet it has been rare to study how outcomes vary with dosage. In trials to obtain drug approval, the norm has been to compare some dose of a new drug with an established therapy or placebo.

Toward Patient-Centered Drug Approval for Treatment of Rare Diseases.

Objectives: This commentary seeks to improve the design and analysis of trials undertaken to obtain approval of drugs for treatment of rare diseases.

Methods: Methodological analysis reveals that use of hypothesis testing in the Food and Drug Administration drug approval process is harmful. Conventional asymmetric error probabilities bias the approval process against approval of new drugs.

Navigating Uncertainty in Immunotherapy Regimens.

Uncertainty in Immunotherapy RegimensIn this Patient Platform, Charles Manski, Ph.D., shares his experience in navigating uncertainty from the perspective of a patient who has had to cope with a challenging clinical problem and an academic whose research aims to improve the evidence and methods used to inform patient care.

Using measures of race to make clinical predictions: Decision making, patient health, and fairness.

The use of race measures in clinical prediction models is contentious. We seek to inform the discourse by evaluating the inclusion of race in probabilistic predictions of illness that support clinical decision making. Adopting a static utilitarian framework to formalize social welfare, we show that patients of all races benefit when clinical decisions are jointly guided by patient race and other observable covariates.

Partial Identification of Personalized Treatment Response with Trial-reported Analyses of Binary Subgroups.

Medical journals have adhered to a reporting practice that seriously limits the usefulness of published trial findings. Medical decision makers commonly observe many patient covariates and seek to use this information to personalize treatment choices. Yet standard summaries of trial findings only partition subjects into broad subgroups, typically binary categories.

Tail and Center Rounding of Probabilistic Expectations in the Health and Retirement Study.

We study rounding of numerical expectations in the Health and Retirement Study (HRS) between 2002 and 2014. We document that respondent-specific rounding patterns across questions in individual waves are quite stable across waves. We discover a tendency by about half of the respondents to provide more refined responses in the tails of the 0-100 scale than the center.

Balancing data privacy and usability in the federal statistical system.

The federal statistical system is experiencing competing pressures for change. On the one hand, for confidentiality reasons, much socially valuable data currently held by federal agencies is either not made available to researchers at all or only made available under onerous conditions. On the other hand, agencies which release public databases face new challenges in protecting the privacy of the subjects in those databases, which leads them to consider releasing fewer data or masking the data in ways that will reduce their accuracy.

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.

Precise or Imprecise Probabilities? Evidence from Survey Response Related to Late-Onset Dementia.

We elicit numerical expectations for late-onset dementia and long-term-care (LTC) outcomes in the US Health and Retirement Study. We provide the first empirical evidence on dementia-risk perceptions among dementia-free older Americans and establish important patterns regarding imprecision of subjective probabilities. Our elicitation distinguishes between precise and imprecise probabilities, while accounting for rounding of reports.

Definition of a critical bleed in patients with immune thrombocytopenia: Communication from the ISTH SSC Subcommittee on Platelet Immunology.

Background: Immune thrombocytopenia (ITP) is an autoimmune disease characterized by low platelet counts and increased risk of bleeding. In preparation for an upcoming guideline, the ITP Emergency Management Guideline Panel, including clinical experts in hematology, emergency medicine, research methodology, and patient representatives, identified the need for a standardized definition of a critical ITP bleed. The goal of the definition was to distinguish critical bleeds from bleeds that may not require urgent treatment, typically in the context of severe thrombocytopenia.

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.

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.

Bounding the Accuracy of Diagnostic Tests, With Application to COVID-19 Antibody Tests.

Tests used to diagnose illness commonly have imperfect accuracy, with some false-positive and negative results. For risk assessment and clinical decisions, predictive values are of interest. Positive predictive value (PPV) is the chance that a member of a relevant population who tests positive has been ill.

Estimating the COVID-19 infection rate: Anatomy of an inference problem.

As a consequence of missing data on tests for infection and imperfect accuracy of tests, reported rates of cumulative population infection by the SARS CoV-2 virus are lower than actual rates of infection. Hence, reported rates of severe illness conditional on infection are higher than actual rates. Understanding the time path of the COVID-19 pandemic has been hampered by the absence of bounds on infection rates that are credible and informative.

Toward Credible Patient-centered Meta-analysis.

Meta-analysis is widely used to combine the findings of multiple disparate studies of health risks or treatment response. Meta-analysis often uses a random-effects model to express heterogeneity across studies. The model interprets a weighted average of study-specific estimates as an estimate of a mean parameter across a hypothetical population of studies.

Tuberculosis diagnosis and treatment under uncertainty.

In 2017, 1.6 million people worldwide died from tuberculosis (TB). A new TB diagnostic test-Xpert MTB/RIF from Cepheid-was endorsed by the World Health Organization in 2010.

Predicting kidney transplant outcomes with partial knowledge of HLA mismatch.

We consider prediction of graft survival when a kidney from a deceased donor is transplanted into a recipient, with a focus on the variation of survival with degree of human leukocyte antigen (HLA) mismatch. Previous studies have used data from the Scientific Registry of Transplant Recipients (SRTR) to predict survival conditional on partial characterization of HLA mismatch. Whereas earlier studies assumed proportional hazards models, we used nonparametric regression methods.

Communicating uncertainty in policy analysis.

The term "policy analysis" describes scientific evaluations of the impacts of past public policies and predictions of the outcomes of potential future policies. A prevalent practice has been to report policy analysis with incredible certitude. That is, exact predictions of policy outcomes are routine, while expressions of uncertainty are rare.