Changing colorectal cancer (CRC) incidence rates, including recent increases for people younger than 50 y, need to be considered in planning for future cancer control and screening initiatives. Reliable estimates of the impact of changing CRC trends on the National Bowel Cancer Screening Program (NBCSP) are essential for programmatic planning in Australia. An existing microsimulation model of CRC, , was updated to reproduce Australian CRC trends data and provide updated projections of CRC- and screening-related outcomes to inform clinical practice guidelines for the prevention of CRC.

Patient decision aids (PtDAs) are effective interventions to help people participate in health care decisions. Although there are quality standards, PtDAs are complex interventions with variability in their attributes.

The purpose of external validation of a risk prediction model is to evaluate its performance before recommending it for use in a new population. Sample size calculations for such validation studies are currently based on classical inferential statistics around metrics of discrimination, calibration, and net benefit (NB). For NB as a measure of clinical utility, the relevance of inferential statistics is doubtful. Value-of-information methodology enables quantifying the value of collecting validation data in terms of expected gain in clinical utility.

Hepatitis C virus (HCV) infection is associated with reduced quality of life and health utility. It is unclear whether this is primarily due to HCV infection itself or commonly co-occurring patient characteristics such as low income and mental health issues. This study aims to estimate and separate the effects of HCV infection on health utility from the effects of clinical and sociodemographic factors using real-world population-level data.

A decision model for patients with advanced melanoma to estimate outcomes of a wide range of treatment sequences is lacking.

We aimed to illustrate that health economists should consider individual heterogeneity when solving the problem of finding the optimal combination of sensitivity and specificity that maximizes the average health utility of a target population in a screening program.

Estimates of the future prevalence of childhood cancer are informative for health system planning but are underutilized. We describe the development of a pediatric oncology microsimulation model for prevalence (POSIM-Prev) and illustrate its application to produce projections of incidence, survival, and limited-duration prevalence of childhood cancer in Ontario, Canada, until 2040.

The nonparametric sampling method is generic and can sample times to an event from any discrete (or discretizable) hazard without requiring any parametric assumption.The method is showcased with 5 commonly used distributions in discrete-event simulation models.The method produced very similar expected times to events, as well as their probability distribution, compared with analytical results.We provide a multivariate categorical sampling function for R and Python programming languages to sample times to events from processes with different hazards simultaneously.

In microsimulation models of diseases with an early, acute phase requiring short cycle lengths followed by a chronic phase, fixed short cycles may lead to computational inefficiency. Examples include epidemic or resource constraint models with early short cycles where long-term economic consequences are of interest for individuals surviving the epidemic or ultimately obtaining the resource. In this article, we demonstrate methods to improve efficiency in such scenarios. Furthermore, we show that care must be taken when applying these methods to epidemic or resource constraint models to avoid bias.

We explored preferences around the benefit-risk ratio (BRR) of vaccination among the general adult population and health care sector workers (HCSWs). We estimated preference weights and expected vaccine uptake for different BRR levels for a vaccine recommended during an infectious disease emergence. In addition, we explored how far qualitative information about disease severity, epidemiological context, and indirect protection interacts with these preferences.

Patient decision aids (PtDAs) can support shared decision making. We aimed to explore how inclusive PtDAs are for people with limited health literacy (LHL) by analyzing 1) the understandability of PtDAs using established criteria, 2) how options and probabilities of outcomes are communicated, and 3) the extent to which risk communication (RC) guidelines are followed.

Important barriers to the use of QALYs in the United States include concerns about disability and age discrimination.Modifications to the utility function underlying QALYs have been proposed to mitigate these concerns, but some find them challenging to consider and/or to apply.Unrelated to these concerns, QALYs have been adapted within the framework of distributional cost-effectiveness analysis to allow consideration of inequality as well as efficiency.I outline how this framework can also remediate concerns about disability and age discrimination.

After a new drug or medical device is approved by the US Food and Drug Administration (FDA), physician-patient communication about benefits and risks is critical, including whether the product was approved through an expedited pathway based on limited evidence. In addition, physician reporting of drug- and device-related adverse events in real-world use is necessary to have a complete safety profile. We studied physician-reported communication and safety-reporting practices related to drugs and devices.

In the United States, human papillomavirus (HPV) vaccination among 27- to 45-y-olds (mid-adults) is recommended based on shared clinical decision making with a health care provider. We developed a patient decision aid tool to support the implementation of this mid-adult HPV vaccination guideline. The purpose of this study was to evaluate the effect of a patient decision aid tool for HPV vaccination, HPV DECIDE, compared with an information fact sheet among mid-adults who have not received the HPV vaccine.

Individual-level state-transition microsimulations (iSTMs) have proliferated for economic evaluations in place of cohort state transition models (cSTMs). Probabilistic economic evaluations quantify decision uncertainty and value of information (VOI). Previous studies show that iSTMs provide unbiased estimates of expected incremental net monetary benefits (EINMB), but statistical properties of iSTM-produced estimates of decision uncertainty and VOI remain uncharacterized.

Prior to COVID-19, little was known about how risks associated with such a pandemic would compete with and influence patient decision making regarding cancer risk reducing medical decision making. We investigated how the pandemic affected preferences for medical risk-reducing strategies among women at elevated risk of breast or ovarian cancer.

Simulation modeling is a promising tool to help policy makers and providers make evidence-based decisions when evaluating integrated care programs. The functionality of such models, however, depends on 2 prerequisites: 1) the analytical segmentation of populations to capture both health and health-related social service (HASS) needs and 2) the precise estimation of transition probabilities among the various states of need.

Multi-indication cancer drugs receive licensing extensions to include additional indications, as trial evidence on treatment effectiveness accumulates. We investigate how sharing information across indications can strengthen the inferences supporting health technology assessment (HTA).

Clinical uncertainty is associated with increased resource utilization, worsened health-related quality of life for patients, and provider burnout, particularly during critical illness. Existing data are limited, because determining uncertainty from notes typically requires manual, qualitative review. We sought to develop a consensus list of descriptors of clinical uncertainty and then, using a thematic analysis approach, describe how respondents consider their use in intensive care unit (ICU) notes, such that future work can extract uncertainty data at scale.

The Veterans Health Administration (VA) recommends lung cancer screening (LCS), including shared decision making between clinicians and veteran patients. We sought to characterize 1) veteran conceptualization of lung cancer risk and 2) veteran and clinician accounts of shared decision-making discussions about LCS to assess whether they reflect veteran concerns.

Our commentary proposes the application of directed acyclic graphs (DAGs) in the design of decision-analytic models, offering researchers a valuable and structured tool to enhance transparency and accuracy by bridging the gap between causal inference and model design in medical decision making.The practical examples in this article showcase the transformative effect DAGs can have on model structure, parameter selection, and the resulting conclusions on effectiveness and cost-effectiveness.This methodological article invites a broader conversation on decision-modeling choices grounded in causal assumptions.