Unbiased Analysis of Today's Healthcare Issues
That is the question asked by a 2022 NBER working paper by Pierre Dubois, Ashvin Gandhi and Shoshana Vasserman. Methodology: Demand Side Model The authors used IQVIA data on revenues and quantities of drugs sold to hospitals from 2002 to 2013 across US, Canada, France, Germany, Italy, Spain and UK markets. The authors define a…
FDA’s January 2026 draft guidance on Bayesian methodology in drug and biologics trials signals a clear willingness to see Bayesian methods used for regulatory decisionmaking around clinical trials. The guidance lays out how sponsors (i.e., pharmaceutical manufacturers) should pre-specify priors, success criteria, and simulations so that Bayesian designs remain interpretable, control error rates when needed,…
That is the title of a helpful article by Karnon and Haji Ali Afzali (Pharmacoeconomics 2014) with a subtitle A Review and Critique of the Costs and Benefits of DES. The paper starts by highlighting the 3 most common types of models: decision trees, Markovian cohort models, and individual level models. Decisions trees are useful…
There are four primary types of uncertainty in health economic modelling: Heterogeneity: Variation between individuals that can be explained by their characteristics Stochastic uncertainty: Variation between individuals that cannot be explained by their characteristics, Parameter uncertainty: Uncertainty in the estimated values for the parameters that define the model Structural uncertainty: Uncertainty in model outcomes that…
The free online book R for Health Technology Assessment has the answer: Markov models are characterised by a memoryless property called the Markov assumption or Markov property. Under the Markov assumption, movement of an individual from their current health state to a future one, conditional on both past and current health states, depends only on…
Catastrophic healthcare expenditures (CHE) are highly problematic for families are are unequally distributed throughout society. However, how can we quantify the incidence, intensity and inequality of CHE in a society? A paper by Ogwang and Mwabu (2025) provide one methodology by using the Watts poverty measure and adapting it to measure CHE. We first describe…
What is the impact of changing one treatment attribute on patient preferences for that treatment? This is a key question of interest for many patient preference studies. In practice, this is most often done by estimating the causal effect of changing one attribute of a profile while averaging over the distribution of the remaining profile…
Randomized controlled trials are the gold standard for evaluating treatment efficacy, but effectiveness in the real-world may vary. One reason for this is that clinical trials often have stricter inclusion criteria than is the case for the target treated population. Policymakers, payers, and clinicians may wonder how well the results from the narrower clinical trial…
TL;DR A paper by Ress and Wild (2024) provide the following recommendations in answering this question. When aiming to control for a large covariate set, consider using the superlearner to estimate nuisance parameters. When employing the superlearner to estimate nuisance parameters, consider using doubly robust estimation approaches, such as AIPW and TMLE. When faced with…
What are the key elements for reporting your objective, attributes, experimental design, survey design, data collection process, econometric analysis, and results? A paper by Ride et al. (2024) conducted a Delphi Panel expert elicitation survey to create the DIRECT checklist for discrete choice experiment (DCE) studies in health. The checklist is pasted below. If you…