ReviewPharmacoEconomics2026
Microsimulation Modeling for Health Decision Sciences Using C++: A Tutorial.
Review in PharmacoEconomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Cardiovascular-Kidney-Metabolic Syndrome: Conceptualising an Approach to Health Economic Modelling.Diabetes, obesity & metabolism · 2026Review
- Lowering the Barriers to the Appropriate Use of Individual-Level Simulation for Health Economic Evaluation.PharmacoEconomics · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Microsimulation models have become increasingly common in the field of decision modeling for health. Because microsimulation models are computationally more demanding than traditional Markov cohort models, the use of computer programming languages in their development has become more common. C++ is a programming language that has gained widespread recognition in computationally intensive fields, including systems modeling and performance-critical applications. It offers powerful tools for building high-performance microsimulation models, outpacing many traditional modeling software solutions, such as native R, in terms of speed and control over memory management. However, there is limited accessible guidance for implementing microsimulation models in C++. This tutorial offers a step-by-step approach to constructing microsimulation models in C++ and demonstrates its application through simplified but adaptable example decision models. We walk the reader through essential steps and provide generic C++ code that is flexible and suitable for adapting to a range of models. Finally, we present the standalone C++ models and their Rcpp counterparts run within R, and compare their performance to equivalent R implementations in terms of speed and memory efficiency.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.