Evidence map›Paper›PMID 40715941›Full record

ReviewPharmacoEconomics2026

Microsimulation Modeling for Health Decision Sciences Using C++: A Tutorial.

Aku-Ville Lehtimäki, Janne Martikainen

Abstract readReview
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Aku-Ville LehtimäkiSchool of Pharmacy, Faculty of Health Sciences, Kuopio Campus, University of Eastern Finland, 70211, Kuopio, Finland. aku-ville.lehtimaki@uef.fi.ORCID http://orcid.org/0000-0003-0995-3935
Janne MartikainenSchool of Pharmacy, Faculty of Health Sciences, Kuopio Campus, University of Eastern Finland, 70211, Kuopio, Finland.ORCID http://orcid.org/0000-0003-0855-3921

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

Computer SimulationDecision Support TechniquesProgramming LanguagesSoftwareHumansMarkov Chains

Identifiers

PMID40715941
PMCPMC13013250

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.