Microsimulation Model Predictions for Multicancer Screening Harms

Comprising R objects and code from a stochastic microsimulation model that estimates the impacts of multicancer early detection tests on cancer incidence and outcomes. It was created by Talía Malagon to generate figures and tables for a submitted paper on screening harms from overdiagnosis and false positives.

Use Cases

• Reproduce the figures and tables from the paper 'Assessing potential screening harms from overdiagnosis and false positives with multicancer early detection tests' using the provided R code and model objects.
• Analyze the microsimulation model's predictions for cancer incidence and outcomes under different screening scenarios.
• Validate the stochastic microsimulation model's structure and assumptions by examining the underlying R code.

Strengths

• Data and code are directly linked to a specific, submitted academic paper, ensuring research transparency.
• Includes the full R code required to regenerate all results and visualizations from the study.
• Focuses on a specific, policy-relevant research question regarding harms from multicancer early detection tests.

Limitations

• The dataset consists of model outputs and code, not raw observational or clinical data, limiting direct analysis of real-world patient information.
• The underlying microsimulation model's predictions are contingent on its specific assumptions and parameter inputs, which may not be fully detailed.
• The dataset's utility is primarily for reproducing the specific study's results rather than for general-purpose machine learning training.

Provenance

Source

Borealis Harvested Dataverse

Collection Method

Generated by a stochastic microsimulation model developed for the research.

Freshness

Last updated on 2026-01-31.