Evidence map›Paper›PMID 35530306›Full record

ArticleFrontiers in oncology2022

Microsimulation Model for Prevention and Intervention of Coloretal Cancer in China (MIMIC-CRC): Development, Calibration, Validation, and Application.

Bin Lu, Le Wang, Ming Lu, Yuhan Zhang, Jie Cai, Chenyu Luo, Hongda Chen, Min Dai

Abstract read
In one paragraph

Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. A Scoping Review on Calibration Methods for Cancer Simulation Models.Medical decision making : an international journal of the Society for Medical Decision Making · 2025
    Article
  3. Calculating epidemiological outcomes from simulated longitudinal data.medRxiv : the preprint server for health sciences · 2025
    Article
  4. Article
  5. Article
  6. Article
  7. Calibration and Validation of the Colorectal Cancer and Adenoma Incidence and Mortality (CRC-AIM) Microsimulation Model Using Deep Neural Networks.Medical decision making : an international journal of the Society for Medical Decision Making · 2023
    Article
  8. Article
  9. Article
  10. 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

8 authors.

Bin LuOffice of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Le WangDepartment of Cancer Prevention, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Cancer and Basic Medicine, Chinese Academy of Sciences, Hangzhou, China.
Ming LuOffice of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Yuhan ZhangOffice of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Jie CaiDepartment of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Chenyu LuoOffice of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Hongda ChenMedical Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Min DaiMedical Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: A microsimulation model provides important references for decision-making regarding colorectal cancer (CRC) prevention strategies, yet such a well-validated model is scarce in China. Methods: We comprehensively introduce the development of MIcrosimulation Model for the prevention and Intervention of Colorectal Cancer in China (MIMIC-CRC). The MIMIC-CRC was first constructed to simulate the natural history of CRC based on the adenoma-carcinoma pathway. The parameters were calibrated and validated using data from population-based cancer registry data and CRC screening programs. Furthermore, to assess the model's external validity, we compared the model-derived results to outcome patterns of a sigmoidoscopy screening trial in the UK [UK Flexible Sigmoidoscopy Screening (UKFSS) trial]. Finally, we evaluated the application potential of the MIMIC-CRC model in CRC screening by comparing the 8 different strategies. Results: We found that most of the model-predicted colorectal lesion prevalence was within the 95% CIs of observed prevalence in a large population-based CRC screening program in China. In addition, model-predicted sex- and age-specific CRC incidence and mortality were equivalent to the registry-based data. The hazard ratios of model-estimated CRC-related incidence and mortality for sigmoidoscopy screening compared to no screening were 0.60 and 0.51, respectively, which were comparable to the reported results of the UKFSS trial. Moreover, we found that all 8 strategies could reduce CRC incidence and mortality compared to no screening. Conclusions: The well-calibrated and validated MIMIC-CRC model may represent a valid tool to assess the comparative effectiveness of CRC screening strategies and will be useful for further decision-making to CRC prevention.

Indexed as

colorectal cancerMarkov modelmicrosimulation modelnatural historyscreening

Identifiers

PMID35530306
PMCPMC9072786

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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.