ArticleBMC medical research methodology2023
A new cure model that corrects for increased risk of non-cancer death: analysis of reliability and robustness, and application to real-life data.
Article in BMC medical research methodology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 14 citations in OpenAlex.
- Fitting high-dimensional mixture cure models using the hdcuremodelsR package.Computer methods and programs in biomedicine · 2026Article
- Multiple imputation for missing values in ordinal variables from cancer registry data when performing Cox proportional hazards regression.BMC medical research methodology · 2026Article
- The impact of cancer survivors' extra risk of noncancer mortality on net survival estimation.American journal of epidemiology · 2025Article
- Review
- Mixture and Non-mixture Cure Models for Health Technology Assessment: What You Need to Know.PharmacoEconomics · 2024Article
- Indicators of cure for women living after uterine and ovarian cancers: a population-based study.American journal of epidemiology · 2024Article
- Evidence of being cured for nasopharyngeal carcinoma: results of a multicenter patient-based study in China.The Lancet regional health. Western Pacific · 2024Article
- Patients with cancer who will be cured and projections of complete prevalence in Italy from 2018 to 2030.ESMO open · 2024Article
- Article
- A new cure model that corrects for increased risk of non-cancer death: analysis of reliability and robustness, and application to real-life data.BMC medical research methodology · 2023Article
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Authors and funding
8 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNon-cancer mortality in cancer patients may be higher than overall mortality in the general population due to a combination of factors, such as long-term adverse effects of treatments, and genetic, environmental or lifestyle-related factors. If so, conventional indicators may underestimate net survival and cure fraction. Our aim was to propose and evaluate a mixture cure survival model that takes into account the increased risk of non-cancer death for cancer patients.
methodsWe assessed the performance of a corrected mixture cure survival model derived from a conventional mixture cure model to estimate the cure fraction, the survival of uncured patients, and the increased risk of non-cancer death in two settings of net survival estimation, grouped life-table data and individual patients' data. We measured the model's performance in terms of bias, standard deviation of the estimates and coverage rate, using an extensive simulation study. This study included reliability assessments through violation of some of the model's assumptions. We also applied the models to colon cancer data from the FRANCIM network.
resultsWhen the assumptions were satisfied, the corrected cure model provided unbiased estimates of parameters expressing the increased risk of non-cancer death, the cure fraction, and net survival in uncured patients. No major difference was found when the model was applied to individual or grouped data. The absolute bias was < 1% for all parameters, while coverage ranged from 89 to 97%. When some of the assumptions were violated, parameter estimates appeared more robust when obtained from grouped than from individual data. As expected, the uncorrected cure model performed poorly and underestimated net survival and cure fractions in the simulation study. When applied to colon cancer real-life data, cure fractions estimated using the proposed model were higher than those in the conventional model, e.g. 5% higher in males at age 60 (57% vs. 52%).
conclusionsThe present analysis supports the use of the corrected mixture cure model, with the inclusion of increased risk of non-cancer death for cancer patients to provide better estimates of indicators based on cancer survival. These are important to public health decision-making; they improve patients' awareness and facilitate their return to normal life.
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