Accommodating the Analysis Model in Multiple Imputation for the Weibull Mixture Cure Model: Performance Under Penalized Likelihood
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Wiley Open Access
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| Natura: | Artículo Open Access |
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Wiley
2026
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| author | Changchang Xu Laurent Briollais Irene L. Andrulis Shelley B. Bull |
| author_facet | Changchang Xu Laurent Briollais Irene L. Andrulis Shelley B. Bull Changchang Xu Laurent Briollais Irene L. Andrulis Shelley B. Bull |
| collection | Wiley Open Access |
| contents | Accommodating the Analysis Model in Multiple Imputation for the Weibull Mixture Cure Model: Performance Under Penalized Likelihood Changchang Xu Laurent Briollais Irene L. Andrulis Shelley B. Bull Statistics in Medicine ABSTRACT Introduction In analysis of time‐to‐event outcomes, a mixture cure (MC) model is preferred over a standard survival model when the sample includes individuals who will never experience the event of interest. Motivated by a cohort study of breast cancer patients with incomplete biomarkers, we develop multiple imputation (MI) methods assuming a Weibull proportional hazards (PH‐MC) analysis model with multiple prognostic factors. However, for MI with fully conditional specification, an incorrectly specified imputation model can impair accuracy of point and interval estimates. Objectives and Methods Our goal is to propose imputation models that are compatible with the Weibull PH‐MC analysis models. We derive an exact conditional distribution (ECD) imputation model which involves the analysis model likelihood. Using simulation studies, we compare effect estimate bias and confidence interval (CI) coverage under alternative imputation models including the ECD model, an approximation that includes a cure indicator (cECD), and a comprehensive simple (CS) model. For robust parameter estimation in finite and/or sparse samples, we incorporate the Firth‐type penalized likelihood (FT‐PL) and combined likelihood profile (CLIP) methods into the MI. Results Compared to complete case analysis, MI with penalization reduces estimation bias and improves coverage. Although ECD and cECD perform similarly at higher event rates, ECD generates smaller bias and higher coverage at lower rates. CS has larger bias and lower coverage than ECD and cECD, but CIs are narrower than for cECD. Conclusions In analyses of biomarkers and composite subtypes for prognosis studies such as in breast cancer, the use of compatible imputation models and penalization methods is recommended for MC modeling in samples with low event numbers and/or with covariate imbalance. 10.1002/sim.70437 http://creativecommons.org/licenses/by-nc/4.0/ |
| doi_str_mv | 10.1002/sim.70437 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_sim_70437 |
| institution | Wiley Open Access |
| license_str_mv | http://creativecommons.org/licenses/by-nc/4.0/ |
| publishDate | 2026 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Accommodating the Analysis Model in Multiple Imputation for the Weibull Mixture Cure Model: Performance Under Penalized Likelihood Changchang Xu Laurent Briollais Irene L. Andrulis Shelley B. Bull Statistics in Medicine Accommodating the Analysis Model in Multiple Imputation for the Weibull Mixture Cure Model: Performance Under Penalized Likelihood Changchang Xu Laurent Briollais Irene L. Andrulis Shelley B. Bull Statistics in Medicine ABSTRACT Introduction In analysis of time‐to‐event outcomes, a mixture cure (MC) model is preferred over a standard survival model when the sample includes individuals who will never experience the event of interest. Motivated by a cohort study of breast cancer patients with incomplete biomarkers, we develop multiple imputation (MI) methods assuming a Weibull proportional hazards (PH‐MC) analysis model with multiple prognostic factors. However, for MI with fully conditional specification, an incorrectly specified imputation model can impair accuracy of point and interval estimates. Objectives and Methods Our goal is to propose imputation models that are compatible with the Weibull PH‐MC analysis models. We derive an exact conditional distribution (ECD) imputation model which involves the analysis model likelihood. Using simulation studies, we compare effect estimate bias and confidence interval (CI) coverage under alternative imputation models including the ECD model, an approximation that includes a cure indicator (cECD), and a comprehensive simple (CS) model. For robust parameter estimation in finite and/or sparse samples, we incorporate the Firth‐type penalized likelihood (FT‐PL) and combined likelihood profile (CLIP) methods into the MI. Results Compared to complete case analysis, MI with penalization reduces estimation bias and improves coverage. Although ECD and cECD perform similarly at higher event rates, ECD generates smaller bias and higher coverage at lower rates. CS has larger bias and lower coverage than ECD and cECD, but CIs are narrower than for cECD. Conclusions In analyses of biomarkers and composite subtypes for prognosis studies such as in breast cancer, the use of compatible imputation models and penalization methods is recommended for MC modeling in samples with low event numbers and/or with covariate imbalance. 10.1002/sim.70437 http://creativecommons.org/licenses/by-nc/4.0/ |
| title | Accommodating the Analysis Model in Multiple Imputation for the Weibull Mixture Cure Model: Performance Under Penalized Likelihood |
| topic | Statistics in Medicine |
| url | https://onlinelibrary.wiley.com/doi/10.1002/sim.70437 |