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Open access Jul 2026

Association of comorbidity burden with initial treatment allocation in older women with gynecologic cancer: a retrospective cohort study

Background Comorbidity is a key determinant of treatment decisions in older cancer patients; however, its role in shaping initial treatment allocation among older women with gynecologic malignancies remains insufficiently characterized. We aimed to evaluate the association between comorbidity burden and treatment selection and to develop a clinically interpretable predictive model. Methods We retrospectively analyzed 972 women aged ≥65 years with newly diagnosed cervical, ovarian, or endometrial cancer treated at a tertiary hospital in Southwest China between 2019 and 2024. Disease-specific guideline-concordant standard treatment was defined as curative-intent initial treatment appropriate for tumor type and FIGO stage, including surgery, platinum-based chemotherapy, concurrent chemoradiotherapy, brachytherapy, or combined-modality treatment when indicated. Multivariable logistic regression was used to identify independent predictors of disease-specific guideline-concordant standard treatment, and a predictive model was developed and internally validated with assessments of discrimination, calibration, and clinical utility. Results Of the 972 patients, 63.7% received disease-specific guideline-concordant standard treatment. Higher comorbidity burden [Charlson Comorbidity Index (CCI) ≥ 4], ECOG performance status ≥2, age ≥75 years, and pulmonary disease were independently associated with lower odds of receiving disease-specific guideline-concordant standard treatment (all p < 0.05). In contrast, higher body mass index and serum albumin levels were associated with higher odds of receiving disease-specific guideline-concordant standard treatment. The final model demonstrated strong internally validated discriminatory performance (AUC = 0.933, 95% CI: 0.917–0.948), good calibration, and meaningful clinical utility across a wide range of decision thresholds. Conclusion In this pooled real-world observational cohort of older women with cervical, ovarian, or endometrial cancer, comorbidity burden was independently associated with receipt of disease-specific guideline-concordant standard treatment. The internally validated host-factor-oriented model may support individualized treatment discussions as an adjunct to multidisciplinary assessment, but it should not be interpreted as a tumor-specific treatment algorithm or as a replacement for disease-specific guideline-based decision-making. Further external validation and tumor-specific prospective studies are needed before broader clinical implementation.

Lin Tang, Yuhang Liu, Bin Chen et al. · 0 citations
Preprint Aug 2026

Double Machine Learning with High-dimensional Interactive Fixed Effects

Factor structures are central to empirical work in economics and finance, and are usually used to model time-varying unobserved heterogeneity through interactive fixed effects (IFE). Existing IFE estimators rest on low-dimensional and linear specifications in the covariates, assumptions which are increasingly restrictive in applications drawing on rich datasets with controls of unknown functional form. This paper develops a Double Machine Learning estimator for the high-dimensional partially linear panel model with interactive fixed effects (panel DML-IFE). The method combines projection-based defactorisation of the data, in the spirit of Common Correlated Effects (CCE), with a Neyman-orthogonal score function and cross-fitting procedure, and accommodates low-rank factor structures in outcomes and treatments alongside high-dimensional, potentially nonlinear covariate effects estimated by machine learning algorithms. Monte Carlo simulations show that panel DML-IFE outperforms conventional IFE estimator outside the correctly-specified linear case, with bias reduction driven primarily by the time and covariate dimensions. An empirical application to U.S. stock returns shows that several effects documented under linear specifications lose statistical significance once high-dimensional nonlinear confounding and the presence of IFE are jointly accounted for.

Bin Chen, Annalivia Polselli, P. Clarke · 0 citations

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