Efficient t0$$ {t}_0 $$‐year risk regression using the logistic model
From MaRDI portal
Publication:6140345
DOI10.1111/sjos.12658MaRDI QIDQ6140345
Thomas H. Scheike, Torben Martinussen
Publication date: 2 January 2024
Published in: Scandinavian Journal of Statistics (Search for Journal in Brave)
augmentationefficient estimationcensoringdouble robustnessinverse probability of censoring weighting\(t_0\)-year riskfixed time regression
Cites Work
- Improved precision in the analysis of randomized trials with survival outcomes, without assuming proportional hazards
- Pseudo-observations under covariate-dependent censoring
- Semiparametric theory and missing data.
- On the estimation of average treatment effects with right‐censored time to event outcome and competing risks
- Predicting cumulative incidence probability by direct binomial regression
- Generalised linear models for correlated pseudo-observations, with applications to multi-state models
- Evaluating Prediction Rules fort-Year Survivors With Censored Regression Models
- Direct Modelling of Regression Effects for Transition Probabilities in Multistate Models
- Application of the Time‐Dependent ROC Curves for Prognostic Accuracy with Multiple Biomarkers
- Efficient Estimation in the Fine and Gray Model
- On logistic regression with right censored data, with or without competing risks, and its use for estimating treatment effects
This page was built for publication: Efficient t0$$ {t}_0 $$‐year risk regression using the logistic model
