Estimation of covariate effects on net survivals in the relative survival progressive illness-death model
- 8 May 2021
- journal article
- research article
- Published by SAGE Publications in Statistical Methods in Medical Research
- Vol. 30 (6), 1538-1553
- https://doi.org/10.1177/09622802211003608
Abstract
Recently, there has been a lot of development in relative survival field. In the absence of data on the cause of death, the research has tended to focus on the estimation of survival probability of a cancer (as a disease of interest). In many cancers, one nonfatal event that decreases the survival probability can occur. There are a few methods that assess the role of prognostic factors for multiple types of clinical events while dealing with uncertainty about the cause of death. However, these methods require proportional hazard or Markov assumptions. In practice, one or both of these assumptions might be violated. Violation of the proportional hazard assumption can lead to estimates that are biased, and difficult to interpret and violation of Markov assumption results in inconsistent estimators. In this work, we propose a semi-parametric approach to estimate the possibly time-varying regression coefficients in the likely non-Markov relative survival progressive illness-death model. The performance of the proposed estimator is investigated through simulations. We illustrate our approach using data from a study on rectal cancer resected for cure conducted in two French population-based digestive cancer registries.Funding Information
- BCAM Severo Ochoa excellence accreditation (SEV-2013-0323)
- French Agence Nationale de la Recherche (ANR-12-BSV1-0028)
This publication has 27 references indexed in Scilit:
- Breast cancer survival in the US and Europe: A CONCORD high‐resolution studyInternational Journal of Cancer, 2012
- On Estimation in Relative SurvivalBiometrics, 2011
- Predicting cumulative incidence probability by direct binomial regressionBiometrika, 2008
- Ten-year survival and risk of relapse for testicular cancer: A EUROCARE high resolution studyEuropean Journal of Cancer, 2007
- Regression Modeling of Competing Risks Data Based on Pseudovalues of the Cumulative Incidence FunctionBiometrics, 2005
- Regression models for relative survivalStatistics in Medicine, 2003
- Generalised linear models for correlated pseudo-observations, with applications to multi-state modelsBiometrika, 2003
- Multi-state models for bleeding episodes and mortality in liver cirrhosisStatistics in Medicine, 2000
- A Proportional Hazards Model for the Subdistribution of a Competing RiskJournal of the American Statistical Association, 1999
- Survival Curve for Cancer Patients Following TreatmentJournal of the American Statistical Association, 1952