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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
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A consistent NPMLE of the joint distribution function with competing risks data under the dependent masking and
1Department of Mathematical Sciences, SUNY, Binghamton, NY, 13902, USA. jiali@celgene.com.
Lifetime Data Analysis
|August 28, 2014
Summary
Dinse's estimators for right-censored and masked competing risks data lack consistent asymptotic properties. A new non-parametric maximum likelihood estimator (NPMLE) is developed, showing improved consistency under a dependent masking and right-censoring model.
Area of Science:
- Biostatistics
- Survival Analysis
- Statistical Modeling
Background:
- Competing risks data with right-censoring and masking present unique statistical challenges.
- Existing non-parametric maximum likelihood estimators (NPMLE) by Dinse lack studied asymptotic properties.
- Previous masking models, like conditional masking probability (CMP) and random partition masking (RPM), are limited.
Purpose of the Study:
- To investigate the asymptotic properties of Dinse's estimators for right-censored and masked competing risks data.
- To develop a consistent NPMLE for such complex data structures.
- To introduce a more general dependent masking and right-censoring model.
Main Methods:
- Analysis of Dinse's estimators under extensions of CMP and RPM models.
- Development of a novel NPMLE under a new dependent masking and right-censoring model.
- Comparative analysis using simulation studies and real-world data.
Main Results:
- Dinse's estimators are not generally consistent, particularly for continuous failure times.
- A new, consistent NPMLE was constructed and validated.
- The proposed consistent NPMLE demonstrated good approximation for moderate sample sizes in simulations.
Conclusions:
- Dinse's estimators have limitations in consistency for masked and right-censored competing risks data.
- The newly developed consistent NPMLE offers a reliable alternative under a generalized masking model.
- The findings advance statistical methods for complex survival data analysis.
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