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Promotion time cure rate model with a neural network estimated nonparametric component
Author(s) -
Xie Yujing,
Yu Zhangsheng
Publication year - 2021
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8980
Subject(s) - nonparametric statistics , computer science , artificial neural network , context (archaeology) , maximization , component (thermodynamics) , expectation–maximization algorithm , artificial intelligence , machine learning , fraction (chemistry) , key (lock) , data mining , econometrics , pattern recognition (psychology) , mathematical optimization , statistics , mathematics , maximum likelihood , paleontology , physics , chemistry , organic chemistry , biology , thermodynamics , computer security
Promotion time cure rate models (PCM) are often used to model the survival data with a cure fraction. Medical images or biomarkers derived from medical images can be the key predictors in survival models. However, incorporating images in the PCM is challenging using traditional nonparametric methods such as splines. We propose to use neural network to model the nonparametric or unstructured predictors' effect in the PCM context. Expectation‐maximization algorithm with neural network for the M‐step is used for parameter estimation. Asymptotic properties of the proposed estimates are derived. Simulation studies show good performance in terms of both prediction and estimation. We finally apply our methods to analyze the brain images from open access series of imaging studies data.