Semiparametric methods for survival data with measurement error under additive hazards cure rate models

Barui, Sandip; Yi, Grace Y.

doi:10.1007/s10985-019-09482-0

Cited by 9 publications

(9 citation statements)

References 38 publications

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“…In addition, DT is expected to be computationally much less expensive when compared to random forests and neural networks. Motivated by this, we propose a novel decision trees (DT)‐based mixture cure model, where we model the incidence using the DT classifier and the latency using a semi‐parametric proportional hazards structure with an unspecified baseline hazard function 21 . To the best of our knowledge, this is the first work that employs DT to capture non‐linearity in the incidence part of MCM.…”

Section: Introductionmentioning

confidence: 99%

On the integration of decision trees with mixture cure model

Aselisewine,

Pal

2023

Statistics in Medicine

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The mixture cure model is widely used to analyze survival data in the presence of a cured subgroup. Standard logistic regression‐based approaches to model the incidence may lead to poor predictive accuracy of cure, specifically when the covariate effect is non‐linear. Supervised machine learning techniques can be used as a better classifier than the logistic regression due to their ability to capture non‐linear patterns in the data. However, the problem of interpret‐ability hangs in the balance due to the trade‐off between interpret‐ability and predictive accuracy. We propose a new mixture cure model where the incidence part is modeled using a decision tree‐based classifier and the proportional hazards structure for the latency part is preserved. The proposed model is very easy to interpret, closely mimics the human decision‐making process, and provides flexibility to gauge both linear and non‐linear covariate effects. For the estimation of model parameters, we develop an expectation maximization algorithm. A detailed simulation study shows that the proposed model outperforms the logistic regression‐based and spline regression‐based mixture cure models, both in terms of model fitting and evaluating predictive accuracy. An illustrative example with data from a leukemia study is presented to further support our conclusion.

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Section: Introductionmentioning

confidence: 99%

On the integration of decision trees with mixture cure model

Aselisewine,

Pal

2023

Statistics in Medicine

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“…Regarding the insusceptible/cure fraction in survival analysis, most previous researches concentrate on the cure fraction to the outcome (i.e., dependent variables) [ 24 – 26 ] instead of the insusceptible fraction to the intermediate event (i.e., independent variables). The logistic regression model (LRM) has been widely used to identify the cure fraction to the outcome [ 20 , 24 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

An improved method for the effect estimation of the intermediate event on the outcome based on the susceptible pre-identification

et al. 2021

BMC Med Res Methodol

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Background In follow-up studies, the occurrence of the intermediate event may influence the risk of the outcome of interest. Existing methods estimate the effect of the intermediate event by including a time-varying covariate in the outcome model. However, the insusceptible fraction to the intermediate event in the study population has not been considered in the literature, leading to effect estimation bias due to the inaccurate dataset. Methods In this paper, we propose a new effect estimation method, in which the susceptible subpopulation is identified firstly so that the estimation could be conducted in the right population. Then, the effect is estimated via the extended Cox regression and landmark methods in the identified susceptible subpopulation. For susceptibility identification, patients with observed intermediate event time are classified as susceptible. Based on the mixture cure model fitted the incidence and time of the intermediate event, the susceptibility of the patient with censored intermediate event time is predicted by the residual intermediate event time imputation. The effect estimation performance of the new method was investigated in various scenarios via Monte-Carlo simulations with the performance of existing methods serving as the comparison. The application of the proposed method to mycosis fungoides data has been reported as an example. Results The simulation results show that the estimation bias of the proposed method is smaller than that of the existing methods, especially in the case of a large insusceptible fraction. The results hold for small sample sizes. Besides, the estimation bias of the new method decreases with the increase of the covariates, especially continuous covariates, in the mixture cure model. The heterogeneity of the effect of covariates on the outcome in the insusceptible and susceptible subpopulation, as well as the landmark time, does not affect the estimation performance of the new method. Conclusions Based on the pre-identification of the susceptible, the proposed new method could improve the effect estimation accuracy of the intermediate event on the outcome when there is an insusceptible fraction to the intermediate event in the study population.

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“…semiparametric cure models with PH structure of the latency is studied extensively by Kuk & Chen (1992), Peng & Dear (2000) and Sy & Taylor (2000), to name a few. Generalizations to semiparametric PO (Gu et al, 2011;Mao & Wang, 2010), AFT (Li & Taylor, 2002;Zhang & Peng, 2007, 2009, transformation class (Lu & Ying, 2004) and additive hazards (Barui & Yi, 2020) under mixture cure rate model were also investigated with various estimation techniques and model considerations.…”

Section: Introductionmentioning

confidence: 99%

A Support Vector Machine Based Cure Rate Model For Interval Censored Data

Pal

Peng²,

Barui³

et al. 2021

Preprint

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The mixture cure rate model is the most commonly used cure rate model in the literature. In the context of mixture cure rate model, the standard approach to model the effect of covariates on the cured or uncured probability is to use a logistic function. This readily implies that the boundary classifying the cured and uncured subjects is linear. In this paper, we propose a new mixture cure rate model based on interval censored data that uses the support vector machine (SVM) to model the effect of covariates on the uncured or the cured probability (i.e., on the incidence part of the model). Our proposed model inherits the features of the SVM and provides flexibility to capture classification boundaries that are non-linear and more complex. Furthermore, the new model can be used to model the effect of covariates on the incidence part when the dimension of covariates is high. The latency part is modeled by a proportional hazards structure. We develop an estimation procedure based on the expectation maximization (EM) algorithm to estimate the cured/uncured probability and the latency model parameters. Our simulation study results show that the proposed model performs better in capturing complex classification boundaries when compared to the existing logistic regression based mixture cure rate model. We also show that our model's ability to capture complex classification boundaries improve the estimation results corresponding to the latency parameters. For illustrative purpose, we present our analysis by applying the proposed methodology to an interval censored data on smoking cessation.

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Semiparametric methods for survival data with measurement error under additive hazards cure rate models

Cited by 9 publications

References 38 publications

On the integration of decision trees with mixture cure model

On the integration of decision trees with mixture cure model

An improved method for the effect estimation of the intermediate event on the outcome based on the susceptible pre-identification

A Support Vector Machine Based Cure Rate Model For Interval Censored Data

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