Survival analysis with change-points in covariate effects

Lee, Chun Yin; Lam, Kwok Fai

doi:10.1177/0962280220922258

Cited by 5 publications

(12 citation statements)

References 28 publications

(64 reference statements)

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“…Other than the coefficient of the age, there is no change for the coefficients of other explanatory variables following our penalized procedure. The estimation results are displayed in Table 5, which are very close to the result obtained by Lee and Lam 55 . The optimal segment length

m=112

and the estimators of these two thresholds correspond to

{Z}_{(132)}

and

{Z}_{(412)}

.…”

Section: Real Data Analysissupporting

confidence: 79%

“…In this section, we apply our method to analyze the German breast cancer dataset, 55 which was collected between July 1984 and December 1989. The response variable of interest is the disease‐free survival time.…”

Section: Real Data Analysismentioning

confidence: 99%

“…Similar observations were made in those based on a large-scale population study with n = 57 068. 69 Lee and Lam 55 considered the same German breast cancer data by combining the likelihood ratio test and sequential analysis and detected two thresholds at 46 years old and 58 years old. By using the classical bootstrap method, they also obtained that the 95% confidence intervals (CI) for these two estimators are [32,49] and [46,64], respectively.…”

Section: German Breast Cancer Datamentioning

confidence: 99%

“…Goodman et al 54 applied a sequential test to determine the number of thresholds in the piecewise constant hazard model. Lee and Lam 55 proposed the sequential test and the likelihood ratio test to deal with the multi‐threshold problem in Cox model. Previously, Li and Jin 56 proposed the multi‐threshold accelerated failure time model with a modified Stute estimator and established the consistency of their procedure.…”

Section: Introductionmentioning

confidence: 99%

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Multi‐threshold proportional hazards model and subgroup identification

Wang

2022

Statistics in Medicine

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We propose a novel two-stage procedure for change point detection and parameter estimation in a multi-threshold proportional hazards model. In the first stage, we estimate the number of thresholds by formulating the threshold detection problem as a variable selection problem and applying the penalized partial likelihood approach. In the second stage, the change point locations are refined by a grid search and the standard inference for segment regression can then follow.The proposed model and estimation procedure could lend support to subgroup identification and personalized treatment recommendation in medical research.We establish the consistency of the threshold estimators and regression coefficient estimators under technical conditions. The finite sample performance of the method is demonstrated via simulation studies and two cancer data examples.

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m=112

and the estimators of these two thresholds correspond to

{Z}_{(132)}

and

{Z}_{(412)}

.…”

Section: Real Data Analysissupporting

confidence: 79%

Section: Real Data Analysismentioning

confidence: 99%

Section: German Breast Cancer Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi‐threshold proportional hazards model and subgroup identification

Wang

2022

Statistics in Medicine

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“…In general, the classical bootstrap proposed by Efron and Tibshirani 13 produces inconsistent estimators in such problem. Based on the change-point Cox regression model, Lee and Lam 14 shows empirically that the classical bootstrap estimator for the change-point parameter can be inconsistent. For clustered survival data, Deng et al.…”

Section: The δAuc Testmentioning

confidence: 99%

Nested logistic regression models and ΔAUC applications: Change-point analysis

Lee

2021

Stat Methods Med Res

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The area under the receiver operating characteristic curve (AUC) is one of the most popular measures for evaluating the performance of a predictive model. In nested models, the change in AUC (ΔAUC) can be a discriminatory measure of whether the newly added predictors provide significant improvement in terms of predictive accuracy. Recently, several authors have shown rigorously that ΔAUC can be degenerate and its asymptotic distribution is no longer normal when the reduced model is true, but it could be the distribution of a linear combination of some [Formula: see text] random variables [ 1 , 2 ]. Hence, the normality assumption and existing variance estimate cannot be applied directly for developing a statistical test under the nested models. In this paper, we first provide a brief review on the use of ΔAUC for comparing nested logistic models and the difficulty of retrieving the reference distribution behind. Then, we present a special case of the nested logistic regression models that the newly added predictor to the reduced model contains a change-point in its effects. A new test statistic based on ΔAUC is proposed in this setting. A simple resampling scheme is proposed to approximate the critical values for the test statistic. The inference of the change-point parameter is done via m-out-of- n bootstrap. Large-scale simulation is conducted to evaluate the finite-sample performance of the ΔAUC test for the change-point model. The proposed method is applied to two real-life datasets for illustration.

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Efficient estimation of Cox model with random change point

Chen,

Ping,

Sun

2024

Statistics in Medicine

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In clinical studies, the risk of a disease may dramatically change when some biological indexes of the human body exceed some thresholds. Furthermore, the differences in individual characteristics of patients such as physical and psychological experience may lead to subject‐specific thresholds or change points. Although a large literature has been established for regression analysis of failure time data with change points, most of the existing methods assume the same, fixed change point for all study subjects. In this paper, we consider the situation where there exists a subject‐specific change point and two Cox type models are presented. The proposed models also offer a framework for subgroup analysis. For inference, a sieve maximum likelihood estimation procedure is proposed and the asymptotic properties of the resulting estimators are established. An extensive simulation study is conducted to assess the empirical performance of the proposed method and indicates that it works well in practical situations. Finally the proposed approach is applied to a set of breast cancer data.

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Survival analysis with change-points in covariate effects

Cited by 5 publications

References 28 publications

Multi‐threshold proportional hazards model and subgroup identification

Multi‐threshold proportional hazards model and subgroup identification

Nested logistic regression models and ΔAUC applications: Change-point analysis

Efficient estimation of Cox model with random change point

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