Multi-threshold accelerated failure time model

Li, Jialiang; Jin, Baisuo

doi:10.1214/17-aos1632

Cited by 26 publications

(48 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When the thresholding variable Z is given, we may apply the TSMCD method in the work of Li and Jin to find change points and estimate the regression parameters. In practice, the choice of thresholding variable is crucial on the identification and interpretation of the subgroups.…”

Section: Model and Methodsmentioning

confidence: 99%

“…After a decision is made on the thresholding variable Z , we follow up with a TSMCD procedure to estimate the regression coefficients and the number and locations of the thresholds . In the first splitting stage, we set

r = ⌊ k \sqrt{n} ⌋

and set q n = ⌊ n / r ⌋ − 1, where k is constant.…”

Section: Model and Methodsmentioning

confidence: 99%

“…Second, the TSMCD method has theoretical support and fast computing speed. Under mild technical conditions, the estimated thresholding locations (change points) from TSMCD converge almost surely to the true thresholding locations . Furthermore, the estimated regression coefficients work as well as the oracle estimators, which can only be obtained when the change point structures are known.…”

Section: Introductionmentioning

confidence: 94%

“…Recent authors adopted the penalization method, which accelerates the change point detection. In this paper, we consider a recently proposed method called two‐stage multiple change‐point detection (TSMCD) method, which enjoys solid theoretical advantages and nice practical performance. There are other methods for subgroup identification, including Bayesian approaches …”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A model‐based multithreshold method for subgroup identification

Wang

et al. 2019

Statistics in Medicine

Self Cite

View full text Add to dashboard Cite

Thresholding variable plays a crucial role in subgroup identification for personalized medicine. Most existing partitioning methods split the sample based on one predictor variable. In this paper, we consider setting the splitting rule from a combination of multivariate predictors, such as the latent factors, principle components, and weighted sum of predictors. Such a subgrouping method may lead to more meaningful partitioning of the population than using a single variable. In addition, our method is based on a change point regression model and thus yields straight forward model-based prediction results. After choosing a particular thresholding variable form, we apply a two-stage multiple change point detection method to determine the subgroups and estimate the regression parameters. We show that our approach can produce two or more subgroups from the multiple change points and identify the true grouping with high probability. In addition, our estimation results enjoy oracle properties. We design a simulation study to compare performances of our proposed and existing methods and apply them to analyze data sets from a Scleroderma trial and a breast cancer study.

show abstract

Section: Model and Methodsmentioning

confidence: 99%

r = ⌊ k \sqrt{n} ⌋

and set q n = ⌊ n / r ⌋ − 1, where k is constant.…”

Section: Model and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A model‐based multithreshold method for subgroup identification

Wang

et al. 2019

Statistics in Medicine

Self Cite

View full text Add to dashboard Cite

show abstract

“…The model is still widely used and adapted to new problems in medical research. A recent modification of the AFT model has been introduced in Li and Jin (2018) and recent applications include AIDS research (Fulcher et al 2017) and cancer research (Cho et al 2018) among many others.…”

Section: Introductionmentioning

confidence: 99%

In-Sample Hazard Forecasting Based on Survival Models with Operational Time

Bischofberger

2020

Risks

View full text Add to dashboard Cite

We introduce a generalization of the one-dimensional accelerated failure time model allowing the covariate effect to be any positive function of the covariate. This function and the baseline hazard rate are estimated nonparametrically via an iterative algorithm. In an application in non-life reserving, the survival time models the settlement delay of a claim and the covariate effect is often called operational time. The accident date of a claim serves as covariate. The estimated hazard rate is a nonparametric continuous-time alternative to chain-ladder development factors in reserving and is used to forecast outstanding liabilities. Hence, we provide an extension of the chain-ladder framework for claim numbers without the assumption of independence between settlement delay and accident date. Our proposed algorithm is an unsupervised learning approach to reserving that detects operational time in the data and adjusts for it in the estimation process. Advantages of the new estimation method are illustrated in a data set consisting of paid claims from a motor insurance business line on which we forecast the number of outstanding claims.

show abstract

Continuous threshold models with two‐way interactions in survival analysis

Liu

Chen

2020

Can J Statistics

View full text Add to dashboard Cite

Proportional hazards model with the biomarker-treatment interaction plays an important role in the survival analysis of the subset treatment effect. A threshold parameter for a continuous biomarker variable defines the subset of patients who can benefit or lose from a certain new treatment. In this article, we focus on a continuous threshold effect using the rectified linear unit and propose a gradient descent method to obtain the maximum likelihood estimation of the regression coefficients and the threshold parameter simultaneously. Under certain regularity conditions, we prove the consistency, asymptotic normality and provide a robust estimate of the covariance matrix when the model is misspecified. To illustrate the finite sample properties of the proposed methods, we simulate data to evaluate the empirical biases, the standard errors and the coverage probabilities for both the correctly specified models and misspecified models. The proposed continuous threshold model is applied to a prostate cancer data with serum prostatic acid phosphatase as a biomarker.

show abstract

Multi-threshold accelerated failure time model

Cited by 26 publications

References 31 publications

A model‐based multithreshold method for subgroup identification

A model‐based multithreshold method for subgroup identification

In-Sample Hazard Forecasting Based on Survival Models with Operational Time

Continuous threshold models with two‐way interactions in survival analysis

Contact Info

Product

Resources

About