Restricted mean survival time regression model with time‐dependent covariates

Zhang, Chengfeng; Wu, Hongji; Huang, Baoyi; Yuan, Hao; Hou, Yufei; Chen, Zheng

doi:10.1002/sim.9495

Cited by 8 publications

(3 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…

in the presence of censoring. However, when applying IPCW to the estimating equation, its expectation is 0 ( 15 ). Therefore, the estimating equation changes to…”

Section: Methodsmentioning

confidence: 99%

Time-varying effect in older patients with early-stage breast cancer: a model considering the competing risks based on a time scale

Yu,

Geng,

et al. 2024

Front. Oncol.

Self Cite

View full text Add to dashboard Cite

BackgroundPatients with early-stage breast cancer may have a higher risk of dying from other diseases, making a competing risks model more appropriate. Considering subdistribution hazard ratio, which is used often, limited to model assumptions and clinical interpretation, we aimed to quantify the effects of prognostic factors by an absolute indicator, the difference in restricted mean time lost (RMTL), which is more intuitive. Additionally, prognostic factors of breast cancer may have dynamic effects (time-varying effects) in long-term follow-up. However, existing competing risks regression models only provide a static view of covariate effects, leading to a distorted assessment of the prognostic factor.MethodsTo address this issue, we proposed a dynamic effect RMTL regression that can explore the between-group cumulative difference in mean life lost over a period of time and obtain the real-time effect by the speed of accumulation, as well as personalized predictions on a time scale.ResultsA simulation validated the accuracy of the coefficient estimates in the proposed regression. Applying this model to an older early-stage breast cancer cohort, it was found that 1) the protective effects of positive estrogen receptor and chemotherapy decreased over time; 2) the protective effect of breast-conserving surgery increased over time; and 3) the deleterious effects of stage T2, stage N2, and histologic grade II cancer increased over time. Moreover, from the view of prediction, the mean C-index in external validation reached 0.78.ConclusionDynamic effect RMTL regression can analyze both dynamic cumulative effects and real-time effects of covariates, providing a more comprehensive prognosis and better prediction when competing risks exist.

show abstract

“…

in the presence of censoring. However, when applying IPCW to the estimating equation, its expectation is 0 ( 15 ). Therefore, the estimating equation changes to…”

Section: Methodsmentioning

confidence: 99%

Time-varying effect in older patients with early-stage breast cancer: a model considering the competing risks based on a time scale

Yu,

Geng,

et al. 2024

Front. Oncol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…When considering the impacts of these covariates on patients' life expectancy, most studies directly model the RMST and make predictions based on the covariate information at the baseline (Andersen, Hansen and Klein, 2004;Tian, Zhao and Wei, 2014;Wang and Schaubel, 2018;Zhong and Schaubel, 2022). These models are limited, however, because they do not account for the changes in these covariates (Thomas and Reyes, 2014), or they only consider the dichotomous time-varying covariate with, at most, one change from untreated to treated (Zhang et al, 2022). To overcome this challenge, Lin et al (2018) extended the traditional regression model by using functional principal component analysis to extract the dominant features of the biomarker trajectory of each individual as time-dependent covariates to conduct dynamic predictions.…”

Section: Introductionmentioning

confidence: 99%

“…When considering the impacts of these covariates on patients’ life expectancy, most studies directly model the RMST and make predictions based on the covariate information at the baseline (Andersen, Hansen and Klein, 2004; Tian, Zhao and Wei, 2014; Wang and Schaubel, 2018; Zhong and Schaubel, 2022). These models are limited, however, because they do not account for the changes in these covariates (Thomas and Reyes, 2014), or they only consider the dichotomous time‐varying covariate with, at most, one change from untreated to treated (Zhang et al., 2022). To overcome this challenge, Lin et al.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Dynamic Restricted Mean Survival Time Based on Pseudo-Observations

et al. 2023

Self Cite

View full text Add to dashboard Cite

In clinical follow-up studies with a time-to-event end point, the difference in the restricted mean survival time (RMST) is a suitable substitute for the hazard ratio (HR).However, the RMST only measures the survival of patients over a period of time from the baseline and cannot reflect changes in life expectancy over time. Based on the RMST, we study the conditional restricted mean survival time (cRMST) by estimating life expectancy in the future according to the time that patients have survived, reflecting the dynamic survival status of patients during follow-up. In this paper, we introduce the estimation method of cRMST based on pseudo-observations, the construction of test statistics according to the difference in the cRMST (cRMSTd), and the establishment of the robust dynamic prediction model using the landmark method. Simulation studies are employed to evaluate the statistical properties of these methods, which are also applied to two real examples. The simulation results show that the estimation of the cRMST is accurate and the cRMSTd test performs well. In addition, the dynamic RMST model has high accuracy in coefficient estimation and better predictive performance than the "static" RMST model. The hypothesis test proposed in this paper has a wide range of applicability, and the dynamic RMST model can predict patients' life expectancy from any prediction time, considering the time-dependent covariates and time-varying effects of covariates.

show abstract

Restricted mean survival time regression model with time‐dependent covariates

Zhang

Huang

et al. 2022

Statistics in Medicine

View full text Add to dashboard Cite

In clinical or epidemiological follow-up studies, methods based on time scale indicators such as the restricted mean survival time (RMST) have been developed to some extent. Compared with traditional hazard rate indicator system methods, the RMST is easier to interpret and does not require the proportional hazard assumption. To date, regression models based on the RMST are indirect or direct models of the RMST and baseline covariates. However, time-dependent covariates are becoming increasingly common in follow-up studies. Based on the inverse probability of censoring weighting (IPCW) method, we developed a regression model of the RMST and time-dependent covariates. Through Monte Carlo simulation, we verified the estimation performance of the regression parameters of the proposed model. Compared with the time-dependent Cox model and the fixed (baseline) covariate RMST model, the time-dependent RMST model has a better prediction ability. Finally, an example of heart transplantation was used to verify the above conclusions. K E Y W O R D Sinverse probability of censoring weighting, restricted mean survival time, survival analysis, time-dependent covariates 1

show abstract

Restricted mean survival time regression model with time‐dependent covariates

Cited by 8 publications

References 32 publications

Time-varying effect in older patients with early-stage breast cancer: a model considering the competing risks based on a time scale

Time-varying effect in older patients with early-stage breast cancer: a model considering the competing risks based on a time scale

Analysis of Dynamic Restricted Mean Survival Time Based on Pseudo-Observations

Restricted mean survival time regression model with time‐dependent covariates

Contact Info

Product

Resources

About