Analysis of Restricted Mean Survival Time for Length-biased Data

Lee, Chi Hyun; Ning, Jing; Shen, Yu

doi:10.1111/biom.12772

Cited by 14 publications

(12 citation statements)

References 33 publications

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“…However, little work is available on regression analysis of RMST for left‐truncated right‐censored data. As discussed in Lee et al, 14 the generalization of existing methods based on weighted estimating equations to left‐truncated right‐censored data is more challenging and complex. The estimation of weight functions would involve estimating the survival function of failure time, distribution of truncation time, and survival function of residual censoring time.…”

Section: Discussionmentioning

confidence: 99%

“…Although much research has been conducted into both regression analysis of left-truncated right-censored data [10][11][12][13] and direct regression analysis of RMST for right-censored data, 3,7,8 relatively little work is available on direct regression modeling of RMST for left-truncated right-censored data. To our knowledge, only one paper by Lee et al 14 studied direct regression analysis of RMST for length-biased right-censored data, a special type of left-truncated right-censored data that assumes a constant disease incidence rate. That paper was mostly concerned with covariate-independent censoring and constructed unbiased estimating equations to obtain consistent estimators of covariate effects on RMST.…”

Section: Introductionmentioning

confidence: 99%

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Regression modeling of restricted mean survival time for left‐truncated right‐censored data

Rong

Ning

Zhu

2022

Statistics in Medicine

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The restricted mean survival time (RMST) is a clinically meaningful summary measure in studies with survival outcomes. Statistical methods have been developed for regression analysis of RMST to investigate impacts of covariates on RMST, which is a useful alternative to the Cox regression analysis. However, existing methods for regression modeling of RMST are not applicable to left-truncated right-censored data that arise frequently in prevalent cohort studies, for which the sampling bias due to left truncation and informative censoring induced by the prevalent sampling scheme must be properly addressed.The pseudo-observation (PO) approach has been used in regression modeling of RMST for right-censored data and competing-risks data. For left-truncated right-censored data, we propose to directly model RMST as a function of baseline covariates based on POs under general censoring mechanisms. We adjust for the potential covariate-dependent censoring or dependent censoring by the inverse probability of censoring weighting method. We establish large sample properties of the proposed estimators and assess their finite sample performances by simulation studies under various scenarios. We apply the proposed methods to a prevalent cohort of women diagnosed with stage IV breast cancer identified from surveillance, epidemiology, and end results-medicare linked database. K E Y W O R D Sgeneral censoring mechanisms, inverse probability of censoring weighting, left-truncated right-censored data, pseudo-observations, restricted mean survival time 1

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Regression modeling of restricted mean survival time for left‐truncated right‐censored data

Rong

Ning

Zhu

2022

Statistics in Medicine

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“…mean progression-free survival time) is a biased measure in the presence of right censoring [17]. Instead we calculate the restricted mean progression free-survival time () which is interpreted as the mean progression-free survival time if observation is restricted to a truncation time [18]. Since the exit time from health is exponentially distributed, the can be calculated asBy definition, the area under the PFS curve is equal to when is set to the maximum observation time in the PFS analysis, [19, 20].…”

Section: Methodsmentioning

confidence: 99%

A technique for approximating transition rates from published survival analyses

Pahuta

Werier

Wai

et al. 2019

Cost Eff Resour Alloc

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Background Quality-adjusted-life-years (QALYs) are used to concurrently quantify morbidity and mortality within a single parameter. For this reason, QALYs can facilitate the discussion of risks and benefits during patient counseling regarding treatment options. QALYs are often calculated using partitioned-survival modelling. Alternatively, QALYs can be calculated using more flexible and informative state-transition models populated with transition rates estimated using multistate modelling (MSM) techniques. Unfortunately the latter approach is considered not possible when only progression-free survival (PFS) and overall survival (OS) analyses are reported. Methods We have developed a method that can be used to estimate approximate transition rates from published PFS and OS analyses (we will refer to transition rates estimated using full multistate methods as true transition rates ). Results The approximation method is more accurate for estimating the transition rates out of health than the transition rate out of illness. The method tends to under-estimate true transition rates as censoring increases. Conclusions In this article we present the basis for and use of the transition rate approximation method. We then apply the method to a case study and evaluate the method in a simulation study. Electronic supplementary material The online version of this article (10.1186/s12962-019-0182-7) contains supplementary material, which is available to authorized users.

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“…The restricted mean survival time is the sum of the areas under the survival curve in a specific time period [27][28][29][30][31]. As a valuable prognostic index, restricted mean survival time was widely applied to different prognostic studies [27][28][29][30][31].…”

Section: Artificial Intelligence Algorithms and Restricted Mean Survi...mentioning

confidence: 99%

Improve individual treatment by comparing treatment benefits: cancer artificial intelligence survival analysis system for cervical carcinoma

Liang

et al. 2022

J Transl Med

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Purpose The current study aimed to construct a novel cancer artificial intelligence survival analysis system for predicting the individual mortality risk curves for cervical carcinoma patients receiving different treatments. Methods Study dataset (n = 14,946) was downloaded from Surveillance Epidemiology and End Results database. Accelerated failure time algorithm, multi-task logistic regression algorithm, and Cox proportional hazard regression algorithm were used to develop prognostic models for cancer specific survival of cervical carcinoma patients. Results Multivariate Cox regression identified stage, PM, chemotherapy, Age, PT, and radiation_surgery as independent influence factors for cervical carcinoma patients. The concordance indexes of Cox model were 0.860, 0.849, and 0.848 for 12-month, 36-month, and 60-month in model dataset, whereas it were 0.881, 0.845, and 0.841 in validation dataset. The concordance indexes of accelerated failure time model were 0.861, 0.852, and 0.851 for 12-month, 36-month, and 60-month in model dataset, whereas it were 0.882, 0.847, and 0.846 in validation dataset. The concordance indexes of multi-task logistic regression model were 0.860, 0.863, and 0.861 for 12-month, 36-month, and 60-month in model dataset, whereas it were 0.880, 0.860, and 0.861 in validation dataset. Brier score indicated that these three prognostic models have good diagnostic accuracy for cervical carcinoma patients. The current research lacked independent external validation study. Conclusion The current study developed a novel cancer artificial intelligence survival analysis system to provide individual mortality risk predictive curves for cervical carcinoma patients based on three different artificial intelligence algorithms. Cancer artificial intelligence survival analysis system could provide mortality percentage at specific time points and explore the actual treatment benefits under different treatments in four stages, which could help patient determine the best individualized treatment. Cancer artificial intelligence survival analysis system was available at: https://zhangzhiqiao15.shinyapps.io/Tumor_Artificial_Intelligence_Survival_Analysis_System/.

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Analysis of Restricted Mean Survival Time for Length-biased Data

Cited by 14 publications

References 33 publications

Regression modeling of restricted mean survival time for left‐truncated right‐censored data

Regression modeling of restricted mean survival time for left‐truncated right‐censored data

A technique for approximating transition rates from published survival analyses

Improve individual treatment by comparing treatment benefits: cancer artificial intelligence survival analysis system for cervical carcinoma

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