G‐estimation of structural nested restricted mean time lost models to estimate effects of time‐varying treatments on a failure time outcome

Biometrics ◽  
2019 ◽  
Vol 76 (3) ◽  
pp. 799-810
Author(s):  
Yasuhiro Hagiwara ◽  
Tomohiro Shinozaki ◽  
Yutaka Matsuyama
Keyword(s):  
Failure Time ◽  
Time Varying ◽  
Mean Time

scholarly journals Gray’s Time-Varying Coefficients Model for Posttransplant Survival of Pediatric Liver Transplant Recipients with a Diagnosis of Cancer

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Yi Ren ◽  
Chung-Chou H. Chang ◽  
Gabriel L. Zenarosa ◽  
Heather E. Tomko ◽  
Drew Michael S. Donnell ◽  
...  
Keyword(s):  
Liver Transplant ◽  
Failure Time ◽  
Penalized Splines ◽  
Time Varying ◽  
Piecewise Constant ◽  
Varying Coefficients ◽  
Covariate Effects ◽  
Pediatric Liver ◽  
Time Varying Coefficients ◽  
Pediatric Liver Transplant

Transplantation is often the only viable treatment for pediatric patients with end-stage liver disease. Making well-informed decisions on when to proceed with transplantation requires accurate predictors of transplant survival. The standard Cox proportional hazards (PH) model assumes that covariate effects are time-invariant on right-censored failure time; however, this assumption may not always hold. Gray’s piecewise constant time-varying coefficients (PC-TVC) model offers greater flexibility to capture the temporal changes of covariate effects without losing the mathematical simplicity of Cox PH model. In the present work, we examined the Cox PH and Gray PC-TVC models on the posttransplant survival analysis of 288 pediatric liver transplant patients diagnosed with cancer. We obtained potential predictors through univariable(P<0.15)and multivariable models with forward selection(P<0.05)for the Cox PH and Gray PC-TVC models, which coincide. While the Cox PH model provided reasonable average results in estimating covariate effects on posttransplant survival, the Gray model using piecewise constant penalized splines showed more details of how those effects change over time.

Multistate Models: Accurate and Dynamic Methods to Improve Predictions of Thrombotic Risk in Patients with Cancer

2019 ◽  
Vol 119 (11) ◽  
pp. 1849-1859 ◽  
Author(s):  
Alberto Carmona-Bayonas ◽  
Paula Jimenez-Fonseca ◽  
Marcelo Garrido ◽  
Ana Custodio ◽  
Raquel Hernandez ◽  
...  
Keyword(s):  
Competing Risks ◽  
Cumulative Incidence ◽  
Proportional Hazards ◽  
Failure Time ◽  
Dynamic Effect ◽  
Progression Free Survival ◽  
Time Varying ◽  
Multistate Models ◽  
Thrombotic Risk ◽  
Prognostic Effect

AbstractResearch into cancer-associated thrombosis (CAT) entails managing dynamic data that pose an analytical challenge. Thus, methods that assume proportional hazards to investigate prognosis entail a risk of misinterpreting or overlooking key traits or time-varying effects. We examined the AGAMENON registry, which collects data from 2,129 patients with advanced gastric cancer. An accelerated failure time (AFT) multistate model and flexible competing risks regression were used to scrutinize the time-varying effect of CAT, as well as to estimate how covariates dynamically predict cumulative incidence. The AFT model revealed that thrombosis shortened progression-free survival and overall survival with adjusted time ratios of 0.72 and 0.56, respectively. Nevertheless, its prognostic effect was nonproportional and disappeared over time if the subject managed to survive long enough. CAT that occurred later had a more pronounced prognostic effect. In the flexible competing risks model, multiple covariates were seen to have significant time-varying effects on the cumulative incidence of CAT (Khorana score, secondary thromboprophylaxis, high tumor burden, and cisplatin-containing regimen), whereas other predictors exerted a constant effect (signet ring cells and primary thromboprophylaxis). The model that assumes proportional hazards was incapable of capturing the effect of these covariates and predicted the cumulative incidence in a biased way. This study evinces that flexible and multistate models are a useful and innovative method to describe the dynamic effect of variables associated with CAT and should be more widely used.

ON ADAPTIVE ESTIMATION FOR LOCALLY STATIONARY WAVELET PROCESSES AND ITS APPLICATIONS

2004 ◽  
Vol 02 (04) ◽  
pp. 545-565 ◽  
Author(s):  
SÉBASTIEN VAN BELLEGEM ◽  
RAINER VON SACHS
Keyword(s):  
Time Series ◽  
Spectral Density ◽  
Real Time ◽  
Adaptive Estimation ◽  
Local Existence ◽  
Real Data ◽  
Time Varying ◽  
Mean Time

The class of locally stationary wavelet processes is a wavelet-based model for covariance nonstationary zero mean time series. This paper presents an algorithm for the pointwise adaptive estimation of their time-varying spectral density. The performance of the procedure is evaluated on simulated and real time series. Two applications of the procedure are also presented and evaluated on real data. The first is a test of local existence for the coefficients of the so-called wavelet periodogram. The second is a new test of covariance stationarity.

Estimating the causal effect of a time-varying treatment on time-to-event using structural nested failure time models

2004 ◽  
Vol 58 (3) ◽  
pp. 271-295 ◽  
Author(s):  
Judith Lok ◽  
Richard Gill ◽  
Aad van der Vaart ◽  
James Robins
Keyword(s):  
Failure Time ◽  
Causal Effect ◽  
Time Varying ◽  
Time To Event

scholarly journals Instrumental variable estimation for a time-varying treatment and a time-to-event outcome via structural nested cumulative failure time models

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Joy Shi ◽  
Sonja A. Swanson ◽  
Peter Kraft ◽  
Bernard Rosner ◽  
Immaculata De Vivo ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Instrumental Variable ◽  
Failure Time ◽  
Cumulative Risk ◽  
Treatment Strategies ◽  
Simulated Data ◽  
Health Study ◽  
Time Varying ◽  
Data Application ◽  
Cumulative Risks

Abstract Background In many applications of instrumental variable (IV) methods, the treatments of interest are intrinsically time-varying and outcomes of interest are failure time outcomes. A common example is Mendelian randomization (MR), which uses genetic variants as proposed IVs. In this article, we present a novel application of g-estimation of structural nested cumulative failure models (SNCFTMs), which can accommodate multiple measures of a time-varying treatment when modelling a failure time outcome in an IV analysis. Methods A SNCFTM models the ratio of two conditional mean counterfactual outcomes at time k under two treatment strategies which differ only at an earlier time m. These models can be extended to accommodate inverse probability of censoring weights, and can be applied to case-control data. We also describe how the g-estimates of the SNCFTM parameters can be used to calculate marginal cumulative risks under nondynamic treatment strategies. We examine the performance of this method using simulated data, and present an application of these models by conducting an MR study of alcohol intake and endometrial cancer using longitudinal observational data from the Nurses’ Health Study. Results Our simulations found that estimates from SNCFTMs which used an IV approach were similar to those obtained from SNCFTMs which adjusted for confounders, and similar to those obtained from the g-formula approach when the outcome was rare. In our data application, the cumulative risk of endometrial cancer from age 45 to age 72 under the “never drink” strategy (4.0%) was similar to that under the “always ½ drink per day” strategy (4.3%). Conclusions SNCFTMs can be used to conduct MR and other IV analyses with time-varying treatments and failure time outcomes.

NOTE ON MTTF OF A PARALLEL SYSTEM

2011 ◽  
Vol 18 (01) ◽  
pp. 43-50 ◽  
Author(s):  
TOSHIO NAKAGAWA ◽  
WON YOUNG YUN
Keyword(s):  
Weibull Distribution ◽  
Asymptotic Method ◽  
Failure Time ◽  
Asymptotic Methods ◽  
Parallel System ◽  
Optimal Number ◽  
System Failure ◽  
Expected Cost ◽  
Cost Rate ◽  
Mean Time

A parallel system with n identical units is considered, and its mean time to system failure (MTTF) is obtained when the failure time is exponential. A simple asymptotic method of computing MTTF is proposed and its approximal values are compared with the exact MTTFs when the failure time has a Weibull distribution. It is of great interest that such asymptotic methods give good approximations to exact MTTFs. Furthermore, when the number of units is random, the MTTF and an optimal number of units to minimize the expected cost rate are derived.

Integrated Prognosis for Wind Turbine Gearbox Condition-Based Maintenance Considering Time-Varying Load and Crack Initiation Time Uncertainty

2021 ◽  
pp. 2150024
Author(s):  
Fangfang Ding ◽  
Zhigang Tian
Keyword(s):  
Crack Initiation ◽  
Wind Turbine ◽  
Wind Farm ◽  
Failure Time ◽  
Remaining Useful Life ◽  
Condition Based Maintenance ◽  
Time Varying ◽  
Initiation Time ◽  
Loading Conditions ◽  
Power Cost

Maintenance management in wind energy industry has great impact on the overall wind power cost. Maintenance services are either supported by wind turbine manufacturers within warranty period, or managed by wind farm owners. With condition-based maintenance (CBM) strategy, maintenance activities are scheduled based on the predicted health conditions of wind turbine components, and accurate prognostics methods are critical for effective CBM. The reported studies on integrated health prognostics considered the uncertainty in crack initiation time (CIT) uncertainty, but did not incorporate time-varying loading conditions, which could also have a significant impact on future health condition and remaining useful life (RUL) prediction. Constant loads were generally used to approximate the actual time-varying loading conditions. In this paper, an integrated prognostics method is proposed for wind turbine gearboxes considering both time-varying loading conditions and CIT uncertainty. As new condition monitoring observations are available, the distributions of both material model parameter and CIT are updated via Bayesian inference, and the failure time prediction is updated accordingly. An example is provided to demonstrate that the proposed time-varying load approach presents more benefits considering the uncertainty of CIT, with significant accuracy improvement comparing to the constant-load approach.

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