Results 241 to 250 of about 359,465 (284)

Critical review of oncology clinical trial design under non-proportional hazards

Critical Reviews in Oncology/Hematology, 2021
In trials of novel immuno-oncology drugs, the proportional hazards (PH) assumption often does not hold for the primary time-to-event (TTE) efficacy endpoint, likely due to the unique mechanism of action of these drugs. In practice, when it is anticipated that PH may not hold for the TTE endpoint with respect to treatment, the sample size is often still
Revathi, Ananthakrishnan, Stephanie, Green, Alessandro, Previtali, Rong, Liu, Daniel, Li, Michael, LaValley   +5 more
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Reduced-rank hazard regression for modelling non-proportional hazards

Statistics in Medicine, 2006
The Cox proportional hazards model is the most common method to analyse survival data. However, the proportional hazards assumption might not hold. The natural extension of the Cox model is to introduce time-varying effects of the covariates. For some covariates such as (surgical)treatment non-proportionality could be expected beforehand.
Perperoglou, Aris, Cessie, Saskia le, van Houwelingen, Hans C   +2 more
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Sequential tests for non-proportional hazards data

Lifetime Data Analysis, 2016
In clinical trials survival endpoints are usually compared using the log-rank test. Sequential methods for the log-rank test and the Cox proportional hazards model are largely reported in the statistical literature. When the proportional hazards assumption is violated the hazard ratio is ill-defined and the power of the log-rank test depends on the ...
Brückner, Matthias, Brannath, Werner
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Non-proportional hazards models

2021
The most general model, described in Chapter 4 covers a very broad spread of possibilities and, in this chapter, we consider some special cases.
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A Non‐Proportional Hazards Model with Hazard Ratio Functions Free from Covariate Values

International Statistical Review, 2020
SummaryA brief survey on methods to handle non‐proportional hazards in survival analysis is given with emphasis on short‐term and long‐term hazard ratio modelling. A drawback of the existing model of this nature is that except at time zero or infinity, the hazard ratio for a unit increase in the value of a covariate depends on the starting value.
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Sample Size Determination Under Non-proportional Hazards

2019
The proportional hazards assumption rarely holds in clinical trials of cancer immunotherapy. Specifically, delayed separation of the Kaplan-Meier survival curves and long-term survival have been observed. Routine practice in designing a randomized controlled two-arm clinical trial with a time-to-event endpoint assumes proportional hazards.
Miao Yang, Zhaowei Hua, Saran Vardhanabhuti   +2 more
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Non-proportional hazards models in survival analysis

2000
Cox’ proportional hazard model is usually the model of choice in survival analysis. It is shown that this model can be embedded in a GLMmodel by proper discretization of the time axis. That approach easily allows non-proportional hazard models, that are special cases of time-varying coefficients models.
Hans C. van Houwelingen, Paul H. C. Eilers   +1 more
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Estimation of Main Effect When Covariates Have Non-Proportional Hazards

Communications in Statistics - Simulation and Computation, 2014
The Cox proportional hazards (PH) regression model has been widely used to analyze survival data in clinical trials and observational studies. In addition to estimating the main treatment or exposure group effect, it is common to adjust for additional covariates using the Cox model.
Erika Strandberg, Xinyi Lin, Ronghui Xu
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On a non‐proportional hazards regression model for repeated medical random counts

Statistics in Medicine, 1997
A wholly parametric non-proportional hazards survival model is introduced. The model retains Cox's constant of proportionality as the leading term in the relative risk but permits additional flexibility by modelling the relative risk as a function of time.
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Robust prediction of the cumulative incidence function under non‐proportional subdistribution hazards

Canadian Journal of Statistics, 2016
AbstractPrediction of a cause‐specific cumulative incidence function (CIF) for data containing competing risks is of primary interest to clinicians when making treatment decisions for patients given their prognostic characteristics. The Fine–Gray regression model is widely used to incorporate multiple prognostic factors, yet it is not applicable when ...
Liu, Qing, Tang, Gong, Costantino, Joseph P., Chang, Chung-Chou H.   +3 more
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