Results 251 to 260 of about 220,037 (330)

Independent Increments and Group Sequential Tests. [PDF]

Stat Med
Tsiatis AA, Davidian M.
europepmc   +1 more source

Conditional survival in glioblastoma: The evolution of prognostic factors over time. [PDF]

Int J Cancer
Mueller T, Vasella F, Velz J, Voglis S, Akeret K, Padevit L, Schubert M, Weller J, Brüningk S, Rushing E, Sarnthein J, Gramatzki D, Häni L, Raabe A, Zeitlberger AM, Bozinov O, Le Rhun E, Weller M, Regli L, Neidert MC.   +19 more
europepmc   +1 more source

Visual analytics framework for survival analysis and biomarker discovery from gene expression data. [PDF]

PLoS One
Kokošar J, Turkay C, Ausec L, Štajdohar M, Zupan B.   +4 more
europepmc   +1 more source

Sampling for computational efficiency when conducting analyses in big data. [PDF]

Am J Epidemiol
Rudolph JE, Zhou Y, Palatino M, Yenokyan K, Xu X, Wentz E, Calkins KL, Joshu CE, Lau B.   +8 more
europepmc   +1 more source

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A Bias-Corrected Kaplan-Meier Estimator

2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM), 2020
The Kaplan-Meier estimator (KME) is a classical non-parametric reliability estimator for incomplete data; and it underestimates the reliability. Few estimators have been developed to correct its bias. This paper aims to fill this gap by proposing a bias-corrected estimator.
R. Jiang
openaire   +2 more sources

Bootstrapping the Kaplan-Meier Estimator

Journal of the American Statistical Association, 1986
Abstract Randomly censored data consist of iid pairs of observations (Xi, δi), i = 1, …, n; if δ i = 0, Xi denotes a censored observation, and if δ i = 1, Xi denotes an exact “survival” time, which is the variable of interest. For estimating the distribution F of the survival times, the product-limit estimator proposed by Kaplan and Meier (1958) has ...
M. Akritas
semanticscholar   +3 more sources

Expert Kaplan–Meier estimation [PDF]

Scandinavian Actuarial Journal, 2023
The setting of a right-censored random sample subject to contamination is considered. In various fields, expert information is often available and used to overcome the contamination. This paper integrates expert knowledge into the product-limit estimator in two different ways with distinct interpretations.
Martin Bladt, Christian Furrer
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Reconstructing the Kaplan–Meier Estimator as an M-estimator

American Statistician, 2021
The Kaplan–Meier (KM) estimator, which provides a nonparametric estimate of a survival function for time-to-event data, has broad applications in clinical studies, engineering, economics and many other fields.
Jiaqi Gu, Yiwei Fan, G. Yin
semanticscholar   +1 more source

Alternatives to the Kaplan–Meier estimator of progression-free survival

The International Journal of Biostatistics, 2020
Progression-free survival (PFS), defined as the time from randomization to progression of disease or death, has been indicated as an endpoint to support accelerated approval of certain cancer drugs by the U.S. FDA.
Jenny J. Zhang, Zhuoxin Sun, Han Yuan, Molin Wang   +3 more
semanticscholar   +1 more source

Effective Sample Size for the Kaplan-Meier Estimator: A Valuable Measure of Uncertainty?

American Statistician
Sample size is an essential indicator of the uncertainty in clinical research results. When studies present time-to-event outcomes with Kaplan-Meier curves, these are often accompanied by the remaining number of patients at risk in a table below the ...
Toby Hackmann, Doranne Thomassen, A. Stiggelbout, S. le Cessie, H. Putter, L. D. de Wreede, E. Steyerberg, Barbara Perić, Carlos Romero-Piqueras, Claudia Cruz Oliveira, E. Verheul, Ellen H Kapiteijn, Frank Doornkamp, G. Sroczynski, I. Korfage, Ignacio López-Forniés, I. M. Svane, M. Wouters, J. Rietjens, K. Steffensen, K. F. Rudebeck, L. Hallsson, Laura Hartman, Marieke Bak, Roberto Pazo-Cid, Tamara Wit, T. Puértolas, Uwe Siebert, Yassin Engelberts   +28 more
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