Robust prediction of t-year survival with data from multiple studies

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Standard

Robust prediction of t-year survival with data from multiple studies. / Cai, T; Gerds, Thomas Alexander; Zheng, Y; Chen, J.

I: Biometrics, Bind 67, Nr. 2, 2011, s. 436–444.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Cai, T, Gerds, TA, Zheng, Y & Chen, J 2011, 'Robust prediction of t-year survival with data from multiple studies', Biometrics, bind 67, nr. 2, s. 436–444. https://doi.org/10.1111/j.1541-0420.2010.01462.x

APA

Cai, T., Gerds, T. A., Zheng, Y., & Chen, J. (2011). Robust prediction of t-year survival with data from multiple studies. Biometrics, 67(2), 436–444. https://doi.org/10.1111/j.1541-0420.2010.01462.x

Vancouver

Cai T, Gerds TA, Zheng Y, Chen J. Robust prediction of t-year survival with data from multiple studies. Biometrics. 2011;67(2):436–444. https://doi.org/10.1111/j.1541-0420.2010.01462.x

Author

Cai, T ; Gerds, Thomas Alexander ; Zheng, Y ; Chen, J. / Robust prediction of t-year survival with data from multiple studies. I: Biometrics. 2011 ; Bind 67, Nr. 2. s. 436–444.

Bibtex

@article{202d873d6eab425c8639b79a6f434d3f,
title = "Robust prediction of t-year survival with data from multiple studies",
abstract = "Summary Recently meta-analysis has been widely utilized to combine information across multiple studies to evaluate a common effect. Integrating data from similar studies is particularly useful in genomic studies where the individual study sample sizes are not large relative to the number of parameters of interest. In this article, we are interested in developing robust prognostic rules for the prediction of t-year survival based on multiple studies. We propose to construct a composite score for prediction by fitting a stratified semiparametric transformation model that allows the studies to have related but not identical outcomes. To evaluate the accuracy of the resulting score, we provide point and interval estimators for the commonly used accuracy measures including the time-specific receiver operating characteristic curves, and positive and negative predictive values. We apply the proposed procedures to develop prognostic rules for the 5-year survival of breast cancer patients based on five breast cancer genomic studies. ",
author = "T Cai and Gerds, {Thomas Alexander} and Y Zheng and J Chen",
year = "2011",
doi = "10.1111/j.1541-0420.2010.01462.x",
language = "English",
volume = "67",
pages = "436–444",
journal = "Biometrics",
issn = "0006-341X",
publisher = "Wiley-Blackwell",
number = "2",

}

RIS

TY - JOUR

T1 - Robust prediction of t-year survival with data from multiple studies

AU - Cai, T

AU - Gerds, Thomas Alexander

AU - Zheng, Y

AU - Chen, J

PY - 2011

Y1 - 2011

N2 - Summary Recently meta-analysis has been widely utilized to combine information across multiple studies to evaluate a common effect. Integrating data from similar studies is particularly useful in genomic studies where the individual study sample sizes are not large relative to the number of parameters of interest. In this article, we are interested in developing robust prognostic rules for the prediction of t-year survival based on multiple studies. We propose to construct a composite score for prediction by fitting a stratified semiparametric transformation model that allows the studies to have related but not identical outcomes. To evaluate the accuracy of the resulting score, we provide point and interval estimators for the commonly used accuracy measures including the time-specific receiver operating characteristic curves, and positive and negative predictive values. We apply the proposed procedures to develop prognostic rules for the 5-year survival of breast cancer patients based on five breast cancer genomic studies.

AB - Summary Recently meta-analysis has been widely utilized to combine information across multiple studies to evaluate a common effect. Integrating data from similar studies is particularly useful in genomic studies where the individual study sample sizes are not large relative to the number of parameters of interest. In this article, we are interested in developing robust prognostic rules for the prediction of t-year survival based on multiple studies. We propose to construct a composite score for prediction by fitting a stratified semiparametric transformation model that allows the studies to have related but not identical outcomes. To evaluate the accuracy of the resulting score, we provide point and interval estimators for the commonly used accuracy measures including the time-specific receiver operating characteristic curves, and positive and negative predictive values. We apply the proposed procedures to develop prognostic rules for the 5-year survival of breast cancer patients based on five breast cancer genomic studies.

U2 - 10.1111/j.1541-0420.2010.01462.x

DO - 10.1111/j.1541-0420.2010.01462.x

M3 - Journal article

C2 - 20670303

VL - 67

SP - 436

EP - 444

JO - Biometrics

JF - Biometrics

SN - 0006-341X

IS - 2

ER -

ID: 35058014