Estimating Haplotype Effects for Survival Data

Thomas H Scheike, Torben Martinussen, Jeremy D Silver

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Resumé

Summary. Genetic association studies often investigate the effect of haplotypes on an outcome of interest. Haplotypes are not observed directly, and this complicates the inclusion of such effects in survival models. We describe a new estimating equations approach for Cox's regression model to assess haplotype effects for survival data. These estimating equations are simple to implement and avoid the use of the EM algorithm, which may be slow in the context of the semiparametric Cox model with incomplete covariate information. These estimating equations also lead to easily computable, direct estimators of standard errors, and thus overcome some of the difficulty in obtaining variance estimators based on the EM algorithm in this setting. We also develop an easily implemented goodness-of-fit procedure for Cox's regression model including haplotype effects. Finally, we apply the procedures presented in this article to investigate possible haplotype effects of the PAF-receptor on cardiovascular events in patients with coronary artery disease, and compare our results to those based on the EM algorithm.
OriginalsprogEngelsk
TidsskriftBiometrics
Vol/bind66
Udgave nummer3
Sider (fra-til)705-15
ISSN0006-341X
DOI
StatusUdgivet - 2010

Fingeraftryk

Haplotype
Survival Data
Haplotypes
haplotypes
Estimating Equation
EM Algorithm
Proportional Hazards Models
Cox Regression Model
Genetic Association
Coronary Artery Disease
Cox Model
Survival Model
Variance Estimator
Semiparametric Model
Genetic Association Studies
Standard error
Goodness of fit
Receptor
Covariates
Inclusion

Citer dette

Scheike, T. H., Martinussen, T., & Silver, J. D. (2010). Estimating Haplotype Effects for Survival Data. Biometrics, 66(3), 705-15. https://doi.org/10.1111/j.1541-0420.2009.01329.x
Scheike, Thomas H ; Martinussen, Torben ; Silver, Jeremy D. / Estimating Haplotype Effects for Survival Data. I: Biometrics. 2010 ; Bind 66, Nr. 3. s. 705-15.
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abstract = "Summary. Genetic association studies often investigate the effect of haplotypes on an outcome of interest. Haplotypes are not observed directly, and this complicates the inclusion of such effects in survival models. We describe a new estimating equations approach for Cox's regression model to assess haplotype effects for survival data. These estimating equations are simple to implement and avoid the use of the EM algorithm, which may be slow in the context of the semiparametric Cox model with incomplete covariate information. These estimating equations also lead to easily computable, direct estimators of standard errors, and thus overcome some of the difficulty in obtaining variance estimators based on the EM algorithm in this setting. We also develop an easily implemented goodness-of-fit procedure for Cox's regression model including haplotype effects. Finally, we apply the procedures presented in this article to investigate possible haplotype effects of the PAF-receptor on cardiovascular events in patients with coronary artery disease, and compare our results to those based on the EM algorithm.",
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Scheike, TH, Martinussen, T & Silver, JD 2010, 'Estimating Haplotype Effects for Survival Data', Biometrics, bind 66, nr. 3, s. 705-15. https://doi.org/10.1111/j.1541-0420.2009.01329.x

Estimating Haplotype Effects for Survival Data. / Scheike, Thomas H; Martinussen, Torben; Silver, Jeremy D.

I: Biometrics, Bind 66, Nr. 3, 2010, s. 705-15.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - Estimating Haplotype Effects for Survival Data

AU - Scheike, Thomas H

AU - Martinussen, Torben

AU - Silver, Jeremy D

N1 - Published Online: 17 Sep 2009

PY - 2010

Y1 - 2010

N2 - Summary. Genetic association studies often investigate the effect of haplotypes on an outcome of interest. Haplotypes are not observed directly, and this complicates the inclusion of such effects in survival models. We describe a new estimating equations approach for Cox's regression model to assess haplotype effects for survival data. These estimating equations are simple to implement and avoid the use of the EM algorithm, which may be slow in the context of the semiparametric Cox model with incomplete covariate information. These estimating equations also lead to easily computable, direct estimators of standard errors, and thus overcome some of the difficulty in obtaining variance estimators based on the EM algorithm in this setting. We also develop an easily implemented goodness-of-fit procedure for Cox's regression model including haplotype effects. Finally, we apply the procedures presented in this article to investigate possible haplotype effects of the PAF-receptor on cardiovascular events in patients with coronary artery disease, and compare our results to those based on the EM algorithm.

AB - Summary. Genetic association studies often investigate the effect of haplotypes on an outcome of interest. Haplotypes are not observed directly, and this complicates the inclusion of such effects in survival models. We describe a new estimating equations approach for Cox's regression model to assess haplotype effects for survival data. These estimating equations are simple to implement and avoid the use of the EM algorithm, which may be slow in the context of the semiparametric Cox model with incomplete covariate information. These estimating equations also lead to easily computable, direct estimators of standard errors, and thus overcome some of the difficulty in obtaining variance estimators based on the EM algorithm in this setting. We also develop an easily implemented goodness-of-fit procedure for Cox's regression model including haplotype effects. Finally, we apply the procedures presented in this article to investigate possible haplotype effects of the PAF-receptor on cardiovascular events in patients with coronary artery disease, and compare our results to those based on the EM algorithm.

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DO - 10.1111/j.1541-0420.2009.01329.x

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JO - Biometrics

JF - Biometrics

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