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Items: 1 to 20 of 88

1.

Modeling familial association of ages at onset of disease in the presence of competing risk.

Shih JH, Albert PS.

Biometrics. 2010 Dec;66(4):1012-23. doi: 10.1111/j.1541-0420.2009.01372.x.

2.

A bivariate cure-mixture approach for modeling familial association in diseases.

Chatterjee N, Shih J.

Biometrics. 2001 Sep;57(3):779-86.

PMID:
11550928
3.

Analysis of survival data from case-control family studies.

Shih JH, Chatterjee N.

Biometrics. 2002 Sep;58(3):502-9.

PMID:
12229984
4.

Association analyses of clustered competing risks data via cross hazard ratio.

Cheng Y, Fine JP, Bandeen-Roche K.

Biostatistics. 2010 Jan;11(1):82-92. doi: 10.1093/biostatistics/kxp039. Epub 2009 Oct 13.

5.

Estimation of time-dependent association for bivariate failure times in the presence of a competing risk.

Ning J, Bandeen-Roche K.

Biometrics. 2014 Mar;70(1):10-20. doi: 10.1111/biom.12110. Epub 2013 Dec 18.

7.

Association and aggregation analysis using kin-cohort designs with applications to genotype and family history data from the Washington Ashkenazi Study.

Chatterjee N, Shih J, Hartge P, Brody L, Tucker M, Wacholder S.

Genet Epidemiol. 2001 Sep;21(2):123-38.

PMID:
11507721
8.

Estimating twin concordance for bivariate competing risks twin data.

Scheike TH, Holst KK, Hjelmborg JB.

Stat Med. 2014 Mar 30;33(7):1193-204. doi: 10.1002/sim.6016. Epub 2013 Oct 17.

PMID:
24132877
9.

Adjustment for competing risk in kin-cohort estimation.

Chatterjee N, Hartge P, Wacholder S.

Genet Epidemiol. 2003 Dec;25(4):303-13.

PMID:
14639700
10.

Joint Modeling and Estimation for Recurrent Event Processes and Failure Time Data.

Huang CY, Wang MC.

J Am Stat Assoc. 2004 Dec;99(468):1153-1165.

11.

Competing risk models to estimate the excess mortality and the first recurrent-event hazards.

Belot A, Remontet L, Launoy G, Jooste V, Giorgi R.

BMC Med Res Methodol. 2011 May 25;11:78. doi: 10.1186/1471-2288-11-78.

12.

Quantifying the predictive accuracy of time-to-event models in the presence of competing risks.

Schoop R, Beyersmann J, Schumacher M, Binder H.

Biom J. 2011 Feb;53(1):88-112. doi: 10.1002/bimj.201000073. Epub 2011 Jan 14.

PMID:
21259311
13.

The analysis of survival data with a non-susceptible fraction and dual censoring mechanisms.

Gagnon DR, Glickman ME, Myers RH, Cupples LA.

Stat Med. 2003 Oct 30;22(20):3249-62.

PMID:
14518026
14.

Time-dependent predictive accuracy in the presence of competing risks.

Saha P, Heagerty PJ.

Biometrics. 2010 Dec;66(4):999-1011. doi: 10.1111/j.1541-0420.2009.01375.x.

15.

A bivariate frailty model with a cure fraction for modeling familial correlations in diseases.

Wienke A, Lichtenstein P, Yashin AI.

Biometrics. 2003 Dec;59(4):1178-83; discussion 1184-5.

PMID:
14969499
16.

Full likelihood analysis of genetic risk with variable age at onset disease--combining population-based registry data and demographic information.

Pitkäniemi J, Varvio SL, Corander J, Lehti N, Partanen J, Tuomilehto-Wolf E, Tuomilehto J, Thomas A, Arjas E.

PLoS One. 2009 Aug 31;4(8):e6836. doi: 10.1371/journal.pone.0006836.

17.

A marginal likelihood approach for estimating penetrance from kin-cohort designs.

Chatterjee N, Wacholder S.

Biometrics. 2001 Mar;57(1):245-52.

PMID:
11252606
19.

General joint frailty model for recurrent event data with a dependent terminal event: Application to follicular lymphoma data.

Mazroui Y, Mathoulin-Pelissier S, Soubeyran P, Rondeau V.

Stat Med. 2012 May 20;31(11-12):1162-76. doi: 10.1002/sim.4479. Epub 2012 Feb 3.

PMID:
22307954
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