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Items: 1 to 20 of 116

1.

On estimation of the survivor average causal effect in observational studies when important confounders are missing due to death.

Egleston BL, Scharfstein DO, MacKenzie E.

Biometrics. 2009 Jun;65(2):497-504. doi: 10.1111/j.1541-0420.2008.01111.x.

2.

Meta-analysis of studies with missing data.

Yuan Y, Little RJ.

Biometrics. 2009 Jun;65(2):487-96. doi: 10.1111/j.1541-0420.2008.01068.x. Epub 2008 May 18.

3.

Mixed-effect hybrid models for longitudinal data with nonignorable dropout.

Yuan Y, Little RJ.

Biometrics. 2009 Jun;65(2):478-86. doi: 10.1111/j.1541-0420.2008.01102.x.

4.

On comparison of mixture models for closed population capture-recapture studies.

Xuan Mao C, You N.

Biometrics. 2009 Jun;65(2):547-53. doi: 10.1111/j.1541-0420.2008.01065.x. Epub 2008 May 28.

PMID:
18510651
5.

Principal stratification designs to estimate input data missing due to death.

Frangakis CE, Rubin DB, An MW, MacKenzie E.

Biometrics. 2007 Sep;63(3):641-9; discussion 650-62.

PMID:
17824995
6.

Joint modeling and analysis of longitudinal data with informative observation times.

Liang Y, Lu W, Ying Z.

Biometrics. 2009 Jun;65(2):377-84. doi: 10.1111/j.1541-0420.2008.01104.x.

PMID:
18759841
7.

The balanced survivor average causal effect.

Greene T, Joffe M, Hu B, Li L, Boucher K.

Int J Biostat. 2013 May 7;9(2):291-306. doi: 10.1515/ijb-2012-0013.

PMID:
23658214
8.

Marginal mark regression analysis of recurrent marked point process data.

French B, Heagerty PJ.

Biometrics. 2009 Jun;65(2):415-22. doi: 10.1111/j.1541-0420.2008.01076.x.

PMID:
18573132
9.

Related causal frameworks for surrogate outcomes.

Joffe MM, Greene T.

Biometrics. 2009 Jun;65(2):530-8. doi: 10.1111/j.1541-0420.2008.01106.x.

PMID:
18759836
10.

Identifiability of causal effects for binary variables with baseline data missing due to death.

Yan W, Hu Y, Geng Z.

Biometrics. 2012 Mar;68(1):121-8. doi: 10.1111/j.1541-0420.2011.01653.x. Epub 2011 Aug 12.

PMID:
21838813
11.

Diagnosis of random-effect model misspecification in generalized linear mixed models for binary response.

Huang X.

Biometrics. 2009 Jun;65(2):361-8. doi: 10.1111/j.1541-0420.2008.01103.x.

PMID:
18759837
12.

Presence-only data and the em algorithm.

Ward G, Hastie T, Barry S, Elith J, Leathwick JR.

Biometrics. 2009 Jun;65(2):554-63. doi: 10.1111/j.1541-0420.2008.01116.x.

13.

The sign of the unmeasured confounding bias under various standard populations.

Chiba Y.

Biom J. 2009 Aug;51(4):670-6. doi: 10.1002/bimj.200800195.

PMID:
19650054
14.

Regularized estimation for the accelerated failure time model.

Cai T, Huang J, Tian L.

Biometrics. 2009 Jun;65(2):394-404. doi: 10.1111/j.1541-0420.2008.01074.x.

15.

Clustering in the presence of scatter.

Maitra R, Ramler IP.

Biometrics. 2009 Jun;65(2):341-52. doi: 10.1111/j.1541-0420.2008.01064.x. Epub 2008 May 30.

PMID:
18537949
16.

Marginal hazards regression for retrospective studies within cohort with possibly correlated failure time data.

Kang S, Cai J.

Biometrics. 2009 Jun;65(2):405-14. doi: 10.1111/j.1541-0420.2008.01077.x. Epub 2008 May 19.

17.

Partly functional temporal process regression with semiparametric profile estimating functions.

Yan J, Huang J.

Biometrics. 2009 Jun;65(2):431-40. doi: 10.1111/j.1541-0420.2008.01071.x. Epub 2008 May 10.

PMID:
18547299
18.

On assessing surrogacy in a single trial setting using a semicompeting risks paradigm.

Ghosh D.

Biometrics. 2009 Jun;65(2):521-9. doi: 10.1111/j.1541-0420.2008.01109.x.

19.

Inference for clustered inhomogeneous spatial point processes.

Henrys PA, Brown PE.

Biometrics. 2009 Jun;65(2):423-30. doi: 10.1111/j.1541-0420.2008.01070.x. Epub 2008 May 18.

PMID:
18565167
20.

Likelihood methods for regression models with expensive variables missing by design.

Zhao Y, Lawless JF, McLeish DL.

Biom J. 2009 Feb;51(1):123-36. doi: 10.1002/bimj.200810487.

PMID:
19197954

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