INFERENCE IN REGRESSION MODELS WITH MISSING COVARIATES

Summary

Principal Investigator: Joseph Ibrahim
Abstract: In this proposal, we propose Bayesian and frequentist methodology for local influence diagnostics and develop model assessment tools for complete data settings as well as in the presence of missing covariate and/or response data for a variety of statistical models, including generalized linear models, models for longitudinal data, and survival model. In Specific Aim 1, we develop frequentist local influence measures and goodness of fit statistics based on the general local influence development of Cook (1986), and discuss these measures for i) linear models with missing at random (MAR) and nonignorably missing covariates and ii) generalized linear models with MAR and nonignorably missing covariates. For Specific Aim 2, we develop new classes of Bayesian case influence diagnostics for the complete data setting then generalize these diagnostics to the missing data framework. The proposed methodologies in Aims 1-2 are primarily motivated from several studies in the PI's collaborative work.
Funding Period: ----------------1997 - ---------------2011-
more information: NIH RePORT

Top Publications

  1. pmc Meta-analysis methods and models with applications in evaluation of cholesterol-lowering drugs
    Ming Hui Chen
    Department of Statistics, University of Connecticut, Storrs, CT, USA
    Stat Med 31:3597-616. 2012
  2. pmc Missing data in clinical studies: issues and methods
    Joseph G Ibrahim
    Department of Biostatistics, University of North Carolina, CB 7420, Chapel Hill, NC 27599, USA
    J Clin Oncol 30:3297-303. 2012
  3. pmc Bayesian inference of the fully specified subdistribution model for survival data with competing risks
    Miaomiao Ge
    Clinical Bio Statistics, Boehringer Ingelheim Pharmaceuticals, Inc, Ridgefield, CT 06877, USA
    Lifetime Data Anal 18:339-63. 2012
  4. pmc Bayesian influence measures for joint models for longitudinal and survival data
    Hongtu Zhu
    Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 7420, USA
    Biometrics 68:954-64. 2012
  5. pmc Bayesian lasso for semiparametric structural equation models
    Ruixin Guo
    Department of Biostatistics, University of North Carolina at Chapel Hill, USA
    Biometrics 68:567-77. 2012
  6. pmc Bayesian meta-experimental design: evaluating cardiovascular risk in new antidiabetic therapies to treat type 2 diabetes
    Joseph G Ibrahim
    Department of Biostatistics, University of North Carolina, McGavran Greenberg Hall, Chapel Hill, North Carolina 27599, USA
    Biometrics 68:578-86. 2012
  7. ncbi Maximum likelihood estimation in generalized linear models with multiple covariates subject to detection limits
    Ryan C May
    Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
    Stat Med 30:2551-61. 2011
  8. pmc A bivariate pseudolikelihood for incomplete longitudinal binary data with nonignorable nonmonotone missingness
    Sanjoy K Sinha
    School of Mathematics and Statistics, Carleton University, Ottawa, Ontario K1S 5B6, Canada University of Pennsylvania, School of Medicine, Philadelphia, Pennsylvania 19104, USA
    Biometrics 67:1119-26. 2011
  9. pmc Bayesian gamma frailty models for survival data with semi-competing risks and treatment switching
    Yuanye Zhang
    Novartis Institutes for BioMedical Research, Inc, 220 Massachusetts Avenue, Cambridge, MA, 02139, USA
    Lifetime Data Anal 20:76-105. 2014
  10. pmc Bayesian methods in clinical trials: a Bayesian analysis of ECOG trials E1684 and E1690
    Joseph G Ibrahim
    Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA
    BMC Med Res Methodol 12:183. 2012

Scientific Experts

  • Joseph Ibrahim
  • Ankit Parikh
  • P O Lewis
  • Xiaoyun Li
  • Fang Yu
  • Sungduk Kim
  • Yan Lin
  • Jennifer P Friedberg
  • Wei Sun
  • Hyunsoon Cho
  • Naim U Rashid
  • Andrea B Troxel
  • Ming Hui Chen
  • Hongtu Zhu
  • Stuart R Lipsitz
  • Garrett M Fitzmaurice
  • Qingxia Chen
  • Yu Fan
  • Yueh Yun Chi
  • Yuanye Zhang
  • Xiaojing Wang
  • Michelle F Miranda
  • Miaomiao Ge
  • Ruixin Guo
  • Wangang Xie
  • Lynn Kuo
  • Sanjoy K Sinha
  • Ryan C May
  • Patrick T Bradshaw
  • Ramon I Garcia
  • Niansheng Tang
  • Katharine M McGreevy
  • Charity G Moore
  • Nancy Tran
  • Xiaoyan Shi
  • Weili Lin
  • Ying Yuan
  • P Qu
  • Bradley S Peterson
  • Tathagata Banerjee
  • Abhijit A Patel
  • Jonathan A L Gelfond
  • Michael Parzen
  • Thomas Liu
  • H Amy Xia
  • Violeta Hennessey
  • XiaoDong Xue
  • Zhiying Pan
  • Donglin Zeng
  • David Ohlssen
  • Jun Yan
  • Jianxin Lin
  • Hui Yao
  • Sy Miin Chow
  • Arvind K Shah
  • Debajyoti Sinha
  • Geert Molenberghs
  • Haitao Chu
  • Rui Wu
  • Marilie D Gammon
  • Xiaojun Guan
  • William K Kaufmann
  • Laura Lindsey-Boltz
  • Cheryl L Addy
  • Dennis A Simpson
  • Ping Ping Qu
  • Dinggang Shen
  • Eric Rimm
  • David G Hoel
  • Yimei Li
  • Charles P Schmitt
  • James R Hussey
  • Sundar Natarajan
  • Jeffrey A Linder
  • Garrett Fitzmaurice
  • J Tepper
  • R S Sandler
  • H Chu
  • J Peacock
  • X J Shen
  • T O Keku
  • Daniel B Rowe
  • Anthony V D'Amico
  • David W Threadgill
  • Ravi Bansal
  • Xuejun Hao
  • Andrew A Renshaw
  • Dipak K Dey
  • Anika Bissahoyo
  • Fei Zou

Detail Information

Publications66

  1. pmc Meta-analysis methods and models with applications in evaluation of cholesterol-lowering drugs
    Ming Hui Chen
    Department of Statistics, University of Connecticut, Storrs, CT, USA
    Stat Med 31:3597-616. 2012
    ..The proposed methodology is quite general and can be applied in any meta-analysis setting for a wide range of scientific applications and therefore offers new analytic methods of clinical importance...
  2. pmc Missing data in clinical studies: issues and methods
    Joseph G Ibrahim
    Department of Biostatistics, University of North Carolina, CB 7420, Chapel Hill, NC 27599, USA
    J Clin Oncol 30:3297-303. 2012
    ..Although the main area of application discussed here is cancer, the issues and methods we discuss apply to any type of study...
  3. pmc Bayesian inference of the fully specified subdistribution model for survival data with competing risks
    Miaomiao Ge
    Clinical Bio Statistics, Boehringer Ingelheim Pharmaceuticals, Inc, Ridgefield, CT 06877, USA
    Lifetime Data Anal 18:339-63. 2012
    ..The proposed methodology is applied to analyze a real dataset from a prostate cancer study in detail...
  4. pmc Bayesian influence measures for joint models for longitudinal and survival data
    Hongtu Zhu
    Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 7420, USA
    Biometrics 68:954-64. 2012
    ..Simulation studies and a real data set are used to highlight the broad spectrum of applications for our Bayesian influence methods...
  5. pmc Bayesian lasso for semiparametric structural equation models
    Ruixin Guo
    Department of Biostatistics, University of North Carolina at Chapel Hill, USA
    Biometrics 68:567-77. 2012
    ..Results demonstrate that our method can accurately estimate the unknown parameters and correctly identify the true underlying model...
  6. pmc Bayesian meta-experimental design: evaluating cardiovascular risk in new antidiabetic therapies to treat type 2 diabetes
    Joseph G Ibrahim
    Department of Biostatistics, University of North Carolina, McGavran Greenberg Hall, Chapel Hill, North Carolina 27599, USA
    Biometrics 68:578-86. 2012
    ..The proposed methodology is applied to the design of a phase 2/3 development program including a noninferiority clinical trial for CV risk assessment in T2DM studies...
  7. ncbi Maximum likelihood estimation in generalized linear models with multiple covariates subject to detection limits
    Ryan C May
    Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
    Stat Med 30:2551-61. 2011
    ..Through simulation studies, we show that the proposed approach can lead to a significant reduction in variance for parameter estimates in these models, improving the power of such studies...
  8. pmc A bivariate pseudolikelihood for incomplete longitudinal binary data with nonignorable nonmonotone missingness
    Sanjoy K Sinha
    School of Mathematics and Statistics, Carleton University, Ottawa, Ontario K1S 5B6, Canada University of Pennsylvania, School of Medicine, Philadelphia, Pennsylvania 19104, USA
    Biometrics 67:1119-26. 2011
    ..We illustrate the method using longitudinal data on CD4 counts from two clinical trials of HIV-infected patients...
  9. pmc Bayesian gamma frailty models for survival data with semi-competing risks and treatment switching
    Yuanye Zhang
    Novartis Institutes for BioMedical Research, Inc, 220 Massachusetts Avenue, Cambridge, MA, 02139, USA
    Lifetime Data Anal 20:76-105. 2014
    ..A simulation study is conducted to examine the empirical performance of DIC and LPML and as well as the posterior estimates. The proposed method is further applied to analyze data from a colorectal cancer study. ..
  10. pmc Bayesian methods in clinical trials: a Bayesian analysis of ECOG trials E1684 and E1690
    Joseph G Ibrahim
    Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA
    BMC Med Res Methodol 12:183. 2012
    ..The analyses of E1684 and E1690 were carried out separately when the results were published, and there were no further analyses trying to perform a single analysis of the combined trials...
  11. pmc Bayesian sequential meta-analysis design in evaluating cardiovascular risk in a new antidiabetic drug development program
    Ming Hui Chen
    Department of Statistics, University of Connecticut, Storrs, Connecticut 06269, U S A
    Stat Med 33:1600-18. 2014
    ..We apply the proposed methodology to the design of a hypothetical antidiabetic drug development program for evaluating cardiovascular risk...
  12. pmc Posterior predictive Bayesian phylogenetic model selection
    Paul O Lewis
    Department of Ecology and Evolutionary Biology, University of Connecticut, 75 N Eagleville Road, Unit 3043, Storrs, CT 06269, USA AbbVie, 1 N Waukegan Road, R436 AP9A 2, North Chicago, IL 60064, USA Department of Statistics, University of Connecticut, 215 Glenbrook Road, Unit 4120, Storrs, CT 06269, USA and Department of Bioinformatics and Computational Biology, Division of Quantitative Sciences, The University of Texas M D Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA
    Syst Biol 63:309-21. 2014
    ....
  13. pmc Bayesian spatial transformation models with applications in neuroimaging data
    Michelle F Miranda
    Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, U S A
    Biometrics 69:1074-83. 2013
    ..Our STM is able to reveal important brain regions with morphological changes in children with attention deficit hyperactivity disorder. ..
  14. ncbi Bayesian modeling and inference for clinical trials with partial retrieved data following dropout
    Qingxia Chen
    Departments of Biostatistics and Biomedical Informatics, Vanderbilt University, Nashville, TN 37232, U S A
    Stat Med 32:4180-95. 2013
    ..We develop an efficient Markov chain Monte Carlo sampling algorithm. We analyze in detail via the proposed method a real dataset from a clinical trial. Copyright © 2013 John Wiley & Sons, Ltd. ..
  15. ncbi Bayesian inference for multivariate meta-analysis Box-Cox transformation models for individual patient data with applications to evaluation of cholesterol-lowering drugs
    Sungduk Kim
    Division of Epidemiology, Statistics and Prevention Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Rockville, MD, U S A
    Stat Med 32:3972-90. 2013
    ..We carry out a detailed analysis of these data by using the proposed methodology. Copyright © 2013 John Wiley & Sons, Ltd. ..
  16. ncbi Bayesian dynamic regression models for interval censored survival data with application to children dental health
    Xiaojing Wang
    Google, New York, NY, USA
    Lifetime Data Anal 19:297-316. 2013
    ..5, and that it gradually reduces to one after age 11. These findings were not seen from the existing studies with Cox proportional hazards models. ..
  17. ncbi Pseudo-likelihood methods for longitudinal binary data with non-ignorable missing responses and covariates
    Michael Parzen
    Department of Decision and Information Analysis, Goizueta Business School, USA
    Stat Med 25:2784-96. 2006
    ..to handle binary responses and possibly missing time-varying covariates. The method is illustrated using data from the Six Cities study, a longitudinal study of the health effects of air pollution...
  18. pmc Statistical strategies to improve the efficiency of molecular studies of colorectal cancer prognosis
    P Qu
    Cancer Research and Biostatistics CRAB, 1730 Minor Ave Suite 1900, Seattle, WA 98101, USA
    Br J Cancer 99:2001-5. 2008
    ..Using this method, we identified and abandoned potentially uninformative molecular markers in favour of more promising candidates. This approach conserves tissue resources, time, and money, and may be applicable to other studies...
  19. pmc Bayesian variable selection for the Cox regression model with missing covariates
    Joseph G Ibrahim
    Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA
    Lifetime Data Anal 14:496-520. 2008
    ..Monte Carlo methods are developed for computing the DICs for all possible subset models in the model space. A Bone Marrow Transplant (BMT) dataset is used to illustrate the proposed methodology...
  20. pmc A note on the validity of statistical bootstrapping for estimating the uncertainty of tensor parameters in diffusion tensor images
    Ying Yuan
    Department of Statistics and Operations, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
    IEEE Trans Med Imaging 27:1506-14. 2008
    ..Our findings raise serious concerns about the use of bootstrap methods to quantify the uncertainty of fiber pathways when those pathways pass through voxels that contain either isotropic tensors, oblate tensors, or multiple tensors...
  21. ncbi PSA failure following definitive treatment of prostate cancer having biopsy Gleason score 7 with tertiary grade 5
    Abhijit A Patel
    Department of Radiation Oncology, Brigham and Women s Hospital and Dana Farber Cancer Institute, Boston, Massachusetts 02115, USA
    JAMA 298:1533-8. 2007
    ..Yet, the management of men with Gleason score 7 vs 8 or 9 prostate cancer differs...
  22. ncbi Bayesian hierarchical modeling for time course microarray experiments
    Yueh Yun Chi
    Department of Biostatistics, University of Washington, Seattle, Washington 98195, USA
    Biometrics 63:496-504. 2007
    ..The methodology is applied to a mouse model time course experiment to correlate temporal changes in azoxymethane-induced gene expression profiles with colorectal cancer susceptibility...
  23. pmc A statistical analysis of brain morphology using wild bootstrapping
    Hongtu Zhu
    Department of Biostatistics and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC 27599 7420, USA
    IEEE Trans Med Imaging 26:954-66. 2007
    ..We demonstrate the application of this robust test procedure to the detection of statistically significant differences in the morphology of the hippocampus over time across gender groups in a large sample of healthy subjects...
  24. ncbi Proximity model for expression quantitative trait loci (eQTL) detection
    Jonathan A L Gelfond
    Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
    Biometrics 63:1108-16. 2007
    ..We also discuss an extension of the MOM method to model multiple eQTLs, and find that many transcripts are likely associated with more than one eQTL...
  25. ncbi Estimation in regression models for longitudinal binary data with outcome-dependent follow-up
    Garrett M Fitzmaurice
    Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue and Brigham and Women s Hospital, Boston, MA, USA
    Biostatistics 7:469-85. 2006
    ..Finally, we illustrate the main results using data from a longitudinal observational study that explored the cardiotoxic effects of doxorubicin chemotherapy for the treatment of acute lymphoblastic leukemia in children...
  26. ncbi Semiparametric models for missing covariate and response data in regression models
    Qingxia Chen
    Department of Biostatistics, Vanderbilt University, Nashville, Tennessee 37232, USA
    Biometrics 62:177-84. 2006
    ..Maximum likelihood estimates are then obtained via the EM algorithm. Simulations are given to demonstrate the methodology, and a real data set from a melanoma cancer clinical trial is analyzed using the proposed methods...
  27. ncbi Bayesian dynamic models for survival data with a cure fraction
    Sungduk Kim
    Department of Statistics, University of Connecticut, Storrs, CT 06269, USA
    Lifetime Data Anal 13:17-35. 2007
    ..In addition, an efficient reversible jump computational algorithm is developed for carrying out posterior computation. A real data set from a melanoma clinical trial is analyzed in detail to further demonstrate the proposed methodology...
  28. ncbi A note on permutation tests for variance components in multilevel generalized linear mixed models
    Garrett M Fitzmaurice
    Harvard Medical School, Boston, MA, USA
    Biometrics 63:942-6. 2007
    ..Results from a simulation study suggest that it is more powerful than tests based on mixtures of chi-square distributions. The proposed test is illustrated using data on the familial aggregation of sleep disturbance...
  29. ncbi Bayesian analysis of generalized odds-rate hazards models for survival data
    Tathagata Banerjee
    Department of Statistics, Calcutta University, Calcutta, 700019, India
    Lifetime Data Anal 13:241-60. 2007
    ..The propriety of the posterior has been established under some mild conditions. A simulation study is conducted and a detailed analysis of the data from a prostate cancer study is presented to further illustrate the proposed methodology...
  30. pmc Bayesian case influence diagnostics for survival models
    Hyunsoon Cho
    Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 7420, USA
    Biometrics 65:116-24. 2009
    ..We also present a theoretical relationship between our case-deletion diagnostics and diagnostics based on Cox's partial likelihood. A simulated data example and two real data examples are given to demonstrate the methodology...
  31. ncbi Using median regression to obtain adjusted estimates of central tendency for skewed laboratory and epidemiologic data
    Katharine M McGreevy
    New Jersey Department of Health and Senior Services, Trenton, NJ, USA
    Clin Chem 55:165-9. 2009
    ..When medians are compared across groups, confounding can be an issue, so there is a need for adjusted medians...
  32. pmc In silico construction of a protein interaction landscape for nucleotide excision repair
    Nancy Tran
    Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
    Cell Biochem Biophys 53:101-14. 2009
    ..Our analysis offers a computational framework that can be applied to construct landscapes for other biological processes...
  33. pmc Bayesian hierarchical modeling and selection of differentially expressed genes for the EST data
    Fang Yu
    Department of Biostatistics, University of Nebraska Medical Center, Omaha, Nebraska 68198 4350
    Biometrics 67:142-50. 2011
    ..Our new method with the new gene selection criterion is demonstrated via several simulations to have low false negative and false positive rates. A real EST data set is used to motivate and illustrate the proposed method...
  34. ncbi Association between a DASH-like diet and mortality in adults with hypertension: findings from a population-based follow-up study
    Ankit Parikh
    Department of Internal Medicine, New York University School of Medicine, New York, New York, USA
    Am J Hypertens 22:409-16. 2009
    ..Although the Dietary Approaches to Stop Hypertension (DASH) diet lowers blood pressure (BP) in hypertensive adults, its effect on mortality is unclear...
  35. pmc ZINBA integrates local covariates with DNA-seq data to identify broad and narrow regions of enrichment, even within amplified genomic regions
    Naim U Rashid
    Department of Biostatistics, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
    Genome Biol 12:R67. 2011
    ..ZINBA provides a single unified framework for analyzing DNA-seq experiments in challenging genomic contexts...
  36. pmc Bayesian local influence for survival models
    Joseph G Ibrahim
    Department of Biostatistics, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 7420, USA
    Lifetime Data Anal 17:43-70. 2011
    ....
  37. ncbi A new threshold regression model for survival data with a cure fraction
    Sungduk Kim
    Division of Epidemiology, Statistics and Prevention Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Rockville, MD 20852, USA
    Lifetime Data Anal 17:101-22. 2011
    ..A real data set from a prostate cancer clinical trial is analyzed in detail to demonstrate the proposed methodology...
  38. pmc A weighted combination of pseudo-likelihood estimators for longitudinal binary data subject to non-ignorable non-monotone missingness
    Andrea B Troxel
    Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, U S A
    Stat Med 29:1511-21. 2010
    ..Finally, the proposed method is used to analyze data from two longitudinal clinical trials of HIV-infected patients...
  39. pmc Genomewide multiple-loci mapping in experimental crosses by iterative adaptive penalized regression
    Wei Sun
    Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA
    Genetics 185:349-59. 2010
    ..Although our methods are motivated by multiple-loci mapping, they are general enough to be applied to other variable selection problems...
  40. pmc MARM: multiscale adaptive regression models for neuroimaging data
    Hongtu Zhu
    1 Department of Biostatistics, University of North Carolina at Chapel Hill, USA
    Inf Process Med Imaging 21:314-25. 2009
    ..Our simulation studies with known ground truth confirm that the MARM significantly outperforms the voxel-wise methods...
  41. ncbi Fully specified bootstrap confidence intervals for the difference of two independent binomial proportions based on the median unbiased estimator
    Yan Lin
    Department of Biostatistics, Division of Quantitative Sciences, UT MD Anderson Cancer Center, Houston, TX 77030, USA
    Stat Med 28:2876-90. 2009
    ..The fully specified bootstrap had better coverage probability in the tail area than Chen's quasi-exact method, Wald intervals and Agresti and Caffo's intervals...
  42. ncbi Challenges and recommendations for blinding in behavioral interventions illustrated using a case study of a behavioral intervention to lower blood pressure
    Jennifer P Friedberg
    VA New York Harbor Healthcare System, New York, NY, United States
    Patient Educ Couns 78:5-11. 2010
    ....
  43. pmc Fixed and random effects selection in mixed effects models
    Joseph G Ibrahim
    Department of Biostatistics, University of North Carolina, McGavran Greenberg Hall, Chapel Hill, North Carolina 27599 7420, USA
    Biometrics 67:495-503. 2011
    ..Simulation studies and a real data set from a Yale infant growth study are used to illustrate the proposed methodology...
  44. pmc Choosing among partition models in Bayesian phylogenetics
    Yu Fan
    Department of Ecology and Evolutionary Biology, University of Connecticut
    Mol Biol Evol 28:523-32. 2011
    ..Such dedicated path-based Markov chain Monte Carlo analyses appear to be a cost of estimating marginal likelihoods accurately...
  45. pmc Local influence for generalized linear models with missing covariates
    Xiaoyan Shi
    Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
    Biometrics 65:1164-74. 2009
    ..Simulation studies are conducted to evaluate our methods, and real datasets are analyzed to illustrate the use of our local influence measures...
  46. pmc Iron deficiency in community-dwelling US adults with self-reported heart failure in the National Health and Nutrition Examination Survey III: prevalence and associations with anemia and inflammation
    Ankit Parikh
    Department of Internal Medicine, New York University Langone Medical Center, New York, NY, USA
    Circ Heart Fail 4:599-606. 2011
    ..Iron deficiency has been proposed as a potential therapeutic target in heart failure, but its prevalence and association with anemia and clinical outcomes in community-dwelling adults with heart failure have not been well characterized...
  47. pmc Improving marginal likelihood estimation for Bayesian phylogenetic model selection
    Wangang Xie
    Abbott, 100 Abbott Park, R436 AP9A 2, Abbott Park, IL 60064, USA
    Syst Biol 60:150-60. 2011
    ..We conclude that the greatly increased accuracy of the SS and TI methods argues for their use instead of the HM method, despite the extra computation needed...
  48. ncbi Logistic regression with incomplete covariate data in complex survey sampling: application of reweighted estimating equations
    Charity G Moore
    Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
    Epidemiology 20:382-90. 2009
    ..Adjusting the sampling weights by the inverse probability of being completely observed appears to be effective in accounting for missing data and reducing the bias of the complete case estimate of the regression coefficients...
  49. pmc A Bayesian proportional hazards regression model with non-ignorably missing time-varying covariates
    Patrick T Bradshaw
    Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
    Stat Med 29:3017-29. 2010
    ..Our sensitivity analysis showed only slight differences between models with different assumptions on the missing data mechanism yet the complete-case analysis yielded markedly different results...
  50. pmc Likelihood methods for binary responses of present components in a cluster
    Xiaoyun Li
    Department of Statistics, Florida State University, Tallahassee, Florida 32306, USA
    Biometrics 67:629-35. 2011
    ..The methodology is illustrated via analyzing a study of the periodontal health status in a diabetic Gullah population...
  51. pmc Variable selection in the cox regression model with covariates missing at random
    Ramon I Garcia
    Department of Biostatistics, University of North Carolina at Chapel Hill, North Carolina 27599 7420, USA
    Biometrics 66:97-104. 2010
    ..Simulations are performed to evaluate the finite sample performance of the penalty estimates. Also, two lung cancer data sets are analyzed to demonstrate the proposed methodology...