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The Ohio State University
Center for Biostatistics and Bioinformatics
https://biostatistics.osu.edu/

Home > Methods Research

Methods Research


Some current methods research by our biostatisticians:

  • Adaptive clinical trial design
  • Sample size re-estimation for laboratory experiments
  • Causal inference using propensity score methods
  • Moderated t-statistics for small sample size gene expression arrays
  • Mice-human correlations based on gene expression data
  • Missing data sensitivity analysis
  • Bias with propensity scores used as a regression covariate
  • Sample size re-estimation for clinical trials
  • Robust mixed model hypothesis testing
  • Microarray/microRNA normalization and filtering methods

 

Publications

  • Garbett NC*, Brock GN*, Chaires JB, Mekmaysy CS, DeLeeuw L, Sivils KL, Harley JB, Rovin BH, Kulasekera KB, Jarjour WN. (2017) “Characterization and classification of lupus patients based on plasma thermograms,” PLoS One, 12(11):e0186398. PMID:    29149219.

  • Kendrick SK, Zheng Q, Garbett NC, Brock GN. (2017) “Application and interpretation of functional data analysis techniques to differential scanning calorimetry data from lupus patients,” PLoS One, 12(11):e0186232. PMID: 29121669.

  • Alver SK, Lorenz DJ, Washburn K, Marvin MR, Brock GN (2017). “Comparison of two equivalent MELD scores for hepatocellular carcinoma patients in the liver transplant allocation system” Transplant International, 30(11):1098-1109. PMID: 28403575

  • Shah JS, Rai SN, DeFilippis AP, Hill BG, Bhatnagar A, Brock GN (2017). “Distribution based nearest neighbor imputation for truncated high dimensional data with applications to pre-clinical and clinical metabolomics studies.” BMC Bioinformatics, 18(1):114. PMCID: PMC5319174

  • Alver SK, Lorenz DJ, Marvin MR, Brock GN (2016). “Projected outcomes of six-month delay in exception points vs. an equivalent MELD score for HCC liver transplant candidates.” Liver Transplantation, 22(10):1343-55. PMID:27343202

  • Egger ME, Myers JA, Arnold FW, Pass LA, Ramirez JA, and Brock GN (2016). “Cost effectiveness of adherence to IDSA/ATS guidelines in elderly patients hospitalized for Community-Aquired Pneumonia”, BMC Medical Informatics and Decision Making, 16(1):34. PMID: 26976388

  • Garbett NC, Brock GN (2016). “Differential scanning calorimetry as a complementary diagnostic tool for the evaluation of biological samples.” Biochim Biophys Acta. 1860(5):981-9. PMID: 26459005

  • Marvin MR, Ferguson N, Cannon RM, Jones C, Brock GN (2015). “MELDEQ: An alternative MELD score for patients with hepatocellular carcinoma”, Liver Transplantation. 21(5):612-22.

  • Yang D, Parrish RS, and Brock GN (2014). “Empirical evaluation of consistency and accuracy of methods to detect differentially expressed genes based on microarray data”, Computers in Biology and Medicine 46:1-10.

  • Yu L, Fernandez S, Brock G. Power analysis for RNA-Seq differential expression studies. BMC Bioinformatics. 2017 May 3;18(1):234. doi: 10.1186/s12859-017-1648-2. PubMed PMID: 28468606; PubMed Central PMCID: PMC5415728.

  • Hade EM, Lu B.  Bias associated with using the estimated propensity score as a regression covariate. Statistics in Medicine. 2013 Jun 21. doi: 10.1002/sim.5884. [Epub ahead of print] PMID:23787715

  • Rhoda D, Murray DM, Andridge RR, Pennell ML, Hade EM.  Multiple Baseline Designs and Group-Randomized Trials.  American Journal of Public Health.  2011 Nov; 101(11):2164-9. doi: 10.2105/AJPH.2011.300264. Epub 2011 Sep 22. Review. Erratum in: Am J Public Health. 2014 Mar;104(3):e12. PMID:21940928

  • Pennell M, Hade EM, Murray DM, Rhoda D.  Cutoff Designs for Community-Based Intervention Studies. Statistics in Medicine. 2011; 30: 1865-1882. PMID: 21500240

  • Hade EM, Jarjoura D, Wei L.  Sample size re-estimation in a breast cancer trial.  Clinical Trials. 2010; 7: 219-226.  PMID:20392786

  • Hade EM, Murray DM, Pennell M, Rhoda D, Paskett E. et al.  Intraclass Correlation Estimates for Cancer Screening Outcomes: Estimates and Applications in the Design of Group Randomized Cancer Screening Studies.  J Natl Cancer Inst Monogr. 2010; 40: 97-103. PMID:20386058

  • Murray DM, Pennell M, Rhoda D, Hade EM, Paskett E. Designing Studies that Would Address the Multilayered Nature of Health Care. J Natl Cancer Inst Monogr. 2010; 40: 90-96. PMID:20386057

  • Lai Wei and David Jarjoura. “Options and Considerations for Adaptive Laboratory Experiments”,  Statistics in Biosciences. Volume 7, Issue 2 (2015), Page 348-366. (DOI) 10.1007/s12561-014-9123-3. PMID: 26539252

  • Zhao, J., et al. (2019). "Predicting outcomes of chronic kidney disease from EMR data based on Random Forest Regression." Math Biosci 310: 24-30.

 

 

Moderated t-statistics for small sample size gene expression arrays

 

Gene expression microarray experiments with few replications lead to great variability in estimates of gene variances. We extend the existing methods by allowing the CV to vary with gene expression, which we refer to as the fully moderated t-statistic, and compared to three other methods (ordinary t, and two moderated t predecessors). Our CV varying method had higher power in a spike-in dataset, and in a real dataset better identified higher expressing genes that were consistent with functional pathways associated with the experiments.

Article:
Yu, Lianbo; Gulati, Parul; Fernandez, Soledad; Pennell, Michael; Kirschner, Lawrence; and Jarjoura, David (2011) "Fully Moderated T-statistic for Small Sample Size Gene Expression Arrays," Statistical Applications in Genetics and Molecular Biology: Vol. 10: Iss. 1, Article 42.

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