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5 papers from each faculty member

Selected Papers by Faculty in the Department of Biostatistics

Emine Bayman

  1. Bayman EO, Chaloner K, Cowles MK. Detecting qualitative interaction: a Bayesian approach. Statistics in medicine. 2010;29(4):455-463. Epub 2009/12/02. doi: 10.1002/sim.3787. PubMed PMID: 19950107
  2. Bayman EO, Chaloner KM, Hindman BJ, Todd MM, IHAST I. Bayesian methods to determine performance differences and to quantify variability among centers in multi-center trials: the IHAST trial. BMC medical research methodology. 2013;13:5. Epub 2013/01/18. doi: 10.1186/1471-2288-13-5. PubMed PMID: 23324207; PMCID: 3599203
  3. Bayman EO, Dexter F, Todd MM. Assessing and Comparing Anesthesiologists’ Performance on Mandated Metrics Using a Bayesian Approach. Anesthesiology. 2015;123(1):101-115. Epub 2015/04/24. doi: 10.1097/aln.0000000000000667. PubMed PMID: 25906338
  4. Bayman EO, Dexter F, Todd MM. Prolonged Operative Time to Extubation Is Not a Useful Metric for Comparing the Performance of Individual Anesthesia Providers. Anesthesiology. 2015;124(2):322-338. Epub 2015/11/07. doi: 10.1097/aln.0000000000000920. PubMed PMID: 26545101
  5. Bayman EO, Parekh KR, Keech J, Selte A, Brennan TJ. A Prospective Study of Chronic Pain after Thoracic Surgery. Anesthesiology. 2017;126(5):938-951. doi: 10.1097/ALN.0000000000001576. PubMed PMID: 28248713; PMCID: PMC5395336

Patrick Breheny

  1. Breheny P (2018). Marginal false discovery rates for penalized regression models. Biostatistics, 20: 299-314.
  2. Breheny P (2015). The group exponential lasso for bi-level variable selection. Biometrics, 71: 731–740.
  3. Breheny P and Huang J (2015). Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors. Statistics and Computing, 25: 173-187.
  4. Breheny P and Huang J (2011). Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection. Annals of Applied Statistics, 5: 232–253.
  5. Breheny P and Burchett W (2017). Visualization of regression models using visreg. The R Journal, 9: 56–71.

Grant Brown

  1. Phillip K, Nair N, Kamalika S, Azevedo JF, Brown GD, Petersen CA, Gomes-Solecki M. (2021). Maternal transfer of neutralizing antibodies to B. burgdorferi OspA after oral vaccination of the rodent reservoir. Vaccine. DOI: 10.1016/j.vaccine.2021.06.025
  2. Seedorff N, Brown G D (2021). totalvis: A Principal Components Approach to Visualizing Total Effects in Black Box Models. SN Computer Science. DOI: 10.1007/s42979-021-00560-5
  3. Brown GD, Oleson JJ, Porter AT (2016). An empirically adjusted approach to reproductive number estimation for stochastic compartmental models: A case study of two Ebola outbreaks. Biometrics. DOI: 10.1111/biom.12432
  4. Brown GD, Porter AT, Oleson JJ, Hinman JA, (2018). Approximate Bayesian computation for spatial SEIR(S) epidemic models. Spatial and Spatiotemporal Epidemiology. DOI: 10.1016/j.sste.2017.11.001
  5. Ozanne MV, Brown GD, Toepp AJ, Scorza BM, Oleson JJ, Wilson ME, Petersen CA (2020). Bayesian Compartmental Models and Associated Reproductive Numbers for an Infection with Multiple Transmission Modes. Biometrics. DOI: 10.1111/biom.13192

Joe Cavanaugh

  1. Riedle B, Neath AA, and Cavanaugh JE (2020). Reconceptualizing the p-value from a likelihood ratio test: a probabilistic pairwise comparison of models based on Kullback-Leibler discrepancy measures, Journal of Applied Statistics. DOI: 10.1080/02664763.2020.1754360.
  2. Cavanaugh JE, Neath AA (2019). The Akaike information criterion: Background, derivation, properties, application, interpretation, and refinements. WIREs Computational Statistics, 11:e1460. DOI: 10.1002/wics.1460.
  3. Peterson RA, Cavanaugh JE (2019). Ordered quantile normalization: A semiparametric transformation built for the cross-validation era. Journal of Applied Statistics. DOI: 10.1080/02664763.2019.1630372.
  4. Zhang T, Cavanaugh JE (2016). A multistage algorithm for best–subset model selection based on the Kullback–Leibler discrepancy. Computational Statistics, 31(2):643-669. DOI: 10.1007/s00180-015-0584-8.
  5. Yang M, Cavanaugh JE, Zamba GJ (2015). State-space models for count time series with excess zeros. Statistical Modelling, 15(1):70-90. DOI: 10.1177/1471082X14535530.

Ryan Cho

  1. Cho, H., Kim, S. and Lee, M. (2020). Adjusting a subject-specific time of event in longitudinal studies, Statistical Methods in Medical Research 29, 1787-1798
  2. Kim, S., Cho, H. and Wu, C. (2020). Risk-predictive probabilities and dynamic nonparametric conditional quantile models for longitudinal analysis, Statistica Sinica, in press
  3. Andrews, N. and Cho, H. (2018). Validating effectiveness of subgroup identification for longitudinal data, Statistics in Medicine 37, 98-106
  4. Cho, H. (2018). Statistical inference in a growth curve quantile regression model for longitudinal data, Biometrics 74, 855-862
  5. Cho, H., Hong, H. G. and Kim, M. O. (2016). Efficient quantile marginal regression for longitudinal data with dropouts, Biostatistics 17, 561-575

Jake Oleson

  1. Kliethermes SA, Oleson JJ. A Bayesian approach to functional mixed effect modeling with binomial outcomes. Statistics in Medicine, 33(18):3130-3146, 2014
  2. VanBuren J, Oleson JJ, Zamba GKD, Wall M. Integrating independent spatio-temporal replications to assess population trends in disease spread. Statistics in Medicine. 35(28):5210-5221, 2016. PMID: 27453437
  3. Seedorff M, Oleson JJ, McMurray B. Detecting when timeseries differ: Using the Bootstrapped Differences of Timeseries (BDOTS) to analyze visual world paradigm data (and more). Journal of Memory and Language, 102:55-67, 2018.
  4. Zahrieh D, Oleson JJ, Romitti PA. Quantifying geographic regions of excess stillbirth risk in the presence of spatial and spatio-temporal heterogeneity. Spatial and Spatio-Temporal Epidemiology. 29, 97-109, 2019.
  5. Ozanne M, Brown G, Toepp A, Scorza B, Oleson J, Wilson M, Petersen C. Bayesian compartmental models and associated reproductive numbers for an infection with multiple transmission models. Biometrics. (early view published online) 2020.

Dan Sewell

  1. Sewell D, Chen Y (2015). Latent space models for dynamic networks. The Journal of the American Statistical Association 110(512):1646-1657.
  2. Sewell DK (2017). Heterogeneous susceptibilities in social influence models. Social Networks, 52:135-144.
  3. Sewell DK (2018). Simultaneous and temporal autoregressive network models. Network Science 6(2):204-231.
  4. Jang H, Justice S, Polgreen PM, Segre AM, Sewell DK, Pemmaraju SV (2019). Evaluating architectural changes to reduce infection spread in a dialysis unit. International Conference on Advances in Social Networks Analysis and Mining ‘19
  5. Sewell DK (2020). Model-based edge clustering. Journal of Computational and Graphical Statistics, 30(2):390-405.

Kai Wang

  1. Wang, K. (2021). Relating parameters in conditional, marginalized, and marginal logistic models when the mediator is binary. Statistics and Its Interface, 14(2), 109-114.
  2. Wang K (2020) Direct effect and indirect effect on an outcome under nonlinear modeling. The International Journal of Biostatistics 1 (ahead-of-print)
  3. Chen Z, Wang K (2019) Gene-based sequential burden association test. Statistics in medicine 38 (13):2353-2363
  4. Wang K (2019) Maximum likelihood analysis of linear mediation models with treatment-mediator interaction. Psychometrika 84 (3):719–748
  5. Wang K (2018) Understanding Power Anomalies in Mediation Analysis. Psychometrika 83 (2):387-406