Software

• In many phase I trials, the design goal is to find the dose associated with a certain target toxicity rate. In some trials, the goal can be to find the dose with a certain weighted sum of rates of various toxicity grades. For others, the goal is to find the dose with a certain mean value of the continuous response. This UnifiedDoseFinding package provides the setup and calculations needed to run a dose-finding trial with non-binary endpoints and performs simulations to assess the design's operating characteristics under various scenarios. Three dose-finding designs are included in this package: unified phase I design (Ivanova et al. (2009), Quasi-CRM/Robust-Quasi-CRM (Yuan et al, 2007; Pan et al. 2014) and generalized BOIN design (Mu et al. , 2018). The toxicity endpoints can be handled with these functions including equivalent toxicity score (ETS), total toxicity burden (TTB), general continuous toxicity endpoints, with incorporating ordinal grade toxicity information into the dose-finding procedure. These functions allow customization of design characteristics to vary sample size, cohort sizes, target dose-limiting toxicity (DLT) rates, discrete or continuous toxicity score, and incorporate safety and/or stopping rules. The package can be downloaded from https://cran.r-project.org/web/packages/UnifiedDoseFinding/index.html.
• An R package MinEDfind containing functions for the implementation and simulation of a Bayesian nonparametric two-stage design for minimum effective dose-finding can be downloaded from https://cran.r-project.org/web/packages/MinEDfind/index.html. For Windows, the MinEDfind library can be downloaded from within R GUI by clicking on Packages/Install package(s), choose a mirror site, and locating MinEDfind. Questions regarding the R code, the literature, and the application of MinEDfind are welcome. An R Shiny App for this method can be found at https://sjbiostat.shinyapps.io/mined/.
• An R package Keyboard containing functions for the implementation and simulation of two phase I model-assisted maximum tolerated dose (MTD)-finding designs for single-agent and combination trials, and one biological dose (OBD)-finding phase I/II design. The package can be downloaded from https://cran.r-project.org/web/packages/Keyboard/index.html.
• Ref: Chu, Yiyi, Haitao Pan, and Ying Yuan. "Adaptive dose modification for phase I clinical trials." Statistics in Medicine 35, no. 20 (2016): 3497-3508. An R Shiny app for conducting Phase I dose-finding trials with dose-insertion method can be found at https://sjbiostat.shinyapps.io/adaptive_dose_modification_shinyapp_updated_10022020/

Software for published statistical models

Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies
Zhaohua Lu et al.
NeuroImage. 2017 149(1) 305-322.
Hongtu Zhu, Zakaria Khondker, Zhaohua Lu, Joseph G. Ibrahim.
Journal of the American Statistical Association. 2014; 109 (507) 977-990.

Bayesian variable selection and model comparison for factor analysis modeling
Zhaohua Lu et al.
Psychological methods. 2017; 22(2):361-381.
Zhaohua Lu et al.
Multivariate behavioral research. 2016; 51(4):519-39

Multiplicy-Adjusted Evidence Weights
Wenjian Bi, Guolian Kang, Stanley Pounds
Presented at the BIBM2017 Meeting

A Robust and Powerful Set-Valued Approach to Rare Variant Association Analyses of Secondary Traits in Case-Control Sequencing Studies
Guolian Kang, Wenjian Bi, et al.
Genetics. 2017; 205(3), pp. 1049-1062

SVSI: Fast and Powerful Set-Valued System Identification Approach to Identifying Rare Variants in Sequencing Studies for Binary and Ordered Categorical Traits
Wenjian Bi, Guolian Kang, et al.
Human Heredity 2014; 78:104-116; Annals Of Human Genetics 2015; 79: 294-309, 2015

rctrack: An R Package that Automatically Collects and Archives Details for Reproducible Computing
Zhifa Liu and Stan Pounds
BMC Bioinformatics 2014 Mar

The Most Informative Spacing Test Effectively Discovers Biologically Relevant Outliers or Multiple Modes in Expression
Iwona Pawlikowska, Gang Wu, Michael Edmonson, Zhifa Liu, Tanja Gruber, Jinghui Zhang, Stan Pounds
Bioinformatics 2014 Jan

A genomic random interval model for statistical analysis of genomic lesion data
Stan Pounds, Cheng Cheng, Shaoyu Li, Zhifa Liu, Jinghui Zhang, Charles Mullighan.
Bioinformatics Epub 2013 July 10

Empirical Bayesian Selection of Hypothesis Testing Procedures for Analysis of Sequence Count Expression Data.
Pounds SB, Gao CL, Zhang H.
Statistical Applications in Genetics and Molecular Biology 2012 Oct 19;11(5).

A Procedure to Statistically Evaluate Agreement of Differential Expression for Cross-Species Genomics
Pounds S, Gao C, …, Gilbertson RJ.
Bioinformatics 2011 Aug 1;27(15):2098-103. Epub 2011 Jun 22.

Cross-Species Genomics Matches Driver Mutations and Cell Compartments to Model Ependymoma
Johnson R, …, Pounds SB, …, Gilbertson RJ.
Nature 2010 Jul 29;466(7306):632-6. Epub 2010 Jul 18.

Subtypes of Medulloblastoma have Distinct Developmental Origins
Gibson P, …, Pounds SB, …, Gilbertson RJ.
Nature 2010 Dec 23;468(7327):1095-9. Epub 2010 Dec 8.

PROMISE: a tool to identify genomic features with a specific biologically interesting pattern of associations with multiple endpoint variables.
Pounds S, Cheng C, Cao X, Crews KR, Plunkett W, Gandhi V, Rubnitz J, Ribeiro RC, Downing JR, Lamba J.
Bioinformatics 2009 Aug 15;25(16):2013-9. Epub 2009 Jun 15.

Reference Alignment of SNP Microarray Signals for Copy Number Analysis of Tumors.
Pounds S, Cheng C, Mullighan C, Raimondi SC, Shurtleff S, Downing JR.
Bioinformatics 2009 Feb 1;25(3):315-21. Epub 2008 Dec 3.

Assumption Adequacy Averaging as a Concept for Developing More Robust Methods for Differential Gene Expression Analysis.
Stan Pounds and Shesh N. Rai
Comput Stat Data Anal. 2009 Mar 15;53(5):1604-1612.

Computational Enhancement of a Shrinkage-Based ANOVA F-test Proposed for Differential Gene Expression Analysis.
Stan Pounds
2007 Biostatistics

Genes regulating B cell development are mutated in acute lymphoid leukaemia
Charles G. Mullighan, Salil Goorha, Ina Radtke, Christopher B. Miller, Elaine Coustan-Smith, James D. Dalton, Kevin Girtman, Susan Mathew, Jing Ma, Stanley B. Pounds, Xiaoping Su, Ching-Hon Pui, Mary V. Relling, William E. Evans, Sheila A. Shurtleff, James R. Downing
Nature. 2007 Apr 12;446(7137):758-64.

Estimation and Control of Multiple Testing Error Rates for the Analysis of Microarray Data.
Stan Pounds
Brief Bioinform. 2006 Mar;7(1):25-36. Review

Robust Estimation of the False Discovery Rate.
Stan Pounds, Cheng Cheng
Bioinformatics 2006 Aug 15;22(16):1979-87. Epub 2006 Jun 15.

Statistical Development and Evaluation of Gene Expression Data Filters.
Stan Pounds, Cheng Cheng
J Comput Biol 2005 May;12(4):482-95. Review.

Sample Size Determination for the False Discovery Rate.
Stan Pounds, Cheng Cheng
Bioinformatics 2005 Dec 1;21(23):4263-71. Epub 2005 Oct 4. Erratum in: Bioinformatics. 2009 Mar 1;25(5):698-9.

Improving False Discovery Rate Estimation.
Stan Pounds, Cheng Cheng
Bioinformatics 2004 Jul 22;20(11):1737-45. Epub 2004 Feb 26.

Estimating the Occurrence of False Positives and False Negatives in Microarray Studies by Approximating and Partitioning the Empirical Distribution of p-values.
Stan Pounds, Steve Morris
Bioinformatics 2003 Jul 1;19(10):1236-42.

Department of Biostatistics
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