Karl Peace
Professor, Georgia Cancer Coalition Distinguished Cancer Scholar, Jiann-Ping College of Public Health | Georgia Southern University
Statesboro, GA, UNITED STATES
Karl Peace researches biostatistics, clinical trials and drug research and development.
Spotlight
Media
Publications:
Documents:
Photos:
Videos:
Audio/Podcasts:
Biography
Dr. Peace began a career in biostatistics in the pharmaceutical industry in 1978. After rising from an entry-level biostatistician position at Burroughs-Wellcome to vice president of worldwide technical operations at Parke-Davis/Warner Lambert, he started Biopharmaceutical Research Consultants Inc. in 1989. Dozens of international biotech and pharmaceutical companies relied on his expertise and he played a key role in the development and regulatory approval of dozens of medicines, including drugs used to treat Alzheimer’s disease, hypertension, arthritis, anxiety, depression and panic attacks, and gastrointestinal ulcers. At the same time, Peace kept one foot in the classroom, serving as an adjunct faculty member at Duke University, the MCV, the University of Michigan, The University of North Carolina, Temple University, and Virginia Commonwealth University. Peace approached Georgia Southern University officials in 1998 with a plan to establish a biostatistics center at the university, a corresponding program of study in biostatistics at the graduate level, and a school of public health. He returned to the Statesboro, Georgia, campus in the fall of 2000, when Georgia Southern began to offer the masters of public health degree in biostatistics that he and Charles Hardy developed. In 2004, his endowment to honor his late wife led to the creation of the Jiann-Ping Hsu College of Public Health (JPHCOPH), the first college or school of public health in the University System of Georgia (USG). In creating the JPHCOPH, the board of regents also named the center for biostatistics the Karl E. Peace Center for Biostatistics. He is author, co-author or editor of 15 books, 22 book chapters and over 100 articles in the scientific, medical or biostatistical literature; and is the recipient of over 50 prestigious awards. He has donated much of his time, talent and treasure to create opportunities for others, particularly our youth, to improve their lots and selves.
Areas of Expertise (6)
Clinical Trials
Pharmaceutical Research
Health & Disease
Health Sciences
Biostatistics
Clinical Development
Accomplishments (11)
Commendation from the Virginia General Assembly
2014 For “a prolific biostatistician and devoted educator, [who] has contributed immensely to his field and inspired countless students at the Medical College of Virginia and other universities to achieve greatness in science and medicine.
GPHA Sellers-McCroan Award Recipient
2014 For Improving Public Health through academic and laboratory advancement
Special Presidential Award for Service: International Chinese Statistical Association
2012
Delta Omega chapter Karl E. Peace Leadership Award
2012
Regent’s Hall of Fame Alumni Award
2012 USG Foundation Board of Trustees
Tito Mijares Life Time Achievement Award Recipient
2008 Philippine Statistical Association
Academic Year 2008-2009 Award Recipient
2008-2009 Excellence In Research And/Or Creative Scholarly Activity Georgia Southern University
Georgia House Of Representatives Passed House Resolution #2118
2008 The Resolution Recognizes Karl E. Peace For His Contributions To The Medical Field, To The Jiann-Ping Hsu College Of Public Health, To Georgia Southern University, And To The Citizens Of The State Of Georgia
Georgia House Resolution HR #2118
2008 Georgia General Assembly Recognizes Dr. Karl E. Peace As A Distinguished Georgian
APHA Statistics Section Award
2007 For Contributions To The Pharmaceutical Industry, Biostatistics & Public Health, American Public Health Association
The President’s Medal For Outstanding And Meritorious Contributions To Georgia Southern University
2005 First Recipient
Education (3)
Medical College of Virginia: Ph.D., Biostatistics 1976
Clemson University: M.S., Mathematics 1964
Georgia Southern University: B.S., Chemistry 1963
Links (4)
Articles (10)
Number Needed to Treat in Multiple Sclerosis Clinical Trials
Neurol Ther.Okuokenye M, Zhang A, Pace, A, Peace KE
2017 Clinicians are expected to select a therapy based on their appraisal of evidence on benefit-to-risk profiles of therapies. In the management of relapsing-remitting multiple sclerosis (RRMS), evidence is typically expressed in terms of risk (proportion) of event, risk reduction, relative and hazard rate reduction, or relative reduction in the mean number of magnetic resonance imaging lesions. Interpreting treatment effect using these measures from a RRMS clinical trial is fairly reliable; however, this might not be the case when treatment effect is expressed in terms of the number needed to treat (NNT).
A Serious Flaw in Nutrition Epidemiology: A Meta-Analysis Study.
American Journal of NutritionPeace KE, Yin JJ, Roschani H, Pandeya S, Young SSY
2017 Many researchers have studied the relationship between diet and health. Specifically, there are papers showing an association between the consumption of sugar sweetened beverages and Type 2 diabetes. Many meta-analyses use individual studies that do not attempt to adjust for multiple testing or multiple modeling. Hence the claims reported in a meta-analysis paper may be unreliable as the base papers do not ensure unbiased statistics.
A comparison of size and power of tests of hypotheses on parameters based on two generalized Lindley distributions
Communications for Statistical Applications and MethodsM Okwuokenye, KE Peace
2016 This study compares two generalized Lindley distributions and assesses consistency between theoretical and analytical results. Data (complete and censored) assumed to follow the Lindley distribution are generated and analyzed using two generalized Lindley distributions, and maximum likelihood estimates of parameters from the generalized distributions are obtained. Size and power of tests of hypotheses on the parameters are assessed drawing on asymptotic properties of the maximum likelihood estimates. Results suggest that whereas size of some of the tests of hypotheses based on the considered generalized distributions are essentially α-level, some are possibly not; power of tests of hypotheses on the Lindley distribution parameter from the two distributions differs.
Size and power of tests of hypotheses on survival parameters from the Lindley distribution with covariates
Austin Biometrics and BiostatisticsM Okwuokenye, KE Peace
2015 The Lindley model is considered as an alternative model facilitating analyses of time-to-event data with covariates. Covariate information is incorporated using the Cox’s proportional hazard model with the Lindley model at the timedependent component. Simulation studies are performed to assess the size and power of tests of hypotheses on parameters arising from maximum likelihood estimators of parameters in the Lindley model. Results are contrasted with that arising from Cox’s partial maximum likelihood estimator. The Linley model is used to analyze a publicly available data set and contrasted with other models.
A SASR Algorithm For Imputing Discrete Missing Outcomes Based On Minimum Distance
Proceedings Of The Southeastern SAS Users GroupOkwuokenye M And Peace KE
2015
Joint Modeling Of Treatment Effect On Time-To-Event Endpoint And Safety Covariates In Control Clinical Trial Data Analysis
Austin Biom And BiostatTsai KT And Peace KE
2015
Analysis Of Subgroup Data Of Clinical Trials
Journal Of Causal InferenceTsai KT, Peace KE
2013 Large randomized controlled clinical trials are the gold standard to evaluate and compare the effects of treatments. It is common practice for investigators to explore and even attempt to compare treatments, beyond the first round of primary analyses, for various subsets of the study populations based on scientific or clinical interests to take advantage of the potentially rich information contained in the clinical database. Although subjects are randomized to treatment groups in clinical trials, this does not imply the same degree of randomization among sub-populations of the original trials.
Approximating The Baseline Hazard Function By Taylor Series For Interval-Censored Time-To-Event Data
J Biopharm StatChen DG, Yu L, Peace KE, Lio YL, Wang Y
2013
Spline Nonparametric Quasi-Likelihood Regression Within The Frame Of The Accelerated Failure Time Model
Computational Statistics And Data AnalysisYu L, Peace KE
2012
Genetic Matching: A Better Algorithm For Adjusting Covariate Imbalance For Statistical Data Analysis And Modeling
Proceedings Of The International ConferenceTsai KT, Peace KE
2011
Social