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Your Environment. Your Health.

Gregg E. Dinse, Sc.D.

Biostatistics & Computational Biology Branch

Gregg E. Dinse, Sc.D.
Gregg Dinse, Sc.D.
Principal Investigator – Retired
Tel (919) 541-2411
1009 Slater Road
Durham, NC

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Research Summary

Most of Gregg Dinse's research focuses on developing improved statistical methods for analyzing data from animal carcinogenicity experiments, multi-chemical toxicity studies, and population-based cancer registries. Several of his recent projects include:


  • Comparing temporal and demographic patterns of non-Hodgkin's lymphoma incidence in Pennsylvania with national results from the United States.
  • Summarizing log-linear time trends in cancer incidence and mortality rates in the United States for cancers not related to tobacco, screening, or treatment.
  • Assessing sex and race differences via age-period-cohort analyses of cancers not related to tobacco, screening, or HIV in the United States.
  • Adapting an additive hazards regression model to analyze survival data with some censoring indicators missing at random.
  • Comparing tumor incidence rates in female Harlan Sprague-Dawley rats with those in female Fischer 344/N rats based on NTP historical control data.
  • Employing kernel smoothing techniques to estimate hazard functions when some cause-of-death indicators are missing at random.
  • Adjusting for covariates in a linear regression analysis of survival data when some censoring indicators are missing.
  • Using a rabbit model to study the feasibility of a silver ion delivery device for treating osteomyelitis.
  • Developing statistical methods for comparing tumor incidence rates in current and historical control groups from rodent carcinogenicity bioassays.
  • Fitting a 4-parameter logistic model (or Hill model) to binary dose-response data via an EM algorithm.
  • Characterizing relative potency for chemicals with non-similar dose-response curves.
  • Evaluating the effects of birth weight and early weight gain on pubertal maturation.
  • Using a power model to estimate relative potency.


  • Hill Viewer
    ("/Rhythmyx/assembler/render?sys_contentid=34951&sys_revision=4&sys_variantid=639&sys_context=0&sys_authtype=0&sys_siteid=&sys_folderid=" sys_dependentvariantid="639" sys_dependentid="34951" inlinetype="rxhyperlink" rxinlineslot="103" sys_dependentid="34951" sys_siteid="" sys_folderid="")Used to visualize dose-response curves and relative potency functions based on two sets of Hill model parameters.

Selected Publications

  1. Han, YY, Dinse, GE, and Davis, DL: Temporal and demographic patterns of non-Hodgkin's lymphoma incidence in Pennsylvania. International Journal of Occupational and Environmental Health 16: 75-84, 2010.[Abstract]  
  2. Han, YY, Davis, DL, Weissfeld, JL, and Dinse, GE: Generational risks for cancers not related to tobacco, screening, or treatment in the United States. Cancer 116: 940-948, 2010.[Abstract]  
  3. Han, YY, Dinse, GE, Umbach, DM, Davis, DL, and Weissfeld, JL: Age-period-cohort analysis of cancers not related to tobacco, screening, or HIV: Sex and race differences. Cancer Causes and Control 21: 1227-1236, 2010.[Abstract]  
  4. Song, X, Sun, L, Mu, X, and Dinse, GE: Additive hazards regression with censoring indicators missing at random. The Canadian Journal of Statistics = Revue canadienne de statistique 38(3):333-351, 2010.[Abstract]  
  5. Dinse, GE, Peddada, SD, Harris, SF, and Elmore, SA: Comparison of NTP historical control tumor incidence rates in female Harlan Sprague-Dawley and Fischer 344/N rats. Toxicologic Pathology 38: 765-775, 2010.[Abstract]  
  6. Wang, QH, Dinse, GE, and Liu, C: Hazard function estimation with cause-of-death data missing at random. Annals of the Institute of Statistical Mathematics 64:415-438, 2012.[Abstract]  
  7. Wang, QH, and Dinse, GE: Linear regression analysis of survival data with missing censoring indicators. Lifetime Data Analysis 17: 256-279, 2011.[Abstract]  
  8. Maoz, G, Brin, YS, Dinse, GE, Lazarovich, T, Barsky, V, Schwartz, L, Schwartz, A, Nyska, A, and Nyska, M: Feasibility of a silver ion delivery device in a rabbit osteomyelitis model: A preliminary study (submitted).  
  9. Dinse, GE, and Peddada, SD: Comparing tumor rates in current and historical control groups in rodent cancer bioassays. Statistics in Biopharmaceutical Research 3: 97-105, 2011.[Abstract]  
  10. Dinse, GE: An EM algorithm for fitting a four-parameter logistic model to binary dose-response data. Journal of Agricultural, Biological and Environmental Statistics 16: 221-232, 2011.  
  11. Dinse, GE, and Umbach, DM: Characterizing non-constant relative potency. Regulatory toxicology and pharmacology 60(3): 342-352, 2011.[Abstract]  
  12. Wang, Y, Dinse, GE, and Rogan, WJ: Birth weight, early weight gain and pubertal maturation: a longitudinal study. Pediatric Obesity 7:101-109, 2012.[Abstract]
  13. Dinse, GE and Umbach, DM: Parameterizing dose-response models to estimate relative potency functions directly. Toxicological Sciences 129:447-455, 2012.[Abstract]
  14. Dinse GE, Jusko TA, Ho LA, Annam K, Graubard BI, Hertz-Picciotto I, Miller FW, Gillespie BW, Weinberg CR. Accommodating measurements below a limit of detection: a novel application of cox regression. American journal of epidemiology 2014 179(8):1018-1024.[Abstract]

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