Harvard Catalyst Profiles

Contact, publication, and social network information about Harvard faculty and fellows.

Robert James Glynn, Sc.D., Ph.D.

Co-Author

This page shows the publications co-authored by Robert Glynn and Bernard Rosner.
Connection Strength

7.778
  1. Comparison of Questionnaire-Based Breast Cancer Prediction Models in the Nurses' Health Study. Cancer Epidemiol Biomarkers Prev. 2019 07; 28(7):1187-1194.
    View in: PubMed
    Score: 0.840
  2. Extensions of the Rosner-Colditz breast cancer prediction model to include older women and type-specific predicted risk. Breast Cancer Res Treat. 2017 Aug; 165(1):215-223.
    View in: PubMed
    Score: 0.738
  3. Estimation of rank correlation for clustered data. Stat Med. 2017 06 30; 36(14):2163-2186.
    View in: PubMed
    Score: 0.730
  4. Regression methods when the eye is the unit of analysis. Ophthalmic Epidemiol. 2012 Jun; 19(3):159-65.
    View in: PubMed
    Score: 0.521
  5. Power and sample size estimation for the clustered wilcoxon test. Biometrics. 2011 Jun; 67(2):646-53.
    View in: PubMed
    Score: 0.464
  6. Power and sample size estimation for the Wilcoxon rank sum test with application to comparisons of C statistics from alternative prediction models. Biometrics. 2009 Mar; 65(1):188-97.
    View in: PubMed
    Score: 0.395
  7. Interval estimation for rank correlation coefficients based on the probit transformation with extension to measurement error correction of correlated ranked data. Stat Med. 2007 Feb 10; 26(3):633-46.
    View in: PubMed
    Score: 0.361
  8. Comparison of risk factors for the competing risks of coronary heart disease, stroke, and venous thromboembolism. Am J Epidemiol. 2005 Nov 15; 162(10):975-82.
    View in: PubMed
    Score: 0.329
  9. Multiple imputation to estimate the association between eyes in disease progression with interval-censored data. Stat Med. 2004 Nov 15; 23(21):3307-18.
    View in: PubMed
    Score: 0.309
  10. Methods to evaluate risks for composite end points and their individual components. J Clin Epidemiol. 2004 Feb; 57(2):113-22.
    View in: PubMed
    Score: 0.293
  11. Tutorial on Biostatistics: Receiver-Operating Characteristic (ROC) Analysis for Correlated Eye Data. Ophthalmic Epidemiol. 2021 May 12; 1-11.
    View in: PubMed
    Score: 0.242
  12. Calculating Sensitivity, Specificity, and Predictive Values for Correlated Eye Data. Invest Ophthalmol Vis Sci. 2020 09 01; 61(11):29.
    View in: PubMed
    Score: 0.231
  13. Tutorial on Biostatistics: Longitudinal Analysis of Correlated Continuous Eye Data. Ophthalmic Epidemiol. 2021 02; 28(1):3-20.
    View in: PubMed
    Score: 0.230
  14. Methods to quantify the relation between disease progression in paired eyes. Am J Epidemiol. 2000 May 15; 151(10):965-74.
    View in: PubMed
    Score: 0.226
  15. Tutorial on Biostatistics: Statistical Analysis for Correlated Binary Eye Data. Ophthalmic Epidemiol. 2018 02; 25(1):1-12.
    View in: PubMed
    Score: 0.184
  16. Multivariate methods for clustered ordinal data with applications to survival analysis. Stat Med. 1997 Feb 28; 16(4):357-72.
    View in: PubMed
    Score: 0.181
  17. A Comprehensive Model of Colorectal Cancer by Risk Factor Status and Subsite Using Data From the Nurses' Health Study. Am J Epidemiol. 2017 02 01; 185(3):224-237.
    View in: PubMed
    Score: 0.180
  18. Tutorial on Biostatistics: Linear Regression Analysis of Continuous Correlated Eye Data. Ophthalmic Epidemiol. 2017 04; 24(2):130-140.
    View in: PubMed
    Score: 0.180
  19. Comparison of alternative regression models for paired binary data. Stat Med. 1994 May 30; 13(10):1023-36.
    View in: PubMed
    Score: 0.150
  20. Breast cancer risk prediction with heterogeneous risk profiles according to breast cancer tumor markers. Am J Epidemiol. 2013 Jul 15; 178(2):296-308.
    View in: PubMed
    Score: 0.139
  21. Accounting for the correlation between fellow eyes in regression analysis. Arch Ophthalmol. 1992 Mar; 110(3):381-7.
    View in: PubMed
    Score: 0.128
  22. Risk factors for mortality in the nurses' health study: a competing risks analysis. Am J Epidemiol. 2011 Feb 01; 173(3):319-29.
    View in: PubMed
    Score: 0.118
  23. Evaluation of risk factors for cataract types in a competing risks framework. Ophthalmic Epidemiol. 2009 Mar-Apr; 16(2):98-106.
    View in: PubMed
    Score: 0.104
  24. A nonparametric test for observational non-normally distributed ophthalmic data with eye-specific exposures and outcomes. Ophthalmic Epidemiol. 2007 Jul-Aug; 14(4):243-50.
    View in: PubMed
    Score: 0.093
  25. Extension of the rank sum test for clustered data: two-group comparisons with group membership defined at the subunit level. Biometrics. 2006 Dec; 62(4):1251-9.
    View in: PubMed
    Score: 0.089
  26. The Wilcoxon signed rank test for paired comparisons of clustered data. Biometrics. 2006 Mar; 62(1):185-92.
    View in: PubMed
    Score: 0.084
  27. Incorporation of clustering effects for the Wilcoxon rank sum test: a large-sample approach. Biometrics. 2003 Dec; 59(4):1089-98.
    View in: PubMed
    Score: 0.072
  28. Systolic and diastolic blood pressure, pulse pressure, and mean arterial pressure as predictors of cardiovascular disease risk in Men. Hypertension. 2000 Nov; 36(5):801-7.
    View in: PubMed
    Score: 0.058
  29. Evidence for a positive linear relation between blood pressure and mortality in elderly people. Lancet. 1995 Apr 01; 345(8953):825-9.
    View in: PubMed
    Score: 0.040
  30. Burden of smoking on cause-specific mortality: application to the Nurses' Health Study. Tob Control. 2010 Jun; 19(3):248-54.
    View in: PubMed
    Score: 0.028
  31. Smoking cessation in a prospective study of healthy adult males: effects of age, time period, and amount smoked. Am J Public Health. 1983 Apr; 73(4):446-50.
    View in: PubMed
    Score: 0.017
  32. Changes in cholesterol and triglyceride as predictors of ischemic heart disease in men. Circulation. 1982 Oct; 66(4):724-31.
    View in: PubMed
    Score: 0.017
  33. A prospective study of cigarette smoking and risk of cataract in men. JAMA. 1992 Aug 26; 268(8):989-93.
    View in: PubMed
    Score: 0.008
Connection Strength
The connection strength for co-authors is the sum of the scores for each of their shared publications.

Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.
Funded by the NIH National Center for Advancing Translational Sciences through its Clinical and Translational Science Awards Program, grant number UL1TR002541.