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Connection

David Hoaglin to Models, Statistical

This is a "connection" page, showing publications David Hoaglin has written about Models, Statistical.
Connection Strength

2.752
  1. Dong G, Huang B, Chang YW, Seifu Y, Song J, Hoaglin DC. The win ratio: Impact of censoring and follow-up time and use with nonproportional hazards. Pharm Stat. 2020 05; 19(3):168-177.
    View in: PubMed
    Score: 0.581
  2. Hoaglin DC. Shortcomings of an approximate confidence interval for moment-based estimators of the between-study variance in random-effects meta-analysis. Res Synth Methods. 2016 Dec; 7(4):459-461.
    View in: PubMed
    Score: 0.458
  3. Hoaglin DC. Misunderstandings about Q and 'Cochran's Q test' in meta-analysis. Stat Med. 2016 Feb 20; 35(4):485-95.
    View in: PubMed
    Score: 0.435
  4. Dong G, Cui Y, Gamalo-Siebers M, Liao R, Liu D, Hoaglin DC, Lu Y. On approximate equality of Z-values of the statistical tests for win statistics (win ratio, win odds, and net benefit). J Biopharm Stat. 2025 May; 35(3):457-464.
    View in: PubMed
    Score: 0.205
  5. Song J, Verbeeck J, Huang B, Hoaglin DC, Gamalo-Siebers M, Seifu Y, Wang D, Cooner F, Dong G. The win odds: statistical inference and regression. J Biopharm Stat. 2023 03; 33(2):140-150.
    View in: PubMed
    Score: 0.176
  6. Bakbergenuly I, Hoaglin DC, Kulinskaya E. On the Q statistic with constant weights for standardized mean difference. Br J Math Stat Psychol. 2022 11; 75(3):444-465.
    View in: PubMed
    Score: 0.170
  7. Kulinskaya E, Hoaglin DC, Bakbergenuly I, Newman J. A Q statistic with constant weights for assessing heterogeneity in meta-analysis. Res Synth Methods. 2021 Nov; 12(6):711-730.
    View in: PubMed
    Score: 0.163
  8. Kulinskaya E, Hoaglin DC, Bakbergenuly I. Exploring consequences of simulation design for apparent performance of methods of meta-analysis. Stat Methods Med Res. 2021 07; 30(7):1667-1690.
    View in: PubMed
    Score: 0.163
  9. Dong G, Mao L, Huang B, Gamalo-Siebers M, Wang J, Yu G, Hoaglin DC. The inverse-probability-of-censoring weighting (IPCW) adjusted win ratio statistic: an unbiased estimator in the presence of independent censoring. J Biopharm Stat. 2020 09 02; 30(5):882-899.
    View in: PubMed
    Score: 0.152
  10. Trikalinos TA, Hoaglin DC, Small KM, Terrin N, Schmid CH. Methods for the joint meta-analysis of multiple tests. Res Synth Methods. 2014 Dec; 5(4):294-312.
    View in: PubMed
    Score: 0.099
  11. Bakbergenuly I, Hoaglin DC, Kulinskaya E. Methods for estimating between-study variance and overall effect in meta-analysis of odds ratios. Res Synth Methods. 2020 May; 11(3):426-442.
    View in: PubMed
    Score: 0.037
  12. Berkey CS, Hoaglin DC, Antczak-Bouckoms A, Mosteller F, Colditz GA. Meta-analysis of multiple outcomes by regression with random effects. Stat Med. 1998 Nov 30; 17(22):2537-50.
    View in: PubMed
    Score: 0.034
  13. Berkey CS, Hoaglin DC, Mosteller F, Colditz GA. A random-effects regression model for meta-analysis. Stat Med. 1995 Feb 28; 14(4):395-411.
    View in: PubMed
    Score: 0.026
  14. Bozeman SR, Hoaglin DC, Burton TM, Pashos CL, Ben-Joseph RH, Hollenbeak CS. Predicting waist circumference from body mass index. BMC Med Res Methodol. 2012 Aug 03; 12:115.
    View in: PubMed
    Score: 0.022
  15. Pine M, Jordan HS, Elixhauser A, Fry DE, Hoaglin DC, Jones B, Meimban R, Warner D, Gonzales J. Modifying ICD-9-CM coding of secondary diagnoses to improve risk-adjustment of inpatient mortality rates. Med Decis Making. 2009 Jan-Feb; 29(1):69-81.
    View in: PubMed
    Score: 0.017
  16. Emerson JD, Hoaglin DC, Mosteller F. Simple robust procedures for combining risk differences in sets of 2 x 2 tables. Stat Med. 1996 Jul 30; 15(14):1465-88.
    View in: PubMed
    Score: 0.007
  17. Emerson JD, Burdick E, Hoaglin DC, Mosteller F, Chalmers TC. An empirical study of the possible relation of treatment differences to quality scores in controlled randomized clinical trials. Control Clin Trials. 1990 Oct; 11(5):339-52.
    View in: PubMed
    Score: 0.005
Connection Strength

The connection strength for concepts is the sum of the scores for each matching publication.

Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.