David Hoaglin to Computer Simulation
This is a "connection" page, showing publications David Hoaglin has written about Computer Simulation.
Connection Strength
1.303
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Kulinskaya E, Hoaglin DC. Estimation of heterogeneity variance based on a generalized Q statistic in meta-analysis of log-odds-ratio. Res Synth Methods. 2023 Sep; 14(5):671-688.
Score: 0.176
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Kulinskaya E, Hoaglin DC. On the Q statistic with constant weights in meta-analysis of binary outcomes. BMC Med Res Methodol. 2023 06 21; 23(1):146.
Score: 0.175
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Dong G, Huang B, Verbeeck J, Cui Y, Song J, Gamalo-Siebers M, Wang D, Hoaglin DC, Seifu Y, M?tze T, Kolassa J. Win statistics (win ratio, win odds, and net benefit) can complement one another to show the strength of the treatment effect on time-to-event outcomes. Pharm Stat. 2023 01; 22(1):20-33.
Score: 0.164
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Dong G, Huang B, Wang D, Verbeeck J, Wang J, Hoaglin DC. Adjusting win statistics for dependent censoring. Pharm Stat. 2021 05; 20(3):440-450.
Score: 0.147
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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.
Score: 0.142
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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.
Score: 0.107
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Hoaglin DC. Misunderstandings about Q and 'Cochran's Q test' in meta-analysis. Stat Med. 2016 Feb 20; 35(4):485-95.
Score: 0.102
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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.
Score: 0.048
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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.
Score: 0.040
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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.
Score: 0.038
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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.
Score: 0.038
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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.
Score: 0.035
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Bakbergenuly I, Hoaglin DC, Kulinskaya E. Estimation in meta-analyses of mean difference and standardized mean difference. Stat Med. 2020 01 30; 39(2):171-191.
Score: 0.034
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Bakbergenuly I, Hoaglin DC, Kulinskaya E. Pitfalls of using the risk ratio in meta-analysis. Res Synth Methods. 2019 Sep; 10(3):398-419.
Score: 0.033
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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.
Score: 0.023