| Quantifying publication bias in meta-analysis L Lin, H Chu Biometrics 74 (3), 785-794, 2018 | 1115 | 2018 |
| The trim-and-fill method for publication bias: practical guidelines and recommendations based on a large database of meta-analyses L Shi, L Lin Medicine 98 (23), e15987, 2019 | 624 | 2019 |
| Bias caused by sampling error in meta-analysis with small sample sizes L Lin PLoS One 13 (9), e0204056, 2018 | 296 | 2018 |
| Empirical comparison of publication bias tests in meta-analysis L Lin, H Chu, MH Murad, C Hong, Z Qu, SR Cole, Y Chen Journal of General Internal Medicine 33 (8), 1260-1267, 2018 | 279 | 2018 |
| Arcsine‐based transformations for meta‐analysis of proportions: Pros, cons, and alternatives L Lin, C Xu Health Science Reports 3 (3), e178, 2020 | 236 | 2020 |
| Meta-analysis of proportions using generalized linear mixed models L Lin, H Chu Epidemiology 31 (5), 713-717, 2020 | 197 | 2020 |
| When continuous outcomes are measured using different scales: guide for meta-analysis and interpretation MH Murad, Z Wang, H Chu, L Lin BMJ 364, k4817, 2019 | 193 | 2019 |
| The effect of publication bias magnitude and direction on the certainty in evidence MH Murad, H Chu, L Lin, Z Wang BMJ Evidence-Based Medicine 23 (3), 84-86, 2018 | 183 | 2018 |
| Evaluation of various estimators for standardized mean difference in meta‐analysis L Lin, AM Aloe Statistics in Medicine 40 (2), 403-426, 2021 | 166 | 2021 |
| Controversy and debate: questionable utility of the relative risk in clinical research: paper 1: a call for change to practice SA Doi, L Furuya-Kanamori, C Xu, L Lin, T Chivese, L Thalib Journal of Clinical Epidemiology 142, 271-279, 2022 | 142 | 2022 |
| Performing arm-based network meta-analysis in R with the pcnetmeta package L Lin, J Zhang, JS Hodges, H Chu Journal of Statistical Software 80 (5), 2017 | 126 | 2017 |
| Comparison of four heterogeneity measures for meta‐analysis L Lin Journal of Evaluation in Clinical Practice 26 (1), 376-384, 2020 | 115 | 2020 |
| Alternative measures of between‐study heterogeneity in meta‐analysis: reducing the impact of outlying studies L Lin, H Chu, JS Hodges Biometrics 73 (1), 156-166, 2017 | 114 | 2017 |
| An adaptive two-sample test for high-dimensional means G Xu, L Lin, P Wei, W Pan Biometrika 103 (3), 609-624, 2016 | 113 | 2016 |
| P value–driven methods were underpowered to detect publication bias: analysis of Cochrane review meta-analyses L Furuya-Kanamori, C Xu, L Lin, T Doan, H Chu, L Thalib, SAR Doi Journal of Clinical Epidemiology 118, 86-92, 2020 | 108 | 2020 |
| A proposed framework to guide evidence synthesis practice for meta-analysis with zero-events studies C Xu, L Furuya-Kanamori, L Zorzela, L Lin, S Vohra Journal of Clinical Epidemiology 135, 70-78, 2021 | 74 | 2021 |
| Exclusion of studies with no events in both arms in meta-analysis impacted the conclusions C Xu, L Li, L Lin, H Chu, L Thabane, K Zou, X Sun Journal of Clinical Epidemiology 123, 91-99, 2020 | 62 | 2020 |
| Laplace approximation, penalized quasi-likelihood, and adaptive Gauss–Hermite quadrature for generalized linear mixed models: Towards meta-analysis of binary outcome with … K Ju, L Lin, H Chu, LL Cheng, C Xu BMC Medical Research Methodology 20, 152, 2020 | 57 | 2020 |
| The magnitude of small-study effects in the Cochrane Database of Systematic Reviews: an empirical study of nearly 30 000 meta-analyses L Lin, L Shi, H Chu, MH Murad BMJ Evidence-Based Medicine 25 (1), 27-32, 2020 | 53 | 2020 |
| Hybrid test for publication bias in meta-analysis L Lin Statistical Methods in Medical Research 29 (10), 2881-2899, 2020 | 46 | 2020 |
Haitao ChuProfessor of Biostatistics, University of Minnesota在 umn.edu 的电子邮件经过验证
