Lifeng Lin

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Associate Professor

University of Arizona

United States

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Dr. Lifeng Lin is an Associate Professor at the University of Arizona, United States. His research focuses on evidence synthesis, systematic reviews, and meta-analysis, as highlighted in his recent articles on topics such as the evaluation of AI in detecting publication bias and the lifecycle of Cochrane systematic reviews. He also explores methodological advancements in randomised controlled trials and the implications of trial sequential analysis.

Recent Grants

Grant: Close

Joint modeling of continuous and binary data in meta-analysis

Open Date: 2021-12-08

Close Date: 2023-11-30

Grant: Close

Statistical Methods and Software for Multivariate Meta-analysis

Open Date: 2019-09-10

Close Date: 2023-05-31

Lifeng Lin

Research Interests

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About

Recent Grants

Articles (28)

Prospective registration was associated with a reduced risk of bias for randomized controlled trials: a meta-research study

Confidence intervals of the relative risk and odds ratio can predict when the optimal information size in a meta-analysis is not met

Leveraging AI for Meta‐Analysis: Evaluating LLMs in Detecting Publication Bias for Next‐Generation Evidence Synthesis

Lifecycles of Cochrane Systematic Reviews (2003–2024): A Bibliographic Study

Retiring the Term “Weighted Mean Difference” in Contemporary Evidence Synthesis

Tipping point analysis in network meta-analysis

Post‐cesarean skin closure with metal staples versus subcuticular suture in obese patients: A systematic review and meta‐analysis of randomized controlled trials

Prediction Performance of Earlier Studies for Later Studies in Cochrane Reviews

Lack of reproducibility of trial sequential analyses: a meta-epidemiological study

Trial sequential analysis involving same-year studies requires careful temporal ordering

Sensitivity analysis with iterative outlier detection for systematic reviews and meta‐analyses

The impact of studies with no events in both arms on meta-analysis of rare events: A simulation study using generalized linear mixed model

Meta-analysis of continuous outcomes: a user’s guide for analysis and interpretation

A brief note on the common (fixed)-effect meta-analysis model: comment on Veroniki and McKenzie

Comparisons of various estimates of the I2 statistic for quantifying between-study heterogeneity in meta-analysis

Comparisons of the mean differences and standardized mean differences for continuous outcome measures on the same scale

Limits in the search date for rapid reviews of diagnostic test accuracy studies

The Active Ingredient in Reading Comprehension Strategy Intervention for Struggling Readers: A Bayesian Network Meta-analysis

Bayesian hierarchical models incorporating study‐level covariates for multivariate meta‐analysis of diagnostic tests without a gold standard with application to COVID‐19

Methods for deriving risk difference (absolute risk reduction) from a meta-analysis

The normality assumption on between-study random effects was questionable in a considerable number of Cochrane meta-analyses

A Bayesian model for combining standardized mean differences and odds ratios in the same meta-analysis

Assessing the robustness of results from clinical trials and meta-analyses with the fragility index

THE EFFECT DIRECTION SHOULD BE TAKEN INTO ACCOUNT WHEN ASSESSING SMALL-STUDY EFFECTS

Meta-analysis methods for risk difference: A comparison of different models

Choice of link functions for generalized linear mixed models in meta-analyses of proportions

Empirical comparisons of heterogeneity magnitudes of the risk difference, relative risk, and odds ratio

Collaborators (7)

Justin Clark

Peng Peng

Lianne Siegel

Chong Wu

Haitao Chu

George Tomlinson

Yoon Kong Loke

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