19 citations · 22 across the 6 of their papers we have counts for
6 papers
Copas-Jackson-type bounds for publication bias over a general class of selection models
Taojun Hu, Yi Zhou, Xiao-Hua Zhou +1
Publication bias (PB) is one of the most vital threats to the accuracy of meta-analysis. Adjustment or sensitivity analysis based on selection models, which describe the probabilit…
CSTEapp: An interactive R-Shiny application of the covariate-specific treatment effect curve for visualizing individualized treatment rule
Yi Zhou, Yuhao Deng, Yu-Shi Tian +5
In precision medicine, deriving the individualized treatment rule (ITR) is crucial for recommending the optimal treatment based on patients' baseline covariates. The covariate-spec…
Phased Instruction Fine-Tuning for Large Language Models
Wei Pang, Chuan Zhou, Xiao-Hua Zhou +1
Instruction Fine-Tuning enhances pre-trained language models from basic next-word prediction to complex instruction-following. However, existing One-off Instruction Fine-Tuning (On…
A likelihood-based sensitivity analysis for addressing publication bias in meta-analysis of diagnostic studies using exact likelihood
Taojun Hu, Yi Zhou, Xiao-Hua Zhou +1
Publication bias (PB) poses a significant threat to meta-analysis, as studies yielding notable results are more likely to be published in scientific journals. Sensitivity analysis…
Copas-Heckman-type sensitivity analysis for publication bias in rare-event meta-analysis under generalized linear mixed models
Yi Zhou, Taojun Hu, Yuji Sakamoto +3
In systematic reviews and meta-analyses, publication bias (PB) is one of the serious concerns and mainly induced by selective publication of academic literatures. Although many met…
Unveiling LLM Evaluation Focused on Metrics: Challenges and Solutions
Taojun Hu, Xiao-Hua Zhou
Natural Language Processing (NLP) is witnessing a remarkable breakthrough driven by the success of Large Language Models (LLMs). LLMs have gained significant attention across acade…