PROJECT SUMMARY
Systematic reviews (SRs) and meta-analyses (MAs) are essential tools in evidence-based medicine and
comparative effectiveness research, enabling thorough evaluations of treatment benefits and risks. In the context
of the COVID-19 pandemic, SRs and MAs have been extensively utilized to provide timely insights on preventive
interventions, therapeutic medications, and vaccines, aiding informed decision-making and response strategies.
However, traditional SRs have limitations such as redundant publications, inconsistent inclusion criteria, and
inadequate investigation of evidence accumulation. Living systematic reviews (LSRs) have emerged as a
dynamic approach to continuously update and synthesize evidence, addressing these limitations. LSRs offer
benefits such as timeliness, reduction of research waste, identification of research gaps, and integration of the
latest evidence. Trial sequential analysis (TSA) is a valuable tool to derive conclusive evidence for assessing
the adequacy of studies in LSRs. By providing monitoring and futility boundaries, TSA ensures reliable decision-
making regarding a treatment’s effectiveness or futility. Conclusive evidence can save patients from harmful
treatments or placebos, and redirect resources to other research areas. However, the current TSA methods have
significant limitations, mainly stemming from their heavy reliance on interim analyses of RCTs, where patients
tend to be more homogeneous than those in MAs that consist of multiple studies on different populations.
This proposed project aims to advance TSA methods by developing innovative approaches to establishing
decision boundaries, specifically targeting the improvement of early-stage TSAs and reducing the risk of
premature termination of LSRs. By addressing between-study heterogeneity and enhancing statistical methods,
this project seeks to enhance the reliability and timeliness of living evidence. Rigorous validation of the proposed
methods for TSAs will be conducted through extensive simulation studies carefully designed to evaluate the
overall type I and type II error rates of treatment effect estimates. Additionally, the performance of the proposed
methods will be assessed using diverse real-world datasets. Furthermore, user-friendly, open-source software
will be developed, accompanied by comprehensive instructions and examples, ensuring accessibility and ease
of implementation for biostatisticians and clinicians. In conclusion, these novel TSA methods hold broad
applicability across medical fields, including infectious diseases and cancers, facilitating more robust
assessments of existing evidence, guiding decisions regarding the necessity of new randomized controlled trials,
and ultimately advancing comparative effectiveness research and evidence-based medicine.