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

Xing Xing1, Yipeng Wang2, Lifeng Lin3

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|December 20, 2024
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Summary
This summary is machine-generated.

Trial sequential analysis (TSA) helps monitor evidence in systematic reviews. Incorrectly ordering same-year studies alphabetically, instead of chronologically, can alter TSA conclusions, impacting timely research like COVID-19 reviews.

Keywords:
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Area of Science:

  • Biostatistics
  • Evidence Synthesis
  • Medical Informatics

Background:

  • Trial sequential analysis (TSA) is crucial for monitoring synthesized evidence in systematic reviews.
  • Current TSA software often defaults to alphabetical ordering of same-year studies, ignoring temporal sequence.
  • This alphabetical ordering is inappropriate and can misrepresent evidence accumulation.

Purpose of the Study:

  • To highlight the impact of study ordering on Trial Sequential Analysis (TSA) conclusions.
  • To demonstrate how alphabetical ordering of same-year studies can lead to erroneous TSA results.
  • To advocate for chronological ordering of studies in TSA, especially for time-sensitive topics.

Main Methods:

  • A case study was employed to illustrate the effects of study ordering on TSA.
  • Comparison of TSA results using alphabetical versus chronological ordering of same-year studies.
  • Analysis of evidence accumulation patterns under different ordering scenarios.

Main Results:

  • Alphabetical ordering of same-year studies significantly altered TSA patterns compared to chronological ordering.
  • The choice of study order demonstrably impacted the interpretation of synthesized evidence.
  • The findings underscore the critical influence of temporal sequencing in TSA.

Conclusions:

  • The default alphabetical ordering of same-year studies in TSA software is methodologically flawed.
  • Accurate temporal ordering of studies is essential for reliable TSA and evidence synthesis.
  • Authors should prioritize chronological ordering in TSAs to ensure valid conclusions, particularly in rapidly evolving research fields.