Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Practical methods for incorporating summary time-to-event data into meta-analysis.

Jayne F Tierney1, Lesley A Stewart, Davina Ghersi

  • 1Meta-analysis Group, MRC Clinical Trials Unit, London, UK. jt@ctu.mrc.ac.uk

Trials
|June 9, 2007
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Measurement of ethnicity in clinical trials: Delphi survey and consensus statement.

BMJ (Clinical research ed.)·2026
Same author

Comparing risk factors in severe COVID-19 using machine learning and non-machine learning methods: analysis from 2 international randomized controlled trials.

JAMIA open·2026
Same author

PROTOCOL: Comparative Effectiveness of Tools for Communicating Health Research Results to Study Participants and Others With Relevant Lived Experience: A Living Systematic Review of Randomized Controlled Trials.

Campbell systematic reviews·2026
Same author

Reporting of Cohort and Routinely Collected Data in Randomised Controlled Trial Protocols (SPIRIT-ROUTINE): extension checklist with explanation and elaboration.

BMJ (Clinical research ed.)·2026
Same author

Author adherence and experiences with PRISMA-P 2015: a cross-sectional study.

Journal of clinical epidemiology·2026
Same author

Language-related eligibility criteria in UK randomised trials: a systematic review of extended research reports.

Trials·2026

This guide translates complex statistical methods for estimating hazard ratios (HRs) from time-to-event data into practical advice. It aims to improve meta-analysis quality when individual patient data is unavailable.

Area of Science:

  • Biostatistics
  • Clinical Epidemiology
  • Medical Research

Background:

  • Time-to-event outcomes in systematic reviews and meta-analyses are best analyzed using hazard ratios (HRs).
  • Methods exist to derive HRs from summary data when individual patient data (IPD) is absent.
  • Limited awareness of these statistical methods hinders their adoption in meta-analysis.

Purpose of the Study:

  • To translate complex statistical methods for estimating HRs from published time-to-event analyses.
  • To provide practical guidance and an accessible spreadsheet for calculating HRs and associated statistics.
  • To facilitate the use of time-to-event data in meta-analyses.

Main Methods:

  • Translating statistical notation into practical guidance for estimating hazard ratios.

Related Experiment Videos

  • Developing an easy-to-use spreadsheet for calculations.
  • Referencing relevant statistical literature for specific circumstances.

    • Main Results:

    • Improved understanding and appropriate use of published time-to-event data in meta-analysis.
    • Guidance for handling specific circumstances in data analysis.
    • A spreadsheet tool to aid in computational aspects of HR estimation.

    Conclusions:

    • The methods do not eliminate potential biases from using published data.
    • This practical guide enhances the quality of analysis and interpretation of meta-analyses with time-to-event outcomes.
    • Facilitates more appropriate use of time-to-event data in systematic reviews.