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

Adjusting for partially missing baseline measurements in randomized trials.

Ian R White1, Simon G Thompson

  • 1MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge CB2 2SR, U.K. ian.white@mrc-bsu.cam.ac.uk

Statistics in Medicine
|December 1, 2004
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

European Society of Contact Dermatitis Guideline for Diagnostic Patch Testing-Recommendations on Best Practice (Update 2026).

Contact dermatitis·2026
Same author

SPIRIT 2025 statement: updated guideline for protocols of randomised trials.

Lancet (London, England)·2026
Same author

Sample Size Calculation for the ROCI Design.

Statistics in medicine·2026
Same author

Bayesian analysis in confirmatory clinical trials: A narrative review and discussion of current practice.

Clinical trials (London, England)·2026
Same author

Adjusting for confounding in population administrative data when confounders are only measured in a linked cohort.

International journal of population data science·2026
Same author

An investigation of the impact of using contrast- and arm-synthesis models for network meta-analysis.

Research synthesis methods·2026

Adjusting for baseline data in randomized trials improves power. Joint modeling is most efficient for missing data, with mean imputation a viable alternative under specific conditions for accurate treatment effect detection.

Area of Science:

  • Biostatistics
  • Clinical Trials
  • Psychiatry Research

Background:

  • Adjusting for baseline variables in randomized trials enhances statistical power to detect treatment effects.
  • Incomplete baseline data in clinical trials leads to inefficient complete-case analysis.

Purpose of the Study:

  • To evaluate efficient statistical methods for handling partially missing baseline data in randomized trials.
  • To identify optimal strategies for maintaining statistical power when baseline data are incomplete.

Main Methods:

  • Comparison of joint modeling, mean imputation, and complete-case analysis for normally distributed data.
  • Investigation of conditions for mean imputation effectiveness, including correlation thresholds and imputation methods.
  • Application of the missing indicator method when baseline data are not missing at random.

Related Experiment Videos

Main Results:

  • Joint modeling of baseline and outcome variables is identified as the most statistically efficient method.
  • Mean imputation offers an excellent alternative when baseline-outcome correlation is below 0.6 and imputation is deterministic.
  • The missing indicator method is crucial when missingness is not completely at random.

Conclusions:

  • Efficient handling of missing baseline data is critical for accurate treatment effect estimation in randomized trials.
  • Joint modeling and carefully applied mean imputation are recommended strategies for incomplete baseline data.
  • Methodological choices depend on data characteristics, including correlation and missingness patterns.