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

On combining dose-response data from epidemiological studies by meta-analysis

S J Smith1, S P Caudill, K K Steinberg

  • 1Centers for Disease Control and Prevention, National Center for Environmental Health, Atlanta, GA 30333, USA.

Statistics in Medicine
|March 15, 1995
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

Workshop on estimating the health burden of overweight and obesity.

International journal of obesity (2005)·2008
Same author

WITHDRAWN: Continuous electronic heart rate monitoring for fetal assessment during labor.

The Cochrane database of systematic reviews·2007
Same author

Prevention of knee injuries in sports. A systematic review of the literature.

The Journal of sports medicine and physical fitness·2003
Same author

Feasibility of the family-centered model for genetic testing.

The American journal of bioethics : AJOB·2002
Same author

Fifty years of epidemiology at the Centers for Disease Control and Prevention: significant and consequential.

American journal of epidemiology·2001
Same author

Epidemic intelligence service of the Centers for Disease Control and Prevention: 50 years of training and service in applied epidemiology.

American journal of epidemiology·2001
Same journal

Latent Class Log-Linear Models for Estimating Diagnostic Test Accuracy Without a Gold Standard: A Simulation Study.

Statistics in medicine·2026
Same journal

Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.

Statistics in medicine·2026
Same journal

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026
Same journal

Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.

Statistics in medicine·2026
Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026
See all related articles

When analyzing dose-response data for hormone replacement therapy and breast cancer, methods incorporating study variability are recommended. However, extreme variability may bias results, requiring careful interpretation of combined epidemiological findings.

Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Hormone replacement therapy (HRT) has been linked to breast cancer risk, necessitating robust meta-analysis methods.
  • Combining dose-response data from epidemiological studies presents statistical challenges.

Purpose of the Study:

  • To compare alternative statistical methods for combining dose-response slopes from epidemiological studies.
  • To evaluate methods for summarizing data within single studies and combining results across multiple studies.

Main Methods:

  • Meta-analysis of HRT and breast cancer data.
  • Comparison of weighing methods for dose-response slopes.
  • Evaluation of regression models with variable vs. zero intercepts.
  • Assessment of random-effects, fixed-effects, and components-of-variance models.

Related Experiment Videos

  • Bootstrap resampling for validation.

    • Main Results:

    • Weighing methods can be sensitive to non-linearity in relative risk.
    • Variable-intercept models yield significantly different slopes compared to zero-intercept models.
    • Random-effects models account for heterogeneity but may introduce bias; components-of-variance models offer a practical alternative.
    • Study quality scores had minimal impact on results.

    Conclusions:

    • Methods incorporating heterogeneity are recommended for combining dose-response slopes to avoid underestimating standard errors.
    • Caution is advised with highly heterogeneous data due to potential bias in point estimates.
    • Statistical power is influenced by effect size, impacting the number of studies needed.