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Cited by 28Meta-analysis and systematic reviewsStatistical Methods in EpidemiologyMenopause: Health Impacts and Treatments

On combining dose—response data from epidemiological studies by meta‐analysis

Smith, S. Jay, Caudill, Samue L. P., Steinberg, Karen K., Thacker, Stephen B.·1995·Statistics in Medicine
DOI 10.1002/sim.4780140513
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Abstract

Using data from a meta‐analysis of the effects of oestrogen replacement therapy on the development of breast cancer, we compared alternative methods for combining dose—response slopes from epidemiological studies. We evaluated issues related both to summarizing data from single studies and to combining results from multiple studies. Findings related to the analysis of individual dose—response studies include: (1) a method of weighing studies that gives greater influence to dose—response slopes that conform to the linear relation of relative risk to duration can lead to large differences in calculated weights as a function of non‐linearity; (2) a regression model using a variable‐intercept resulted in a mean dose—response slope that increased as much as threefold when compared with the values obtained with a zero‐intercept model. When combining results from multiple studies, we found: (1) calculating standard errors of mean dose—response slopes by methods that allow for both among‐study and within‐study variability (a random‐effects type model) gave values different from a method that assumes homogeneity and equal within‐study precision (a fixed‐effects model); (2) the random‐effects model gives mean and standard error results most similar to a bootstrap resampling method as increasing heterogeneity is observed (however, this model could give biased mean estimates compared with the bootstrap method); (3) a components‐of‐variance model compares favourably with the bootstrap and is easier to apply than the random‐effects model. Based on these findings, we recommend the use of methods which incorporate heterogeneity to guard against underestimating the standard error. However, caution is urged because bias in point estimates can occur if extreme heterogeneity is present. Two other observations affect the interpretation of data combined from multiple studies. First, inclusion into a model of quality scores assigned by blinded reviewers had little effect on the mean dose—response slope and its standard error. Second, the number of studies required to achieve desired statistical power, varies with effect size.

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  • Other topics included meta-regression, while few articles focused on more specific statistical issues, such as dose-response analysis, credibility ceilings, bias adjustment, hierarchical models, or regression bias.

    “Other topics included meta-regression, while few articles focused on more specific statistical issues, such as dose-response analysis [81, 109, 111, 113], credibility ceilings [110], bias adjustment [112], hierarchical models [117], or regression bias [108].”

    From Methods to systematically review and meta-analyse observational studies: a systematic scoping review of recommendations (2018) · cites this paper as [111]

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Authors
Smith, S. J., Caudill, S. L. P., Steinberg, K. K., & Thacker, S. B.
Affiliations
Centers for Disease Control and Prevention (US)
Venue
Statistics in Medicine
Publisher
Wiley
Published
1995-03-15
Volume
14
Issue
5-7
Pages
531-544
ISSN
  • 0277-6715 (ISSN-L)
  • 1097-0258
DOI
10.1002/sim.4780140513
OpenAlex
W2041753036
Cited by
28
References
11
Citations by year
2023: 1 · 2019: 1 · 2018: 1 · 2015: 1 · 2014: 2 · 2013: 1
FWCI
4.69
Type
journal article
Language
en
Open access
closed
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor, 4368 d)
Locations
  • Statistics in Medicine (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Menopause: Health Impacts and Treatments
Keywords
estrogen replacement therapy, breast cancer, dose-response slope, random effects models, fixed effects model, bootstrapping, heterogeneity, standard error estimation, statistical power
MeSH terms
Analysis of Variance, Breast Neoplasms, Data Interpretation, Statistical, Dose-Response Relationship, Drug, Epidemiologic Methods, Female, Humans, Menopause, Regression Analysis, Meta-Analysis as Topic, Models, Statistical, Estrogen Replacement Therapy, Case-Control Studies

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