Detecting small‐study effects and funnel plot asymmetry in meta‐analysis of survival data: A comparison of new and existing tests

Detecting small‐study effects and funnel plot asymmetry in meta‐analysis of survival data: A... Small‐study effects are a common threat in systematic reviews and may indicate publication bias. Their existence is often verified by visual inspection of the funnel plot. Formal tests to assess the presence of funnel plot asymmetry typically estimate the association between the reported effect size and their standard error, the total sample size, or the inverse of the total sample size. In this paper, we demonstrate that the application of these tests may be less appropriate in meta‐analysis of survival data, where censoring influences statistical significance of the hazard ratio. We subsequently propose 2 new tests that are based on the total number of observed events and adopt a multiplicative variance component. We compare the performance of the various funnel plot asymmetry tests in an extensive simulation study where we varied the true hazard ratio (0.5 to 1), the number of published trials (N=10 to 100), the degree of censoring within trials (0% to 90%), and the mechanism leading to participant dropout (noninformative versus informative). Results demonstrate that previous well‐known tests for detecting funnel plot asymmetry suffer from low power or excessive type‐I error rates in meta‐analysis of survival data, particularly when trials are affected by participant dropout. Because our novel test (adopting estimates of the asymptotic precision as study weights) yields reasonable power and maintains appropriate type‐I error rates, we recommend its use to evaluate funnel plot asymmetry in meta‐analysis of survival data. The use of funnel plot asymmetry tests should, however, be avoided when there are few trials available for any meta‐analysis. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Research Synthesis Methods Wiley

Detecting small‐study effects and funnel plot asymmetry in meta‐analysis of survival data: A comparison of new and existing tests

Loading next page...
 
/lp/wiley/detecting-small-study-effects-and-funnel-plot-asymmetry-in-meta-WYSBklFaDk
Publisher
Wiley Subscription Services, Inc., A Wiley Company
Copyright
Copyright © 2018 John Wiley & Sons, Ltd.
ISSN
1759-2879
eISSN
1759-2887
D.O.I.
10.1002/jrsm.1266
Publisher site
See Article on Publisher Site

Abstract

Small‐study effects are a common threat in systematic reviews and may indicate publication bias. Their existence is often verified by visual inspection of the funnel plot. Formal tests to assess the presence of funnel plot asymmetry typically estimate the association between the reported effect size and their standard error, the total sample size, or the inverse of the total sample size. In this paper, we demonstrate that the application of these tests may be less appropriate in meta‐analysis of survival data, where censoring influences statistical significance of the hazard ratio. We subsequently propose 2 new tests that are based on the total number of observed events and adopt a multiplicative variance component. We compare the performance of the various funnel plot asymmetry tests in an extensive simulation study where we varied the true hazard ratio (0.5 to 1), the number of published trials (N=10 to 100), the degree of censoring within trials (0% to 90%), and the mechanism leading to participant dropout (noninformative versus informative). Results demonstrate that previous well‐known tests for detecting funnel plot asymmetry suffer from low power or excessive type‐I error rates in meta‐analysis of survival data, particularly when trials are affected by participant dropout. Because our novel test (adopting estimates of the asymptotic precision as study weights) yields reasonable power and maintains appropriate type‐I error rates, we recommend its use to evaluate funnel plot asymmetry in meta‐analysis of survival data. The use of funnel plot asymmetry tests should, however, be avoided when there are few trials available for any meta‐analysis.

Journal

Research Synthesis MethodsWiley

Published: Jan 1, 2018

Keywords: ; ; ; ; ;

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off