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Consequences of sequential sampling for meta-analysis.

Lorenzo Braschi1, Juan Botella, Manuel Suero

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Behavior Research Methods
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Summary
This summary is machine-generated.

Sequential stopping rules can reduce sample sizes but introduce bias and higher variance in meta-analyses. Researchers should use initial sample data for meta-analysis and report it when using sequential rules.

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Area of Science:

  • Statistics
  • Biostatistics
  • Medical Research Methodology

Background:

  • Sequential stopping rules offer efficiency by allowing early study termination while controlling error rates.
  • Their impact on meta-analysis, particularly on effect size estimates and variances, is not well-understood.

Purpose of the Study:

  • To investigate the bias and variance of effect size estimates when using sequential stopping rules in meta-analysis.
  • To evaluate the CLAST sequential stopping rule for paired samples.

Main Methods:

  • Simulated data under various sample sizes and population effect sizes.
  • Applied the CLAST rule to paired sample data.
  • Analyzed bias and variance of effect size estimates.

Main Results:

  • Sequential stopping rules, like CLAST, introduce a small bias in effect size estimates.
  • The variance of effect size estimates is significantly higher with sequential stopping rules compared to fixed-sample rules.

Conclusions:

  • Studies using sequential stopping rules can be incorporated into meta-analyses by using only the initial sample's data.
  • Authors using sequential rules should report the initial sample's information for meta-analytic transparency.