Introduction to Meta-Analysis¶
Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to Meta-Analysis. Wiley.
Cited by¶
2 citations across 2 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Bias
- Borenstein and colleagues (2009) show in their treatment of meta-analysis that pooling many studies sharpens precision relentlessly yet leaves any shared bias untouched, the clearest demonstration that the offset is structural rather than statistical accident.
This sourceTreatment of meta-analytic pooling showing that combining many studies sharpens precision relentlessly while leaving any shared bias untouched — evidence that the offset is structural rather than a statistical accident.
- Borenstein and colleagues (2009) show in their treatment of meta-analysis that pooling many studies sharpens precision relentlessly yet leaves any shared bias untouched, the clearest demonstration that the offset is structural rather than statistical accident.
- Precision Weighting
- The precision-weighted integration rule is inverse-variance weighting: the pooled effect is \(\hat{\theta} = \frac{\sum w_i \theta_i}{\sum w_i}\) with \(w_i = 1/\sigma_i^2\), structurally identical to the Gaussian fusion above — a large trial dominates a small one in exact proportion to precision, which is why naively averaging trial results (equal weights) is wrong and drives the estimate toward the noisy small studies.
This sourceStandard reference deriving inverse-variance weighting for pooling effect sizes across studies.
- The precision-weighted integration rule is inverse-variance weighting: the pooled effect is \(\hat{\theta} = \frac{\sum w_i \theta_i}{\sum w_i}\) with \(w_i = 1/\sigma_i^2\), structurally identical to the Gaussian fusion above — a large trial dominates a small one in exact proportion to precision, which is why naively averaging trial results (equal weights) is wrong and drives the estimate toward the noisy small studies.
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