# Hartung-Knapp method in meta-analysis

**URL:** <https://discourse.datamethods.org/t/hartung-knapp-method-in-meta-analysis/3989>\
**Category:** meta-analysis\
**Created:** [December 16, 2020, 3:32pm UTC](https://discourse.datamethods.org/t/hartung-knapp-method-in-meta-analysis/3989 "2020-12-16T15:32:42Z")\
**Posts on this page:** 1\
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**Author:** ![R\_cubed](https://discourse.datamethods.org/user_avatar/discourse.datamethods.org/r_cubed/32/1518_2.png) [@R\_cubed](https://discourse.datamethods.org/u/R_cubed)\
**Post date:** [December 16, 2020, 10:58pm UTC](https://discourse.datamethods.org/t/hartung-knapp-method-in-meta-analysis/3989/2 "2020-12-16T22:58:18Z")

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Some quick thoughts:

1. If you are going to use a frequentist analysis for this, Hartaung Knapp has been reported to have better coverage for small sample sizes compared to the DerSimonian-Laird approach. But you might want to read this:

[https://colloquium2019.cochrane.org/abstracts/comparison-hartung-knapp-sidik-jonkman-method-meta-analysis-conventional-frequentist](https://colloquium2019.cochrane.org/abstracts/comparison-hartung-knapp-sidik-jonkman-method-meta-analysis-conventional-frequentist)

> **[The Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis is...](https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/1471-2288-14-25?optIn=false)**
>
> Background The DerSimonian and Laird approach (DL) is widely used for random effects meta-analysis, but this often results in inappropriate type I error rates. The method described by Hartung, Knapp, Sidik and Jonkman (HKSJ) is known to perform...

1. The problem – you only have 3 studies. So of course the CI is going to be wide.

2. You will need to find more studies, but I think a meta-regression approach would be more useful to explore the heterogeneity.

**Addendum** This will be a great start to doing a meta-analysis in a rigorous way, in that it doesn’t naively substitute _estimates_ of components for variance, and treat them as _population_ parameters.

> **[Meta-analysis of Odds Ratios: Current Good Practices](https://pmc.ncbi.nlm.nih.gov/articles/PMC5352535/)**
>
> Many systematic reviews of randomized clinical trials lead to meta-analyses of odds ratios. The customary methods of estimating an overall odds ratio involve weighted averages of the individual trials’ estimates of the logarithm of the odds ratio....

1. Think carefully about the effect size measure. Maybe using the log odds instead of the hazard ratio will reduce heterogeneity?

> [@When to use absolute vs relative effect measures](https://discourse.datamethods.org/t/when-to-use-absolute-vs-relative-effect-measures/2955):
>
> After watching BBR Session 8, I had a general question about effect measures in biomedical research. I understand the mathematical difference between the two, but I’m having trouble understanding when to use an absolute vs relative measure in study/experimental design. Are there any general guidelines about choosing between an absolute vs relative effect measure? What are good examples of scenarios (pre-clinical or clinical) where an absolute measure would be more appropriate? What about scenar…

1. Ignore the “significance” of the aggregate CI. If you extracted the one tailed p-values from each of the CI and used a combination procedure, I’m sure it would indicate that there is a positive association before a CI would reject the null.

I put together a thread with a lot of references on statistical issues related to meta-analysis. Suffice it to say – the common textbook procedures will lead you astray if you are not careful.

> [@Nonparametric Effect Size estimation, Likelihood Methods, and Meta Analysis](https://discourse.datamethods.org/t/nonparametric-effect-size-estimation-likelihood-methods-and-meta-analysis/1525/8):
>
> Edit: 11/3/2019 – I’m thinking of creating a BibTeX database of all the links related to meta-analysis in this thread. If anyone is interested, I’d be happy to share it on github. I’m adding some especially relevant citations related to the assumption of normality in meta-analytic procedures: Especially useful is Table 3: (Eight main assumptions made by conventional methods for meta‐analysis) The recommendation in the discussion section was the use of logistic regression. Blockquote Com…

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