Today, I released a new module in the Statistics Globe Hub that explains how to perform sample size calculation using power analysis.

More info about the Statistics Globe Hub: https://statisticsglobe.com/hub

#rstats #datascience #statistics #poweranalysis #samplesize #studyplanning #statisticsglobehub

#statstab #492 A tiny review on e-values and e-processes

Thoughts: How to be a bayesian while wearing a frequentist hat.

#evalues #eprocess #samplesize #evidence #power #sequential #error #type1

https://www.math.uwaterloo.ca/~wang/files/e-review.pdf

sample size

https://piefed.zip/c/NiceMemes/p/1126620/sample-size

#statstab #489 On the performance of the Neyman Allocation with small pilots

Thoughts: If you know your treatment condition will have larger variance you can optimise your sample size.

#nhst #samplesize #neynan #heterogeneity #welch #variance #pilot #se

https://www.sciencedirect.com/science/article/pii/S0304407624001398

#statstab #477 Don’t calculate post-hoc power using observed estimate of effect size

Thoughts: Good discussion and many useful references. Even big journals print stupid stuff.

#posthoc #power #sensitivity #samplesize #consort #medicine #bias

https://statmodeling.stat.columbia.edu/2018/09/24/dont-calculate-post-hoc-power-using-observed-estimate-effect-size/

#statstab #470 Low power bias {shiny} app

Thoughts: easily show what conducting an underpowered study would do to your effect size (type M error).

#teaching #bias #power #typeM #typeS #QRPs #underpowered #samplesize

https://c-jaksic.shinyapps.io/small_power_bias/

Another #PeerReview done.

Manuscript c4,000 words
Review c2,700 words
5hrs

Paper in a key area of my methodological work, so it was really interesting. But I really needed to get stuck in.

Two collaboration projects on the design and reporting of #RCTs that might be useful for others:

https://pubmed.ncbi.nlm.nih.gov/37982521/
presents 19 factors to aid trial design, and the DELTA2 Guidance specifying a target difference and reporting the #SampleSize calculation for RCTs
https://trialsjournal.biomedcentral.com/articles/10.1186/s13063-018-2884-0

#StudyDesign

#statstab #448 {metaforest} Small sample meta-analysis

Thoughts: "a machine-learning based, exploratory approach to identify relevant moderators in meta-analysis"

#ML #MachineLearning #metaanalysis #smallsample #samplesize #heterogeneity #moderator

https://cjvanlissa.github.io/metaforest/articles/Introduction_to_metaforest.html

#statstab #448 {metaforest} Small sample meta-analysis

Thoughts: "a machine-learning based, exploratory approach to identify relevant moderators in meta-analysis"

#ML #MachineLearning #metaanalysis #smallsample #samplesize #heterogeneity #moderator

https://cjvanlissa.github.io/metaforest/articles/Introduction_to_metaforest.html