#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
#SampleSize
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
#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
#samplesize and #ethics question: You plan for a study needing n=100 (50 per cell). Power analysis is all set up and pre-reg. But, because you do research in a uni, you are told you need to allow for more participants (students) as there is a set number of credits all need to reach. What do you do?
#statstab #440 Computing Statistical Power for the Difference in Differences Design
Thoughts: DiD studies are all the rage in Obs research. But how does the concept of power apply to them?
#poweranalysis #DiD #causalinference #samplesize #observational
#statstab #426 Execution of Replications
Thoughts: A good resource for conducting replications. Different ways to plan your sample size and consider "success/failure".
#replication #OpenScience #metaanalysis #samplesize #sesoi #smalltelescope
https://forrt.org/replication_handbook/execution_replications.html
#statstab #421 Sample Size Planning for Statistical Power and Accuracy in Parameter Estimation
Thoughts: AIPE is based on controlling the width of the CI.
Sample size can be computed independent of effect size!
#samplesize #confidenceintervals #AIPE #power #poweranalysis #precision #accuracy #research #design
https://www.annualreviews.org/content/journals/10.1146/annurev.psych.59.103006.093735
Which #SampleSize to use in your pilot or feasibility trial?
Well, you won't find the answer in this review of studies in #ISRCTN (2013 to 2020)
https://pilotfeasibilitystudies.biomedcentral.com/articles/10.1186/s40814-023-01416-w
But it is a good intro into the topic, and with 57% not reaching their target sample size, they may interestingly not provide the information they were designed to offer!
#statstab #417 {pwrss} Practical Power Analysis in R
Thoughts: Some useful vignettes for conducting power analyses for various designs, constraints, and data types.
#poweranalysis #samplesize #r #power #guide #tutorial #sesoi #equivalence #tost #rstats
https://cran.r-project.org/web/packages/pwrss/vignettes/examples.html
#statstab #371 Safeguard Power as a Protection Against Imprecise Power Estimates
Thoughts: tl;dr - when replicating a study, use the lower end of the CI of the original study as your effect in a power analysis.
#statstab #360 Bayes Factor Design Analysis {bfda}
Thoughts: Sample size planning is confusing at first with Bayesian. But BFDA is the quick answer.
🚨New blog post 📝: Your Study Is Too Small (If You Care About Practically Significant Effects)
#effectsize #precision #poweranalysis #research #Psychology #MCID #SESOI #samplesize
How large should your sample size be?
https://vickiboykis.com/2015/08/04/how-large-should-your-sample-size-be/
#HackerNews #sampleSize #statistics #research #dataScience #methodology
#statstab #353 The Abuse of Power; The Pervasive Fallacy of Power Calculations for Data Analysis
Thoughts: An seminal paper on "post hoc" power calculations.
#power #QRPs #NHST #posthoc #samplesize #effectsize
https://www.tandfonline.com/doi/abs/10.1198/000313001300339897
#statstab #324 Information loss due to dichotomization of the outcome of clinical trials. Also it costs more! 💰
Thoughts: Killing the variance in your outcome measure is *not* data transformation.
#statstab #312 {presize} pkg: Understanding Precision-Based Sample Size Calculations
Thoughts: Do you care about effect sizes? Then precision-based planning is for you. Expect higher Ns!
#r #samplesize #precision #estimation #power #Confidenceintervals
https://library.virginia.edu/data/articles/understanding-precision-based-sample-size-calculations
#statstab #297 Sample sizes for saturation in qualitative research
Thoughts: A complicated (and contentious) topic for quals research.
#qualitative #research #sample #samplesize #saturation #methodology #guide #review
https://www.sciencedirect.com/science/article/pii/S0277953621008558
Application of JNDs to meta-science. Very sensible!
"For example, in clinical settings researchers may specify this smallest effect size of interest as the smallest difference in a health condition that patients themselves notice...this practice has been used at least since the advent of psychophysics in the second half of the 19th century"
#metascience #psychology #neuroscience #effectsize #psychophysics #samplesize
`This review holds two main aims. The first aim is to explain the importance of sample size and its relationship to effect size (ES) and statistical significance. The second aim is to assist researchers planning to perform sample size estimations by suggesting and elucidating available alternative software, guidelines and references that will serve different scientific purposes.`
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