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#effectsize

1 post1 participant0 posts today
Dr Mircea Zloteanu ☀️ 🌊🌴

#statstab #393 Statistically Efficient Ways to Quantify Added Predictive Value of New Measurements [actual post]

Thoughts: #392 has the comments, but this is where the magic happens.

#modelselection #modelcomparison #variance #effectsize #tutorial

fharrell.com/post/addvalue/

Statistical ThinkingStatistically Efficient Ways to Quantify Added Predictive Value of New Measurements – Statistical ThinkingResearchers have used contorted, inefficient, and arbitrary analyses to demonstrated added value in biomarkers, genes, and new lab measurements. Traditional statistical measures have always been up to the task, and are more powerful and more flexible. It’s time to revisit them, and to add a few slight twists to make them more helpful.
Nicola Romanò

When designing a scientific experiment, a key factor is the sample size to be used for the results of the experiment to be meaningful.

How many cells do I need to measure? How many people do I interview? How many patients do I try my new drug on?

This is of great importance especially for quantitative studies, where we use statistics to determine whether a treatment or condition has an effect. Indeed, when we test a drug on a (small) number of patients, we do so in the hope our results can generalise to any patient because it would be impossible to test it on everyone.

The solution is to perform a "power analysis", a calculation that tells us whether given our experimental design, the statistical test we are using is able to see an effect of a certain magnitude, if that effect is really there. In other words, this is something that tells us whether the experiment we're planning to do could give us meaningful results.

But, as I said, in order to do a power analysis we need to decide what size of effect we would like to see. So... do scientists actually do that?

We explored this question in the context of the chronic variable stress literature.

We found that only a few studies give a clear justification for the sample size used, and in those that do, only a very small fraction used a biologically meaningful effect size as part of the sample size calculation. We discuss challenges around identifying a biologically meaningful effect size and ways to overcome them.

Read more here!
physoc.onlinelibrary.wiley.com

#experiments#ExperimentalDesign#effectsize
Bjørn Sætrevik

It always takes me some minutes to look up the interpretation guidelines for various effect size measures (yes, I know the rules of thumb are somewhat arbitrary). Today I edited Wikipedia to show three different guidelines for four different measures in the same table. Hopefully this can save some time for other researchers.

#methodology#psychometrics#EffectSize
Dr Mircea Zloteanu ☀️ 🌊🌴

#statstab #260 Effect size measures in a two-independent-samples case with nonnormal and nonhomogeneous data

Thoughts: "A_w and d_r were generally robust to these violations"

#robust #effectsize #ttest #2groups #metaanalysis #assumptions #ttest #cohend

link.springer.com/article/10.3

SpringerLinkEffect size measures in a two-independent-samples case with nonnormal and nonhomogeneous data - Behavior Research MethodsIn psychological science, the “new statistics” refer to the new statistical practices that focus on effect size (ES) evaluation instead of conventional null-hypothesis significance testing (Cumming, Psychological Science, 25, 7–29, 2014). In a two-independent-samples scenario, Cohen’s (1988) standardized mean difference (d) is the most popular ES, but its accuracy relies on two assumptions: normality and homogeneity of variances. Five other ESs—the unscaled robust d (d r * ; Hogarty & Kromrey, 2001), scaled robust d (d r ; Algina, Keselman, & Penfield, Psychological Methods, 10, 317–328, 2005), point-biserial correlation (r pb ; McGrath & Meyer, Psychological Methods, 11, 386–401, 2006), common-language ES (CL; Cliff, Psychological Bulletin, 114, 494–509, 1993), and nonparametric estimator for CL (A w ; Ruscio, Psychological Methods, 13, 19–30, 2008)—may be robust to violations of these assumptions, but no study has systematically evaluated their performance. Thus, in this simulation study the performance of these six ESs was examined across five factors: data distribution, sample, base rate, variance ratio, and sample size. The results showed that A w and d r were generally robust to these violations, and A w slightly outperformed d r . Implications for the use of A w and d r in real-world research are discussed.
#psychology#statistics#research
Dr Mircea Zloteanu ☀️ 🌊🌴

#statstab #254 Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations

Thoughts: I share tutorial papers, as people resonate with different writing styles and explanations.

#statistics #guide #tutorial #effectsize

link.springer.com/article/10.1

SpringerLinkStatistical tests, P values, confidence intervals, and power: a guide to misinterpretations - European Journal of EpidemiologyMisinterpretation and abuse of statistical tests, confidence intervals, and statistical power have been decried for decades, yet remain rampant. A key problem is that there are no interpretations of these concepts that are at once simple, intuitive, correct, and foolproof. Instead, correct use and interpretation of these statistics requires an attention to detail which seems to tax the patience of working scientists. This high cognitive demand has led to an epidemic of shortcut definitions and interpretations that are simply wrong, sometimes disastrously so—and yet these misinterpretations dominate much of the scientific literature. In light of this problem, we provide definitions and a discussion of basic statistics that are more general and critical than typically found in traditional introductory expositions. Our goal is to provide a resource for instructors, researchers, and consumers of statistics whose knowledge of statistical theory and technique may be limited but who wish to avoid and spot misinterpretations. We emphasize how violation of often unstated analysis protocols (such as selecting analyses for presentation based on the P values they produce) can lead to small P values even if the declared test hypothesis is correct, and can lead to large P values even if that hypothesis is incorrect. We then provide an explanatory list of 25 misinterpretations of P values, confidence intervals, and power. We conclude with guidelines for improving statistical interpretation and reporting.
Dr Mircea Zloteanu ☀️ 🌊🌴

#statstab #243 Approaches to Calculating Number Needed to Treat (NNT) with Meta-Analysis

Thoughts: Ppl love a one-number-summary. NNT has won out in medical/clinical. So, here are some ways to compute them (for what they're worth)

#NNT #metaanalysis #R #effectsize #statistics #clinical #clinicaltrials

kenkoonwong.com/blog/metannt/

Everyday Is A School DayApproaches to Calculating Number Needed to Treat (NNT) with Meta-Analysis | Everyday Is A School DayHere, we have demonstrated three different methods for calculating NNT with meta-analysis data. I learned a lot from this experience, and I hope you find it enjoyable and informative as well. Thank you, @wwrighID, for initiating the discussion and providing a pivotal example by using the highest weight control event proportion to back-calculate ARR and, eventually, NNT. I also want to express my gratitude to @DrToddLee for contributing a brilliant method of pooling a single proportion from the control group for further estimation. Special thanks to @MatthewBJane, the meta-analysis maestro, for guiding me toward the correct equation to calculate event proportions, with weight estimated by the random effect model. 🙏
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