A new tool for power analysis of longitudinal linear mixed-effects models (LMMs) – with support for missing data, plus non-inferiority and equivalence tests.
powerlmmjs.rpsychologist.com
Would really appreciate your feedback as I refine this app! Details below 🧵👇
Visualize how statistical power changes across parameter combinations.
powerlmmjs.rpsychologist.com?view=contour
Visualize how statistical power changes across parameter combinations.
powerlmmjs.rpsychologist.com?view=contour
Visualize how statistical power changes across parameter combinations.
powerlmmjs.rpsychologist.com?view=contour
Visualize how statistical power changes across parameter combinations.
powerlmmjs.rpsychologist.com?view=contour
powerlmmjs.rpsychologist.com
- Calculate power (etc) for multilevel models
- Examine effects of dropout and other important parameters
- Fast! (Instant results)
powerlmmjs.rpsychologist.com
- Calculate power (etc) for multilevel models
- Examine effects of dropout and other important parameters
- Fast! (Instant results)
Now includes:
- Power analysis summary report
- Reproducible & shareable configs (URL/JSON)
- Calculations validated against R
- Hypothesis region visualization
powerlmmjs.rpsychologist.com
Now includes:
- Power analysis summary report
- Reproducible & shareable configs (URL/JSON)
- Calculations validated against R
- Hypothesis region visualization
powerlmmjs.rpsychologist.com
Now includes:
- Power analysis summary report
- Reproducible & shareable configs (URL/JSON)
- Calculations validated against R
- Hypothesis region visualization
powerlmmjs.rpsychologist.com
"Globally, approximately 3.3% of the population reports using AAS, with prevalence rates of 6.4% among men [2] and 4% among women [3]."
"Globally, approximately 3.3% of the population reports using AAS, with prevalence rates of 6.4% among men [2] and 4% among women [3]."
”I sometimes say that 'screen use', when putting that into statistical models, it’s sort of like looking at 'car use' when trying to find out why injuries are happening from car accidents” 🇳🇴
www.youtube.com/live/XKo6oUY...
”I sometimes say that 'screen use', when putting that into statistical models, it’s sort of like looking at 'car use' when trying to find out why injuries are happening from car accidents” 🇳🇴
www.youtube.com/live/XKo6oUY...
"Before and after causal inference: recognizing causal questions and answering them when assumptions are violated"
www.youtube.com/watch?v=9B-X...
"Before and after causal inference: recognizing causal questions and answering them when assumptions are violated"
www.youtube.com/watch?v=9B-X...
Below is a visual comparison of the ICD-11 criteria for gaming and gambling disorder—essentially a substitution of terms.
Below is a visual comparison of the ICD-11 criteria for gaming and gambling disorder—essentially a substitution of terms.
But if it’s that untrustworthy, why are you publishing the results it generates?
But if it’s that untrustworthy, why are you publishing the results it generates?
The *Simplest* and *Most Correct* Way to Do Causal Mediation Analysis
Are you tired of explaining mediation analysis to your colleagues? Just send them this package.
github.com/rpsychologis...
The *Simplest* and *Most Correct* Way to Do Causal Mediation Analysis
Are you tired of explaining mediation analysis to your colleagues? Just send them this package.
github.com/rpsychologis...
The *Simplest* and *Most Correct* Way to Do Causal Mediation Analysis
Are you tired of explaining mediation analysis to your colleagues? Just send them this package.
github.com/rpsychologis...
Step 2: Toss in random values
Step 3: Call it conservative
I’ll definitely adopt this model-free way of handling MNAR, what could go wrong?
Step 2: Toss in random values
Step 3: Call it conservative
I’ll definitely adopt this model-free way of handling MNAR, what could go wrong?
I’ve also observed that people read magazines they’d never touch anywhere else while waiting. Proposing a new disorder: "Compulsive Magazine Reading". Urgent need for intervention programs
I’ve also observed that people read magazines they’d never touch anywhere else while waiting. Proposing a new disorder: "Compulsive Magazine Reading". Urgent need for intervention programs
Clinical prediction: Hard.
Causal inference: Hard.
Causal prediction: Combines the hardest parts of both. Predict outcomes we can’t observe, and evaluate models by making even more assumptions about an unobservable target.
Clinical prediction: Hard.
Causal inference: Hard.
Causal prediction: Combines the hardest parts of both. Predict outcomes we can’t observe, and evaluate models by making even more assumptions about an unobservable target.
—> www.bmj.com/content/389/...
#openscience #transparency #medsky #statssky #episky
—> www.bmj.com/content/389/...
#openscience #transparency #medsky #statssky #episky
Sometimes, when reviewing a manuscript, it's really unclear to me what precisely the authors are trying to do -- which makes it hard to evaluate the work properly.
So, here's some advice for how to ensure that readers don't get lost.
www.the100.ci/2025/02/17/r...
Sometimes, when reviewing a manuscript, it's really unclear to me what precisely the authors are trying to do -- which makes it hard to evaluate the work properly.
So, here's some advice for how to ensure that readers don't get lost.
www.the100.ci/2025/02/17/r...