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Collaborator

Power Calculation : 8 Reasons Why You Should Care About It

Power calculation is crucial in study planning—it helps you determine the ideal sample size to ensure your research is feasible and meaningful. By doing this ...
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Electronic Data Capture (EDC) in Clinical Trials: Everything to Know

Electronic Data Capture (EDC) systems are transforming clinical trials by replacing traditional paper-based methods with efficient, accurate digital data collection. EDC uses electronic Case Report ...
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Collaborator

The Complete Biostatistics Roadmap, or How to Become a Great Biostatistician (with Books and Resources)

Becoming a skilled biostatistician requires more than just learning statistical methods; it's about developing a deep understanding of a wide range of topics, from foundational ...
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Predictive Analytics in Healthcare: Uses and Benefits

Predictive analytics is transforming healthcare by using data to forecast future health trends and improve decision-making. Through techniques like machine learning and data mining, it ...
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Causal Inference

The Influence of Confounding Variables in Observational Studies

Observational studies help identify associations when RCTs are impractical, but they are often challenged by confounding variables. A confounder is a factor linked to both ...
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Collaborator

Do Power Calculation Before Data Collection, Not After – With Downloadable R Code Notebook

Don’t Compute the Statistical Power of Your Experiment…Even if SPSS Allows It And Your Editor Requires It! Introduction Download the R Markdown notebook here used ...
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Collaborator

Sample Size Calculation for Bioequivalence Tests : A powerTOST Tutorial With Downloadable R Code

This tutorial explores the methodology of sample size calculation for bioequivalence studies using the two one-sided tests (TOST) procedure in R. It examines key factors ...
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Causal Inference

Once Upon a Time Series

A Journey Through Causal Inference and Inspiration In 2015, on the brink of defending a dissertation in biostatistics, the author found new hope and direction. ...
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Collaborator

Who’s the Culprit for Bias in Aging Clocks?

Predicting biological age with machine learning How and why do we age? This is a lofty biological question, so what might it have to do ...
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