Volume 1 · complete · 90 slides

Correlations

Matching the coefficient to the question, interpreting what it gives you, and knowing what quietly breaks it.

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Contents

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01What is a correlation coefficient?The coefficient, its assumptions, and where it shows up in clinical outcome assessmentslide 7, 12 in all 02Methods for correlation-based questionsChoosing the right correlation for the question you are askingslide 19, 23 in all 03Types of correlationMatching the coefficient to the data and the questionslide 42, 14 in all 04Correlation outputsShowing, estimating, and interpreting a correlationslide 56, 14 in all 05Challenges and solutionsWhat distorts a correlation, and what to do about itslide 70, 11 in all 06Further Relevant ConceptsConcepts that sit alongside correlation and change how a coefficient should be interpretedslide 81, 10 in all

Try it live

Slide 50 argues that Pearson can understate a real association. The demo lets you break it yourself.

Pearson vs Spearman

Reproduce every figure

Every figure in this volume comes from one synthetic dataset. It and the code are yours.

# long data, so pivot to wide first
wide <- adcorr |>
  filter(AVISIT == "Baseline") |>
  pivot_wider(id_cols = USUBJID,
    names_from = PARAMCD, values_from = AVAL)

cor(wide$TARGTOT, wide$RELATEDA)  # 0.73

Get the dataset and code

Downloads

volume-1-correlations.pdf3.3 MB volume-1-correlations.pptx3.9 MB Dataset and code (zip) 97 KB

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With thanks to our contributors

A collaborative effort

32 people. 17 organizations. One handbook.

Acaster Lloyd Consulting

Adelphi Values

Altoida

Boehringer Ingelheim

Candlagan Consulting LLC

Compass Strategy and Research

Duke University

ECNP Clinical Outcomes TWG

GSK

Ionis Pharmaceuticals

Leeds Beckett University

Leoben Research Aurora

Sprout Health Solutions

Thermo Fisher Scientific

TiliaHealth

Universidad FUCS

University of Sunderland

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