Describe the cohort

Before we model an outcome, who is in the cohort and how do the measurements relate to one another? Start with the dataset and its Table 1, then choose the display that lets a clinical reader see the distribution, comparison, trajectory, or study flow that matters.

Choose the next recipe

Your question	Recipe	Why this one
Who is in this dataset and what values need attention?	Describing a dataset	Takes inventory before a figure or model hides the detail.
How do I make a publication table from a data summary?	Publication tables	Formats a summary for a report or manuscript.
How do I prepare the manuscript’s Table 1?	Manuscript tables	Builds the descriptive table readers expect.
What does the distribution of one continuous measure look like?	Histogram plots	Shows counts across meaningful bins.
Is the distribution smooth, skewed, or multimodal?	Density plots	Shows the shape without committing to bins.
How do groups compare on a continuous measure?	Box plots	Shows center, spread, and outlying values by group.
How many observations fall in each category?	Bar chart plots	Makes categorical counts or proportions easy to compare.
Do two continuous measures move together?	Scatter plots	Shows the relationship one observation at a time.
How does each patient change across repeated measurements?	Spaghetti plots	Preserves the within-patient trajectory.
How do several outcomes fit into a compact small-multiple display?	Postage stamp plots	Repeats one clear view across outcomes or groups.
How did patients move through the study?	CONSORT patient-flow diagrams	Documents cohort assembly and exclusions.
Which sets of patients overlap?	Venn diagrams and UpSet plots	Shows intersections more clearly than a long list of counts.
How does a cohort move between categories?	Alluvial and Sankey plots	Traces flows from one state to the next.
Did treatment groups balance before and after adjustment?	Covariate balance and follow-up	Checks the comparability needed before outcome interpretation.