Proposal Case 002: When Clarity Meets Efficiency

Some projects stand out for their precision — not in complexity, but in clarity.

This doctor was already active in research and had a strong command of his study’s analytical needs. He approached me with a clear analysis plan, well-defined table shells, and a high-quality dataset that he personally curated. Each variable was properly labelled, and every subset clearly identified — a level of preparation that made statistical programming both smooth and rewarding.

The analysis involved comparison between two study groups.

• For categorical variables, results were summarised as n (%).

• For continuous variables, results were summarised as mean (SD), median, minimum, and maximum.

• Statistical tests applied included the independent t-test for continuous variables, and either the Pearson chi-squared test or Fisher’s exact test for categorical variables, depending on expected cell counts.

All tables were generated in R using the gtsummary package, providing publication-quality output with consistent formatting and automatic p-values.

The workflow was straightforward — within a weekend, the complete deliverables were ready. When updated data arrived later, the same R script could be rerun at minimal cost, ensuring reproducibility and efficiency.

What impressed me most was the doctor’s deep understanding of statistical reasoning — he knew precisely which variables linked to each table row and which subset applied. The final paper was successfully published, and he graciously acknowledged my contribution in the article.