Clinical Outcomes Statistical Summarization

I have a set of anonymised patient records and I want to convert the raw tables into a clear, statistically-sound story about how diagnoses link to treatment outcomes.

Your main task is to run a full statistical analysis on these records, focusing on trend detection around diagnosis types, interventions chosen, and the resulting outcomes. I need more than basic counts: confidence intervals, significance testing where appropriate, and clear visuals that highlight any meaningful patterns. Regression or survival analysis may be useful if the data allow, but I’m open to your professional recommendations.

Preferred stack is Python (pandas, numpy, SciPy, statsmodels, matplotlib/seaborn) or R (tidyverse, ggplot2, survival, lme4). If you are more comfortable in SAS or Stata, let me know—what matters is methodological rigour and clear documentation of every step so results can be reproduced.

Deliverables
• Cleaned dataset with a brief data-quality summary
• Statistical analysis script(s) with comments
• Visual dashboards or static charts illustrating key trends
• A concise report (PDF or Word) that interprets the findings in plain language, including limitations and next-step suggestions

I will supply the de-identified CSV files and the data dictionary once we start. Let me know your estimated timeline and any clarifying questions you have about the variables or the depth of analysis.