Six projects, each with something I checked
Each repository has a written README with its limits. The label under each name says what kind of data it uses.
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pv-regulatory-lifecycle
Synthetic safety data, fictional drug
Twelve linked documents on a made-up drug: a synthetic safety database, data checks, signal screening, reporting deadlines, a draft periodic safety report, a risk management plan summary, a label change, a letter to prescribers and a dossier tracker.
All 50 planted data problems found. Rash was the only event that met the screening rule, mostly at 25 mg and age 65 and over (PRR 6.96). Where the Indian guidance is silent, the documents say so instead of guessing. One script re-runs everything and confirms identical results.
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clinical-research-analysis
Published teaching dataset
A survival analysis written up like a paper: analysis plan first, then Kaplan-Meier curves, Cox regression, and a meta-analysis of four published trials.
Women compared with men, adjusted hazard ratio 0.58 (95% CI 0.41 to 0.80). Pooled SGLT2 inhibitor trials: 0.91 (0.85 to 0.97). A flagged assumption check led to a documented follow-up analysis.
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clinical-data-management
Synthetic trial data
What a data management team does on a trial: edit checks, a numbered query log, simplified SDTM-style datasets, a dashboard and a Data Validation Plan.
11 edit checks found all 36 planted errors. 88 of 120 subjects were clean enough to map, and 32 were held back for open queries.
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drug-adverse-event-explorer
Real public FDA reports
Charts the most reported side effects of a drug, compares two drugs, and calculates a PRR score, a standard way to flag possible safety signals.
Ibuprofen and acetaminophen are compared as percentages of each drug’s own reports, so a drug with more reports does not look worse. The README explains why reports cannot prove causation.
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clinical-trials-tracker
Real public registry data
A pipeline and dashboard on ClinicalTrials.gov: which trials are recruiting and where, which records look out of date, and how long trials take.
581 diabetes trials with an Indian site. 35 are recruiting now, and 66 records were flagged as possibly out of date. A script writes the findings report from the data.
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healthcare-sql-analysis
Synthetic hospital data
Five SQL queries on a made-up hospital database, using JOINs, CASE, CTEs and a window function.
Admissions by department, city and age group all add up to the same 863 admissions.
These are self-directed learning projects and not work experience. Three use synthetic data and one uses a published teaching dataset, so no patient information is involved in any of them.
How I work
Plan before results
I write the question and the method before I run the analysis, and I record every change I make afterwards.
Check against a known answer
I plant errors and see whether my checks find them, make totals agree across queries, and compare numbers with the original papers.
State the limits
Every project says what it cannot show: synthetic data, observational results, flags that are leads and not proof.
Skills
- Programming
- Python (pandas, statsmodels, matplotlib, Streamlit), SQL (SQLite, JOINs, CTEs, window functions), Git and GitHub
- Methods
- Survival analysis, Cox regression, meta-analysis, data validation, signal detection with PRR
- Standards
- Working knowledge of CDISC SDTM concepts (simplified, not full conformance)
- Next
- GCP training and R
About me
I am a medical graduate (MBBS) moving into clinical research and health data roles. I like questions where a number has to stand up to checking.
I started with coding and found that research questions were the part I enjoyed most. These six projects are how I am learning the work of drug safety, clinical data management and clinical research teams.
I am looking for entry-level roles in drug safety, clinical data management or clinical operations in India.