Clinical trial data analysis is the job of turning a locked study dataset into a defensible answer to one question: did the treatment work, or was the difference chance? It is not the work of collecting, cleaning, or locking that dataset.
If you meant collecting / cleaning / locking the trial database → clinical trial data management. If you meant healthcare data management (EHR / silos / patient care) → healthcare data management. If you meant clinical decision support at the bedside → what is clinical decision support. If you meant imaging software to buy → medical imaging software / image analysis software.
This page is the what-is / how-to explainer for analysis: hypothesis testing, the methods table, the statistical analysis plan, interim vs final, missing data, and SDTM/ADaM. It is not a stats-software listicle. PYCAD does not sell biostats.
Hypothesis testing
You start from the opposite of what you hope to show. The null hypothesis says the treatment has no effect; any gap between arms is noise. The alternative says the effect is real. A p-value is the probability of seeing a result at least this extreme if the null were true. A conventional cutoff is p < 0.05 — not a law of nature, a pre-agreed bar so you do not keep testing until something “works.”
That bar is only as honest as the plan behind it. The test, the endpoint, and the handling of missing data have to be locked before unblinding. Running a menu of tests after the fact is p-hacking, not analysis.
Which test
The method follows the data type and the design, not a favourite package.
| Method | Question | Example |
|---|---|---|
| t-test | Do two groups differ on a continuous mean? | Mean cholesterol drop, drug vs placebo |
| ANOVA | Do three or more groups differ on a continuous mean? | Low dose vs high dose vs placebo, blood pressure |
| Chi-square | Are two categorical variables associated? | Share of patients with a side effect, treatment vs control |
| Kaplan–Meier | How does the chance of an event change over time? | Survival (or progression) curves, experimental vs standard care |
Oncology and other time-to-event trials live on survival analysis. A Kaplan–Meier curve plots the probability of staying event-free. Overlay the arms and a clinician can see whether the new therapy delays the event — before anyone argues about a hazard ratio.
The statistical analysis plan
The statistical analysis plan (SAP) is written before analysis code. It names the primary and secondary endpoints, the tests, the multiplicity rules, and how missing data will be handled. That is the defence against data dredging: you do not get to pick the endpoint that happened to light up.
Sensitivity analyses are the stress test. Re-run the primary analysis under a different (still valid) missing-data rule or a slightly different subgroup definition. If the conclusion holds, you have more than one pretty p-value.
Interim vs final
An interim analysis is a planned look while the trial is still running — usually for a data and safety monitoring board. Overwhelming benefit or a safety problem can stop the trial. It is not the submission analysis.
The final analysis happens after the last visit is in, the data are cleaned, and the database is locked. That locked file is the one regulators review. Collection and lock live on the management page linked above; this page starts after that file exists.
Missing data
How you treat gaps can move the result. Write the rule in the SAP before you see the holes. Last observation carried forward (LOCF) is largely retired: it pretends a dropout stayed at their last value and can hide decline or recovery.
Two methods that are actually used:
- Multiple imputation (MI) — fill the gaps several plausible ways, analyse each completed set, then combine. The uncertainty of the missingness stays in the result.
- Mixed models for repeated measures (MMRM) — model the longitudinal outcome with the gaps left in, under stated assumptions, instead of inventing a single fill-in.
SDTM and ADaM
Regulators do not want a private spreadsheet dialect. CDISC is the common language. Two models matter for analysis:
- SDTM (Study Data Tabulation Model) — how the collected, subject-level data are organised for review.
- ADaM (Analysis Data Model) — how the analysis-ready datasets are structured, with a traceable path from raw tabulations to the numbers in the report.
For many FDA / EMA submissions this is required, not optional. It is also why a reviewer can reconstruct how a p-value was born. Git (or any versioned code trail) plus independent double programming is how you prove the same path a second time.
AI, one block
Useful AI here is analysis support, not a second statistician: site-selection models from historical enrolment, NLP that pulls adverse-event phrases out of notes, a forecast that a cohort will miss a visit window. A model that cannot show its work is a problem in a GCP setting. Validate it the way you validate any analysis tool — and do not treat a black box as the primary efficacy test.
GCP and the audit trail
Good Clinical Practice is the ethics-and-science rulebook: participant rights, a reconstructable record, no silent edits. Every change to analysis datasets and code should be attributable — who, when, why — the same spirit as a 21 CFR Part 11 audit trail on the capture system. Analysis that cannot be replayed is not submission-ready.
PYCAD builds custom web DICOM viewers and medical-imaging AI (annotation → model deployment). That is a connector / imaging stack, not a trial EDC and not a biostats platform. If the work is a viewer or a model on trial images, that is the services side. Case studies.