Data analysis
Data and statistical analysis
Upload your data. The studio cleans it step by step, shows every change, and gives you a cleaned file and an audit report.
1Upload your data
The first row must hold the variable names. For Excel, the first sheet is read.
If the buttons above do not open your files, copy the cells from Excel (include the header row) or paste CSV text here, then press Load.
2Review and approve the cleaning steps
Nothing is deleted without your approval. The original file is never changed. Every action is logged.
Intake and profiling
Categorical harmonization
Plausibility limits (draft, pending clinical review)
Missing value audit
3Results
Data Quality Scorecard
Activity log
Cleaned data
4Choose your analysis
Analyses use the cleaned data from step 3. Each result is added to the audit report.
Independent checks run on the analyses
Each statistic was computed by this page and by Python on the same random test data. Each row used one random data set, not many. R was not available. Differences are the largest absolute difference found.
| Analysis | Compared with | Largest difference |
|---|---|---|
| Welch t-test, Mann-Whitney, ANOVA, Kruskal-Wallis, chi-square, Fisher exact, Pearson, Spearman | scipy 1.18 | 3.3e-9 (17 values) |
| Paired t-test (t, p, 95% CI) | scipy | 1.2e-13 |
| Wilcoxon signed-rank (exact for up to 50 pairs without ties or zeros; normal approximation otherwise) | scipy | 0 (exact), 6.7e-15 (approximation) |
| Kaplan-Meier survival, Greenwood standard error, median and its 95% CI | statsmodels 0.15 | 1.1e-16 (survival), 1.4e-17 (SE), medians and CIs identical |
| Log-rank test | statsmodels | 2.7e-15 |
| Linear regression (coefficients, SE, p, CI, R-squared, F test) | statsmodels | 5e-14 |
| Variance inflation factor | statsmodels | agrees to 6 decimals |
| Logistic regression (coefficients, SE, p, log-likelihood, likelihood ratio test) | statsmodels | 8.4e-9 |
| AUC | scikit-learn 1.9 | 1.1e-16 |
| Cox regression with Breslow ties (coefficients, SE, p, log-likelihood) | statsmodels | 1.5e-14 |
| Concordance index | Independent pair-counting code in Python | 0 |
| Newcombe 95% CI for a risk difference (3 tables) | statsmodels | 2.1e-9 |
| Decision curve net benefit (6 thresholds) | Direct calculation in Python | 1.1e-16 |
| Principal components (variance explained, eigenvalues, loadings) | scikit-learn | 1.7e-16, 1.8e-15, 2.9e-15 |
| k-means sum of squares; silhouette from the same labels | scikit-learn | identical on 3 separated clusters; silhouette 1e-15 |
| Ridge, LASSO and elastic net coefficients at fixed penalty strength | scikit-learn (ridge: closed form) | 3.9e-11 |
| Cross-validated error across the penalty path (same folds) | scikit-learn | 7.5e-13 |
| Gradient boosting predictions (same settings, regression and classification) | scikit-learn | 3.6e-15 and 2.2e-16 |
Python scripts (members): each of the 13 script types was run in Python on the 90-row test file. Coefficients, test statistics and p-values matched the studio. Cross-validated figures, the random forest and the penalty choice differ slightly because scikit-learn draws its own folds and random numbers.
Checked end to end: on a 90-row file, the linear, logistic and Cox regression tables produced by this page (including dummy coding of categories) agree with statsmodels to the rounding shown.
Not identical by design: the random forest uses random resampling, so it is checked by performance, not value by value. On the test data the out-of-bag AUC was 0.708 here and 0.707 in scikit-learn, with the same top variable and the same variable ranking. For a numeric outcome, out-of-bag R-squared was 0.633 here and 0.597 in scikit-learn. For k-means on overlapping data with 5 clusters, the best of 20 random starts here was 0.2% above the best of 30 starts in scikit-learn (the two agreed for 2, 3 and 4 clusters).
Not covered: the automatic choice between mean-based and rank-based methods is a rule of thumb. Effect sizes, the Table 1 layout, the bootstrap AUC interval, the calibration table, cross-validation fold assignment, the charts, the cost-effectiveness arithmetic (checked by hand on one example) and the proportional hazards assumption were not compared with other software.
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The plausibility limits are a draft based on clinical judgment and have not been reviewed or sourced. Uniqueness is added to the Kahn categories. Version 0.4. Datathrob. Clinical research made simple.