Biostatistics Analyzer

Clinical Use Cases

• Clinical trial analysis: A research team analyzing a randomized controlled trial comparing two anti-hypertensive agents uses the skill to perform the primary efficacy analysis (between-group difference in systolic BP at 12 weeks, adjusted for baseline), generate Kaplan-Meier curves for the secondary MACE endpoint, and calculate the number needed to treat
• Retrospective cohort study: An epidemiologist studying risk factors for post-operative complications uses the skill to build a multivariable logistic regression model, check for confounders, assess model discrimination (C-statistic), and produce a forest plot of adjusted odds ratios
• Diagnostic accuracy study: A pathologist evaluating a new biomarker for pancreatic cancer uses the skill to generate ROC curves comparing the novel biomarker against CA 19-9, calculate DeLong's test for AUC comparison, and determine the optimal cutoff point that maximizes sensitivity at a clinically acceptable specificity threshold
• Grant proposal sample size justification: An investigator planning a non-inferiority trial needs to calculate the required sample size assuming a non-inferiority margin of 5%, expected event rate of 15% in both arms, 80% power, and one-sided alpha of 0.025, accounting for 15% dropout

Safety & Evidence

• Safety Classification: Safe — The skill performs mathematical computations and statistical analyses. It does not make clinical decisions, treat patients, or generate clinical recommendations. Statistical results and interpretations must be reviewed by qualified biostatisticians or researchers with statistical training.
• Evidence Level: Moderate — Statistical methods implemented follow established textbook approaches (Altman, Bland, Hosmer-Lemeshow) and published reporting guidelines (CONSORT, STROBE, STARD, TRIPOD). Calculations use standard statistical formulas. However, proper application of statistical methods requires understanding of assumptions, study design, and clinical context that the skill supports but does not replace.

Example Usage

Sample size calculation:

Calculate required sample size for:
Study design: Randomized controlled trial, parallel groups
Primary outcome: Proportion of patients achieving HbA1c < 7.0% at 6 months
Expected rate in control group: 35%
Expected rate in intervention group: 50%
Alpha: 0.05 (two-sided)
Power: 80%
Allocation ratio: 1:1
Expected dropout rate: 10%

Sample Size Calculation:

Method: Two-proportion z-test (chi-square test for equality of proportions)

Parameters:

• p1 (control): 0.35
• p2 (intervention): 0.50
• Absolute difference: 0.15
• Alpha: 0.05 (two-sided)
• Power: 0.80
• Allocation ratio: 1:1

Result (before dropout adjustment):

• Per group: 170 patients
• Total: 340 patients

Result (after 10% dropout adjustment):

• Per group: 189 patients
• Total: 378 patients

Reporting language: "A sample size of 170 per group (340 total) provides 80% power to detect a 15 percentage point difference in the proportion of patients achieving HbA1c <7.0% (50% vs. 35%) using a two-sided chi-square test at the 5% significance level. Accounting for an anticipated 10% dropout rate, we plan to enroll 189 patients per group (378 total)."

Sensitivity analysis: At 90% power, the required sample size increases to 227 per group (454 total, or 504 after dropout adjustment).

ROC curve analysis:

Evaluate diagnostic performance of troponin-I for acute MI diagnosis:
- AUC for troponin-I: 0.92 (95% CI: 0.89-0.95)
- AUC for CK-MB: 0.78 (95% CI: 0.73-0.83)
- Compare the two biomarkers

ROC Curve Comparison:

Biomarker	AUC	95% CI	Optimal Cutoff (Youden)	Sensitivity	Specificity
Troponin-I	0.92	0.89-0.95	0.04 ng/mL	93.2%	84.6%
CK-MB	0.78	0.73-0.83	5.0 ng/mL	72.1%	76.3%

DeLong's test for AUC comparison:

• Difference in AUC: 0.14 (95% CI: 0.08-0.20)
• Z-statistic: 4.67
• P-value: <0.001

Interpretation: Troponin-I demonstrates significantly superior discriminative ability for acute MI diagnosis compared to CK-MB (AUC 0.92 vs. 0.78, p<0.001). At the optimal cutoff of 0.04 ng/mL, troponin-I achieves 93.2% sensitivity and 84.6% specificity.

Technical Details

• Category: Clinical Research Summarizing
• Author: OMS Contributors
• License: MIT
• Version: 1.0.0
• Specialty: Research, Biostatistics

References

• Altman DG. Practical Statistics for Medical Research. Chapman and Hall/CRC, 1991.
• Hosmer DW, Lemeshow S, Sturdivant RX. Applied Logistic Regression. 3rd ed. Wiley, 2013.
• Bland JM, Altman DG. "Statistical methods for assessing agreement between two methods of clinical measurement." Lancet. 1986;327(8476):307-310.
• DeLong ER, DeLong DM, Clarke-Pearson DL. "Comparing the areas under two or more correlated receiver operating characteristic curves." Biometrics. 1988;44(3):837-845.
• Schulz KF, et al. "CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials." BMJ. 2010;340:c332.
• Collins GS, et al. "Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD)." BMJ. 2015;350:g7594.

This skill is part of Open Medical Skills, a curated marketplace of medical AI skills maintained by physicians for physicians and the healthcare industry.