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Survival Analysis Power Analysis/Evidence
Method evidence record

Survival Analysis Power Analysis

Power analysis for survival studies determines how many participants — and how many observed events — are required so that a log-rank test or Cox regression has a sufficient probability of detecting a clinically meaningful difference in survival between groups. The foundational formulas were derived by Schoenfeld (1981) and Lachin (1981) and remain the standard approach in clinical trial planning.

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Source record

Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.

Sample Size and Power Analysis for Survival Analysis (Log-rank and Cox Regression)
Taxonomic method record · hypothesis-test / statistics
  • Schoenfeld, D. A. (1981). The asymptotic properties of nonparametric tests for comparing survival distributions. Biometrika, 68(1), 316–319. · DOI 10.1093/biomet/68.1.316
  • Lachin, J. M. (1981). Introduction to sample size determination and power analysis for clinical trials. Controlled Clinical Trials, 2(2), 93–113. · DOI 10.1016/0197-2456(81)90001-5
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Related methods

Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.

See alsoCox proportional hazardsmachine-suggested · Relational suggestion, not evidence.See alsoKaplan-Meiermachine-suggested · Relational suggestion, not evidence.See alsoLog-Rank Testmachine-suggested · Relational suggestion, not evidence.Same method familyPower Analysis for Proportionsmachine-suggested · Relational suggestion, not evidence.Same method familyPower Analysis for t-testmachine-suggested · Relational suggestion, not evidence.Same method familySimulation-Based Power Analysismachine-suggested · Relational suggestion, not evidence.

Evidence status

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Bibliographic sources are present. Claim-level evidence review has not been performed.

Sources

2 recorded citations, copied from the method source record.

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