Bandingkan metode

	Analisis Daya untuk Studi Kelangsungan Hidup	Analisis kekuatan berbasis simulasi (Kekuatan Monte Carlo)
Bidang	Statistika	Statistika
Keluarga	Hypothesis test	Hypothesis test
Tahun asal	1981	2011
Pencetus	—	Arnold et al. (2011); Green & MacLeod (2016) for mixed-model extension
Tipe	Sample size determination for survival outcomes	Simulation-based (Monte Carlo)
Sumber perintis	Schoenfeld, D. A. (1981). The asymptotic properties of nonparametric tests for comparing survival distributions. Biometrika, 68(1), 316–319.	Arnold, B.F. et al. (2011). Simulation Methods to Estimate Design Power: An Overview for Applied Research. BMC Medical Research Methodology, 11, 94.
Alias	log-rank power analysis, cox regression power analysis, survival power analysis, Sağkalım Analizi Güç Analizi	Monte Carlo power analysis, Monte Carlo simulation power, MC power, Simülasyon Tabanlı Güç Analizi (Monte Carlo Power)
Terkait	6	6
Ringkasan	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.	Simulation-based power analysis estimates the statistical power and required sample size of a study by repeating a full analysis pipeline thousands of times on artificially generated data. Because it relies on Monte Carlo simulation rather than closed-form equations, it is applicable to designs — mixed models, complex measurement structures, non-standard outcomes — where analytical power formulas do not exist. The approach was systematically described for applied research by Arnold et al. in 2011, and the mixed-model implementation via the SIMR package was formalised by Green and MacLeod in 2016.