Survival Analyses in MerQur: The Full Repertoire of Time-to-Event Data
DOI:
https://doi.org/10.53463/merqur.20260452Keywords:
survival analysis, Kaplan-Meier, Cox regresyon, frailtyAbstract
Survival analysis is a family of statistical methods that handles time-to-event data: an event whose occurrence time is to be observed (death, disease recurrence, machine failure, species establishment, customer churn) and censored units whose event has not yet occurred by the end of follow-up. Classical regression cannot correctly handle censoring; survival analysis is therefore a separate methodological tradition. This study introduces in detail the 8 analyses offered in MerQur’s Survival category: Kaplan-Meier survival analysis, Cox proportional-hazards regression, Parametric Survival (AFT — Weibull/lognormal/log-logistic), Competing Risks (Fine-Gray), Time-dependent Cox (time-varying covariates), Survey-PHREG (complex-sample weighted Cox), Interval-Censored survival, and Shared-Frailty Cox (Cox with random effects). For each: (i) mathematical basis, (ii) types of censoring (right, left, interval) and application context, (iii) form fields and options in MerQur, (iv) reported statistics (hazard ratio, 95% CI, median survival, RMST), and (v) interpretation guidance for a typical research question. The proportional-hazards assumption of the Cox model is automatically tested via Schoenfeld residuals; the most appropriate distribution for parametric AFT is selected by AIC comparison. Within the competing-risks framework, the Fine-Gray subdistribution hazard provides different information from classical Cox. MerQur offers an integrated tool environment in Turkish for survival research ranging from medical research to forest ecology, from financial survival to technical reliability.
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