ArticleBMC medical research methodology2026
Survival analysis of left-truncated and right-censored HIV data: comparison of Cox regression and alternative models.
Article in BMC medical research methodology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThis study examined the impact of delayed entry in HIV cohorts by analyzing left-truncated and right-censored (LTRC) survival data from 69 HIV-positive male patients (24 deaths) followed at a tertiary infectious diseases center. The primary objective was to empirically compare commonly used survival models in estimating survival and identifying prognostic factors under LTRC conditions.
methodsThree LTRC-adapted modeling frameworks were applied with identical covariates: the semiparametric Cox proportional hazards model, accelerated failure time (AFT) models, and parametric proportional hazards models. Median survival time was estimated using Kaplan-Meier methods under both right-censoring-only and LTRC specifications to assess truncation-related differences. Hazard ratios (HRs) and 95% confidence intervals (CIs) were obtained from the LTRC-adjusted Cox model. Model performance was evaluated using information criteria (AIC, BIC, HQIC).
resultsIgnoring left truncation substantially inflated median survival estimates (3,885 vs. 2,626 days). In the LTRC-adjusted Cox model, age (HR = 1.049, 95% CI: 1.011-1.089) and log-transformed HIV RNA (HR = 1.214, 95% CI: 1.055-1.400) were significant predictors, whereas CD4 count and comorbidity status were not. Among the evaluated models, the Cox model showed the lowest information criterion values within this dataset.
conclusionsAppropriate risk-set specification under left truncation is essential for reliable survival estimation in delayed-entry HIV cohorts. Within this empirical dataset, the LTRC-adapted Cox model showed favorable performance relative to AFT and parametric PH alternatives.
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