PubMed · 16890820
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Desmond Whyms, Ini Huijts, Steven L B Jensen. 2006-08-05. Coordinating HIV control efforts.. https://doi.org/10.1016/s0140-6736(06)69141-1
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Interval censoring arises when a subject misses prescheduled visits at which the failure is to be assessed. Most existing approaches for analysing interval-censored failure time data assume that the censoring mechanism is independent of the true failure time. However, there are situations where this assumption may not hold. In this paper, we consider such a situation in which the dependence structure between the censoring variables and the failure time can be modelled through some latent variables and a method for regression analysis of failure time data is proposed. The method makes use of the proportional hazards frailty model and an EM algorithm is presented for estimation. Finite sample properties of the proposed estimators of regression parameters are examined through simulation studies and we illustrate the method with data from an AIDS study.
To compare the survival functions based on right-truncated data, Lagakos et al. proposed a weighted logrank test based on a reverse time scale. This is in contrast to Bilker and Wang, who suggested a semi-parametric version of the Mann-Whitney test by assuming that the distribution of truncation times is known or can be estimated parametrically. The approach of Lagakos et al. is simple and elegant, but the weight function in their method depends on the underlying cumulative hazard functions even under proportional hazards models. On the other hand, a semi-parametric test may have better efficiency, but it may be sensitive to misspecification of the distribution of truncation times. Therefore, this paper proposes a non-parametric test statistic based on the integrated weighted difference between two estimated survival functions in forward time. The comparative results from a simulation study are presented and the implementation of these methods to a real data set is demonstrated.
OBJECTIVE: To quantify cause-specific mortality risk attributable to non-AIDS-related and AIDS-related causes before and after the advent of highly active antiretroviral therapy (HAART). METHODS: Competing-risk methods were used to determine the cumulative AIDS-related and non-AIDS-related risk of mortality between 1990 and the end of 2003 in the Johns Hopkins HIV Clinical Cohort, a prospective cohort study. RESULTS: Beginning in 1997 with the introduction of HAART, all-cause mortality declined and has remained stable at approximately 39 deaths per 1000 person-years. AIDS-related mortality continued to decline in this period (P = 0.008), whereas non-AIDS-related mortality increased (P < 0.001). Using competing-risk methods, the risk of dying attributable to AIDS-related causes remains significantly higher than the risk of dying attributable to non-AIDS-related causes for patients with a CD4 count 200 cells/mm, however, non-AIDS-related mortality was greater than AIDS-related mortality, particularly among injection drug users. Other transmission categories had similar AIDS-related and non-AIDS-related cumulative mortalities. CONCLUSIONS: HAART has reduced mortality rates among HIV-infected individuals, but further efforts to reduce mortality in this population require increased attention to conditions that have not traditionally been considered to be HIV related.