This situation can be seen as an extreme form of ‘informative’ censoring, where censoring is associated with the probability of the outcome. Analyses that ignore competing events are however regularly published even though they may produce misleading results. We examined how the competing risk of death affected estimates of loss to follow-up in cohorts of patients starting ART in Zambia and Switzerland. We compared outcomes in ART programme from Zambia and Switzerland to illustrate the importance of death as a competing risk when estimating loss to follow-up. Standard Kaplan-Meier analyses that ignored the competing risk of death substantially overestimated the cumulative incidence of loss to follow-up in patients starting with low CD4 counts in Zambia. In contrast, there was little bias among populations experiencing lower mortality, including patients starting ART with high CD4 counts in Zambia and all patients in Switzerland. The results from the cause-specific Cox models and the more complex Fine and Gray model were comparable, both when analyzing the effect of CD4 count strata on the rate of loss to follow-up, and when comparing the Swiss with the Zambian cohort. We estimated the cumulative incidence of developing the event of interest in the presence of a competing risk. The cumulative incidence represents the probability that an individual will experience an event of interest by time. In contrast to the standard Kaplan-Meier approach, the cumulative incidence from the competing risk analysis depends not only on the number of patients who experienced an event, but also on the number of patients who did not experience a competing event. Similarly, we used two approaches to model the effect of covariates in the present of the competing risk. The causespecific Cox model, in which competing causes are censored, is a reasonable and practical choice but is restricted to modelling instantaneous risk functions. The Fine and Gray model makes use of the subdistribution hazard to model cumulative incidence and thus quantify the overall benefit or harm of an exposure, however, it is considerably more complex. Of note, the effect of a covariate on cumulative incidence will also depend on its effect on the competing risk. In other words, the effect of a covariate on the cause-specific hazard may be different from the PF-04217903 citations corresponding effect on cumulative incidence. This was recently illustrated using the example of the competing risks of stopping first line ART or switching to second-line ART. An important strength of our study was the analysis of a combined dataset, which allowed using the same definitions and coding of variables in the Zambian and Swiss cohorts. We could thus examine the risk of loss to follow-up and death across the same CD4 categories, while adjusting for a common set of confounding variables. The CIDRZ programme is typical of many sites involved in the scale-up of ART in resource-limited settings.