This paper provides a systematic comparison of cancer mortality and incidence projection methods used at major national health agencies. These methods include Poisson regression using an age-period-cohort model as well as a simple log-linear trend, a joinpoint technique, which accounts for sharp changes, autoregressive time series and state-space models. We assess and compare the reliability of these projection methods by using Canadian cancer mortality data for 12 cancer sites at both the national and regional levels. Cancer sites were chosen to provide a wide range of mortality frequencies. We explore specific techniques for small case counts and for overall national-level projections based on regional-level data. No single method is omnibus in terms of superior performance across a wide range of cancer sites and for all sizes of populations. However, the procedures based on age-period-cohort models used by the Association of the Nordic Cancer Registries tend to provide better performance than the other methods considered. The exception is when case counts are small, where the average of the observed counts over the recent 5-year period yields better predictions.

The use of single raw cortisol values is inadequate to compare physiological stress levels across individuals. If the distributions of individuals' cortisol values are approximately normal, then the standardized ranking method is most appropriate; otherwise, the sample percentile method is advised. These methods may be applied to compare stress levels across individuals in other populations and species.

The authors link time‐to‐event models with longitudinal models through shared latent variables when the time of the event of interest is known only to lie within an interval. The context of tree growth and mortality studies presents a natural application of shared parameter joint modelling where a latent feature of each tree impacts both mortality and growth. The authors' developments are motivated by such an application, with the additional caveat that event‐times are not known exactly, since the trees are subject to intermittent observation, with the time between measurements extending into decades or longer. Such interval censoring is a common occurrence in similar long‐term experiments in resource management, ecology and health research. The additional numerical complexity resulting from interval censored time‐to‐event data often makes inference for joint models prohibitive. The authors examine properties of three event‐time imputation methods that enable application of now standard joint modelling techniques to interval censored time‐to‐event data. The imputation techniques include the midpoint method, a kernel smoothing method, and a backsolve method which incorporates information from the longitudinal trajectory. Joint analysis of a designed, long‐term, forestry experiment is presented, accompanied by a simulation study investigating the properties of the three event‐time imputation techniques. The Canadian Journal of Statistics 39: 438–457; 2011 © 2011 Statistical Society of Canada