On the incidence-prevalence relation and length-biased sampling

For many diseases, logistic and other constraints often render large incidence studies difficult, if not impossible, to carry out. This becomes a drawback, particularly when a new incidence study is needed each time the disease incidence rate is investigated in a different population. However, by carrying out a prevalent cohort study with follow-up it is possible to estimate the incidence rate if it is constant. In this paper we derive the maximum likelihood estimator (MLE) of the overall incidence rate, $\lambda$, as well as age-specific incidence rates, by exploiting the well known epidemiologic relationship, prevalence = incidence $\times$ mean duration ($P = \lambda \times \mu$). We establish the asymptotic distributions of the MLEs, provide approximate confidence intervals for the parameters, and point out that the MLE of $\lambda$ is asymptotically most efficient. Moreover, the MLE of $\lambda$ is the natural estimator obtained by substituting the marginal maximum likelihood estimators for P and $\mu$, respectively, in the expression $P = \lambda \times \mu$. Our work is related to that of Keiding (1991, 2006), who, using a Markov process model, proposed estimators for the incidence rate from a prevalent cohort study \emph{without} follow-up, under three different scenarios. However, each scenario requires assumptions that are both disease specific and depend on the availability of epidemiologic data at the population level. With follow-up, we are able to remove these restrictions, and our results apply in a wide range of circumstances. We apply our methods to data collected as part of the Canadian Study of Health and Ageing to estimate the incidence rate of dementia amongst elderly Canadians.