Determining disease prevalence from incidence and survival using simulation techniques.

OBJECTIVES

We present a new method for determining prevalence estimates together with estimates of their precision, from incidence and survival data using Monte-Carlo simulation techniques. The algorithm also provides for the incidence process to be marked with the values of subject level covariates, facilitating calculation of the distribution of these variables in prevalent cases.

METHODS

Disease incidence is modelled as a marked stochastic process and simulations are made from this process. For each simulated incident case, the probability of remaining in the prevalent sub-population is calculated from bootstrapped survival curves. This algorithm is used to determine the distribution of prevalence estimates and of the ancillary data associated with the marks of the incidence process. This is then used to determine prevalence estimates and estimates of the precision of these estimates, together with estimates of the distribution of ancillary variables in the prevalent sub-population. This technique is illustrated by determining the prevalence of acute myeloid leukaemia from data held in the Haematological Malignancy Research Network (HMRN). In addition, the precision of these estimates is determined and the age distribution of prevalent cases diagnosed within twenty years of the prevalence index date is calculated.

CONCLUSION

Determining prevalence estimates by using Monte-Carlo simulation techniques provides a means of calculation more flexible that traditional techniques. In addition to automatically providing precision estimates for the prevalence estimates, the distribution of any measured subject level variables can be calculated for the prevalent sub-population. Temporal changes in incidence and in survival offer no difficulties for the method.