# About BAMP

**BAMP** is a software package to analyze incidence or mortality data on the Lexis diagram, using a Bayesian version of an age-period-cohort model. Such models have been described in Berzuini and Clayton (1994), Besag et al. (1995) and Knorr-Held and Rainer (2001). For each pixel in the Lexis diagram (that is for a specific age group and specific period) data must be available on the number of persons under risk (population number) and the number of disease cases (typically cancer incidence or mortality). A hierarchical model is assumed with a binomial model in the first-stage.

As smoothing priors for the age, period and cohort parameters random walks of first and second order (RW1 or RW2) available. **BAMP** also allows to drop one or more of the latent components, for example to drop the cohort effect and to analyze a age-period model. Additional unstructured prior distributions are assumed for each pixel in the Lexis diagram. Note that there is a nonidentifiability in the likelihood of the APC-model, see Clayton and Schifflers (1987), which indices some problems in interpreting the latent effects. Only for RW1 model, the parameters are (weakly) identifiable.

**BAMP** has several features which are described more detailed in Knorr-Held and Rainer (2001):

- The data does not need to be on the same grid, for example period can be in one year intervals and age group in five year intervals.

- **BAMP** allows for prediction of the future number of cases

- **BAMP** allows for a retrospective prediction for model checking

Additionally to the model described in Knorr-Held and Rainer (2001), **BAMP** can handle

- AP and AC models

- models with and without global heterogenity parameter (overdispersion)

- models with additional age, period and/or cohohort heterogenity

- including covariates (still in development)

Detail about this feature can be found in Schmid (2004 - in german)

There are some graphical routines available in order to

- plot estimated age, period and cohort effects (only for RW1 model)

- compare observed and fitted rates

- predict rates

- assess the "significance" of the unstructured parameters. This helps to identify variation in the data, which is not supported by the age, period and cohort parameters.