Models for assessing temporal trends in familial aggregation are described for both cross-sectional and longitudinal family data. Simultaneous linear structural equations on latent variables are used to model the dependence among family members. The coefficients of the equations are assumed to be parametric functions of time, so that quite complex temporal trends in familial aggregations can be accommodated. Variable family sizes and missing data values pose no problem as the parameters of the models are estimated via maximum likelihood techniques. One of the models is applied to systolic blood pressure data in 542 Japanese-American nuclear families. The results indicate limited evidence for temporal variation in the genetic expression, but that there is substantial temporal variation in environmental influences, which appear to peak at middle age.