A major goal of the pharmaceutical scientist is "nding a relationship between an in vitro characteristic of an oral dosage form and its in vivo performance. One such relationship between drug dissolution (or absorption) in vivo and that in vitro is known as an &in vitro}in vivo correlation' (IVIVC) whose importance stems from the fact that it may be used to minimize the number of human studies required during product development, assist in setting meaningful in vitro dissolution speci"cations and justify biowaivers for scale-up and post approval changes. A number of ways of describing an IVIVC have been reported with &level A' being the most informative and therefore most desirable. In the majority of cases reported to date, both the model and the statistical methods employed for level A IVIVC are very simplistic. The model assumes that the rate and extent of dissolution in vivo are the same as those in vitro. The statistical methods ignore the repeated measures nature of the data and use a response variable as an independent variable without accounting for measurement error. This paper describes some new models which include the simple model as a special case. The modelling approach is based on considering the time at which a drug molecule enters solution (in vitro or in vivo) to be a random variable. The in vitro and in vivo distributions are then related to one another using a proportional odds, proportional hazards or proportional reversed hazards model. The models can be extended by adding a linear time component which describes a time varying relationship. Following the addition of random e!ects to these structural models in order to account for the repeated measures nature of the data collected, the models may be described as generalized linear mixed e!ects models. The models were "tted to some data sets using a maximum likelihood based method and the results indicate that these models have potential for describing an in vitro}in vivo relationship which cannot be described using the currently available models.