Re: Probabilistic model

From: "Leonid Gibiansky" Subject: Re: [NMusers] Probabilistic model Date: Tue, May 17, 2005 8:40 pm Let me add an example in support of Nick's suggestion: In the project (real data, consecutive PK, then PK/PD) that motivated my small example we noticed that the expected score ESC= SUM(SCORE_i*P_i) defined as a sum of (level * probability of the score at that level) described the observed data with a very good accuracy. That motivated two continuous models. In one, we fitted ESC as defined above to the observed DV (score). The second model was a model for ESC as an EMAX function of concentration. Individual predictions of these two continuous models were as good as individual predictions of the probabilistic model. We tried predictive check simulations and found out that all three models over-estimated the frequency of the highest scores (with the strongest effect). The probabilistic model was slightly better than continuous in this regard. Continuous models took much less time (many hours instead of many days) and efforts to converge (e.g., initial values of the parameters were obtained by FO; then FOCEI converged starting from the FO final estimates): this was much simpler than guessing initial conditions for the probabilistic model. Both types of models predicted a very similar covariate PD effect (requiring about 25-30% dose adjustment for a subgroup of patients). Continuous models were more stable and they actually converged (i.e., start from different initial conditions led to similar solutions) while the probabilistic model exhibited behavior described in the original example that started this discussion. Based on this example, it would be hard to recommend any of the approaches over the other: each has own advantages and problems. Leonid