RE: Missing Gender (Categorical values)

From:"diane r mould" Subject:RE: [NMusers] Missing Gender (Categorical values) Date:Mon, 5 Aug 2002 21:10:12 -0400 Dear Alan Height was not available in the data, if it had been then we could have just back-calculated weight from the Dubois and Dubois formula without imputing it. We had BSA for all of the patients, although we did not have the original values of weight that had been used to calculate BSA. We would rather have done that than pay the price in the long run times and other model qualification aspects involved with using joint functions. We did test age, sex, creatinine clearance as covariates for the joint model for the same reasons that you cite. This joint model was built in the same fashion that any model would be built. You should have seen that BSA, sex and creatinine clearance were included in the model - age and the other covariates did not improve the fit. We used the observed covariates in the pk part of the model if they were available. BSA was available for all patients. creatinine clearance and sex were estimated for the few individuals that were missing it. There were 2 patients who were missing weight and were also missing sex and ECOG performance status. All of the patients who were missing weight did have creatinine clearance however. The patient who was missing creatinine clearance had the other covariates available. Patients missing ECOG performance status had all other covariates (with the exception of the two who were missing weight and sex). We did not back calculate from creatinine clearance for several reasons - the first was that we did not have sex for 2 of the patients and we did not have the serum creatinine data for any of them. Without serum creatinine, its hard to back estimate weight even if you happen to know the sex, creatinine clearance, and age of a patient. The second reason was that we were also missing ECOG performance status from a fairly large percentage of patients. This latter covariate historically had been shown to influence the safety of the drug. Therefore we had to handle at least 2 missing covariates that were potentially meaningful. Many of the studies used in that work had been conducted a long time ago, and data bases change. Its hard to answer your question given the age and number of data bases that these data were extracted from. Some of the data was faxed to me on paper because the data bases were not easily available. We felt that this was the 'best imputation model' for the same reasons that a modeler would decide that his final pop pk model was 'best'. It was used because the model described the data, the covariates were physiologically reasonable and because changing that model (ie adding other factors) did not improve it further. The manuscript was fairly clear about the fact that we tested a lot of covariates both for the joint model and for the pk model. The second aspect of your question seems to deal with the selection of the covariates for the PK part of the model. Weight was not added just because we had imputed it. This covariate was added because it explained a lot of the inter-individual variability and its inclusion reduced the objective function. We tested a rather long list of covariates, including sex, age, weight, creatinine clearance, BSA, etc. Other covariates and other functions did not do the job as well. If it helps, I did fit a much reduced data set (where all the imputed data had been removed) and came to the same conclusions that were drawn from the larger data set. In addition, I have completed a second analysis using a new data set, with no missing data and the functions are nearly the same - with the same results on IIV. You may be confusing regenerating missing data with standard model building practices. The covariate model that was ultimately used for weight was an allometric model - which is why the exponential terms for clearance were fixed. I did try other models for weight (and I also tried BSA), but they were not as good as this allometric model. Changing the PK model does have some impact on the imputation model but its not that profound. We noticed that the estimated weights did change slightly from the base to the final model but the observed weights do help keep the individual estimates of weight in line. Unless the model is grossly misspecified, I dont think that you are going to do a lot to change the individual estimates if the imputation model is not perfect. Actually - I think I misspoke - we did impute sex using the joint function for the two subjects who were missing it. Sex was not statistically significant as a covariate in the pk model. You seem to be saying that one could dismiss a covariate because of imputation. I dont think so. Perhaps you are missing an important point - the imputed value of a covariate that is used in the pk is the INDIVIDUAL predicted value, not a typical value. Even a base PK model (with no covariates) should provide good individual predicted values of a concentration. Furthermore, in imputation, one would use the observed values of a covariate when they are available. Sex, in our case, was missing only for 2 patients - a very small percentage. if sex was not significant (and it was not) then its not dismissed because we used imputed weight as a covariate in the final model. Covariate effects are checked individually too. Good model building practices should help prevent the sort of thing that you describe from happening. I am not sure that I can answer that. I would imagine that it would be case dependent but a simulation study would need to be done to test that, or perhaps some other person could answer this. True enough. You seem to be referring to informative missingness, such as missing creatinine clearance information because all of the patients with low CLCR values dropped out due to high drug levels leading to adverse events or something of that sort. Is that right? this is not the case with topotecan - it was missing completely at random as far as we could tell. However, it may be a problem or an issue with Atul's data - but that would make it even more reasonable to impute, in order to avoid bias as Lewis suggested earlier. Best Regards Diane ------------------ "See also: 99aug222002 "Imputation of missing sex covariate"