This paper suggests an approach to deal with an estimation problem which is often encountered in analyzing the longitudinal cost data gathered in a clinical trial. The source of that estimation problem is twofold: 1) a considerable number of missing data due to treatment-related withdrawal of severely affected patients with high health care costs in only one the treatment groups and 2) a heavily skewed cost distribution due to rare high-cost events. The approach is illustrated using data from a trial comparing 3 different drug regimes. In order to calculate costs per patient-year in case of selectively missing data we extrapolated the costs of patients with incomplete follow-up. Due to the skewness and the associated large variance in costs per patient-year, these costs cannot be analyzed using common parametric statistical methods relying on underlying normal distributions. A logarithmic transformation was performed to approximate a normal distribution, reduce the impact of extreme values and create similar size variances in the treatment groups. An ordinary least squares regression analysis of transformed data then standardized for differences in patient characteristics between the groups. For the retransformation, the so-called smearing estimate was used. This 'transformation-standardization-retransformation' approach enabled us to provide more consistent and efficient estimates of cost differences that were shown to be statistically significant and judged to be important.