We correct for leading-track misidentification with a data-driven procedure. Starting from the measured distributions, for each event the track loss due to inefficiency is applied a second time to the **data** (having been applied the first time naturally by the detector) by rejecting tracks randomly. If the leading track is considered reconstructed it is used as before to define the different regions. Otherwise the sub-leading track is used. Since the tracking inefficiency is quite small (about 20%) applying it on the reconstructed **data** a second time does not alter the results significantly. To verify this statement we compared our results with a two step procedure. In this case the inefficiency is applied two times on measured data, half of its value at a time. The correction factor obtained in this way is compatible with the one step procedure. Furthermore, the data-driven procedure has been tested on simulated **data** where the true leading particle is known. We observed a discrepancy between the two methods, especially at low leading-track p T values, which is taken into account in the systematic error. The maximum leading-track misidentification correction is 8% on the final distributions.