  Handling of missed data is one of the main tasks in data preprocessing
especially in large public service datasets. We have analysed data from the
Trauma Audit and Research Network (TARN) database, the largest trauma database
in Europe. For the analysis we used 165,559 trauma cases. Among them, there are
19,289 cases (13.19\%) with unknown outcome. We have demonstrated that these
outcomes are not missed `completely at random' and, hence, it is impossible
just to exclude these cases from analysis despite the large amount of available
data. We have developed a system of non-stationary Markov models for the
handling of missed outcomes and validated these models on the data of 15,437
patients which arrived into TARN hospitals later than 24 hours but within 30
days from injury. We used these Markov models for the analysis of mortality. In
particular, we corrected the observed fraction of death. Two na\"ive approaches
give 7.20\% (available case study) or 6.36\% (if we assume that all unknown
outcomes are `alive'). The corrected value is 6.78\%. Following the seminal
paper of Trunkey (1983) the multimodality of mortality curves has become a much
discussed idea. For the whole analysed TARN dataset the coefficient of
mortality monotonically decreases in time but the stratified analysis of the
mortality gives a different result: for lower severities the coefficient of
mortality is a non-monotonic function of the time after injury and may have
maxima at the second and third weeks. The approach developed here can be
applied to various healthcare datasets which experience the problem of lost
patients and missed outcomes.
