Estimating the Decayed, Missing, and Filled permanent Teeth (DMFT) index of the University of Split student population using the Monte Carlo method

Abstract

Aim: To generate data for the Decayed, Missing, and Filled Permanent Teeth (DMFT) index of the entire student popu-lation of the University of Split from three samples of differ-ent sizes using the Monte Carlo method.Methods: We collected data during clinical exercises in the courses ‘Restorative Dental Medicine 2’ and ‘Endodontics 2.’ All participants (n = 200) were students at the University of Split. We derived three samples from the collected data on the DMFT index – small (n = 50), medium-sized (n = 100), and the entire sample (n = 200). Afterwards, we ran Monte Carlo simulations (MCS) to derive DMFT index data for three student populations of 20 000 individuals each from those samples. The probabilities of individual outcomes for the DMFT index and its components, as well as their correla-tions, were entered as assumptions for the MCS.Results: The estimated mean DMFT index of the student population was 8.96 (standard deviation (SD) = 0.69, 99% CI = 8.91–9.00) for MCS 50, 9.12 (SD = 0.47, 99% CI = 9.08–9.21) for MCS 100, and 8.82 (SD = 0.36, 99% CI = 8.77–8.87) for MCS 200. Regarding the components of the DMFT index, the number of repaired teeth in MCS 50, MCS 100, and MCS 200 was most strongly correlated with the DMFT index, with R values of 0.84, 0.82, and 0.77, respectively. The estimated mean DMFT indices by MCS differed from the mean DMFT indices of the corresponding samples and each other by less than 1 point.Conclusions: The Monte Carlo method may be useful in es-timating the population means of clinical indices in dental medicine, including the DMFT index. According to the as-sumptions made for MCS in this study, the size of the origi-nal samples did not significantly affect the estimates of the parameters of the DMFT index.