Statistical analyses
Binary logistic regression models were computed to assess the
associations between demographics, olfactory-related factors and
clinically relevant changes in overall olfactory function (TDI) and the
sub-dimensions threshold (T), discrimination (D), and identification
(I). Clinically relevant changes were defined based on the following
cut-off scores: (i) for overall olfactory function: TDI improvement
greater or equal 5.5 points at follow up visit, (ii) for threshold
function: T improvement greater or equal 2.5 points at follow up visit,
and (iii) for discrimination and identification function: improvement
greater or equal 3 points at follow up visit.16Olfactory-related variables included: age (years), gender (male and
female), olfactory function at first visit (baseline olfactory function,
TDI), duration of olfactory training (weeks), duration of smell loss
(month), reason for OD (postinfectious, posttraumatic, and idiopathic),
and presence of parosmia or phantosmia at first visit. All demographics
and olfactory-related variables were entered in the models, and
statistical estimates were generated to calculate adjusted odds ratios
(aOR) with 95% confidence interval. Hierarchical cluster analysis and
the associated dendrogram were computed based on the Ward clustering
method and the Squared Euclidian distance to identify possible groupings
between changes after OT in T, D and I in terms of similarity. Data were
analyzed using SPSS (SPSS version 23.0 for Windows; IBM Corp., Armonk,
NY, USA). This study employed a level of significance of 0.05. According
to the previously reported and widely used sample size
calculation-criterion of ten events per variable in logistic regression
analysis, we needed at least 80 patients with parosmia. Because we
included 81 patients with parosmia, this study is sufficiently powered
to conduct the described analysis for parosmia as predictive
value.17