Statistical analysis
An overall model to assess differences in force according to the method of teaching was used. The dependent variable was the “post-teaching measurements”, categorical variables indicated patient status (“awake” and “anaesthetised”) and teaching method (“biofeedback”, “nose” and “syringe”), and their interaction, and “baseline measurements” were considered as a continuous covariate. This model is conventionally formulated as an analysis of covariance (ANCOVA) and in Stata can be estimated using the “anova” (analysis of variance), “regress” (linear regression) or “mixed” (linear mixed [random effects] model, LMM)[21]. For the LMM, a random coefficient model was formulated with participants considered as random intercepts and baseline measurements as random effect slopes (for each participant). The incorporation of both “anaesthetised” and “awake” tests into the overall model allowed the parts of the model to borrow strength from each other. Model predictions were calculated using the “margins” command of Stata™[22]. Model adequacy for the ANCOVA, linear regression and LMM models was adjudged according to the information criteria; lower scalar values preferred (AIC, assessing best model prediction), Bayesian information criterion (BIC, best overall model) and the likelihood ratio test[23]. Model estimates for each “method” and “status” are displayed in graph format using the Stata™ “marginsplot” command.
Exact P-values are reported for parameter estimates; P-values <0.05 were deemed to be significant. Statistical analysis was performed using Stata™ (V 15.1) software.