Statistical and sample size considerations
cough
Statistical analyses were carried out using the software SAS v9.4 (SAS Institute, Cary, North Carolina, USA).For cough count data, a lognormal distribution was assumed, i.e., the data were log-transformed and a mixed model was applied, using time point, treatment sequence, and their interaction as fixed effects. Dependence within subjects was modeled using normally distributed subject effects; between subjects, independence was assumed. This model was analyzed using a Bayesian approach with non-informative prior. A similar method was used for VAS data. The LCQ total and domain scores were analyzed by a hierarchical model, assuming normally distributed errors, using the above approach. Responder analyses were run for 24-h cough count and VAS data. The cut-off points for a positive response were: (1) > 30% reduction in cough frequency, (2) > 50% reduction in cough frequency, and (3) > 30 mm reduction in VAS cough severity (from baseline).Statistical methods were chosen to facilitate the internal proof-of-concept decision. Due to the Bayesian statistical approach that was used throughout the study, 90% credible intervals were calculated. The required sample size was estimated by simulations with SAS v9.4. Based on cough frequency data reported for another P2X3 receptor antagonist [