Statistical Analysis
cardiovascular comorbidity, diabetes, pulmonary comorbidity, HNC, Comorbidity, neurological comorbidity
REGRESSION
The feasibility of ePRO monitoring during radiotherapy was prespecified as the primary end point of this trial. As there is no uniformly accepted definition of feasibility for this type of trial, we have defined the feasibility threshold based on a previous trial from our group. Similar analyses have defined lower thresholds for feasibility, for example, 70% of patients answering 60% of questions, as reported by Tran et al [Feasibility was assessed by the percentage of patients providing answers to ≥80% of ePRO items during the course of radiotherapy, and feasibility was defined as the ability of ≥80% of patients in the experimental arm to answer ≥80% of ePRO questions. Sample size calculation was based on an assumed percentage of 89% for the primary end point, and a sample size of 50 patients was calculated to provide a power of 80% (1-β) to a 1-sided significance level of 5% (α).The primary end point was analyzed using a binomial test. Descriptive statistics were reported as means (SDs) in the case of continuous variables as well as absolute and relative numbers for categorical variables. Logistic regression models were used to estimate the effect of each covariate (eg, histology, smoking, systematic therapy, therapy, cardiovascular comorbidity, neurological comorbidity, nephrological comorbidity, diabetes, pulmonary comorbidity, and Charlson Comorbidity Index) on the odds of answering more than 80% of the questionnaires, including the experimental arm. The models were adjusted by age and gender.Secondary end points for this trial were global quality of life and quality of life or symptom burden for patients with HNC as well as patient satisfaction at the completion of radiotherapy. Secondary end points were compared between both trial groups; for statistical analysis, sum scores for each functional and symptom scale were calculated from the questionnaires, as defined by the EORTC scoring manual and compared using Wilcoxon rank sum tests with continuity correction. The statistical analysis was conducted using R (version 4.2.2; R Core Team). For the effect size calculation, the R package effectsize (version 0.8.3) was used.