The simultaneous modeling of continuous and count data was permitted in NONMEM by the indication variable F_FLAG.
If a patient had multiple colonoscopies during the observation period, we derived a single overall indication variable to characterize his/her colonoscopy use (patient-level classification, described later).
Least squares multiple regression was used to explain the influence of the treatment (i.e. the device in use), demographic data and additional medical variables (indication, therapeutic range, medication) on TTR, with dichotomous and polytomous variables being transformed into groups of dummy indicator variables.
However, the robustness of stepwise regression is related to the choice of procedure options, and therefore influence the indication of variables' sensitivities.
There was no evidence of confounding by indication, as variables included in model 2 did not substantially modify the HR.
Additionally, work-related and indication-specific variables were collected.
Indication-specific variables were duration of LBP (≤12 months/> 12 months) and intensity of pain.
Furthermore, sociodemographic and indication-specific variables as well as work-related aspects were assessed.
In the second model (question 2), besides PA, the sociodemographic, work-related and indication-specific variables were included stepwise to get insight in what variables explained HRQoL best.
All of trainees filled the self assessment form which included patients demographics, histories, indications, and variables related to procedure, complication and pathologic report.
Sociodemographic and indication-specific variables as well as work-related aspects of the sample are shown in Table 1.> The prevalence of physical inactivity across different life areas is presented in Table 2.
