Statistical Analysis
chronic diseases
REGRESSION, SECONDARY, CHRONIC DISEASES
With an observation period of 12 months, 0.75 hospitalizations per patient were assumed, derived from a comparable population (AgeCoDe study).Descriptive statistical methods and multilevel (mixed) regression models were used for data analysis. Depending on the distribution of the outcome variable, Poisson regression, linear regression, or logistic regression models were applied. To take into account the cluster effect, GP practice identification was included in the model as a random effect. For the primary outcome, an intention-to-treat (ITT) analysis was performed, including all participants recruited and participating at baseline and with information about the hospitalization status at the end of the observation period. Missing values were not imputed.The adjusted number of hospitalizations was calculated by dividing the number of hospitalizations by the actual number of observation days and then multiplying it by 365.25 days. The multivariate analyses were performed using the unadjusted number of hospitalizations; for the adjustment, the actual observation time was included as covariate. Other covariates used for adjustment were age in years (integer), sex (nominal), number of chronic diseases (integer), and number of hospitalizations (integer) in the 6 months before baseline. A sensitivity analysis was performed with the per-protocol (PP) population consisting of all participants participating in the T2 visit and participants in the intervention group who had at least 2 family conferences.Data on the secondary outcomes were not collected for participants from whom no data were collected at T2 (dropouts), which means that the ITT analyses for the secondary outcomes would more or less consist of the participants in the PP population due to the number of missing values. For the PP analyses, only a few participants who did not complete the intervention were excluded. Therefore, we decided to present the PP analyses for the secondary outcomes.