Specification of Regression Models
heart failure
INTERACTIONS, EVENT, DISEASE, REGRESSION, EVENTS, HEART FAILURE
The generalized linear model (GLM) framework was adopted to facilitate modeling the QoL and annual hospital costs. For the QoL analysis, QoL utility at final follow‐up was modeled using the GLM with a Gaussian family distribution and linear link function, adjusting for participant QoL at randomization. Study participants who died before QoL measurement or did not provide QoL measure at study end were excluded from QoL analysis. For the hospital cost analysis, in view of the substantial proportion of annual periods without hospital care costs, the 2‐part (part 1: logistic model for likelihood of incurring any cost, part 2: magnitude of cost, conditional on incurring cost) GLM specifications with Gaussian, Poisson, and gamma family distributions, in conjunction with identity and log link functions, were compared. Tests of appropriateness of distribution and link function and model fit statistics guided the choice of the final GLM specifications. Urgent and nonurgent CRV events and hospital admissions for heart failure always occurred in hospital. Therefore, these events' respective annual periods of occurrence were excluded from the estimation of the first part of the 2‐part models with the probability of incurring hospital costs then assumed certain (100%). SEs in the hospital cost regression model were adjusted for clustering by participant to account for correlation between the multiple annual periods for individual participants during follow‐up.In both the QoL and hospital cost regression models, adjustments were considered for participants' demographic and clinical characteristics and hierarchical temporal event history categories. The small amount of missing data for covariates was imputed with mean values by sex, smoking status, and trial arm. Backward and forward covariate selections were carried out on all events of interest with their hierarchical temporal event history categories and the full set of participant characteristics. With the exception of covariates for age and sex that were always retained, only covariates statistically significant at 1% level were retained in final models. Interactions between co‐occurring events were tested when such co‐occurrences exceeded 5% of the total combined number of contributing events. The F‐test (at 1% significance level) was used to assess statistical differences in the effects between each 2 consecutive hierarchical temporal event history categories from the most distant temporal category (more than 3 years since the qualifying event) backwards to the most recent category (ie, ≤1 year from event/year of event). Lack of evidence for a statistically significant difference at each step led to combining respective temporal categories and reestimating the model before the next step.The additional annual hospital costs in year of occurrence of disease events were estimated using the recycled prediction method