Statistical analysis
lung cancer
LUNG CANCER, PROGRESSION, REGRESSION
The ILA prevalence at baseline was described for the full cohort using frequencies and percentages by descriptor. Both presence and absence of ILA, as well as phenotypes, were described for the entire cohort. Progression of ILA, defined as worsening of existing ILA or incidence of ILA over 4 years, was described for the subset with T4 scans.The primary analyses examined the association between baseline indeterminate ILA and ILA (presence/absence) and time to mortality, time to first hospitalization, and time to development of lung cancer in the full cohort. Kaplan–Meier plots were generated to visualize the associations between ILA variables and lung cancer, hospital admission and all-cause mortality. Cox regression proportional hazards models were used to test for this association in both univariate and multivariable models. The multivariable model was adjusted for age, sex, BMI, smoking status and pack years exposure; for the cox regression proportional hazards model for all-cause mortality, age was included as a time-dependent covariate.Secondary analyses included Cox regression models to examine the association between phenotypes of ILA as the independent variables (separately) with outcomes of time to first hospitalization, lung cancer, and mortality adjusted for age, sex, BMI, smoking status and pack years exposure.The Kappa (κ) value was calculated for agreement between first and second readers for the baseline scan. Interobserver agreement was categorized as poor (κ = 0–0.20), fair (κ = 0.21–0.40), moderate (κ = 0.41–0.60), good (κ = 0.61–0.80), or excellent (κ = 0.81–1).To adjust for multiplicity, Bonferroni correction was utilized for our three primary outcomes and p-value significance was set at < 0.017. Otherwise, significance levels were set at a p-value ≤ 0.05. All statistical analyses were performed using STATA14.1 software (StataCorp LLC, College Station, TX).