Sas 9.4 ts level 1m5

To compare socioeconomic factors for cases and controls on the redemption of prescriptions, we used a conditional logistic regression model with case = 1, control = 0. We corrected for socioeconomic factors using Statistics Denmark model Socio13.39 Statistical analysis was performed using SAS 9.4 TS Level 1M5 (SAS, Inc., Cary, NC, USA).
An attempt to quit smoking was defined as the redemption of a prescription for smoking cessation medication in the period between 2009 and 2015. Subsequent attempts were defined as redemption of a prescription for smoking cessation medication more than six months after redeeming the previous prescription, plus the defined daily dosage (DDD) of the previously redeemed prescriptions.

Costs are presented as annual means for cases and controls. Statistical significance of the cost estimates and DCCI was assessed by non‐parametric bootstrap analysis. A significance level of 0.05 was assumed for all tests. In the before and after analysis, only cases with at least a 5‐year follow‐up period prior to diagnosis were included. A Generalized Estimating Equation (two‐step model) for gamma regression was used to compare healthcare costs, income from employment and public transfer income in female cases and controls with male cases and controls within age groups and to assess gender differences in healthcare costs (adjusted for age). The two‐step model takes individuals with no costs or income (= 0) into account. Statistical analyses were performed using SAS 9.4 TS Level 1M5 (SAS, Inc., Cary, NC, USA).

Cases and matched comparators were followed until A/D, death, migration, or end of follow-up in 2018, whichever came first. Baseline characteristics were presented as proportion, except age, which was summarised as a mean with standard deviation (SD). Differences in SES and DCCI between cases and matched comparators were estimated using the Chi2 test. The cumulative incidence of A/D was estimated starting on the day of inclusion using a Cumulative Incidence Function (CIF), which takes death into account as a competing risk. Cumulative incidence curves were plotted to describe the incidence of A/D over time. We used the Fine-Gray subdistribution hazard regression model (28 (link)) to estimate the association between sarcoidosis and A/D. The subdistribution hazard ratio (sHR) for A/D was adjusted for DCCI and SES. DCCI was treated as a categorical variable of three groups: 0, 1-2, and >2 points. We performed a sensitivity analysis estimating the sHR for A/D where cases and comparators who only redeemed one prescription of antidepressants within one year were included in the cohorts. The cumulative incidence and sHR were stratified by age-group (18-29, 30-39, 40-49, 50-59 and ≥60 years) and gender. We used 95% confidence intervals (95% CI) and a significance level of 0.05. Statistical software: SAS 9.4 TS Level 1M5 (SAS, Inc., Cary, NC, USA).