Statistical Analysis
In brief, we present encounter characteristics for our overall sample and a propensity score matched sample (PS-matched sample). We generated a PS-matched sample to account for potential confounding, specifically confounding by indication, and selection bias. When modeling the association between steroid administration and our outcomes, we used only the PS-matched sample of encounters.
Descriptive statistics were used to summarize characteristics of our overall sample of encounters and our PS-matched sample; frequencies and percentages were calculated for categorical variables, while means and standard deviations were calculated for continuous variables. Encounters in which patients received a corticosteroid were compared with encounters in which patients did not receive a corticosteroid by using t-tests or Somers’ D for continuous variables and chi-square tests for categorical variables. As a patient may be represented more than once in our study sample, leading to a potential correlation of encounters within a patient, we adjusted these tests for clustered errors as suggested by Donner & Klar 7 and Newsom8.
We estimated average length of stay in our overall sample for a patient who received a steroid and the average day in which that steroid was administered using an intercept only regression with cluster-robust standard errors. We then regressed day of administration on length of stay to determine the correlation between these clinical course measures. A cluster-robust variance estimator was used to account for the possible repeated encounters within a patient.
In order to assess the association between steroid use and our outcomes, we built a series of regression models, which included an unadjusted (Model 1) and a fully-adjusted model (Model 2). Restoration of patients to their baseline FEV1pp after hospitalization and FEV1pp at follow-up were modeled using linear regression. Time to next APE was modeled using a Cox’s proportional hazard (PH) regression. In each model, we included only our PS-matched cohort (see section below for details). Additionally, we accounted for the correlation of encounters within patients who were hospitalized more than once using cluster-robust variance estimators. We utilized a change-in-estimate variable selection strategy 9 to create our fully-adjusted models (Model 2), in which a covariate or combination of covariates were retained in the model if they changed the regression coefficient for steroid by approximately 20% or more. Model assumptions and fit were assessed; steroid administration violated the proportional hazards assumption, which we addressed by splitting our follow-up period at the median event time, creating two Cox PH models 10. We censored patients still at risk after the median event time in the first Cox PH model, and included only patients still at risk beyond the median event time in the second Cox PH model.