2.3 Statistical Analysis
Patient-level characteristics for the hip and knee osteoarthritis
patient cohorts were compared via analysis of variance (ANOVA) for
continuous variables and chi square tests for categorical variables
where appropriate. These analyses accounted for patient clustering
within HVHC systems through the use of robust standard errors.
Multivariate logistic regression examined the association between
patient-level characteristics and alignment between patient-expressed
post-decision aid treatment choices and treatments received among
patients with hip and knee osteoarthritis who were exposed to decision
aids across HVHC systems between 2012-2015. The outcome of interest –
alignment between post-decision aid treatment choice and treatment
received – is a dichotomous variable where alignment = 1 if patients
chose surgery and received surgery or chose non-surgical treatment and
received non-surgical treatment, and alignment = 0 if patients’
treatment choices did not match received treatments (e.g., chose
non-surgical treatment and received surgery). Another multivariate
logistic regression was run to explore the association between
patient-level characteristics and choice-treatment alignment for hip or
knee patients who specifically chose surgery since the majority of both
patient cohorts preferred surgery after exposure to decision aids.
Patient-level characteristics included in all final models are age, sex,
race, marital status, health insurance type, educational attainment,
Charlson comorbidity score, HOOS or KOOS pain score and decision-making
stage.
All regression models utilized organization-level fixed effects to
account for clustering of patients within systems. The output of these
models is reported as odds ratios (OR) with corresponding 95%
confidence intervals (CI). Any missingness across variables of interest
was assumed to be random and these observations were excluded from final
analyses. Analyses were conducted using SAS version 9.4.