Statistical analysis
Descriptions of continuous variables (time between transplant and QoL
survey) were expressed as the means and standard deviations. Discrete
qualitative variables (age at transplant, QoL scores via PedsQL TM 4.0)
were expressed as numbers and percentages. Characteristics of pediatric
HSCT patients were grouped by patient race. Fisher’s exact tests were
then used to examine potential differences among categorical variables
(including sex, type of diagnosis, conditioning regimen, donor type,
donor source, CMV status, insurance type, and household income
classification) and one way analysis of variance (ANOVA) were used to
test for differences in age at transplant and QoL scores. SAS version
9.4 (SAS Institute Inc; Cary, NC) was used for data summary and
analysis.
Proc Regression in SAS was used to estimate unadjusted and multivariate
adjusted models of the four QoL and overall functioning scores as
dependent variables. Independent predictors included patient race, age
at the time of transplant, sex, diagnosis (malignant or non-malignant),
conditioning regimen, and time since transplant. Models were
additionally run assessing the interaction of race and estimated income
level (Table 3) and the interaction of race and type of insurance (Table
4).
In multivariate modeling, non-White (including Hispanic, Black, or
Native American) patient QoL outcomes were compared to those of
non-Hispanic White patients in separate models for each QoL outcome. In
these supplementary analyses, multivariate models compared Hispanic,
Black, or Native American patient QoL outcomes individually to those of
non-Hispanic White patients. For the sets of multivariate analyses in
both the manuscript and the supplement, the primary multivariate
analysis was adjusted for age, sex, type of disease, and conditioning
(Supplemental Table S2) while a secondary analysis further adjusted for
insurance type and estimated household income (Supplemental Table S3).
An a priori alpha level was set to 0.05.