Block 2: 10-item OSPRO-YF and 10-item OSPRO-ROS
at baseline.
Block 3: Remaining items from the OSPRO-YF
(+ 7 items) and OSPRO-ROS (+ 13 items). These were
included to determine whether full-length versions of
the tools provided better prediction over shortened
versions.
Block 4: Baseline-to-4 week change in pain intensity,
region-specific disability, and OSPRO-YF scores. Early
changes in these variables may be associated with
improved prediction of outcomes over baseline vari-
ables alone [ 38]. This approach modeled change in
these variables as a measure of treatment response and
allowed us to assess the relative value of treatment
monitoring for the prediction of healthcare utilization
outcomes.
For the first analysis, binary logistic regression was used
to determine predictors of any healthcare utilization fol-
lowing physical therapy, with the dependent variable
defined as reporting one or more utilization events for any
of the potential healthcare services over the entire
follow-up period. For analyses of specific services,
utilization was dichotomized for each service. Specific
service utilization over early (through 6 months) and late
(6 months to 12 months) phases following physical ther-
apy were collapsed to create a single dichotomous
utilization indicator for each service over the entire study
follow-up period. Any utilization of the service over that
period was categorized as YES. Separate multivariate bin-
ary logistic regression models were then fitted for the
dichotomous utilization indicator (i.e. YES or NO) of each
healthcare service (e.g. opioid use, injection, imaging, sur-
gery, and emergency room visits).
For all analyses, full hierarchical multivariate models
were first fit to assess the unique contributions of each
block. This approach allowed us to determine the rela-
tive contributions of baseline demographic and
health-related variables, the newly developed
OSPRO-ROS and OSPRO-YF tools, and response to
treatment via time varying variables (e.g., pain intensity
and region specific function). However, since our pri-
mary aim was to develop concise and accurate
utilization prediction models for efficient assessment of
risk, we then separately developed stepwise models using
backward selection for each dependent variable to derive
parsimonious prediction item sets. Parsimonious models
were chosen as a more conservative approach to identi-
fying individual predictors given the potential for overfit-
ting full multivariate models because of high subject
attrition. For stepwise models, the p-value threshold was
0.05 for entry and 0.10 for removal. Overall fit for each
model was examined with Hosmer & Lemeshow test,
chi-square and pseudo-R 2 values (e.g. Nagelkerke) when
appropriate. Comparison of adjusted odds ratios (OR)
and 95% confidence interval (CI) were used to determine
the relative strength of each predictor in parsimonious
models. Multicollinearity was assessed using variance
inflation factor (VIF) and tolerance, where VIFs < 10 and
tolerances > 0.1 suggested no significant collinearity
among independent variables [ 39].
Planned sensitivity analyses for missing data
The electronic OPT-IN data collection forms required
complete data from respondents before they were
allowed to proceed to subsequent survey pages. There-
fore, the occurrence of missing data for independent
predictor variables was minimal (< 1% of sample). How-
ever, for subjects who were lost to follow-up, we planned
two approaches to assess the potential influence of miss-
ing data on study outcomes. First, demographic and
baseline health variables would be compared between
those with complete follow-up at 1 year and those with-
out follow-up at 1 year to identify any potential group
differences related to completion of follow-up. Second,
sensitivity analyses would be conducted by repeating
each analysis using inverse probability of attrition
weighting (IPAW). This propensity scoring approach