Psychometric properties of a brief measure of autonomy support in breast cancer patients

Study Sample and Data Collection

The target sample was women aged 20–79 who were diagnosed withbreast cancer within
the previous 18 months. The sample included patients with American Joint Committee
on Cancer (AJCC) stage 0 – III breast cancer, with the intention to evaluate the full
spectrum of experiences in patients who might have many treatment options, as well
as those with more advanced disease in whom treatment recommendations might be more
prescriptive. Participants were recruited from Memorial Sloan-Kettering Cancer Center
(MSKCC) in New York and from Emory University Hospital Midtown, the Winship Cancer
Institute of Emory University, and Grady Memorial Hospital in Georgia between June
and September 2013. Based on an estimated sample size for adequacy of psychometric
analyses, we set a quota sample of 200 completed surveys.

At MSKCC, eligible breast cancer patients were approached in clinic and asked to complete
the survey. Patients who met the inclusion criteria were identified by examining the
clinic schedule for the upcoming day and approaching all eligible patients on a selected
day. These women were given the option to take the survey home to complete if requested.
A $10 incentive was provided to respondents upon completion of their survey.

At the Georgia sites, eligible breast cancer patients were identified by clinic records
and mailed a survey packet which included a $10 pre-incentive. The institutional review
boards of the University of Michigan, MSKCC, and Emory University approved all study
procedures and materials.

The response rate was 83.8 % (93 of 111) in New York and 54.0 % (67 of 124) in Georgia,
for a combined response rate of 67.2 % (158/235). For factor and internal consistency
analysis by physician specialty, which required all items to be completed, the final
analytical sample size was 155, 157, 138, and 106, for the overall treatment experience
(hereafter referred to as “overall”), surgeon, medical oncologist, and radiation oncologist
scales, respectively. For hierarchical factor analysis that required all provider-specific
items, the analytical sample size was 106.

Measures

Patient characteristics

Participants were asked about their age, race, ethnicity, and level of education as
well as the amount of time (in months) since their breast cancer diagnosis. We also
asked yes/no questions to ascertain whether or not they had received various treatments,
specifically lumpectomy, mastectomy, radiation therapy, and chemotherapy, and whether
they experienced moderate or severe toxicity during their treatment (defined as nausea,
vomiting, diarrhea, shortness of breath, pain, or arm swelling).

Perceived autonomy support was assessed with six questions that measured patients’
perceptions of the degree to which their physicians were autonomy supportive. Patients
responded to the six questions for their overall treatment experience, followed by
questions about their surgeon, medical oncologist, and radiation oncologist, in that
order. The six questions, which were provided by the scale’s developer (GW) were asked
as follows:

I feel that my (insert breast cancer treatment doctors, surgeon, medical oncologist,
or radiation oncologist)…

1) …provided me with choices and options for my breast cancer treatment.

2) …understood how I saw things with respect to my breast cancer.

3) …expressed confidence in my ability to make decisions.

4) …listened to how I would like to handle my breast cancer treatment.

5) …encouraged me to ask questions.

6) …tried to understand how I saw things before offering an opinion.

Responses were on a 7 point scale anchored with: not at all true (1), somewhat true
(4), and very true (7).

Analyses

Exploratory factor analysis (EFA) using principal components was used to explore the
factor structure, i.e. the number of underlying constructs measured by the items.
We began by retaining factors with Eigenvalues near to 1 (indicating that approximately
16.7 % of the variance was explained) and required item-loadings of??0.45 as indication
that the items should be retained. After scales were formed from the factor(s), we
measured their internal consistency using Cronbach’s alpha and reported the correlation
between the scales as calculated for the four groups (3 provider groups and the overall
rating) assessed using Spearman’s rank coefficient. We also evaluated the correlation
between provider level scales and the overall scale. We then explored the association
of scales stratified by the surgery, chemotherapy, and radiation received, and by
patient characteristics using the Kruskal-Wallis test. When the Kruskal-Wallis test
suggested a significance difference among the groups, pairwise Wilcoxon Rank-sum tests
were performed.

In order to explore whether assessment was needed on the provider level rather than
simply asking about overall treatment experience, we examined inter-item correlations
for 18 items measured at the provider-specific level using Spearman’s rank coefficient
and hierarchical EFA with first- and second-order factors. The first order EFA was
used to determine the common constructs/factors measured by the 18 items. It was hypothesized
if patients did differentiate between providers that the 6 items asked about each
specific provider would compose common factors, with three common factors, one for
each provider type discovered. Conversely, if patients did not differentiate between
providers, then it was hypothesized that the same questions asked across the three
provider types would compose common factors, resulting in 6 factors, each with three
items. Once the first-order factor solution was decided, those factor solutions were
then used as the inputs into the second-order EFA. If patients did not differentiate
between provider types, it was hypothesized that a single common second-order factor
would explain the majority of the variance of the provider-level first-order factors.
If, however, patients did differentiate between providers, a single common second-order
factor would leave considerable variance unexplained. Additionally the feasibility
of the single second-order factor was determined by considering the interpretability
of the first-order factors loadings. The SAS System version 9.3 (SAS Institute Inc.,
Cary, NC, USA) was used for all statistical analyses. For statistical tests, p-values
at or below 5 % were considered significant.