| Primary objectives To assess if: | Outcomes | Predictors | Covariates1 | Analysis Type |
|---|---|---|---|---|
| navigation improves recommended breast cancer care uptake (screening or diagnostic mammography) and time to diagnosis following an abnormal mammogram | Time to mammography appointment (days) | Study Arm2 | Hospital site, age, race/ethnicity, insurance status, and neighborhood-level socioeconomic status (e.g., poverty) | Cox survival analysis |
| Time to diagnosis after abnormal result (days) | ||||
| navigation effects depend on patients’ residential MUA status | Time to mammography appointment (days) | Study Arm*MUA status3,4 | Cox survival analysis (overall & stratified)5 | |
| Time to diagnosis after abnormal result (days) | ||||
| Secondary objectives To assess the efficacy of navigation across | ||||
| different points of the care continuum among patients diagnosed with breast cancer | Adherence to screening, diagnostic care, and treatment guidelines (composite score)6 | Study Arm2 | Ordinal regression | |
| multiple regular screening episodes among patients who did not obtain breast cancer diagnoses. | Receipt of multiple mammograms every 1–2 years (yes/no) | Logistic Regression |
1
Listed covariates will be included in models. Simultaneously other variables listed in Table 2 may be also be included in models, depending on preliminary bivariate analyses.
2
Control/Usual Care group will be the referent group.
3
Affluent/ineligible MUA status will be the referent group.
4
The main effects of study arm and MUA status will be included in these models.
5
If interaction terms are significant, strata-specific Cox regression models will be run to assess study arm differences for the different types of MUA status groups.
6
For this outcome, given ranges differ by age ranges, analyses will be stratified by age group (50–74 years old or other).