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If smoking causes heart disease, we will see an effect on the cancer variable that reflects our meddling with the smokers.
These dummy variables were then multiplied with the cancer variable to create cancer by race/ethnicity interaction terms.
The HPV cancer associations were tested using logistical regression, and models were fit with one cancer variable at a time.
An additional dichotomised cancer variable (eight selected cancer) was created which included a positive answer to having had any of those eight cancers.
Exploration of this analysis identified that the cancer variable was non-significant only when spousal stress was in the model, but it became highly significant (p < 0.001) once spousal stress was removed (adjusted R =22, p < 0.001).
Six high frequency and high cost chronic conditions were selected for study (note: four types of cancer were combined into one "cancer" variable, in order to limit the count of conditions for these analyses).
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Moderators were baseline demographic, exercise/fitness, and cancer variables.
Individuals with missing data for family history of cancer variables or clinical features were excluded from analyses.
We included not only several standard demographic and exercise/fitness variables but also key cancer variables that make the field of exercise oncology unique (Courneya, 2014).
Other studies have also reported that cancer variables moderate the exercise response of lymphoma patients (Courneya et al, 2009, 2012) and breast cancer patients (Segal et al, 2001) but not prostate cancer patients (Segal et al, 2003).
It is possible that cancer variables moderate the effects of exercise interventions differently depending on the type of cancer, the types of treatments, and whether exercise is compared with no exercise or with different types and doses of exercise.
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