A high percentage of employed breast cancer patients returned to work after treatment, and workplace accommodations played an important role in their return. In addition, perceived employer discrimination because of cancer was negatively associated with return to work for breast cancer survivors. Employers seem to have a pivotal role in breast cancer patients' successful return to work.

For analyses of longitudinal repeated-measures data, statistical methods include the random effects model, fixed effects model and the method of generalized estimating equations. We examine the assumptions that underlie these approaches to assessing covariate effects on the mean of a continuous, dichotomous or count outcome. Access to statistical software to implement these models has led to widespread application in numerous disciplines. However, careful consideration should be paid to their critical assumptions to ascertain which model might be appropriate in a given setting. To illustrate similarities and differences that might exist in empirical results, we use a study that assessed depressive symptoms in low-income pregnant women using a structured instrument with up to five assessments that spanned the pre-natal and post-natal periods. Understanding the conceptual differences between the methods is important in their proper application even though empirically they might not differ substantively. The choice of model in specific applications would depend on the relevant questions being addressed, which in turn informs the type of design and data collection that would be relevant.

We discuss how cancer affected the employment of almost 800 employed patients who participated in a longitudinal study. The greatest reduction in patients' labor supply (defined as employment and weekly hours worked) was observed 6 months following diagnosis. At 12 and 18 months following diagnosis, many patients returned to work. Based on these and other findings related to patients' employment situations, we suggest 4 areas for future research: 1) collection of employment information in cancer studies; 2) research into racial and ethnic minority patients and employment outcomes; 3) interventions to reduce the effects of cancer and its treatment on employment; and 4) investigations into the influence of employment-contingent health insurance on cancer treatment and recovery.