• Parul Agarwal, Erin Moshier, Meng Ru, Nisha Ohri, Ronald Ennis, Kenneth Rosenzweig, and Madhu Mazumdar.
• 1 Department of Population Health Science and Policy, Institute for Healthcare Delivery Science, Tisch Cancer Institute (TCI), Icahn School of Medicine at Mount Sinai, New York, NY, USA.
• Cancer Control. 2018 Jan 1; 25 (1): 1073274818789355.

AbstractThe objectives of this study are to illustrate the effects of immortal time bias (ITB) using an oncology outcomes database and quantify through simulations the magnitude and direction of ITB when different analytical techniques are used. A cohort of 11 626 women who received neoadjuvant chemotherapy and underwent mastectomy with pathologically positive lymph nodes were accrued from the National Cancer Database (2004-2008). Standard Cox regression, time-dependent (TD), and landmark models were used to compare overall survival in patients who did or did not receive postmastectomy radiation therapy (PMRT). Simulation studies showing ways to reduce the effect of ITB indicate that TD exposures should be included as variables in hazard-based analyses. Standard Cox regression models comparing overall survival in patients who did and did not receive PMRT showed a significant treatment effect (hazard ratio [HR]: 0.93, 95% confidence interval [CI]: 0.88-0.99). Time-dependent and landmark methods estimated no treatment effect with HR: 0.97, 95% CI: 0.92 to 1.03 and HR: 0.98, 95% CI, 0.92 to 1.04, respectively. In our simulation studies, the standard Cox regression model significantly overestimated treatment effects when no effect was present. Estimates of TD models were closest to the true treatment effect. Landmark model results were highly dependent on landmark timing. Appropriate statistical approaches that account for ITB are critical to minimize bias when examining relationships between receipt of PMRT and survival.

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