Modeling Treatment Efficacy Under Screening

• Activity Number: 312
• Type: Contributed
• Date/Time: Tuesday, August 3, 2010 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Shih-Yuan Lee*+ and Alexander Tsodikov
• Companies: University of Michigan and University of Michigan
• Keywords: Screening ; Treatment efficacy ; Statistical modeling

Abstract

Modeling the treatment null hypothesis and the alternative when population is subject to cancer screening is a challenge. Survival is subject to length and lead-time bias, and there is a shift of the distribution of disease stage towards earlier stages under screen-based diagnosis. However, screening is not a treatment, and all these dynamic changes are expected to occur under the null hypothesis of no treatment effect. Under the alternative hypothesis, treatment effect for an unscreened person may be different from the screen-detected one, as early detection may enhance the effect. The challenge is that these treatments are applied at different points of disease development. We provide a statistical modeling approach to address the question of treatment efficacy in this dynamic situation.