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Thursday, September 23
Thu, Sep 23, 1:30 PM - 2:45 PM
Virtual
Development and Validation of Novel Early Endpoints to Expedite Oncology Drug Development

Statistical Inference Based on Mean Duration of Response and Probability of Being in Response for Drug Development (302433)

*Bo Huang, Pfizer Inc. 
Lu Tian, Stanford University 
L.J. Wei, Harvard University 

Keywords: Mean Duration of Response, Probability of Being in Response, Intent-to-Treat, Restricted Mean Survival Time

In comparative studies, treatment effect is often assessed using a binary outcome that indicates response to the therapy. Commonly used summary measures for response include the cumulative and current response rates at a specific time point. The current response rate is sometimes called the probability of being in response (PBIR), which regards a patient as a responder only if they have achieved and remain in response at present. The methods used in practice for estimating these rates, however, may not be appropriate. Moreover, whereas an effective treatment is expected to achieve a rapid and sustained response, the response at a fixed time point does not provide information about the duration of response (DOR). As an alternative, a curve constructed from the PBIR over the entire study period may be considered, which can be used for visualizing how rapidly patients responded to therapy and how long responses were sustained. The area under the PBIR curve is the mean DOR in the ITT population, which enables valid statistical comparison between treatment groups based on DOR, making it a powerful and useful endpoint for assessing treatment effect of drugs that have higher response rate, shorter time-to-response, and longer time being in response. Statistical inference and applications in oncology clinical trials will be presented.

Reference:

• Huang B, Tian L, Talukder E, Rothenberg M, Kim DH, Wei LJ. Evaluating treatment effect based on duration of response for a comparative oncology study. JAMA oncology. 2018 Jun 1;4(6):874-6. • Huang B, Tian L, McCaw ZR, Luo X, Talukder E, Rothenberg M, Xie W, Choueiri TK, Kim DH, Wei LJ. Analysis of response data for assessing treatment effects in comparative clinical studies. Annals of Internal Medicine. 2020 Sep 1;173(5):368-74.