Analysis of Response Data for Assessing Treatment Effects in Comparative Clinical Studies

• DOI: 10.7326/m20-0104
• Publication Date: 2020-08-31T21:04:08Z
• Authors: Bo Huang; Lu Tian; Zachary R. McCaw; Xiaodong Luo; Enayet Talukder; Mace Rothenberg; Wanling Xie; Toni K. Choueiri; Dae Hyun Kim; Lee-Jen Wei
• Keywords: Comparative Effectiveness Research; Time Factors; Statistics as Topic; Antineoplastic Agents; Equivalence Trials as Topic; 01 natural sciences; Kidney Neoplasms; Treatment Outcome; Data Interpretation, Statistical; Humans; 0101 mathematics; Carcinoma, Renal Cell; Probability; Randomized Controlled Trials as Topic

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 current response rates 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. This connection between response and DOR makes this curve attractive for assessing the treatment effect. In contrast to the conventional method for analyzing the DOR data, which uses responders only, the above procedure includes all patients in the study. Although discussed extensively in the statistical literature, estimation of the current response rate curve has garnered little attention in the medical literature. This article illustrates how to construct and analyze such a curve using data from a recent study for treating renal cell carcinoma. Clinical trialists are encouraged to consider this robust and clinically interpretable procedure as an additional tool for evaluating treatment effects in clinical studies.