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

Abstract

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.

Figure 1.

Possible Patterns for Sequential Occurrences of Response (R), Progression/death (P/D), and Censoring Events (C).

Figure 2.

A. Conventional Cumulative Response Rate (CRR) Curve (solid) and Cumulative Incidence Curve (CIC, dashed). B. Probability-of-Being-in-Response (PBIR) over Time with Data from the JAVELIN Renal-101 Study.

Figure 3.

Using Duration of Response (DOR) Data from the JAVELIN Renal-101 Study to illustrate potentially biased comparisons via standard Kaplan-Meier procedure among A. Responders only (subject to patients’ selection bias); and B. All Patients (subject to bias due to dependent censoring)

Figure 4.

Mean Duration of Response (mDOR) from the JAVELIN Renal-101 Study. A. mDOR as the Area between two Kaplan-Meier curves for Progression/Death and the Progression/Death/Response in the avelumab plus axitinib arm. B. mDOR as the Area under the probability-of-being-in-response (PBIR) Curve in the avelumab plus axitinib arm. C. mDOR as the Area between two Kaplan-Meier curves for Progression/Death and Progression/Death/Response in the sunitinib arm. D. mDOR as the Area under probability-of-being-in-response Curve in the sunitinib arm.

Figure 4.

Mean Duration of Response (mDOR) from the JAVELIN Renal-101 Study. A. mDOR as the Area between two Kaplan-Meier curves for Progression/Death and the Progression/Death/Response in the avelumab plus axitinib arm. B. mDOR as the Area under the probability-of-being-in-response (PBIR) Curve in the avelumab plus axitinib arm. C. mDOR as the Area between two Kaplan-Meier curves for Progression/Death and Progression/Death/Response in the sunitinib arm. D. mDOR as the Area under probability-of-being-in-response Curve in the sunitinib arm.