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Analysis of Response Data for Assessing Treatment Effects in Comparative Clinical Studies.
Ann Intern Med. 2020 Jul 07 [Online ahead of print]AIM

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.

Authors+Show Affiliations

Pfizer, Groton, Connecticut (B.H., E.T.).Stanford University, Stanford, California (L.T.).Harvard T.H. Chan School of Public Health, Boston, Massachusetts (Z.R.M.).Sanofi, Bridgewater, New Jersey (X.L.).Pfizer, Groton, Connecticut (B.H., E.T.).Pfizer, New York, New York (M.R.).Dana-Farber Cancer Institute, Boston, Massachusetts (W.X., T.K.C.).Dana-Farber Cancer Institute, Boston, Massachusetts (W.X., T.K.C.).Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Harvard Medical School, Boston, Massachusetts (D.H.K.).Harvard University, Boston, Massachusetts (L.W.).

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32628533

Citation

Huang, Bo, et al. "Analysis of Response Data for Assessing Treatment Effects in Comparative Clinical Studies." Annals of Internal Medicine, 2020.
Huang B, Tian L, McCaw ZR, et al. Analysis of Response Data for Assessing Treatment Effects in Comparative Clinical Studies. Ann Intern Med. 2020.
Huang, B., Tian, L., McCaw, Z. R., Luo, X., Talukder, E., Rothenberg, M., Xie, W., Choueiri, T. K., Kim, D. H., & Wei, L. J. (2020). Analysis of Response Data for Assessing Treatment Effects in Comparative Clinical Studies. Annals of Internal Medicine. https://doi.org/10.7326/M20-0104
Huang B, et al. Analysis of Response Data for Assessing Treatment Effects in Comparative Clinical Studies. Ann Intern Med. 2020 Jul 7; PubMed PMID: 32628533.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Analysis of Response Data for Assessing Treatment Effects in Comparative Clinical Studies. AU - Huang,Bo, AU - Tian,Lu, AU - McCaw,Zachary R, AU - Luo,Xiaodong, AU - Talukder,Enayet, AU - Rothenberg,Mace, AU - Xie,Wanling, AU - Choueiri,Toni K, AU - Kim,Dae Hyun, AU - Wei,Lee-Jen, Y1 - 2020/07/07/ PY - 2020/7/7/pubmed PY - 2020/7/7/medline PY - 2020/7/7/entrez JF - Annals of internal medicine JO - Ann. Intern. Med. N2 - 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. SN - 1539-3704 UR - https://www.unboundmedicine.com/medline/citation/32628533/Analysis_of_Response_Data_for_Assessing_Treatment_Effects_in_Comparative_Clinical_Studies L2 - https://www.acpjournals.org/doi/10.7326/M20-0104?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub=pubmed DB - PRIME DP - Unbound Medicine ER -
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