Dataset Information

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.

SUBMITTER: Huang B

PROVIDER: S-EPMC7773521 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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

Huang Bo B Tian Lu L McCaw Zachary R ZR Luo Xiaodong X Talukder Enayet E Rothenberg Mace M Xie Wanling W Choueiri Toni K TK Kim Dae Hyun DH Wei Lee-Jen LJ

Annals of internal medicine 20200707 5

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, ...[more]

PMID: 32628533

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Project description:ObjectivesMethods for pharmacoepidemiologic studies of large-scale data repositories are established. Although clinical cohorts of older adults often contain critical information to advance our understanding of medication risk and benefit, the methods best suited to manage medication data in these samples are sometimes unclear and their degree of validation unknown. We sought to provide researchers, in the context of a clinical cohort study of delirium in older adults, with guidance on the methodological tools to use data from clinical cohorts to better understand medication risk factors and outcomes.DesignProspective cohort study.SettingThe Successful Aging After Elective Surgery (SAGES) prospective cohort.ParticipantsA total of 560 older adults (aged ≥70 years) without dementia undergoing elective major surgery.MeasurementsUsing the SAGES clinical cohort, methods used to characterize medications were identified, reviewed, analyzed, and distinguished by appropriateness and degree of validation for characterizing pharmacoepidemiologic data in smaller clinical data sets.ResultsMedication coding is essential; the American Hospital Formulary System, most often used in the United States, is not preferred over others. Use of equivalent dosing scales (e.g., morphine equivalents) for a single medication class (e.g., opioids) is preferred over multiclass analgesic equivalency scales. Medication aggregation from the same class (e.g., benzodiazepines) is well established; the optimal prevalence breakout for aggregation remains unclear. Validated scale(s) to combine structurally dissimilar medications (e.g., anticholinergics) should be used with caution; a lack of consensus exists regarding the optimal scale. Directed acyclic graph(s) are an accepted method to conceptualize causative frameworks when identifying potential confounders. Modeling-based strategies should be used with evidence-based, a priori variable-selection strategies.ConclusionAs highlighted in the SAGES cohort, the methods used to classify and analyze medication data in clinically rich cohort studies vary in the rigor by which they have been developed and validated.

Dataset Information

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

Publications

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

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