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| Title: | Assessing treatment effect heterogeneity in clinical trials with blocked binary outcomes |
| Author (s): | Albert, Jeffrey M. Gadbury, Gary L. Mascha, Edward J. |
| Department/Lab Affiliations: | Mathematics & Statistics |
| Keywords: | Bounds Causal effects Randomized block design Subject-treatment interaction |
| Subject Terms: | Counterfactuals logic. |
| Issue Date: | 2005 |
| Publisher: | John Wiley & Sons |
| Citation: | Jeffrey M. Albert, Gary L. Gadbury, and Edward J. Mascha (2005). Assessing Treatment Effect Heterogeneity in Clinical Trials with Blocked Binary Outcomes. Biometrical Journal, 47, 662 – 673. |
| Abstract: | This paper addresses treatment effect heterogeneity (also referred to, more compactly, as treatment heterogeneity) in the context of a controlled clinical trial with binary endpoints. Treatment heterogeneity, variation in the true (causal) individual treatment effects, is explored using the concept of the potential outcome. This framework supposes the existance of latent responses for each subject corresponding to each possible treatment. In the context of a binary endpoint, treatment heterogeniety may be represented by the parameter, 2, the probability that an individual would have a failure on the experimental treatment, if received, and would have a success on control, if received. Previous research derived bounds for 2 based on matched pairs data. The present research extends this method to the blocked data context. Estimates (and their variances) and confidence intervals for the bounds are derived. We apply the new method to data from a renal disease clinical trial. In this example, bounds based on the blocked data are narrower than the corresponding bounds based only on the marginal success proportions. Some remaining challenges (including the possibility of further reducing bound widths) are discussed. (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) |
| Type: | Article - Journal text |
| In Title: | Biometrical Journal |
| Copyright Notice: | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. FULL COPYRIGHT INFORMATION: |
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| title | Assessing treatment effect heterogeneity in clinical trials with blocked binary outcomes |
| contributor.author | Albert, Jeffrey M. |
| contributor.author | Gadbury, Gary L. |
| contributor.author | Mascha, Edward J. |
| contributor.deptlab | Mathematics & Statistics |
| subject | Bounds |
| subject | Causal effects |
| subject | Randomized block design |
| subject | Subject-treatment interaction |
| subject.LCSH | Counterfactuals logic. |
| date.issued | 2005 |
| publisher | John Wiley & Sons |
| identifier.citation | Jeffrey M. Albert, Gary L. Gadbury, and Edward J. Mascha (2005). Assessing Treatment Effect Heterogeneity in Clinical Trials with Blocked Binary Outcomes. Biometrical Journal, 47, 662 – 673. |
| identifier.pub.URI | |
| description.abstract | This paper addresses treatment effect heterogeneity (also referred to, more compactly, as treatment heterogeneity) in the context of a controlled clinical trial with binary endpoints. Treatment heterogeneity, variation in the true (causal) individual treatment effects, is explored using the concept of the potential outcome. This framework supposes the existance of latent responses for each subject corresponding to each possible treatment. In the context of a binary endpoint, treatment heterogeniety may be represented by the parameter, 2, the probability that an individual would have a failure on the experimental treatment, if received, and would have a success on control, if received. Previous research derived bounds for 2 based on matched pairs data. The present research extends this method to the blocked data context. Estimates (and their variances) and confidence intervals for the bounds are derived. We apply the new method to data from a renal disease clinical trial. In this example, bounds based on the blocked data are narrower than the corresponding bounds based only on the marginal success proportions. Some remaining challenges (including the possibility of further reducing bound widths) are discussed. (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) |
| type | Article - Journal |
| type.DCMIType | text |
| type.status | Postprint |
| rights | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. |
| rights.URI | |
| relation.isPartOf | Biometrical Journal |
| date.accessioned | 2007-04-11T17:00:48Z |
| date.available | 2008-05-07T19:02:02Z |
| identifier.persist.URI |