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      A tutorial on sensitivity analyses in clinical trials: the what, why, when and how

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          Abstract

          Background

          Sensitivity analyses play a crucial role in assessing the robustness of the findings or conclusions based on primary analyses of data in clinical trials. They are a critical way to assess the impact, effect or influence of key assumptions or variations—such as different methods of analysis, definitions of outcomes, protocol deviations, missing data, and outliers—on the overall conclusions of a study.

          The current paper is the second in a series of tutorial-type manuscripts intended to discuss and clarify aspects related to key methodological issues in the design and analysis of clinical trials.

          Discussion

          In this paper we will provide a detailed exploration of the key aspects of sensitivity analyses including: 1) what sensitivity analyses are, why they are needed, and how often they are used in practice; 2) the different types of sensitivity analyses that one can do, with examples from the literature; 3) some frequently asked questions about sensitivity analyses; and 4) some suggestions on how to report the results of sensitivity analyses in clinical trials.

          Summary

          When reporting on a clinical trial, we recommend including planned or posthoc sensitivity analyses, the corresponding rationale and results along with the discussion of the consequences of these analyses on the overall findings of the study.

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          Most cited references37

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          Negative Binomial Regression

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            Intention-to-treat principle.

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              The assessment, monitoring, and enhancement of treatment fidelity in public health clinical trials.

              To discuss methods of preservation of treatment fidelity in health behavior change trials conducted in public health contexts. The treatment fidelity framework provided by the National Institutes of Health's Behavioral Change Consortium includes five domains of treatment fidelity (Study Design, Training, Delivery, Receipt, and Enactment). A measure of treatment fidelity was previously developed and validated using these categories. Strategies for assessment, monitoring, and enhancing treatment fidelity within each of the five treatment fidelity domains are discussed. The previously created measure of treatment fidelity is updated to include additional items on selecting providers, additional confounders, theory testing, and multicultural considerations. Implementation of a treatment fidelity plan may require extra staff time and costs. However, the economic and scientific costs of lack of attention to treatment fidelity are far greater than the costs of treatment fidelity implementation. Maintaining high levels of treatment fidelity with flexible adaptation according to setting, provider, and patient is the goal for public health trials.
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                Author and article information

                Contributors
                Journal
                BMC Med Res Methodol
                BMC Med Res Methodol
                BMC Medical Research Methodology
                BioMed Central
                1471-2288
                2013
                16 July 2013
                : 13
                : 92
                Affiliations
                [1 ]Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada
                [2 ]Departments of Pediatrics and Anesthesia, McMaster University, Hamilton, ON, Canada
                [3 ]Center for Evaluation of Medicine, St Joseph’s Healthcare Hamilton, Hamilton, ON, Canada
                [4 ]Biostatistics Unit, Father Sean O’Sullivan Research Center, St Joseph’s Healthcare Hamilton, Hamilton, ON, Canada
                [5 ]Population Health Research Institute, Hamilton Health Sciences, Hamilton, ON, Canada
                [6 ]Department of Psychiatry and Behavioral Neurosciences, McMaster University, Hamilton, ON, Canada
                [7 ]Population Genomics Program, McMaster University, Hamilton, ON, Canada
                [8 ]GSK, Mississauga, ON, Canada
                [9 ]Department of Kinesiology, University of Waterloo, Waterloo, ON, Canada
                [10 ]Department of Nephrology, Toronto General Hospital, Toronto, ON, Canada
                [11 ]Department of Pediatrics, McMaster University, Hamilton, ON, Canada
                [12 ]Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada
                [13 ]McMaster Integrative Neuroscience Discovery & Study (MiNDS) Program, McMaster University, Hamilton, ON, Canada
                [14 ]Department of Biostatistics, Korea University, Seoul, Korea
                [15 ]Department of Clinical Epidemiology, University of Ottawa, Ottawa, ON, Canada
                [16 ]Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada
                Article
                1471-2288-13-92
                10.1186/1471-2288-13-92
                3720188
                23855337
                81660afa-eb7b-4bcd-9b67-e89852319a81
                Copyright © 2013 Thabane et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 11 December 2012
                : 10 July 2013
                Categories
                Commentary

                Medicine
                sensitivity analysis,clinical trials,robustness
                Medicine
                sensitivity analysis, clinical trials, robustness

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