A tutorial on sensitivity analyses in clinical trials: the what, why, when and how
By Lehana Thabane, L. Mbuagbaw, Shiyuan Zhang, Z. Samaan, M. Marcucci, Chenglin Ye, M. Thabane, L. Giangregorio, B. Dennis, Daisy Kosa and 7 more. BMC Medical Research Methodology, 2013
787 citations recorded by the source corpus.
BackgroundSensitivity 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.DiscussionIn 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.SummaryWhen 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.
Subfields: Statistics and Probability
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Citations in the corpus
- Do p Values Lose Their Meaning in Exploratory Analyses? It Depends How You Define the Familywise Error Rate (2017, background): “Specifically, researchers can (a) list all of the variables that they included in their research study (Simmons et al., 2011) and (b) undertake a sensitivity analysis (e.g., Steegen, Tuerlinckx, Gelman, & Vanpaemel, 2016; Thabane et al., 2013).”
- An introduction to multiplicity issues in clinical trials: the what, why, when and how. (2016, neutral): “Sensitivity analyses may include using different definitions for the outcomes, comparing alternative approaches for dealing with missing data or data outliers, adjusting for baseline imbalances, performing competing risk analyses, tackling non-adherence or protocol violation, etc.(47) Findings from ”