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A tutorial on sensitivity analyses in clinical trials: the what, why, when and how
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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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“To test the robustness of the data analysis strategy (given missing data associated with participant attrition), for variables that showed a significant change from T1 to T3 (for any group), a post-hoc sensitivity analysis involving multiple imputation by chained equations was performed using the mice package in R [85, 87].”
From Optimizing parenting and child outcomes following parent–child interaction therapy – toddler: a randomized controlled trial (2024) · cites this paper as [85]
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“Additionally, a complete-case analysis was conducted as a sensitivity analysis on observed data to corroborate the assumption of data missing at random [94].”
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“The generality of a researcher's results under alternative relevant approaches can be checked via robustness analyses (e.g., Thabane et al., 2013, also called sensitivity or multiverse analyses) that indicate how research results vary as a function of reasonable data exclusions and alternative sensible data coding, aggregation, and analysis approaches.”
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“Missing data will be handled following the authors’ recommendations, using the most appropriate method depending on the reasons for the missing data and the sensitivity analysis principles.57”
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Metadata
- Authors
- Thabane, L., Mbuagbaw, L., Zhang, S., Samaan, Z., Marcucci, M., Ye, C., Thabane, M., Giangregorio, L., Dennis, B., Kosa, D., Debono, V. B., Dillenburg, R., Fruci, V., Bawor, M., Lee, J., Wells, G., & Goldsmith, C. H.
- Affiliations
- Population Health Research Institute (CA)
- St. Joseph’s Healthcare Hamilton (CA)
- McMaster University (CA)
- GlaxoSmithKline (Canada) (CA)
- University of Waterloo (CA)
- Korea University (KR)
- University of Ottawa (CA)
- Simon Fraser University (CA)
- Corresponding author
- Thabane, Lehana
- ORCID
- Venue
- BMC Medical Research Methodology
- Published
- 2013-07-16
- Volume
- 13
- Issue
- 1
- Pages
- 92-92
- ISSN
- 1471-2288 (ISSN-L)
- PMC
- PMC3720188
- OpenAlex
- W2095437242
- Cited by
- 893
- References
- 76
- Citations by year
- 2026: 76 · 2025: 113 · 2024: 107 · 2023: 109 · 2022: 90 · 2021: 84 · 2020: 90 · 2019: 52 · 2018: 57 · 2017: 43 · 2016: 31 · 2015: 28 · 2014: 11 · 2013: 1
- FWCI
- 20.82
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Medical Research Methodology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- uO Research (University of Ottawa) (submittedVersion, public-domain)
- Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- sensitivity analysis, clinical trials, missing data, outliers, protocol deviations, outcome definitions
- MeSH terms
- Clinical Trials as Topic, Humans, Outcome and Process Assessment, Health Care, Research Design, Sensitivity and Specificity
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