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A tutorial on sensitivity analyses in clinical trials: the what, why, when and how
Open accessAbstract
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].”
From Lessons Learned from an Attempted Pragmatic Randomized Controlled Trial for Improvement of Chronic Pain-Associated Disability in Green Professions: Long-Term Effectiveness of a Guided Online-Based Acceptance and Commitment Therapy (PACT-A) (2022) · cites this paper as [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.”
From Does preregistration improve the credibility of research findings? (2020) · cites this paper as Thabane et al., 2013
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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”
From Effectiveness of a projection-based augmented reality exposure system in treating cockroach phobia: Study protocol of a randomised controlled trial (2023) · cites this paper as [57]
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A tutorial on pilot studies: the what, why and how
Thabane, L., Ma, J., Chu, R., Cheng, J., Ismaila, A., Rios, L. P., Robson, R., Thabane, M., Giangregorio, L., & Goldsmith, C. H. (2010) · BMC Medical Research Methodology · doi:10.1186/1471-2288-10-1 · Cited by 3,384
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Cited by
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Song, X., Zhang, J., Zhan, C., Xuan, Y., Ye, M., & Xu, C. (2015) · Journal of Hydrology · doi:10.1016/j.jhydrol.2015.02.013 · Cited by 655
Gomila, R. (2020) · Journal of Experimental Psychology General · doi:10.1037/xge0000920 · Cited by 565
Handling missing data in RCTs; a review of the top medical journals
Bell, M. L., Fiero, M., Horton, N. J., & Hsu, C.-H. (2014) · BMC Medical Research Methodology · doi:10.1186/1471-2288-14-118 · Cited by 395
An introduction to multiplicity issues in clinical trials: the what, why, when and how
Li, G., Taljaard, M., Van den Heuvel, E. R., Levine, M. A., Cook, D. J., Wells, G. A., Devereaux, P. J., & Thabane, L. (2016) · International Journal of Epidemiology · doi:10.1093/ije/dyw320 · Cited by 381
Self-citationAddressing the theory crisis in psychology
Oberauer, K., & Lewandowsky, S. (2019) · Psychonomic Bulletin & Review · doi:10.3758/s13423-019-01645-2 · Cited by 462
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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