Cited by 174Statistical Methods in Clinical TrialsPharmacogenetics and Drug MetabolismPharmaceutical studies and practices

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Statistical pitfalls of personalized medicine

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Senn, Stephen·2018·Nature

DOI 10.1038/d41586-018-07535-2

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An Assessment of Clinically Useful Measures of the Consequences of Treatment
Laupacis, A., Sackett, D. L., & Roberts, R. S. (1988) · New England Journal of Medicine · doi:10.1056/nejm198806303182605 · Cited by 1,681
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Estimation of the Percentage of US Patients With Cancer Who Benefit From Genome-Driven Oncology
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Consequences of dichotomization
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Mastering variation: Variance components and personalised medicine
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Esomeprazole Versus Other Proton Pump Inhibitors in Erosive Esophagitis: A Meta-Analysis of Randomized Clinical Trials
Gralnek, I. M., Dulai, G. S., Fennerty, M. B., & Spiegel, B. M. R. (2006) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2006.09.013 · Cited by 198
Individual response to treatment: is it a valid assumption?
Senn, S. J. (2004) · BMJ · doi:10.1136/bmj.329.7472.966 · Cited by 185
Self-citation
A multicenter, placebo-controlled, double-blind, multiple-crossover study of Fentanyl Pectin Nasal Spray (FPNS) in the treatment of breakthrough cancer pain
Portenoy, R. K., Burton, A. W., Gabrail, N. Y., & Taylor, D. R. (2010) · Pain · doi:10.1016/j.pain.2010.07.028 · Cited by 155
A randomized comparative study of esomeprazole 40 mg versus pantoprazole 40 mg for healing erosive oesophagitis: the EXPO study
Labenz, J., Armstrong, D., Lauritsen, K., Katelaris, P., Schmidt, S., Schütze, K., Wallner, G. T., Juergens, H., Preiksaitis, H. G., Keeling, N., et al. (2005) · Alimentary Pharmacology & Therapeutics · doi:10.1111/j.1365-2036.2005.02368.x · Cited by 146
Understanding Variation in Sets of N-of-1 Trials
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“We therefore suggest further research on the group of clients who do not benefit from CFT treatment, especially since classifying clients as responders and non-responders can be problematic (Senn, 2018).”
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History of Oncotherapies in Cancer Biology
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Potter, T. I. T., Horgan, G. W., Wanders, A. J., Zandstra, E. H., Zock, P. L., Fisk, H. L., Minihane, A. M., Calder, P. C., Mathers, J. C., & de Roos, B. (2022) · Frontiers in Nutrition · doi:10.3389/fnut.2022.989716 · Cited by 3
“Change in plasma TG and plasma PC EPA + DHA were the outcomes of interest in this study and were used on a continuous scale rather than being dichotomized into “responders” or “non-responders” to the intervention to maximize statistical power (33, 34).”
The multifaceted nature of recovery after exercise: A need for individualization
Rappelt, L., Javanmardi, S., Heinke, L. N., Baumgart, C., & Freiwald, J. (2023) · Sports Orthopaedics and Traumatology · doi:10.1016/j.orthtr.2023.10.006 · Cited by 3
Treatment effect variation in brain stimulation across psychiatric disordersPreprint
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Ethical considerations on the use of big data and artificial intelligence in kidney research from the ERA ethics committee
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“Profiling also results in uniformization rather than individualization of health care [13 ].”
Interindividual Variability of Acute Cardiovascular Responses to Low-Load Resistance Exercise with Different Methods of Blood Flow Restriction
Hammert, W. B., Kang, A., Yamada, Y., Kataoka, R., Sallberg, R. W., Metcalf, E. E., & Loenneke, J. P. (2025) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000003886 · Cited by 2
Individual responses: transparency is welcome, but inference needs replication and modeling
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Three-Year Outcomes of Proximal Hypoglossal Nerve Stimulation in OSA
Schwartz, A. R., Jacobowitz, O., Mickelson, S. A., Miller, M. B., Oliven, A., Certal, V., Hopp, M. L., Winslow, D. H., Huntley, T. C., Nachlas, N. E., et al. (2026) · CHEST Journal · doi:10.1016/j.chest.2025.11.057 · Cited by 3
“38 Nevertheless, THN3 expanded eligibility criteria compared with other trials, suggesting that a wider range of patients may benefit from HGNS than previously disclosed.”
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Mori, T., Herder, C., Cardoso, P., Dennis, J., & Kuß, O. (2026) · Diabetologia · doi:10.1007/s00125-026-06802-6 · Cited by 2
“For example, repeated crossover trials (or ‘ N -of-1-trials’), in which each individual receives at least one treatment twice, are particularly useful to address precision medicine questions [75, 76].”
Global impact and application of Precision Healthcare
Mentis, A. A., & Liu, L. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13963-5.00001-7 · Cited by 2
Personalized Medicine Beyond Stratification
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Understanding the variation in salivary fluoride levels: A secondary trajectory‐based analysis of trial data
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“Exploration of possible individual or subgroup treatment effect heterogeneity could be done by conducting repeated cross-over trials [39, 40], where participants are treated with 5000-and 1450-ppm fluoride toothpaste during several periods, and their background salivary fluoride trajectories…”
Estimating heterogeneity of treatment effect in psychiatric clinical trials
Siegel, J. S., Zhong, J., Tomioka, S., Ogirala, A., Faraone, S. V., Szabo, S. T., Koblan, K. S., & Hopkins, S. C. (2025) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2025.09.070 · Cited by 2
Advancing Personalized Medicine Through Prediction
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“An individual patient's risk and response to treatment might vary within short time windows (8).”
Background
Are treatment effects consistent with hypothesized mechanisms of action proposed for postoperative delirium interventions? Reanalysis of systematic reviews
Boxell, E., Malik, Y., Wong, J., Lee, M. H., Berntsson, H., Lee, M. J., Bourne, R. S., McCullagh, I. J., Hind, D., & Wilson, M. (2021) · Journal of Comparative Effectiveness Research · doi:10.2217/cer-2021-0161 · Cited by 2
Introduction
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“The literature considers ways to optimize the personalized treatment process, and the main topics of discussion are the issues of diagnosis, methods of genetic testing, protection of sensitive data (Chan, Ginsburg, 2011; Hamburg, Collins, 2010, Gaciong, 2016; Schork, 2015), the use of new technology (Abul-Husn, Kenny,…”
Method
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“…medicine – including personalized psychiatry – the patient’s individual needs are assessed for instance by sequencing their genome to identify individually matching treatments that would not necessarily work for other patients displaying similar symptoms (e.g., Jain, 2002; Senn, 2016; 2018).”
Background
ASPECTS, The Mismeasure of Stroke: A Metrological InvestigationPreprint
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Effects of Moderate and Regular Exercise on Improving Neurocognition and Preventing Neurodegenerative Diseases in the Elderly
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“While individual differences in biomarker trajectories may hint at modifiable pathways, true prediction of treatment responsiveness requires specific designs such as N-of-1 trials or adaptive modeling (e.g., Senn, 2018;).”
Consistency of Blood Pressure Response to Potassium-Enriched Salt in the China Salt Substitute Study
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“12,13 Another option for parallel-group trials is to use multiple measures of BP made during follow-up to calculate the extent to which within-person variation in BP accounts for the apparent between-person variation in BP response to the intervention.”
A systematic review and meta-analysis of clozapine response rates in schizophrenia
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The current status and future prospects of precision medicine
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“In some reports, the patients that benefit are in the order of 7% or less [9], and the benefits may be exaggerated due to statistical pitfalls [12].”
Background
Learning to Personalize Medicine from Aggregate DataPreprint
Colbaugh, R., & Glass, K. (2020) · medRxiv · doi:10.1101/2020.07.07.20148205 · Cited by 1
“) model-development is performed with data assembled prior to the target clinical trial [3,18,33]; ii.”
BackgroundMethodInfluential
Constant effect in randomized clinical trials with quantitative outcome : a methodological review
Cortés, J. (2021) · doi:10.5821/dissertation-2117-349575 · Cited by 1
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Concerning Pharmacogenetics, Pharmacogenomics and Personalized Medicine
Senn, S. J. (2021) · Wiley eBooks · doi:10.1002/9781119238614.ch25 · Cited by 1
Self-citation
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Moeller, J., & Schmidt, D. (2023) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/hj3rw · Cited by 1
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“1), thereby reducing the potential for misinterpretation of the individualized response (5).”
Precision Medicine and Complexity
Aron, D. C. (2019) · Springer eBooks · doi:10.1007/978-3-030-24593-1_13
Using Machine-Learning Techniques to Identify Responders vs. Non-responders in Randomized Clinical Trials.Preprint
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“Even when the division is based on a consistent and clinically correct threshold, it can still fail in discerning between patients who achieve an exceptional beneﬁt to the response vs. ones who just make it past the cut-oﬀ [13].”
Background
References
Riley, R. D., Tierney, J. F., & Stewart, L. A. (2021) · Wiley eBooks · doi:10.1002/9781119333784.refs2
Pharmaco‐economics and Portfolio Management
Senn, S. J. (2021) · doi:10.1002/9781119238614.ch24
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Design and Interpretation of Clinical Trials as Seen by a Statistician
Senn, S. J. (2021) · doi:10.1002/9781119238614.ch3
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The Measurement of Treatment Effects
Senn, S. J. (2021) · doi:10.1002/9781119238614.ch8
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Kaleidoscope
Tracy, D. K., Joyce, D. W., & Shergill, S. S. (2019) · The British Journal of Psychiatry · doi:10.1192/bjp.2018.292
Dose‐Finding
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Self-citation
Secondary Analysis to Inform the Development of Adaptive Preventive Interventions
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“This is a concern identified in the field of personalized medicine in the example of differential response to medication (Senn, 2018).”
Background
Pharmakotherapie: Allgemeine Aspekte
Keck, M. E. (2022) · Elsevier eBooks · doi:10.1016/b978-3-437-23031-8.00015-7
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Cross-sectional evaluation of outcome comparison methodology in dermatologic clinical trials and a simulation-based discussion of why outcome comparison technique mattersPreprint
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“We must, however, be cognizant that these methods have their pitfalls, notably with respect to within-person variance over time (Senn, 2018), and that these approaches must be continuously re ﬁ ned.”
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“…sensitive data (Chan, Ginsburg, 2011; Hamburg, Collins, 2010; Gaciong, 2016; Schork, 2015), developing skills in the use of new technologies (Abul-Husn, Kenny, 2019), as well as in the analysis of large data sets (Cirillo, Valencia, 2019; Suwinski, Ong, Ling, Poh, Khan, Ong, 2019; Senn, 2018).”
Developing the Necessary Information for Sample Size Calculations in Future Spinal Cord Stimulation Clinical Trials
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“Unfortunately, response heterogeneity is often misattributed to the nebulous “non-responders to treatment,” when it is equally likely to be due to study itself [7].”
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“But this is predicated on the assumption that the recorded response for each patient is reliable and reproducible; if the response cannot be replicated, then our identification of ‘ responders ’ is insecure [7].”
Additional file 2 of Genetic susceptibility, elevated blood pressure, and risk of atrial fibrillation: a Mendelian randomization study
Nazarzadeh, M., Pinho‐Gomes, A., Bidel, Z., Canoy, D., Dehghan, A., Smith Byrne, K., Bennett, D. A., Smith, G. D., & Rahimi, K. (2021) · Figshare · doi:10.6084/m9.figshare.14168440
Additional file 1 of Genetic susceptibility, elevated blood pressure, and risk of atrial fibrillation: a Mendelian randomization study
Nazarzadeh, M., Pinho‐Gomes, A., Bidel, Z., Canoy, D., Dehghan, A., Smith Byrne, K., Bennett, D. A., Smith, G. D., & Rahimi, K. (2021) · Figshare · doi:10.6084/m9.figshare.14168434
Additional file 1 of Comparison of causal forest and regression-based approaches to evaluate treatment effect heterogeneity: an application for type 2 diabetes precision medicine
Venkatasubramaniam, A., Mateen, B. A., Shields, B. M., Hattersley, A. T., Jones, A. G., Vollmer, S. J., & Dennis, J. M. (2023) · Figshare · doi:10.6084/m9.figshare.23536129
Additional file 3 of Genetic susceptibility, elevated blood pressure, and risk of atrial fibrillation: a Mendelian randomization study
Nazarzadeh, M., Pinho‐Gomes, A., Bidel, Z., Canoy, D., Dehghan, A., Smith Byrne, K., Bennett, D. A., Smith, G. D., & Rahimi, K. (2021) · Figshare · doi:10.6084/m9.figshare.14168443
Treatment effect heterogeneity of exercise therapy for chronic non-specific low back pain: A meta-analysis
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Online-delivered eye movement desensitization and reprocessing treatment for adults with post-traumatic stress disorder due to multiple traumas: a non-concurrent multiple baseline design
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“In addition, in SCEDs, each person has a large number of repeated observations, taking into account within-person variability and thereby increasing the reliability and interpretation of the results, compared with studies relying on only one or a few outcome assessments (Senn, 2018).”
One size fits none: A call for an idionomic revolution
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Obesity science, research gaps, and opportunities in the new era of obesity medicines: an Endocrine Society scientific statement
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“This may be because much of the treatment response variability is not repeatable or truly driven by biological differences (86).”
Individual effects of a nutritional supplementation blend on sleep in professional under-17 footballers: a replicate crossover trial
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Updated Italian intersocietal recommendations for the appropriate use of anti-amyloid monoclonal antibodies in Alzheimer's disease
Benussi, A., Agosta, F., Agnello, L., Alfano, A. R., Altini, M., Antico, A., Basilico, S., Bellelli, G., Bonanni, L., Bottini, G., et al. (2026) · The Journal of Prevention of Alzheimer s Disease · doi:10.1016/j.tjpad.2026.100685
“Moreover, dichotomizing a continuous outcome such as the CDR-SB may preferentially identify borderline cases in whom small score fluctuations move the participant across an arbitrary threshold, rather than a distinct subgroup experiencing a clearly meaningful benefit [27 – 29].”

Authors: Senn, S.
Affiliations: Luxembourg Institute of Health (LU)
Corresponding author: Senn, Stephen
ORCID: Senn, Stephen
Venue: Nature
Publisher: Springer Science and Business Media LLC
Published: 2018-11-27
Volume: 563
Issue: 7733
Pages: 619-621
ISSN: 0028-0836 (ISSN-L)
1476-4687
DOI: 10.1038/d41586-018-07535-2
OpenAlex: W2903127325
Cited by: 174
References: 12
Citations by year: 2026: 10 · 2025: 17 · 2024: 18 · 2023: 23 · 2022: 18 · 2021: 38 · 2020: 28 · 2019: 22
FWCI: 18.32
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Nature (publishedVersion)
PubMed (publishedVersion)
RePEc: Research Papers in Economics (submittedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmacogenetics and Drug Metabolism
Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Pharmaceutical studies and practices
Keywords: personalized medicine, drug trials, terminology, statistical pitfalls, personalized treatment, clinical trial design
MeSH terms: Clinical Protocols, Clinical Trials as Topic, Humans, Statistics as Topic, Reproducibility of Results, Treatment Outcome, Evidence-Based Medicine, Precision Medicine

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