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Statistical pitfalls of personalized medicine
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“This flawed approach fails to take into account the complexity of neurobehavioural disorders in 3 ways.9,34–37”
From Precision clinical trials: a framework for getting to precision medicine for neurobehavioural disorders (2021) · cites this paper as [34]
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“First, patient subgroups are defined by baseline pre-randomization markers that are hypothesized to modulate the effect size of the intervention (i.e., modulators), an approach commonly used in cardiovascular disease, for example.”
From Precision clinical trials: a framework for getting to precision medicine for neurobehavioural disorders (2021) · cites this paper as [34]
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Issues in the determination of ‘responders’ and ‘non‐responders’ in physiological research
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MethodResilience in Clinical Care: Getting a Grip on the Recovery Potential of Older Adults
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“Overlooking patients’ variable physiological responses over time by performing too few measurements is a common pitfall of biomedical research and hinders progress in the development of personalized medicine.(12) Many of the selected articles directly or indirectly referred to the difficulty of studying real-world fun…”
BackgroundThe efficacy and heterogeneity of antipsychotic response in schizophrenia: A meta-analysis
McCutcheon, R. A., Pillinger, T., Mizuno, Y., Montgomery, A., Pandian, H., Vano, L. J., Marques, T. R., & Howes, O. D. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0502-5 · Cited by 89
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“Other designs, such asN×1 trials and cross-over designs may provide more direct within-person comparability, and thereby also provide information on the stability of treatment response for an individual [68].”
BackgroundHerold, F., Törpel, A., Hamacher, D., Budde, H., Zou, L., Strobach, T., Müller, N. G., & Gronwald, T. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.682891 · Cited by 80
“being a non-responder, seems to be a non-favorable strategy for most outcome parameters as by doing so we fail to understand the “50 shades of responders” (e.g., the contribution of the different determinants that causes interindividual response variability; Senn, 2018; Atkinson et al., 2019).”
BackgroundRole of metabolomics in the delivery of precision nutrition
Brennan, L., & de Roos, B. (2023) · Redox Biology · doi:10.1016/j.redox.2023.102808 · Cited by 72
“However, it may not be appropriate to label someone as a responder or non-responder from a single pair of values (e.g. a baseline versus end value, representing a change from baseline); as responses may vary from occasion to occasion [64].”
BackgroundMethodInfluentialA systems approach to clinical oncology uses deep phenotyping to deliver personalized care
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Structured sparsity regularization for analyzing high-dimensional omics data
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Multifunctional nanostructures: Intelligent design to overcome biological barriers
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Key questions for the evaluation of anti-amyloid immunotherapies for Alzheimer’s disease
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“34 Also, in addition Box 1 Were statistical analyses appropriate and did they support efficacy?”
From Boulder to Stockholm in 70 Years: Single Case Experimental Designs in Clinical Research
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“2015, 2018; Senn 2018).”
Precision nutrition in diabetes: when population-based dietary advice gets personal
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Perspective: Application of N-of-1 Methods in Personalized Nutrition Research
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Essential statistical principles of clinical trials of pain treatments
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Post–Modern Epidemiology: When Methods Meet Matter
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“…right person at the right time is currently a defining trend in many disciplines, including the fields of personalized learning (e.g., Dumont, 2019; Bulger, 2016), personalized medicine (e.g., Senn, 2016; 2018), personalized advertisement (Zhu & Chang, 2016; Bang & Wojdynski, 2016), and many more.”
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“Even constant treatment effects lead to differences in observed response rates, creating the illusion of a differential treatment effect, where none exists [20].”
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“Moving the focus from dichotomies to the level of the individual is required to advance precision medicine (Senn, 2018).”
Simoes, J. P., Daoud, E., Shabbir, M., Amanat, S., Assouly, K. K. S., Biswas, R., Casolani, C., Dode, A., Enzler, F., Jacquemin, L., et al. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.647285 · Cited by 49
“Congruent with the push for individualizedmedicine (Schork, 2015; Senn, 2018), Single-Case Experimental Designs (SCED) offer an alternative to RCTs.”
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Personalized prediction of daily eczema severity scores using a mechanistic machine learning model
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“Response variability is defined as biologically important individual differences in the physiological response to the same intervention and has important implications for stratified, precision or personalized intervention (Erden, 2015; Localio et al., 2020; Senn, 2018).”
BackgroundVenkatasubramaniam, A., Mateen, B. A., Shields, B. M., Hattersley, A. T., Jones, A. G., Vollmer, S. J., & Dennis, J. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02207-2 · Cited by 40
“Our focus was not a comprehensive review of all closely related methods, and so we cannot comment on the performance of other treatment effect heterogeneity focused algorithms, in particular recently developed Bayesian approaches [38– 40] Finally, as our validation dataset was observational, despite extensive sensitiv…”
ResultMargaritelis, N. V., Nastos, G. G., Vasileiadou, O., Chatzinikolaou, P. N., Theodorou, A. A., Paschalis, V., Vrabas, I. S., Kyparos, A., Fatouros, I. G., & Nikolaidis, M. G. (2023) · Acta Physiologica · doi:10.1111/apha.14017 · Cited by 33
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“For example, they imply the existence of among-individual heterogeneity of treatment effects – a core idea in stratified, precision or personalised medicine (Senn, 2018).”
BackgroundScientific challenges for precision public health
Kee, F., & Taylor-Robinson, D. C. (2020) · Journal of Epidemiology & Community Health · doi:10.1136/jech-2019-213311 · Cited by 33
Wang, X., Li, J., Wei, X., Shi, G., Zhang, N., Tu, J., Yan, C.-Q., Zhang, Y., Hong, Y., Yang, J., et al. (2023) · Pain · doi:10.1097/j.pain.0000000000002859 · Cited by 32
Thomas, D. M., Clark, N., Turner, D. S., SIU, C. O., Halliday, T. M., Hannon, B. A., Kahathuduwa, C. N., Kroeger, C. M., Zoh, R. S., & Allison, D. B. (2019) · American Journal of Clinical Nutrition · doi:10.1093/ajcn/nqz196 · Cited by 31
“In these cases, the RTM effect can either be estimated from repeated measurements at baseline or through estimates from comparable national databases or other existing data sets, such as those performed in other studies (6, 43).”
ResultBuckel, M., Maclean, P., Knight, J. C., Lawler, P. R., & Proudfoot, A. G. (2023) · Critical Care · doi:10.1186/s13054-023-04752-8 · Cited by 30
“…in a response variable after a single episode of treatment, this may potentially be explained by statistical noise rather than a true biological difference and can easily be compounded by arbitrary dichotomization of a continuous variable to distinguish ‘responders’ from ‘non-responders’ [132].”
BackgroundClinical trial design, end-points, and emerging therapies in pulmonary arterial hypertension
Weatherald, J. C., Fleming, T. R., Wilkins, M. R., CASCINO, T. M., Psotka, M. A., Zamanian, R. T., Seeger, W., Galiè, N., & Gomberg‐Maitland, M. (2024) · European Respiratory Journal · doi:10.1183/13993003.01205-2024 · Cited by 29
Homan, S., Muscat, W., Joanlanne, A., Marousis, N., Cecere, G., Hofmann, L. A., Ji, E., Neumeier, M. S., Vetter, S., Seifritz, E., et al. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.11.033 · Cited by 29
“More often than not is this assumption the result of a somewhat arbitrary distinction between “responders” and “nonresponders” (Homan and Kane, 2018; Senn, 2018).”
BackgroundPositioning Imatinib for Pulmonary Arterial Hypertension: A Dose-Finding Phase 2 Study
Rothman, A. M. K., Villar, S. S., Middleton, J., Roussakis, A., Varian, F., Zafar, H., Law, M. W.-M., Apperley, J. F., Bartelink, I. H., Said, M. M., et al. (2025) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.202410-1929oc · Cited by 27
“Reproduceable changes on rechallenging highlight another advantage of continuous monitoring of cardiopulmonary hemodynamics in a rare disease; speci fi cally, it enables n -of-1 studies and a convincing evaluation of personalized responses (27).”
Kimberley, T. J., Cramer, S. C., Wolf, S. L., Liu, C. Y., Gochyyev, P., Dawson, J., Dixit, A. K., Majid, A., Brown, B. L., Turner, D. L., et al. (2025) · Stroke · doi:10.1161/strokeaha.124.050479 · Cited by 27
How should we investigate variation in the relation between social media and well-being?Preprint
Johannes, N., Masur, P. K., Vuorre, M., & Przybylski, A. (2021) · Meta-Psychology · doi:10.31234/osf.io/xahbg · Cited by 24
“For example, in the field of personalized medicine, there is a heavy debate on how to understand variation in effects and how to demark effects on the individual level from those on the group-level (Senn, 2016, 2018).”
BackgroundHutchins, K., Betts, J. A., Thompson, D., Hengist, A., & Gonzalez, J. T. (2025) · American Journal of Clinical Nutrition · doi:10.1016/j.ajcnut.2025.02.024 · Cited by 25
“Understanding interindividual variance requires replication at the level of the individual [24,25,27].”
Neumeier, M. S., Homan, S., Vetter, S., Seifritz, E., Kane, J. M., Huhn, M., Leucht, S., & Homan, P. (2021) · Schizophrenia Bulletin · doi:10.1093/schbul/sbab078 · Cited by 24
Replication and generalization in applied neuroimaging
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“…generalization issues in neuroimaging applications are similar to those in other human 2 research fields (He et al., 2015; Shavelson et al., 1989; Shavelson and Webb, 1991; Tipton, 2014); the 3 issues are also closely linked to meta-analysis, which aggregate the outcomes of multiple studies 4…”
Stunnenberg, B. C., Berends, J., Griggs, R. C., Statland, J. M., Drost, G., Nikles, J., Groenewoud, H., van Engelen, B. G. M., van der Wilt, G. J., & Raaphorst, J. (2021) · Neurology · doi:10.1212/wnl.0000000000012998 · Cited by 23
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“6”
Individual response to antidepressants for depression in adults-a meta-analysis and simulation study
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Carmland, M. E., Kreutzfeldt, M. S., Holbech, J. V., Andersen, N. T., Jensen, T. S., Bach, F. W., Sindrup, S. H., & Finnerup, N. B. (2019) · Trials · doi:10.1186/s13063-019-3695-7 · Cited by 22
Ten guidelines for designing motor learning studies
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Schork, N. J. (2022) · Harvard Data Science Review · doi:10.1162/99608f92.f1eef6f4 · Cited by 21
“…of a population-based RCT, as many interventions have been unequivocally shown not to work on all the individuals who take them based on a variety of different population-based metrics exploring intervention responses (Nicholas J. Schork 2015)(Senn 2018)(Subramanian, Kim, and Christakis 2018).”
BackgroundGuo, X., McCutcheon, R. A., Pillinger, T., Mizuno, Y., Natesan, S., Brown, K., & Howes, O. D. (2020) · Journal of Affective Disorders · doi:10.1016/j.jad.2020.07.102 · Cited by 22
Single-Case Experimental Designs: Clinical Research and Practice
Vlaeyen, J. W. S., Onghena, P., Vannest, K. J., & Kratochwill, T. R. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-818697-8.00191-6 · Cited by 22
Responders and non‐responders to aerobic exercise training: beyond the evaluation of
Maturana, F. M., Soares, R. N., Murias, J. M., Schellhorn, P., Erz, G., Burgstahler, C., Widmann, M., Munz, B., Thiel, A., & Niess, A. M. (2021) · Physiological Reports · doi:10.14814/phy2.14951 · Cited by 19
“Individual responses to exercise training have shown to be more robustly analyzed through cross- over designs, where participants act as their own controls (Hecksteden et al., 2015; Senn, 2018).”
BackgroundPark, J., Park, J., Jeon, J., Chae, S., Kim, G. B., Han, G., Park, H., Jeong, Y., & Jeong, K. (2025) · Advanced Science · doi:10.1002/advs.202417625 · Cited by 20
Should responder analyses be conducted on continuous outcomes?
Abugov, R., Clark, J. E., Higginbotham, L., Li, F., Nie, L., Reasner, D. S., Rothmann, M. D., Yuan, X., & Sharretts, J. M. (2022) · Pharmaceutical Statistics · doi:10.1002/pst.2273 · Cited by 17
Hoogland, J., Efthimiou, O., Nguyen, T., & Debray, T. P. A. (2024) · Statistics in Medicine · doi:10.1002/sim.10186 · Cited by 17
Standardization of gut microbiome analysis in sports
Mancin, L., Paoli, A., Berry, S., Gonzalez, J. T., Collins, A. J., Lizarraga, M. A., Mota, J. F., Nicola, S., & Rollo, I. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101759 · Cited by 22
“89 Human individuality is shaped by biological features (i.e., me-tabolome and microbiome composition) as well as lifestyle, the way we eat our food (i.e., fasting period and meal ordering), and the reason we make the dietary choices.”
BackgroundWijn, S. R. W., Rovers, M. M., Le, L. H., Belias, M., Hoogland, J., IntHout, J., Debray, T. P. A., & Reitsma, J. B. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-028751 · Cited by 16
“For each organisation, we used the following strategy to determine whether they had official documents in which they provide research guidance: (1) using the search engine of their official website, (2) Google, (3) PubMed and (4) citations.”
BackgroundMethodResultInfluentialStrategies for Testing Intervention Matching Schemes in Cancer
Schork, N. J., Goetz, L. H., Lowey, J., & Trent, J. M. (2020) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1947 · Cited by 16
“Although there is debate about the best way to quantify the number of individuals likely to respond to specific interventions,[2, 3] there is little doubt that interventions need to be provided to individuals in a way that better match the pathologies they have.”
BackgroundQi, W., Abu–Hanna, A., van Esch, T. E. M., Beurs, D. P. D., Liu, Y., Flinterman, L. E., & Schut, M. C. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102080 · Cited by 16
Inherited Retinal Disorders: Using Evidence as a Driver for Implementation
Sergouniotis, P. I. (2019) · Ophthalmologica · doi:10.1159/000500574 · Cited by 15
The Daily Experience of Subjective Tinnitus: Ecological Momentary Assessment Versus End-of-Day Diary
Lourenco, M. P. C. G., Simoes, J. P., Vlaeyen, J. W. S., & Cima, R. F. F. (2021) · Ear and Hearing · doi:10.1097/aud.0000000000001054 · Cited by 15
“An added benefit of EMA and EDD measures is that it may conform with the push for individualized medicine (Schork 2015; Senn 2018) due to its flexibility in incorporating different items.”
BackgroundSystematic analysis of the number needed to treat
Vancak, V., Goldberg, Y., & Levine, S. Z. (2020) · Statistical Methods in Medical Research · doi:10.1177/0962280219890635 · Cited by 15
“Its asymptotically-correct delta-method-based and bootstrap-based 1 a ð Þ100%-level CIs are defined as in (11) and (6) for W ht; hc ð Þ g ps ht; hc ð Þ ð Þ.”
MethodInfluentialShen, T., Thackray, A. E., King, J. A., Alotaibi, T. F., Alanazi, T. M., Willis, S. A., Roberts, M. J., Lolli, L., Atkinson, G., & Stensel, D. J. (2023) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000003283 · Cited by 15
“We have previously adopted a replicated crossover study design and associated documented analysis approaches (16,24 – 27) to identify the presence of interindividual variability in appetite responses to acute exercise (18) and standardized meal intake (17).”
MethodBiomarkers for treatment response in rheumatoid arthritis: Where are they?
Zhao, S. S., & Moots, R. J. (2020) · Rheumatology and Immunology Research · doi:10.2478/rir-2020-0008 · Cited by 14
“However, dichotomizing to response versus non-response or using percentage-change further sacrifices statistical power that is already limited in many biomarker studies.[10] Using continuous absolute change in an outcome measure, with consideration for baseline differences between biomarker groups, is more valid and s…”
BackgroundHengartner, M. P. (2019) · Acta Neuropsychiatrica · doi:10.1017/neu.2019.23 · Cited by 13
“(quoted by Goodwin and Nutt) that allegedly delineate a subgroup of ‘true benefiters’ are by and large a statistical artefact due to random outcome variation and arbitrary subgroup selection (Senn, 2018).”
BackgroundMethodInfluentialDiscontinuation of antipsychotic medication—time to rethink trial design
Speyer, H., Begemann, M., Albert, N., Weller, A., Nordentoft, M., Veling, W., Sommer, I. E., Allott, K., & Killackey, E. J. (2020) · The Lancet Psychiatry · doi:10.1016/s2215-0366(20)30340-0 · Cited by 13
Lolli, L., Gonzalez, J. T., Shannon, O. M., Spellanzon, B., Stensel, D. J., Thackray, A. E., & Atkinson, G. (2025) · International Journal of Sport Nutrition and Exercise Metabolism · doi:10.1123/ijsnem.2025-0041 · Cited by 12
Alsaeid, M., Sung, S., Bai, W., Tam, M., Wong, Y. J., Cortés, J., Cobo, E., González, J. A., & Abraldeṣ, J. G. (2024) · Hepatology Communications · doi:10.1097/hc9.0000000000000321 · Cited by 12
“The gold standard to address this question would be trials with more than 1 crossover sequence, [48] which are not available.”
The ACTTION Guide to Clinical Trials of Pain Treatments, part II: mitigating bias, maximizing value
Dworkin, R. H., Kerns, R. D., McDermott, M. P., Turk, D. C., & Veasley, C. (2021) · PAIN Reports · doi:10.1097/pr9.0000000000000886 · Cited by 12
“designs in which patients receive each treatment on at least 2 occasions.(11,25,51,52) Other important challenges for the development of precision pain treatments include the need to”
BackgroundÞórarinsdóttir, K., Holmes, E. A., Hardarson, J., Héðinsdóttir, U., Kanstrup, M., Singh, L., Hauksdóttir, A., Halldorsdottir, T., Guðmundsdóttir, B., Valdimarsdóttir, U. A., et al. (2021) · JMIR Formative Research · doi:10.2196/29873 · Cited by 11
Individual physiological and mitochondrial responses during 12 weeks of intensified exercise
Jacques, M., Landen, S., Alvarez‐Romero, J., Yan, X., GARNHAM, A. P., Hiam, D., Siegwald, M., Mercier, E., Hecksteden, A., Eynon, N., et al. (2021) · Physiological Reports · doi:10.14814/phy2.14962 · Cited by 10
“…is to isolate sources of variation in exercise training responses by first quantifying the magnitude of variation in training response, given that if subject- by- training interaction is low assessing response of individuals is futile; and only then quantifying individual responses (Senn, 2018).”
BackgroundExcess significance and power miscalculations in neurofeedback research
Thibault, R. T., & Pedder, H. (2022) · NeuroImage Clinical · doi:10.1016/j.nicl.2022.103008 · Cited by 12
“However, repeated efforts to apply this approach in personalized medicine remain largely unsuccessful (Senn, 2018).”
BackgroundMohandoss, A. A., & Thavarajah, R. (2019) · Journal of Oral Biology and Craniofacial Research · doi:10.1016/j.jobcr.2019.06.009 · Cited by 11
“Using N-of-1 trial must be done, perhaps at institutional settings to determine the actual gene-schizophrenia-drug-SA relationship instead of a cross-sectional methodology.(33)”
MethodCan We Rely on Changes in HVPG in Patients with Cirrhosis?
García–Tsao, G. (2021) · Hepatology · doi:10.1002/hep.32159 · Cited by 11
“This also brings into question the concept of analyzing responders versus nonresponders in clinical trials, a concept that has recently been challenged.(9)”
BackgroundReynolds, P. S. (2023) · doi:10.1002/9781119800002.ch18 · Cited by 11
Raittio, E. J., Leite, F. R. M., Machado, V., Botelho, J., & Nascimento, G. G. (2024) · Journal of Periodontal Research · doi:10.1111/jre.13347 · Cited by 10
Treatment effect heterogeneity of cognitive behavioral therapy for insomnia – A meta-analysis
Steinmetz, L., Simon, L., Baumeister, H., Spiegelhalder, K., & Terhorst, Y. (2024) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2024.101966 · Cited by 11
Polygenic modelling of treatment effect heterogeneity
Xu, Z. M., & Burgess, S. (2020) · Genetic Epidemiology · doi:10.1002/gepi.22347 · Cited by 9
Clinical significance of diabetes control before metabolic surgery
Albaugh, V. L., Sharma, G., Tu, C., & Aminian, A. (2021) · Surgery for Obesity and Related Diseases · doi:10.1016/j.soard.2021.03.031 · Cited by 9
What Is the Numerical Nature of Pain Relief?
Vigotsky, A. D., Tiwari, S. R., Griffith, J. W., & Apkarian, A. V. (2021) · Frontiers in Pain Research · doi:10.3389/fpain.2021.756680 · Cited by 8
Responder Analyses: A Methodological Mess
Cook, C. E., Bejarano, G., Reneker, J. C., Vigotsky, A. D., & Riddle, D. L. (2023) · Journal of Orthopaedic and Sports Physical Therapy · doi:10.2519/jospt.2023.11853 · Cited by 9
Tabb, K. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108750349.028 · Cited by 8
Long, Q., Bhinge, S., Calhoun, V. D., & Adalı, T. (2021) · Brain Connectivity · doi:10.1089/brain.2020.0815 · Cited by 7
“There has been significant interest in studying the subtypes of schizophrenia (Dwyer et al., 2018; Geisler et al., 2015; Jablensky, 2006, 2010) to gain a better understanding of the uncertainty in whether a precision medicine is needed (Senn, 2018) during clinical diagnosis and treatment.”
Profit versus Quality: The Enigma of Scientific Wellness
Carbonara, K., MacNeil, A. J., O’Leary, D. D., & Coorssen, J. R. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12010034 · Cited by 7
“In contrast, personalized medicine is a practice that commercializes precision medicine research with the aim of matching individuals with the prevention and therapy techniques that are “best” suited to them [10,11].”
BackgroundGonzalez, J. T., Lolli, L., Veasey, R. C., Rumbold, P. L. S., Betts, J. A., Atkinson, G., & Stevenson, E. (2024) · European Journal of Nutrition · doi:10.1007/s00394-024-03467-y · Cited by 8
Nielsen, S. M., Storgaard, H., Ellingsen, T., Shea, B., Wells, G. A., Welch, V. A., Furst, D. E., de Wit, M. P. T., Voshaar, M., Juhl, C. B., et al. (2020) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2020-217237 · Cited by 6
Hurault, G., Roekevisch, E., Schram, M. E., Szegedi, K., Kežić, S., Middelkamp-Hup, M. A., Spuls, P. I., & Tanaka, R. J. (2022) · Skin Health and Disease · doi:10.1002/ski2.77 · Cited by 6
“12 Such fluctuations can be stochastic (unpredictable), due to unobserved/unrecorded factors (e.g., environmental factors) or measurement error (cf. inter‐ and intra‐rater variability of severity scores).”
Guo, Y., Waisberg, E., Ong, J., Kumar, R., & Lee, A. G. (2024) · Acta Astronautica · doi:10.1016/j.actaastro.2024.12.033 · Cited by 7
Duisters, K. L. W., Ogino, S., Andou, T., Ito, K., Akabane, T., Harms, A. C., Moerland, M., Hashimoto, Y., Ando, A., Ohtsu, Y., et al. (2019) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1606 · Cited by 5
“Although these fluctuations are considered to be a tremendous source of information in fields such as personalized medicine,(5,6) they simultaneously hamper translation to clinical practice.(7)”
BackgroundLourenço, M. G., Fuller, T., Ranson, S., Vlaeyen, J., & Cima, R. (2022) · Ear and Hearing · doi:10.1097/aud.0000000000001261 · Cited by 5
“Inspired by recent interest in more personalized research and treatment development (Schork 2015; Senn 2018), a SCED was used to explore the differences between group and individual treatment delivery settings of an established CBT for tinnitus protocol (Cima et al.”
MethodÖn, B. I., Havla, J. B., & Mansmann, U. (2024) · Multiple Sclerosis and Related Disorders · doi:10.1016/j.msard.2024.106247 · Cited by 5
Vancak, V., Goldberg, Y., & Levine, S. Z. (2022) · Statistics in Medicine · doi:10.1002/sim.9418 · Cited by 5
Metadata
- Authors
- Senn, S.
- Affiliations
- Luxembourg Institute of Health (LU)
- Corresponding author
- Senn, Stephen
- ORCID
- Senn, Stephen
- Venue
- Nature
- Published
- 2018-11-27
- Volume
- 563
- Issue
- 7733
- Pages
- 619-621
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- 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
- 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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