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How to Cite This Stats-of-1 Resources Webpage

Please cite this webpage as follows, based on desired style (all taken from scribbr.com except Vancouver, which is taken from proofed.com). Replace “Day Month Year” (e.g., “1 February 2020”) and “Month Day, Year” (e.g., February 1, 2020") as appropriate.

How to Add Resources

See this post for instructions on how to submit your own suggested resources and references: https://statsof1.org/2021-09-03-adding-new-esametric-resources/

The following esametric resources are ordered alphabetically, stratified by category. Note that some items are listed multiple times.

Actigraphy + Accelerometry

Publications

  • Barnett I, Torous J, Staples P, Keshavan M, Onnela JP. Beyond smartphones and sensors: Choosing appropriate statistical methods for the analysis of longitudinal data. Journal of the American Medical Informatics Association. 2018 Dec;25(12):1669-74. academic.oup.com/jamia/article/25/12/1669/5110357
  • Catellier DJ, Hannan PJ, Murray DM, Addy CL, Conway TL, Yang S, Rice JC. Imputation of missing data when measuring physical activity by accelerometry. Medicine and science in sports and exercise. 2005 Nov;37(11 Suppl):S555. ncbi.nlm.nih.gov/pmc/articles/PMC2435061
  • Rowe DA, Mahar MT, Raedeke TD, Lore J. Measuring physical activity in children with pedometers: Reliability, reactivity, and replacement of missing data. Pediatric Exercise Science. 2004 Nov 1;16(4):343-54. researchgate.net/profile/David_Rowe/publication/251259986….pdf

Aggregated N-of-1 Analyses

This section covers Aggregated N-of-1, Intensive Longitudinal Data, Meta-analysis, Mixed-Effects Models, Random-Effects Models, Multilevel/Hierarchical Models, Multilevel Time Series, Series-of-n-of-1.

Publications

Applications and Empirical Studies

Publications

Bayesian Approaches

Publications

Causality + Causal Inference + Mediation Analysis

Publications

  • Anjum RL, Copeland S, Rocca E. Rethinking Causality, Complexity and Evidence for the Unique Patient: A CauseHealth Resource for Healthcare Professionals and the Clinical Encounter. Springer Nature. 2020. library.oapen.org/handle/20.500.12657/39574
  • Balandat M. New tools for econometric analysis of high-frequency time series data-application to demand-side management in electricity markets. University of California, Berkeley; 2016. search.proquest.com/openview/17ecbcf034eb521b90b63a6fe9c8a314/1?pq-origsite=gscholar&cbl=18750
  • Bojinov I, Rambachan A, Shephard N. Panel experiments and dynamic causal effects: A finite population perspective. Quantitative Economics. 2021 Nov;12(4):1171-96. onlinelibrary.wiley.com/doi/full/10.3982/QE1744
  • Bojinov I, Shephard N. Time series experiments and causal estimands: exact randomization tests and trading. Journal of the American Statistical Association. 2019 Oct 2;114(528):1665-82. tandfonline.com/doi/abs/10.1080/01621459.2018.1527225
  • Bojinov I, Simchi-Levi D, Zhao J. Design and analysis of switchback experiments. Management Science. 2023 Jul;69(7):3759-77. pubsonline.informs.org/doi/abs/10.1287/mnsc.2022.4583
  • Cai X, Zeng L, Fowler C, Dixon L, Ongur D, Baker JT, Onnela JP, Valeri L. Causal estimands and identification of time-varying effects in non-stationary time series from N-of-1 mobile device data. arXiv preprint arXiv:2407.17666. 2024 Jul 24. arxiv.org/abs/2407.17666
  • Cook TD, Campbell DT, Shadish W. Experimental and quasi-experimental designs for generalized causal inference. Boston, MA: Houghton Mifflin; 2002. onlinelibrary.wiley.com/doi/10.1002/pam.10129
  • Daza EJ. Person as Population: A Longitudinal View of Single-Subject Causal Inference for Analyzing Self-Tracked Health Data. arXiv preprint arXiv:1901.03423. 2019 Jan 10. arxiv.org/abs/1901.03423
  • Daza EJ. Causal analysis of self-tracked time series data using a counterfactual framework for N-of-1 trials. Methods of information in medicine. 2018 Feb;57(Suppl 1):e10. thieme-connect.de/products/ejournals/html/10.3414/ME16-02-0044
    • LaTeX pre-print (identical content) here
  • Daza EJ, inventor; Evidation Health Inc, assignee. Methods and systems for generating personalized treatments via causal inference. United States patent application US 18/365,899. 2024 Feb 8. patents.justia.com/patent/20240047042
  • Daza EJ, Matias I, Schneider L. Model-Twin Randomization (MoTR) for Estimating One’s Own Recurring Individual Treatment Effect. Statistics in Medicine. 2024 Oct 1 under review. arxiv.org/abs/2208.00739 (2-min explainer video here)
  • Eichler M. Causal inference in time series analysis. Causality: Statistical perspectives and applications. 2012 Jul 13:327-54. onlinelibrary.wiley.com/doi/book/10.1002/9781119945710
  • Gärtner T, Schneider J, Arnrich B, Konigorski S. Comparison of Bayesian Networks, G-estimation and linear models to estimate causal treatment effects in aggregated N-of-1 trials with carry-over effects. BMC Medical Research Methodology. 2023 Aug 21;23(1):191. link.springer.com/article/10.1186/s12874-023-02012-5
  • Hall GJ, Putzeys S, Kratochwill TR, Levin JR. Discovering Internal Validity Threats and Operational Concerns in Single-Case Experimental Designs Through Directed Acyclic Graphs. Educational Psychology Review. 2024 Dec;36(4):128. link.springer.com/article/10.1007/s10648-024-09962-2
  • Han K, Basse G, Bojinov I. Population interference in panel experiments. Journal of Econometrics. 2024 Jan 1;238(1):105565. sciencedirect.com/science/article/pii/S0304407623002816
  • Izem R, McCarter R. Randomized and non-randomized designs for causal inference with longitudinal data in rare disorders. Orphanet Journal of Rare Diseases. 2021 Dec;16:1-9. ojrd.biomedcentral.com/articles/10.1186/s13023-021-02124-5
  • Krajewski T, Hudgens M. The augmented synthetic control method in public health and biomedical research. Statistical Methods in Medical Research. 2024 Feb 6:09622802231224638. journals.sagepub.com/doi/full/10.1177/09622802231224638
  • Langenberg B, Wurpts IC, Geuke GG, Onghena P. Estimating and Testing Causal Mediation Effects in Single-Case Experimental Designs Using State-Space Modeling. Evaluation & the Health Professions. 2022:01632787211067533. journals.sagepub.com/doi/10.1177/01632787211067533
  • Liang T, Recht B. Randomization inference when n equals one. arXiv preprint arXiv:2310.16989. 2023 Oct 25. arxiv.org/abs/2310.16989
  • Linden A. A matching framework to improve causal inference in interrupted time‐series analysis. Journal of Evaluation in Clinical Practice. 2018 Apr;24(2):408-15. onlinelibrary.wiley.com/doi/abs/10.1111/jep.12874
  • MacKinnon DP, Smyth HL, Somers J, Ho E, Norget J, Miočević M. A randomization permutation test for single subject mediation. Evaluation & the health professions. 2022 Mar;45(1):54-65. journals.sagepub.com/doi/10.1177/01632787211070811
  • Mahoney J. Strategies of causal inference in small-N analysis. Sociological methods & research. 2000 May;28(4):387-424. journals.sagepub.com/doi/abs/10.1177/0049124100028004001
  • Mahoney J. Toward a unified theory of causality. Comparative Political Studies. 2008 Apr;41(4-5):412-36. journals.sagepub.com/doi/abs/10.1177/0010414007313115
  • Malenica I, Bibaut A, van der Laan MJ. Adaptive Sequential Design for a Single Time-Series. arXiv preprint arXiv:2102.00102. 2021 Jan 29. arxiv.org/abs/2102.00102
  • Matias I, Daza EJ, Wac K. What possibly affects nighttime heart rate? Conclusions from N-of-1 observational data. Digital Health. 2022 Aug 24. journals.sagepub.com/doi/10.1177/20552076221120725
  • Menchetti F, Cipollini F, Mealli F. Combining counterfactual outcomes and ARIMA models for policy evaluation. The Econometrics Journal. 2023 Jan;26(1):1-24. academic.oup.com/ectj/article/26/1/1/6713620
  • Nagesh N, Azimi I, Andriola T, Rahmani AM, Jain R. Towards Deep Personal Lifestyle Models Using Multimodal N-of-1 Data. InInternational Conference on Multimedia Modeling 2023 Jan 9 (pp. 589-600). Cham: Springer International Publishing. link.springer.com/chapter/10.1007/978-3-031-27077-2_46
  • Neto EC, Bot BM, Perumal T, Omberg L, Guinney J, Kellen M, Klein A, Friend SH, Trister AD. Personalized hypothesis tests for detecting medication response in Parkinson disease patients using iPhone Sensor data. InBiocomputing 2016: Proceedings of the Pacific Symposium 2016 (pp. 273-284). ncbi.nlm.nih.gov/pubmed/26776193
  • Neto EC, Prentice RL, Bot BM, Kellen M, Friend SH, Trister AD, Omberg L, Mangravite L. Towards personalized causal inference of medication response in mobile health: an instrumental variable approach for randomized trials with imperfect compliance. arXiv preprint arXiv:1604.01055. 2016 Apr 4. arxiv.org/abs/1604.01055
  • Neto EC, Tummalacherla M, Mangravite L, Omberg L. Causality-based tests to detect the influence of confounders on mobile health diagnostic applications: a comparison with restricted permutations. arXiv. 2019 Nov:arXiv-1911. arxiv.org/abs/1911.05139
  • Piccininni M, Stensrud MJ, Shahn Z, Konigorski S. Causal inference for N-of-1 trials. arXiv preprint arXiv:2406.10360. 2024 Jun 14. arxiv.org/abs/2406.10360
  • Qu K, Schmid CH, Liu T. Instrumental Variable Approach to Estimating Individual Causal Effects in N-of-1 Trials: Application to ISTOP Study. arXiv preprint arXiv:2306.14019. 2023 Jun 24. arxiv.org/abs/2306.14019
  • Rohlfing I. Case studies and causal inference: An integrative framework. Palgrave Macmillan; 2012 Sep 26. link.springer.com/book/10.1057/9781137271327
  • Runge J, Gerhardus A, Varando G, Eyring V, Camps-Valls G. Causal inference for time series. Nature Reviews Earth & Environment. 2023 Jul;4(7):487-505. nature.com/articles/s43017-023-00431-y
  • van der Laan MJ, Malenica I. Robust Estimation of Data-Dependent Causal Effects based on Observing a Single Time-Series. arXiv preprint arXiv:1809.00734. 2018 Sep 3. arxiv.org/abs/1809.00734
  • Vegetabile BG. On the distinction between “conditional average treatment effects” (CATE) and “individual treatment effects” (ITE) under ignorability assumptions. arXiv preprint arXiv:2108.04939. 2021 Aug 10. arxiv.org/abs/2108.04939
  • Wu X, Weinberger KR, Wellenius GA, Dominici F, Braun D. Assessing the causal effects of a stochastic intervention in time series data: Are heat alerts effective in preventing deaths and hospitalizations?. arXiv preprint arXiv:2102.10478. 2021 Feb 20. arxiv.org/abs/2102.10478

Changepoint Approaches

Publications

Clinical Research + Clinical Trials + Clinical Practice

Publications

  • Coravos A, Doerr M, Goldsack J, Manta C, Shervey M, Woods B, Wood WA. Modernizing and designing evaluation frameworks for connected sensor technologies in medicine. npj Digital Medicine. 2020 Mar 13;3(1):1-0. nature.com/articles/s41746-020-0237-3
  • Marra C, Chen JL, Coravos A, Stern AD. Quantifying the use of connected digital products in clinical research. npj Digital Medicine. 2020 Apr 3;3(1):1-0. nature.com/articles/s41746-020-0259-x
  • Selker HP, Cohen T, D’Agostino RB, Dere WH, Ghaemi SN, Honig PK, Kaitin KI, Kaplan HC, Kravitz RL, Larholt K, McElwee NE. A Useful and Sustainable Role for N‐of‐1 Trials in the Healthcare Ecosystem. Clinical Pharmacology & Therapeutics. 2022 Aug;112(2):224-32. ascpt.onlinelibrary.wiley.com/doi/10.1002/cpt.2425
  • Selker HP, Dulko D, Greenblatt DJ, Palm M, Trinquart L. The use of N-of-1 trials to generate real-world evidence for optimal treatment of individuals and populations. Journal of Clinical and Translational Science. 2023 Jan;7(1):e203. ncbi.nlm.nih.gov/pmc/articles/PMC10565195/

Community

Publications

  • Chen C, Haddad D, Selsky J, Hoffman JE, Kravitz RL, Estrin DE, Sim I. Making sense of mobile health data: an open architecture to improve individual-and population-level health. Journal of medical Internet research. 2012;14(4):e112. jmir.org/2012/4/e112

Connectivity Map

Publications

Deep Learning

Publications

  • Matias I, Wac K. Dejà vu: Recurrent Neural Networks for health wearables data forecast. In2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) 2022 Dec 12 (pp. 1724-1731). IEEE. ieeexplore.ieee.org/abstract/document/10069171

Digital Phenotyping

Publications

  • Barnett I, Torous J, Staples P, Keshavan M, Onnela JP. Beyond smartphones and sensors: Choosing appropriate statistical methods for the analysis of longitudinal data. Journal of the American Medical Informatics Association. 2018 Dec;25(12):1669-74. academic.oup.com/jamia/article/25/12/1669/5110357
  • Onnela JP, Rauch SL. Harnessing smartphone-based digital phenotyping to enhance behavioral and mental health. Neuropsychopharmacology. 2016 Jun;41(7):1691-6. nature.com/articles/npp20167

Dynamic Systems

Publications

Education

Publications

  • Rose T. The end of average: How to succeed in a world that values sameness. Penguin UK; 2016 Jan 28. toddrose.com/endofaverage

Effect Size + Tau-U (from Kendall’s τ)

Publications

Functional Data Analysis

Publications

Generalization

Publications

Highly-Repeated Within-Person (HRWP)

Publications

  • Whitsett DD, Shoda Y. An approach to test for individual differences in the effects of situations without using moderator variables. Journal of experimental social psychology. 2014 Jan 1;50:94-104. pmc.ncbi.nlm.nih.gov/articles/PMC3925438/
  • Zayas V, Sridharan V, Lee RT, Shoda Y. Addressing two blind spots of commonly used experimental designs: The Highly‐Repeated Within‐Person approach. Social and Personality Psychology Compass. 2019 Sep;13(9):e12487. compass.onlinelibrary.wiley.com/doi/abs/10.1111/spc3.12487

Longitudinal Analysis

Publications

  • Barnett I, Torous J, Staples P, Keshavan M, Onnela JP. Beyond smartphones and sensors: Choosing appropriate statistical methods for the analysis of longitudinal data. Journal of the American Medical Informatics Association. 2018 Dec;25(12):1669-74. academic.oup.com/jamia/article/25/12/1669/5110357
  • Daza EJ. Person as Population: A Longitudinal View of Single-Subject Causal Inference for Analyzing Self-Tracked Health Data. arXiv preprint arXiv:1901.03423. 2019 Jan 10. arxiv.org/abs/1901.03423
  • Lum K, Dunson DB, Johndrow J. Closer than they appear: A Bayesian perspective on individual-level heterogeneity in risk assessment. arXiv preprint arXiv:2102.01135. 2021 Feb 1. arxiv.org/abs/2102.01135
  • Wang E, Cook D, Hyndman RJ. A new tidy data structure to support exploration and modeling of temporal data. Journal of Computational and Graphical Statistics. 2019 Dec 27:1-3. tandfonline.com/doi/abs/10.1080/10618600.2019.1695624

Machine Learning + Artificial Intelligence

Publications

  • Daza EJ. Causal analysis of self-tracked time series data using a counterfactual framework for n-of-1 trials. Methods of Information in Medicine. 2018 Feb;57(01):e10-21. thieme-connect.de/products/ejournals/html/10.3414/ME16-02-0044
    • LaTeX version (identical content with cleanly formatted notation) here
  • Daza EJ, Matias I, Schneider L. Model-Twin Randomization (MoTR) for Estimating One’s Own Recurring Individual Treatment Effect. Statistics in Medicine. 2024 Oct 1 under review. arxiv.org/abs/2208.00739 (2-min explainer video here)
  • Malenica I, Bibaut A, van der Laan MJ. Adaptive Sequential Design for a Single Time-Series. arXiv preprint arXiv:2102.00102. 2021 Jan 29. arxiv.org/abs/2102.00102
  • Matias I, Daza EJ, Wac K. What possibly affects nighttime heart rate? Conclusions from N-of-1 observational data. Digital Health. 2022 Aug 24. journals.sagepub.com/doi/10.1177/20552076221120725
  • Nagesh N, Azimi I, Andriola T, Rahmani AM, Jain R. Towards Deep Personal Lifestyle Models Using Multimodal N-of-1 Data. InInternational Conference on Multimedia Modeling 2023 Jan 9 (pp. 589-600). Cham: Springer International Publishing. link.springer.com/chapter/10.1007/978-3-031-27077-2_46

Masked Visual Analysis

Publications

Measurement Error

Publications

Missing Data

Publications

  • Catellier DJ, Hannan PJ, Murray DM, Addy CL, Conway TL, Yang S, Rice JC. Imputation of missing data when measuring physical activity by accelerometry. Medicine and science in sports and exercise. 2005 Nov;37(11 Suppl):S555. ncbi.nlm.nih.gov/pmc/articles/PMC2435061
  • Chen LT, Feng Y, Wu PJ, Peng CY. Dealing with missing data by EM in single-case studies. Behavior Research Methods. 2020 Feb;52(1):131-50. link.springer.com/article/10.3758/s13428-019-01210-8
  • De TK, Michiels B, Tanious R, Onghena P. Handling missing data in randomization tests for single-case experiments: A simulation study. Behavior Research Methods. 2020 Jan 2:1-6. link.springer.com/article/10.3758/s13428-019-01320-3
  • Fowler C, Cai X, Baker JT, Onnela JP, Valeri L. Testing unit root non-stationarity in the presence of missing data in univariate time series of mobile health studies. arXiv preprint arXiv:2210.04998. 2022 Oct 10. arxiv.org/abs/2210.04998
  • Rowe DA, Mahar MT, Raedeke TD, Lore J. Measuring physical activity in children with pedometers: Reliability, reactivity, and replacement of missing data. Pediatric Exercise Science. 2004 Nov 1;16(4):343-54. researchgate.net/profile/David_Rowe/publication/251259986….pdf

Mobile Devices + Sensors + Wearables

Publications

  • Coravos A, Doerr M, Goldsack J, Manta C, Shervey M, Woods B, Wood WA. Modernizing and designing evaluation frameworks for connected sensor technologies in medicine. npj Digital Medicine. 2020 Mar 13;3(1):1-0. nature.com/articles/s41746-020-0237-3
  • Daza EJ, Matias I, Schneider L. Model-Twin Randomization (MoTR) for Estimating One’s Own Recurring Individual Treatment Effect. Statistics in Medicine. 2024 Oct 1 under review. arxiv.org/abs/2208.00739 (2-min explainer video here)
  • Matias I, Daza EJ, Wac K. What possibly affects nighttime heart rate? Conclusions from N-of-1 observational data. Digital Health. 2022 Aug 24. journals.sagepub.com/doi/10.1177/20552076221120725
  • Nagesh N, Azimi I, Andriola T, Rahmani AM, Jain R. Towards Deep Personal Lifestyle Models Using Multimodal N-of-1 Data. InInternational Conference on Multimedia Modeling 2023 Jan 9 (pp. 589-600). Cham: Springer International Publishing. link.springer.com/chapter/10.1007/978-3-031-27077-2_46

Mobility

Multiple Baseline Designs

Publications

N-of-1 Trials

Publications

Overviews + Commentaries + Perspectives

Publications

Patents

Publications

  • Daza EJ, inventor; Evidation Health Inc, assignee. Methods and systems for generating personalized treatments via causal inference. United States patent application US 18/365,899. 2024 Feb 8. patents.justia.com/patent/20240047042

Quantitative Idiography

Publications

  • Shoda Y, Wilson NL, Chen J, Gilmore AK, Smith RE. Cognitive‐affective processing system analysis of intra‐individual dynamics in collaborative therapeutic assessment: Translating basic theory and research into clinical applications. Journal of personality. 2013 Dec;81(6):554-68. onlinelibrary.wiley.com/doi/abs/10.1111/jopy.12015
  • Shoda Y, Wilson NL, Whitsett DD, Lee-Dussud J, Zayas V. The person as a cognitive-affective processing system: Quantitative ideography as an integral component of cumulative science. psycnet.apa.org/record/2013-35883-021
  • Brady LM, Fryberg SA, Shoda Y. Expanding the interpretive power of psychological science by attending to culture. Proceedings of the National Academy of Sciences. 2018 Nov 6;115(45):11406-13. pnas.org/doi/10.1073/pnas.1803526115

Randomization Tests

Publications

Repeated Measures + Individual Heterogeneity

Publications

  • Araujo A, Julious S, Senn S. Understanding variation in sets of N-of-1 trials. PLoS One. 2016;11(12). ncbi.nlm.nih.gov/pmc/articles/PMC5131970
  • Lum K, Dunson DB, Johndrow J. Closer than they appear: A Bayesian perspective on individual-level heterogeneity in risk assessment. arXiv preprint arXiv:2102.01135. 2021 Feb 1. arxiv.org/abs/2102.01135
  • Matthews J, Altman DG, Campbell MJ, Royston P. Analysis of serial measurements in medical research. British Medical Journal. 1990 Jan 27;300(6719):230-5. bmj.com/content/300/6719/230.short
  • Shaffer JA, Kronish IM, Falzon L, Cheung YK, Davidson KW. N-of-1 randomized intervention trials in health psychology: a systematic review and methodology critique. Annals of Behavioral Medicine. 2018 Sep;52(9):731-42. academic.oup.com/abm/article/52/9/731/4837296

Sample Size + Statistical Power

Publications

  • Blackston JW, Chapple AG, McGree JM, McDonald S, Nikles J. Comparison of Aggregated N-of-1 Trials with Parallel and Crossover Randomized Controlled Trials Using Simulation Studies. Healthcare 2019 Dec (Vol. 7, No. 4, p. 137). Multidisciplinary Digital Publishing Institute. mdpi.com/2227-9032/7/4/137
  • Bouwmeester S, and Jongerling J. Power of a randomization test in a single case multiple baseline AB design. PLOS ONE. 2020 Feb 6;15(2):e0228355. journals.plos.org/plosone/article?id=10.1371/journal.pone.0228355
  • Ferron J, Onghena P. The power of randomization tests for single-case phase designs. The Journal of Experimental Education. 1996 Apr 1;64(3):231-9. tandfonline.com/doi/abs/10.1080/00220973.1996.9943805
  • Hedges, LV, Shadish, WR & Natesan Batley, P. Power analysis for single-case designs: Computations for (AB)k designs. Behav Res (2022). doi.org/10.3758/s13428-022-01971-9
  • Percha B, Baskerville EB, Johnson M, Dudley JT, Zimmerman N. Designing Robust N-of-1 studies for precision medicine: Simulation study and design recommendations. Journal of Medical Internet Research. 2019;21(4):e12641. jmir.org/2019/4/e12641/
  • Senn S. Sample size considerations for n-of-1 trials. Statistical methods in medical research. 2019 Feb;28(2):372-83. journals.sagepub.com/doi/abs/10.1177/0962280217726801
  • Wang Y, Schork NJ. Power and Design Issues in Crossover-Based N-Of-1 Clinical Trials with Fixed Data Collection Periods. Healthcare 2019 Sep (Vol. 7, No. 3, p. 84). Multidisciplinary Digital Publishing Institute. mdpi.com/2227-9032/7/3/84

Self-Controlled Case Series

Publications

Single-Case Experimental Designs (SCEDs) (SCDs) + Single-Subject Research/Designs

Publications

Small Data

Software Tools

Publications

Special Issues + Collections

Publications

  • Davidson K, Cheung K, Friel C, Suls J. Introducing Data Sciences to N-of-1 Designs, Statistics, Use-Cases, the Future, and the Moniker ‘N-of-1’Trial. Harvard Data Science Review. hdsr.mitpress.mit.edu/specialissue3 + hdsr.mitpress.mit.edu/pub/pv2y77y3/release/1?readingCollection=c31cf5ee
  • Nikles J, McDonald S (editors). Special Issue: N-of-1 Trials in Healthcare. Healthcare. 2019 Jul - 2020 Feb. 13 articles. mdpi.com/journal/healthcare/special_issues/N_of_1
  • Nikles J, Daza EJ, McDonald S, Hekler E, Schork NJ (editors). Research Topic: Creating Evidence from Real World Patient Digital Data. Frontiers in Computer Science (Digital Public Health), Frontiers in Psychiatry (Social Psychiatry and Psychiatric Rehabilitation, Public Mental Health), Frontiers in Psychology (Health Psychology, Psychology for Clinical Settings), Frontiers in Digital Health (Connected Health, Personalized Medicine, Health Informatics), Frontiers in Neurology (Neurorehabilitation), Frontiers in Public Health (Public Mental Health, Digital Public Health), Frontiers in Sociology (Public Mental Health). 2020 Feb - 2020 Oct. 13 articles. frontiersin.org/research-topics/10089

Sports + Athletic Performance

Publications

  • Nakayama S, Trejo-Silva A, Gomez-Ruano MA, Aida H. Impact of a Ball Regulation Change on Game Performance and Shooting Play in Japanese U-15 Girls’ Handball. Perceptual and Motor Skills. 2024 Sep 19:00315125241274215. journals.sagepub.com/doi/abs/10.1177/00315125241274215
  • Tweedy S, Dutia IM, Cairney J, Beckman E. Single case experimental design: a rigorous method for addressing inequity and enhancing precision within Para sport and exercise medicine research. British Journal of Sports Medicine. 2024 Sep 19. bjsm.bmj.com/content/early/2024/09/19/bjsports-2024-108587

Statistical Considerations + Good Statistical Practice

Publications

Study Design, Analysis, Reporting

Publications

Switchback Experiments

Publications

Systematic Reviews

Publications

  • Gabler NB, Duan N, Vohra S, Kravitz RL. N-of-1 trials in the medical literature: a systematic review. Medical care. 2011 Aug 1:761-8. jstor.org/stable/23053842
  • Müller AR, Brands MM, van de Ven PM, Roes KC, Cornel MC, van Karnebeek CD, Wijburg FA, Daams JG, Boot E, van Eeghen AM. Systematic review of N-of-1 studies in rare genetic neurodevelopmental disorders: the power of 1. Neurology. 2021 Mar 16;96(11):529-40. neurology.org/doi/full/10.1212/WNL.0000000000011597
  • Shaffer JA, Kronish IM, Falzon L, Cheung YK, Davidson KW. N-of-1 randomized intervention trials in health psychology: a systematic review and methodology critique. Annals of Behavioral Medicine. 2018 Sep;52(9):731-42. academic.oup.com/abm/article/52/9/731/4837296
  • Smith JD. Single-case experimental designs: a systematic review of published research and current standards. Psychological methods. 2012 Dec;17(4):510. ncbi.nlm.nih.gov/pmc/articles/PMC3652808

Time Series

Publications

  • Beltz AM, Wright AG, Sprague BN, Molenaar PC. Bridging the nomothetic and idiographic approaches to the analysis of clinical data. Assessment. 2016 Aug;23(4):447-58. journals.sagepub.com/doi/abs/10.1177/1073191116648209
  • Bojinov I, Shephard N. Time series experiments and causal estimands: exact randomization tests and trading. Journal of the American Statistical Association. 2019 Oct 2;114(528):1665-82. tandfonline.com/doi/abs/10.1080/01621459.2018.1527225
  • Bojinov I, Simchi-Levi D, Zhao J. Design and analysis of switchback experiments. Management Science. 2023 Jul;69(7):3759-77. pubsonline.informs.org/doi/abs/10.1287/mnsc.2022.4583
  • Cai X, Zeng L, Fowler C, Dixon L, Ongur D, Baker JT, Onnela JP, Valeri L. Causal estimands and identification of time-varying effects in non-stationary time series from N-of-1 mobile device data. arXiv preprint arXiv:2407.17666. 2024 Jul 24. arxiv.org/abs/2407.17666
  • Daza EJ. Person as Population: A Longitudinal View of Single-Subject Causal Inference for Analyzing Self-Tracked Health Data. arXiv preprint arXiv:1901.03423. 2019 Jan 10. arxiv.org/abs/1901.03423
  • Daza EJ. Causal analysis of self-tracked time series data using a counterfactual framework for n-of-1 trials. Methods of Information in Medicine. 2018 Feb;57(01):e10-21. thieme-connect.de/products/ejournals/html/10.3414/ME16-02-0044
    • LaTeX version (identical content with cleanly formatted notation) here
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