×

Adaptive data collection for intraindividual studies affected by adherence. (English) Zbl 1541.62313

MSC:

62P10 Applications of statistics to biology and medical sciences; meta analysis

References:

[1] Ahn, W. Y., Dai, J., Vassileva, J., Busemeyer, J. R., & Stout, J. C. (2016). Computational modeling for addiction medicine. In H.Ekhtiari (ed.) and M. P.Paulus (ed.) (Eds.), Progress in brain research (vol. 224, pp. 53-65). Elsevier. https://linkinghub.elsevier.com/retrieve/pii/S0079612315001387
[2] Brunner, E., Bathke, A. C., & Konietschke, F. (2018). Rank and pseudo‐rank procedures for independent observations in factorial designs: Using R and SAS, 1st ed. Springer Series in Statistics. Springer, 2018. · Zbl 1455.62001
[3] Cavagnaro, D. R., Myung, J. I., Pitt, M. A., & Kujala, J. V. (2010). Adaptive design optimization: A mutual information‐based approach to model discrimination in cognitive science. Neural Computation, 22(4), 887-905. https://www.mitpressjournals.org/doi/abs/10.1162/neco.2009.02‐09‐959 · Zbl 1200.68182
[4] Chaloner, K., & Verdinelli, I. (1995). Bayesian experimental design: A review. Statistical Science, 10(3), 273-304. · Zbl 0955.62617
[5] Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3), 1-58. https://dl.acm.org/doi/10.1145/1541880.1541882
[6] deVries, L. P., Baselmans, B. M. L., & Bartels, M. (2021). Smartphone‐based ecological momentary assessment of well‐being: A systematic review and recommendations for future studies. Journal of Happiness Studies, 22(5), 2361-2408. https://link.springer.com/10.1007/s10902‐020‐00324‐7
[7] Fricker, R. D., Hegler, B. L., & Dunfee, D. A. (2008). Comparing syndromic surveillance detection methods: EARS’versus a CUSUM‐based methodology. Statistics in Medicine, 27(17), 3407-3429. https://onlinelibrary.wiley.com/doi/10.1002/sim.3197
[8] Hekler, E. B., Rivera, D. E., Martin, C. A., Phatak, S. S., Freigoun, M. T., Korinek, E., Klasnja, P., Adams, M. A., & Buman, M. P. (2018). Tutorial for using control systems engineering to optimize adaptive mobile health interventions. Journal of Medical Internet Research, 20(6), e214. http://www.jmir.org/2018/6/e214/
[9] Hulme, W. J., Martin, G. P., Sperrin, M., Casson, A. J., Bucci, S., Lewis, S., & Peek, N. (2021). Adaptive symptom monitoring using hidden Markov models ‐ An application in ecological momentary assessment. IEEE Journal of Biomedical and Health Informatics, 25(5), 1770-1780. https://ieeexplore.ieee.org/document/9226077/
[10] Koizumi, D., Matsuda, T., & Sonoda, M. (2012). On the automatic detection algorithm of cross site scripting (xss) with the non‐stationary bernoulli distribution. In The 5th International Conference on Communications, Computers and Applications (MIC‐CCA2012) (pp. 131-135).
[11] Liu, H., Xie, Q. W., & Lou, V. W. Q. (2019). Everyday social interactions and intra-individual variability in affect: A systematic review and meta-analysis of ecological momentary assessment studies. Motivation and Emotion, 43(2), 339-353.
[12] MacDonald, S. W., Nyberg, L., & Bäckman, L. (2006). Intra‐individual variability in behavior: Links to brain structure, neurotransmission and neuronal activity. Trends in Neurosciences, 29(8), 474-480. https://linkinghub.elsevier.com/retrieve/pii/S0166223606001251
[13] Mohan, S. (2021). Exploring the role of common model of cognition in designing adaptive coaching interactions for health behavior change. ACM Transactions on Interactive Intelligent Systems, 11(1), 1-30. https://dl.acm.org/doi/10.1145/3375790
[14] Probst, T., Pryss, R., Langguth, B., & Schlee, W. (2016). Emotional states as mediators between tinnitus loudness and tinnitus distress in daily life: Results from the “TrackYourTinnitus” application. Scientific Reports, 6(1), 20382. https://www.nature.com/articles/srep20382
[15] Russell, M. A., & Gajos, J. M. (2020). Annual Research Review: Ecological momentary assessment studies in child psychology and psychiatry. Journal of Child Psychology and Psychiatry, 61(3), 376-394.
[16] Sievert, C. (2020). Interactive web‐based data visualization with R, plotly, and shiny. Chapman & Hall/CRC The R series. CRC Press, Taylor and Francis Group.
[17] Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing & Management, 45(4), 427-437. https://linkinghub.elsevier.com/retrieve/pii/S0306457309000259
[18] Sottas, P.‐E., Robinson, N., Rabin, O., & Saugy, M. (2011). The athlete biological passport. Clinical Chemistry, 57(7), 969-976. https://academic.oup.com/clinchem/article/57/7/969/5621042
[19] Sylvester, R. J. (1988). A Bayesian approach to the design of phase II clinical trials. Biometrics, 44(3), 823-836. https://www.jstor.org/stable/2531594?origin=crossref · Zbl 0715.62243
[20] Team, R. C. (2021). R: A language and environment for statistical computing.
[21] Thomas, J. G., & Bond, D. S. (2015). Behavioral response to a just‐in‐time adaptive intervention (JITAI) to reduce sedentary behavior in obese adults: Implications for JITAI optimization. Health Psychology, 34, 1261-1267. http://doi.apa.org/getdoi.cfm?doi=10.1037/hea0000304
[22] Zhang, L., Monacelli, G., Vashisht, H., Schlee, W., Langguth, B., & Ward, T. (2022). The effects of tinnitus in probabilistic learning tasks: Protocol for an ecological momentary assessment study. JMIR Research Protocols, 11(11), e36583. https://www.researchprotocols.org/2022/11/e36583
[23] Zimek, A., Schubert, E., & Kriegel, H.‐P. (2012). A survey on unsupervised outlier detection in high‐dimensional numerical data. Statistical Analysis and Data Mining, 5(5), 363-387. https://onlinelibrary.wiley.com/doi/10.1002/sam.11161 · Zbl 07260336
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.