{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T01:51:56Z","timestamp":1788227516318,"version":"build-2803163510"},"reference-count":58,"publisher":"The Angle Orthodontist (EH Angle Education & Research Foundation)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To suggest a sample size calculation method to develop artificial intelligence (AI) that can predict soft tissue changes after orthognathic surgery with clinically acceptable accuracy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>From data collected from 705 patients who had undergone combined surgical-orthodontic treatment, 10 subsets of the data were generated through random resampling procedures, specifically with reduced data sizes of 75, 100, 150, 200, 300, 400, 450, 500, 600, and 700. Resampling was repeated four times, and each subset was used to create a total of 40 AI models using a deep-learning algorithm. The prediction results for soft tissue change after orthognathic surgery were compared across all 40 AI models based on their sample sizes. Clinically acceptable accuracy was set as a 1.5-mm prediction error. The predictive performance of AI models was evaluated on the lower lip, which was selected as a primary outcome variable and a benchmark landmark. Linear regression analysis was conducted to estimate the relationship between sample size and prediction error.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The prediction error decreased with increasing sample size. A sample size greater than 1700 datasets was estimated as being required for the development of an AI model with a prediction error &lt; 1.5 mm at the lower lip area.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>A fairly large quantity of orthognathic surgery data seemed to be necessary to develop software programs for visualizing surgical treatment objectives with clinically acceptable accuracy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2319\/010125-1","type":"journal-article","created":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T00:30:44Z","timestamp":1756427444000},"page":"467-473","source":"Crossref","is-referenced-by-count":3,"title":["What amount of data is required to develop artificial intelligence that can accurately predict soft tissue changes after orthognathic surgery?"],"prefix":"10.2319","volume":"95","author":[{"given":"Jong-Hak","family":"Kim","sequence":"first","affiliation":[{"name":"Graduate Student (Ph.D), Department of Orthodontics, Seoul National University, Seoul, Korea."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naeun","family":"Kwon","sequence":"additional","affiliation":[{"name":"Graduate Student (Ph.D), Department of Orthodontics, Seoul National University, Seoul, Korea."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ji-Ae","family":"Park","sequence":"additional","affiliation":[{"name":"Clinical Lecturer, Department of Orthodontics, Seoul National University Dental Hospital, Seoul, Korea."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sung Bin","family":"Youn","sequence":"additional","affiliation":[{"name":"Assistant Professor, Center for Orthognathic and Facial Contouring Surgery, Seoul National University Dental Hospital, Seoul, Korea."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Byoung-Moo","family":"Seo","sequence":"additional","affiliation":[{"name":"Professor, Department of Oral and Maxillofacial Surgery, Seoul National University School of Dentistry, Seoul, Korea."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shin-Jae","family":"Lee","sequence":"additional","affiliation":[{"name":"Professor, Department of Orthodontics and Dental Research Institute, Seoul National University School of Dentistry, Seoul, Korea."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1164","reference":[{"key":"i1945-7103-95-5-467-b01","series-title":"J Oral Maxillofac Surg","first-page":"1402","article-title":"Does the sequence of bimaxillary orthognathic surgery affect accuracy in Skeletal Class Iii patients","volume":"82","author":"Youn","year":"2024","unstructured":"Youn SB,\nOh HJ,\nSon IS,\nLee SJ,\nSohn HB,\nSeo BM. Does the sequence of bimaxillary orthognathic surgery affect accuracy in Skeletal Class Iii patients? J Oral Maxillofac Surg. 2024;82:1402\u20131415."},{"key":"i1945-7103-95-5-467-b02","series-title":"J Craniomaxillofac Surg","first-page":"387","article-title":"Effect of maxillary impaction on mandibular surgical accuracy in virtually-planned orthognathic surgery: a retrospective study","volume":"51","author":"Oh","year":"2023","unstructured":"Oh HJ,\nSon IS,\nLee SJ,\nSohn HB,\nSeo BM. Effect of maxillary impaction on mandibular surgical accuracy in virtually-planned orthognathic surgery: a retrospective study. J Craniomaxillofac Surg. 2023;51:387\u2013392."},{"key":"i1945-7103-95-5-467-b03","series-title":"J Oral Maxillofac Surg","first-page":"1146.e1\u20131146","article-title":"Virtually-planned orthognathic surgery achieves an accurate condylar position","volume":"79","author":"Oh","year":"2021","unstructured":"Oh HJ,\nMoon JH,\nHa H, et al. Virtually-planned orthognathic surgery achieves an accurate condylar position. J Oral Maxillofac Surg. 2021;79:1146.e1\u20131146.e25."},{"key":"i1945-7103-95-5-467-b04","series-title":"Angle Orthod","first-page":"549","article-title":"Does artificial intelligence predict orthognathic surgical outcomes better than conventional linear regression methods","volume":"94","author":"Park","year":"2024","unstructured":"Park JA,\nMoon JH,\nLee JM, et al. Does artificial intelligence predict orthognathic surgical outcomes better than conventional linear regression methods? Angle Orthod. 2024;94:549\u2013556."},{"key":"i1945-7103-95-5-467-b05","series-title":"Angle Orthod","first-page":"910","article-title":"Predicting soft tissue changes after orthognathic surgery: the sparse partial least squares method","volume":"89","author":"Suh","year":"2019","unstructured":"Suh HY,\nLee HJ,\nLee YS,\nEo SH,\nDonatelli RE,\nLee SJ. Predicting soft tissue changes after orthognathic surgery: the sparse partial least squares method. Angle Orthod. 2019;89:910\u2013916."},{"key":"i1945-7103-95-5-467-b06","series-title":"Am J Orthod Dentofacial Orthop","first-page":"724","article-title":"A more accurate soft-tissue prediction model for Class III 2-jaw surgeries","volume":"146","author":"Lee","year":"2014","unstructured":"Lee YS,\nSuh HY,\nLee SJ,\nDonatelli RE. A more accurate soft-tissue prediction model for Class III 2-jaw surgeries. Am J Orthod Dentofacial Orthop. 2014;146:724\u2013733."},{"key":"i1945-7103-95-5-467-b07","series-title":"Angle Orthod","first-page":"322","article-title":"A better statistical method of predicting postsurgery soft tissue response in Class II patients","volume":"84","author":"Lee","year":"2014","unstructured":"Lee HJ,\nSuh HY,\nLee YS, et al. A better statistical method of predicting postsurgery soft tissue response in Class II patients. Angle Orthod. 2014;84:322\u2013328."},{"key":"i1945-7103-95-5-467-b08","series-title":"J Oral Maxillofac Surg","first-page":"e553\u2013562","article-title":"A more accurate method of predicting soft tissue changes after mandibular setback surgery","volume":"70","author":"Suh","year":"2012","unstructured":"Suh HY,\nLee SJ,\nLee YS, et al. A more accurate method of predicting soft tissue changes after mandibular setback surgery. J Oral Maxillofac Surg. 2012;70:e553\u2013562."},{"key":"i1945-7103-95-5-467-b09","series-title":"IEEE Access","first-page":"36636","article-title":"Partial least squares regression trees for multivariate response data with multicollinear predictors","volume":"12","author":"Yu","year":"2024","unstructured":"Yu W,\nLee SJ,\nCho H. Partial least squares regression trees for multivariate response data with multicollinear predictors. IEEE Access. 2024;12:36636\u201336644."},{"key":"i1945-7103-95-5-467-b10","series-title":"Commun Stat Appl Methods","first-page":"65","article-title":"Modified partial least squares method implementing mixed-effect model","volume":"30","author":"Kim","year":"2023","unstructured":"Kim K,\nLee SJ,\nEo SH,\nCho SJ,\nLee JW. Modified partial least squares method implementing mixed-effect model. Commun Stat Appl Methods. 2023;30:65\u201373."},{"key":"i1945-7103-95-5-467-b11","series-title":"Angle Orthod","first-page":"380","article-title":"Ethnic and ethical challenges in treatment planning: dealing with diversity in the 21st century","volume":"84","author":"Haskell","year":"2014","unstructured":"Haskell BS,\nSegal ES. Ethnic and ethical challenges in treatment planning: dealing with diversity in the 21st century. Angle Orthod. 2014;84:380\u2013382."},{"key":"i1945-7103-95-5-467-b12","series-title":"Angle Orthod","first-page":"595","article-title":"Evaluation of automated photograph-cephalogram image integration using artificial intelligence models","volume":"94","author":"Moon","year":"2024","unstructured":"Moon JH,\nKim MG,\nCho SJ, et al. Evaluation of automated photograph-cephalogram image integration using artificial intelligence models. Angle Orthod. 2024;94:595\u2013601."},{"key":"i1945-7103-95-5-467-b13","series-title":"Angle Orthod","first-page":"329","article-title":"Evaluation of automated cephalometric analysis based on the latest deep learning method","volume":"91","author":"Hwang","year":"2021","unstructured":"Hwang HW,\nMoon JH,\nKim MG,\nDonatelli RE,\nLee SJ. Evaluation of automated cephalometric analysis based on the latest deep learning method. 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Angle Orthod. 2019;89:903\u2013909."},{"key":"i1945-7103-95-5-467-b16","series-title":"Angle Orthod","first-page":"557","article-title":"Orthodontic treatment outcome predictive performance differences between artificial intelligence and conventional methods","volume":"94","author":"Cho","year":"2024","unstructured":"Cho SJ,\nMoon JH,\nKo DY, et al. Orthodontic treatment outcome predictive performance differences between artificial intelligence and conventional methods. Angle Orthod. 2024;94:557\u2013565."},{"key":"i1945-7103-95-5-467-b17","series-title":"Angle Orthod","first-page":"219","article-title":"Craniofacial growth prediction models based on cephalometric landmarks in Korean and American children","volume":"95","author":"Kim","year":"2025","unstructured":"Kim JH,\nMoon JH,\nRoseth J,\nSuh H,\nOh H,\nLee SJ. Craniofacial growth prediction models based on cephalometric landmarks in Korean and American children. Angle Orthod. 2025;95:219\u2013226"},{"key":"i1945-7103-95-5-467-b18","series-title":"A cephalometric example. Angle Orthod","first-page":"823","article-title":"How much deep learning is enough for automatic identification to be reliable","volume":"90","author":"Moon","year":"2020","unstructured":"Moon JH,\nHwang HW,\nYu Y,\nKim MG,\nDonatelli RE,\nLee SJ. How much deep learning is enough for automatic identification to be reliable? A cephalometric example. Angle Orthod. 2020;90:823\u2013830."},{"issue":"Suppl 2","key":"i1945-7103-95-5-467-b19","series-title":"Orthod Craniofac Res","first-page":"6","article-title":"Factors influencing the development of artificial intelligence in orthodontics","volume":"27","author":"Lee","year":"2024","unstructured":"Lee JM,\nMoon JH,\nPark JA,\nKim JH,\nLee SJ. Factors influencing the development of artificial intelligence in orthodontics. Orthod Craniofac Res. 2024;27 Suppl 2:6\u201312."},{"key":"i1945-7103-95-5-467-b20","series-title":"Am J Orthod Dentofacial Orthop","first-page":"616","article-title":"Sample size calculation for an artificial intelligence study","volume":"167","author":"Kim","year":"2025","unstructured":"Kim JH,\nKwon N,\nPandis N,\nLee SJ. Sample size calculation for an artificial intelligence study. Am J Orthod Dentofacial Orthop. 2025;167:616\u2013620."},{"key":"i1945-7103-95-5-467-b21","first-page":"6679","article-title":"TabNet: attentive interpretable tabular learning","volume":"35","author":"Arik","year":"2021","unstructured":"Arik S\u00d6,\nPfister T. TabNet: attentive interpretable tabular learning. 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Comparison of individualized facial growth prediction models using artificial intelligence and partial least squares based on the Mathews growth collection. Angle Orthod. 2025;95:249\u2013258."},{"key":"i1945-7103-95-5-467-b24","series-title":"Semin Orthod","first-page":"45","article-title":"Reliability statistics every orthodontist should know","volume":"30","author":"Moon","year":"2024","unstructured":"Moon JH,\nLee JM,\nPark JA,\nSuh H,\nLee SJ. Reliability statistics every orthodontist should know. Semin Orthod. 2024;30:45\u201349."},{"key":"i1945-7103-95-5-467-b25","article-title":"R: A language and environment for statistical computing","author":"Development Core Team","year":"2025","unstructured":"Development Core Team R. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2025. https:\/\/www.R-project.org\/"},{"key":"i1945-7103-95-5-467-b26","series-title":"Biostatistics: The Bare Essentials","author":"Norman","year":"1994","unstructured":"Norman GR,\nStreiner DL. Biostatistics: The Bare Essentials. St. Louis, Missouri: Mosby Year Book; 1994."},{"key":"i1945-7103-95-5-467-b27","series-title":"The Elements of Statistical Learning: Data Mining, Inference, and Prediction","volume":"2","author":"Hastie","year":"2009","unstructured":"Hastie T,\nTibshirani R,\nFriedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Vol 2. 2nd ed. New York, NY: Springer; 2009."},{"key":"i1945-7103-95-5-467-b28","series-title":"Angle Orthod","first-page":"207","article-title":"Comparison of individualized facial growth prediction models based on the partial least squares and artificial intelligence","volume":"94","author":"Moon","year":"2024","unstructured":"Moon JH,\nShin HK,\nLee JM, et al. Comparison of individualized facial growth prediction models based on the partial least squares and artificial intelligence. Angle Orthod. 2024;94:207\u2013215."},{"key":"i1945-7103-95-5-467-b29","series-title":"Am J Orthod Dentofacial Orthop","first-page":"519","article-title":"Sample calculations for comparison of 2 means","volume":"141","author":"Pandis","year":"2012","unstructured":"Pandis N. Sample calculations for comparison of 2 means. Am J Orthod Dentofacial Orthop. 2012;141:519\u2013521."},{"key":"i1945-7103-95-5-467-b01","series-title":"J Oral Maxillofac Surg","first-page":"1402","article-title":"Does the sequence of bimaxillary orthognathic surgery affect accuracy in Skeletal Class Iii patients","volume":"82","author":"Youn","year":"2024","unstructured":"Youn SB,\nOh HJ,\nSon IS,\nLee SJ,\nSohn HB,\nSeo BM. Does the sequence of bimaxillary orthognathic surgery affect accuracy in Skeletal Class Iii patients? J Oral Maxillofac Surg. 2024;82:1402\u20131415."},{"key":"i1945-7103-95-5-467-b02","series-title":"J Craniomaxillofac Surg","first-page":"387","article-title":"Effect of maxillary impaction on mandibular surgical accuracy in virtually-planned orthognathic surgery: a retrospective study","volume":"51","author":"Oh","year":"2023","unstructured":"Oh HJ,\nSon IS,\nLee SJ,\nSohn HB,\nSeo BM. Effect of maxillary impaction on mandibular surgical accuracy in virtually-planned orthognathic surgery: a retrospective study. J Craniomaxillofac Surg. 2023;51:387\u2013392."},{"key":"i1945-7103-95-5-467-b03","series-title":"J Oral Maxillofac Surg","first-page":"1146.e1\u20131146","article-title":"Virtually-planned orthognathic surgery achieves an accurate condylar position","volume":"79","author":"Oh","year":"2021","unstructured":"Oh HJ,\nMoon JH,\nHa H, et al. Virtually-planned orthognathic surgery achieves an accurate condylar position. J Oral Maxillofac Surg. 2021;79:1146.e1\u20131146.e25."},{"key":"i1945-7103-95-5-467-b04","series-title":"Angle Orthod","first-page":"549","article-title":"Does artificial intelligence predict orthognathic surgical outcomes better than conventional linear regression methods","volume":"94","author":"Park","year":"2024","unstructured":"Park JA,\nMoon JH,\nLee JM, et al. Does artificial intelligence predict orthognathic surgical outcomes better than conventional linear regression methods? Angle Orthod. 2024;94:549\u2013556."},{"key":"i1945-7103-95-5-467-b05","series-title":"Angle Orthod","first-page":"910","article-title":"Predicting soft tissue changes after orthognathic surgery: the sparse partial least squares method","volume":"89","author":"Suh","year":"2019","unstructured":"Suh HY,\nLee HJ,\nLee YS,\nEo SH,\nDonatelli RE,\nLee SJ. Predicting soft tissue changes after orthognathic surgery: the sparse partial least squares method. Angle Orthod. 2019;89:910\u2013916."},{"key":"i1945-7103-95-5-467-b06","series-title":"Am J Orthod Dentofacial Orthop","first-page":"724","article-title":"A more accurate soft-tissue prediction model for Class III 2-jaw surgeries","volume":"146","author":"Lee","year":"2014","unstructured":"Lee YS,\nSuh HY,\nLee SJ,\nDonatelli RE. A more accurate soft-tissue prediction model for Class III 2-jaw surgeries. Am J Orthod Dentofacial Orthop. 2014;146:724\u2013733."},{"key":"i1945-7103-95-5-467-b07","series-title":"Angle Orthod","first-page":"322","article-title":"A better statistical method of predicting postsurgery soft tissue response in Class II patients","volume":"84","author":"Lee","year":"2014","unstructured":"Lee HJ,\nSuh HY,\nLee YS, et al. A better statistical method of predicting postsurgery soft tissue response in Class II patients. Angle Orthod. 2014;84:322\u2013328."},{"key":"i1945-7103-95-5-467-b08","series-title":"J Oral Maxillofac Surg","first-page":"e553\u2013562","article-title":"A more accurate method of predicting soft tissue changes after mandibular setback surgery","volume":"70","author":"Suh","year":"2012","unstructured":"Suh HY,\nLee SJ,\nLee YS, et al. A more accurate method of predicting soft tissue changes after mandibular setback surgery. J Oral Maxillofac Surg. 2012;70:e553\u2013562."},{"key":"i1945-7103-95-5-467-b09","series-title":"IEEE Access","first-page":"36636","article-title":"Partial least squares regression trees for multivariate response data with multicollinear predictors","volume":"12","author":"Yu","year":"2024","unstructured":"Yu W,\nLee SJ,\nCho H. Partial least squares regression trees for multivariate response data with multicollinear predictors. IEEE Access. 2024;12:36636\u201336644."},{"key":"i1945-7103-95-5-467-b10","series-title":"Commun Stat Appl Methods","first-page":"65","article-title":"Modified partial least squares method implementing mixed-effect model","volume":"30","author":"Kim","year":"2023","unstructured":"Kim K,\nLee SJ,\nEo SH,\nCho SJ,\nLee JW. Modified partial least squares method implementing mixed-effect model. Commun Stat Appl Methods. 2023;30:65\u201373."},{"key":"i1945-7103-95-5-467-b11","series-title":"Angle Orthod","first-page":"380","article-title":"Ethnic and ethical challenges in treatment planning: dealing with diversity in the 21st century","volume":"84","author":"Haskell","year":"2014","unstructured":"Haskell BS,\nSegal ES. Ethnic and ethical challenges in treatment planning: dealing with diversity in the 21st century. Angle Orthod. 2014;84:380\u2013382."},{"key":"i1945-7103-95-5-467-b12","series-title":"Angle Orthod","first-page":"595","article-title":"Evaluation of automated photograph-cephalogram image integration using artificial intelligence models","volume":"94","author":"Moon","year":"2024","unstructured":"Moon JH,\nKim MG,\nCho SJ, et al. Evaluation of automated photograph-cephalogram image integration using artificial intelligence models. Angle Orthod. 2024;94:595\u2013601."},{"key":"i1945-7103-95-5-467-b13","series-title":"Angle Orthod","first-page":"329","article-title":"Evaluation of automated cephalometric analysis based on the latest deep learning method","volume":"91","author":"Hwang","year":"2021","unstructured":"Hwang HW,\nMoon JH,\nKim MG,\nDonatelli RE,\nLee SJ. Evaluation of automated cephalometric analysis based on the latest deep learning method. Angle Orthod. 2021;91:329\u2013335."},{"key":"i1945-7103-95-5-467-b14","series-title":"Angle Orthod","first-page":"69","article-title":"Automated identification of cephalometric landmarks: Part 2-Might it be better than human","volume":"90","author":"Hwang","year":"2020","unstructured":"Hwang HW,\nPark JH,\nMoon JH, et al. Automated identification of cephalometric landmarks: Part 2-Might it be better than human? Angle Orthod. 2020;90:69\u201376."},{"key":"i1945-7103-95-5-467-b15","series-title":"Angle Orthod","first-page":"903","article-title":"Automated identification of cephalometric landmarks: Part 1-Comparisons between the latest deep-learning methods YOLOV3 and SSD","volume":"89","author":"Park","year":"2019","unstructured":"Park JH,\nHwang HW,\nMoon JH, et al. Automated identification of cephalometric landmarks: Part 1-Comparisons between the latest deep-learning methods YOLOV3 and SSD. Angle Orthod. 2019;89:903\u2013909."},{"key":"i1945-7103-95-5-467-b16","series-title":"Angle Orthod","first-page":"557","article-title":"Orthodontic treatment outcome predictive performance differences between artificial intelligence and conventional methods","volume":"94","author":"Cho","year":"2024","unstructured":"Cho SJ,\nMoon JH,\nKo DY, et al. Orthodontic treatment outcome predictive performance differences between artificial intelligence and conventional methods. Angle Orthod. 2024;94:557\u2013565."},{"key":"i1945-7103-95-5-467-b17","series-title":"Angle Orthod","first-page":"219","article-title":"Craniofacial growth prediction models based on cephalometric landmarks in Korean and American children","volume":"95","author":"Kim","year":"2025","unstructured":"Kim JH,\nMoon JH,\nRoseth J,\nSuh H,\nOh H,\nLee SJ. Craniofacial growth prediction models based on cephalometric landmarks in Korean and American children. Angle Orthod. 2025;95:219\u2013226"},{"key":"i1945-7103-95-5-467-b18","series-title":"A cephalometric example. Angle Orthod","first-page":"823","article-title":"How much deep learning is enough for automatic identification to be reliable","volume":"90","author":"Moon","year":"2020","unstructured":"Moon JH,\nHwang HW,\nYu Y,\nKim MG,\nDonatelli RE,\nLee SJ. How much deep learning is enough for automatic identification to be reliable? A cephalometric example. Angle Orthod. 2020;90:823\u2013830."},{"issue":"Suppl 2","key":"i1945-7103-95-5-467-b19","series-title":"Orthod Craniofac Res","first-page":"6","article-title":"Factors influencing the development of artificial intelligence in orthodontics","volume":"27","author":"Lee","year":"2024","unstructured":"Lee JM,\nMoon JH,\nPark JA,\nKim JH,\nLee SJ. Factors influencing the development of artificial intelligence in orthodontics. Orthod Craniofac Res. 2024;27 Suppl 2:6\u201312."},{"key":"i1945-7103-95-5-467-b20","series-title":"Am J Orthod Dentofacial Orthop","first-page":"616","article-title":"Sample size calculation for an artificial intelligence study","volume":"167","author":"Kim","year":"2025","unstructured":"Kim JH,\nKwon N,\nPandis N,\nLee SJ. Sample size calculation for an artificial intelligence study. 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Semin Orthod. 2024;30:45\u201349."},{"key":"i1945-7103-95-5-467-b25","article-title":"R: A language and environment for statistical computing","author":"Development Core Team","year":"2025","unstructured":"Development Core Team R. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2025. https:\/\/www.R-project.org\/"},{"key":"i1945-7103-95-5-467-b26","series-title":"Biostatistics: The Bare Essentials","author":"Norman","year":"1994","unstructured":"Norman GR,\nStreiner DL. Biostatistics: The Bare Essentials. St. Louis, Missouri: Mosby Year Book; 1994."},{"key":"i1945-7103-95-5-467-b27","series-title":"The Elements of Statistical Learning: Data Mining, Inference, and Prediction","volume":"2","author":"Hastie","year":"2009","unstructured":"Hastie T,\nTibshirani R,\nFriedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Vol 2. 2nd ed. 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