2 recherche sur le mot-clé 'propensity score matching'

Assisted reproductive technology has no association with autism spectrum disorders: The Taiwan Birth Cohort Study / For-Wey LUNG in Autism, 22-3 (April 2018)  

Public ISBD [article]  inAutism > 22-3 (April 2018) . - p.377-384 Titre : Assisted reproductive technology has no association with autism spectrum disorders: The Taiwan Birth Cohort Study Type de document : texte imprimé Auteurs : For-Wey LUNG, Auteur ; Tung-Liang CHIANG, Auteur ; Shio-Jean LIN, Auteur ; Meng-Chih LEE, Auteur ; Bih-Ching SHU, Auteur Article en page(s) : p.377-384 Langues : Anglais (eng) Mots-clés : Taiwan Birth Cohort Study  assisted reproductive technology  autism spectrum disorder  low birth weight  propensity score matching Index. décimale : PER Périodiques Résumé : The use of assisted reproduction technology has increased over the last two decades. Autism spectrum disorders and assisted reproduction technology share many risk factors. However, previous studies on the association between autism spectrum disorders and assisted reproduction technology have shown inconsistent results. The purpose of this study was to investigate the association between assisted reproduction technology and autism spectrum disorder diagnosis in a national birth cohort database. Furthermore, the results from the assisted reproduction technology and autism spectrum disorder propensity score matching exact matched datasets were compared. For this study, the 6- and 66-month Taiwan Birth Cohort Study datasets were used (N = 20,095). In all, 744 families were propensity score matching exact matched and selected as the assisted reproduction technology sample (ratio of assisted reproduction technology to controls: 1:2) and 415 families as the autism spectrum disorder sample (ratio of autism spectrum disorder to controls: 1:4). Using a national birth cohort dataset, controlling for the confounding factors of assisted reproduction technology conception and autism spectrum disorder diagnosis, both assisted reproduction technology and autism spectrum disorder propensity score matching matched datasets showed the same results of no association between assisted reproduction technology and autism spectrum disorder. Further study on the detailed information regarding the processes and methods of assisted reproduction technology may provide us with more information on the association between assisted reproduction technology and autism spectrum disorder. En ligne : http://dx.doi.org/10.1177/1362361317690492 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=358 [article] Assisted reproductive technology has no association with autism spectrum disorders: The Taiwan Birth Cohort Study [texte imprimé] / For-Wey LUNG, Auteur ; Tung-Liang CHIANG, Auteur ; Shio-Jean LIN, Auteur ; Meng-Chih LEE, Auteur ; Bih-Ching SHU, Auteur . - p.377-384. Langues : Anglais (eng) in Autism > 22-3 (April 2018) . - p.377-384 Mots-clés : Taiwan Birth Cohort Study  assisted reproductive technology  autism spectrum disorder  low birth weight  propensity score matching Index. décimale : PER Périodiques Résumé : The use of assisted reproduction technology has increased over the last two decades. Autism spectrum disorders and assisted reproduction technology share many risk factors. However, previous studies on the association between autism spectrum disorders and assisted reproduction technology have shown inconsistent results. The purpose of this study was to investigate the association between assisted reproduction technology and autism spectrum disorder diagnosis in a national birth cohort database. Furthermore, the results from the assisted reproduction technology and autism spectrum disorder propensity score matching exact matched datasets were compared. For this study, the 6- and 66-month Taiwan Birth Cohort Study datasets were used (N = 20,095). In all, 744 families were propensity score matching exact matched and selected as the assisted reproduction technology sample (ratio of assisted reproduction technology to controls: 1:2) and 415 families as the autism spectrum disorder sample (ratio of autism spectrum disorder to controls: 1:4). Using a national birth cohort dataset, controlling for the confounding factors of assisted reproduction technology conception and autism spectrum disorder diagnosis, both assisted reproduction technology and autism spectrum disorder propensity score matching matched datasets showed the same results of no association between assisted reproduction technology and autism spectrum disorder. Further study on the detailed information regarding the processes and methods of assisted reproduction technology may provide us with more information on the association between assisted reproduction technology and autism spectrum disorder. En ligne : http://dx.doi.org/10.1177/1362361317690492 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=358

A comprehensive causal and machine learning framework for autism spectrum disorder risk prediction and high-risk subgroup identification from children with chronic health and genetic disorders in the United States / Md Roungu AHMMAD in Research in Autism, 131 (March 2026)  

Public ISBD [article]  inResearch in Autism > 131 (March 2026) . - 202826 Titre : A comprehensive causal and machine learning framework for autism spectrum disorder risk prediction and high-risk subgroup identification from children with chronic health and genetic disorders in the United States Type de document : texte imprimé Auteurs : Md Roungu AHMMAD, Auteur ; Harry PANTAZOPOULOS, Auteur ; Sahil Hareshbhai KOTHIYA, Auteur Article en page(s) : 202826 Langues : Anglais (eng) Mots-clés : Autism Spectrum Disorder  Propensity Score Matching  Machine Learning  Chronic Disease  Genetic Disorder Index. décimale : PER Périodiques Résumé : Purpose Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition often accompanied by diverse comorbidities. This study aimed to develop and evaluate a machine learning framework to identify key predictors of ASD and characterize disease–disease relationships. Methods We analyzed 2022–2023 National Survey of Children’s Health (NSCH) data. Propensity score matching (PSM) was applied to reduce confounding and approximate a randomized controlled design. Multiple machine learning algorithms were implemented to identify important predictors and assess disease–disease interactions. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration plots, and cross-validation. Results In matched cohorts, epilepsy showed the strongest association with ASD (20.0 % vs. 6.1 %; OR = 3.85, 95 % CI: 2.88–5.20). Down syndrome (DS) (14.5 % vs. 4.7 %; OR = 3.45, 95 % CI: 1.75–7.30) and congenital heart disease (CHD) (7.5 % vs. 5.4 %; OR = 1.42, 95 % CI: 1.14–1.77) were also significant, whereas diabetes, cystic fibrosis, and allergies demonstrated no consistent associations. Across machine learning analyses, epilepsy consistently emerged as the top predictor, with DS and CHD also ranked highly. Children with both epilepsy and CHD represented the highest-risk subgroup (>32 %), while those without epilepsy but with both allergies and CHD had moderate risk (>10 %). Model discrimination was good (AUC = 0.771) with satisfactory calibration. Conclusion Epilepsy is the strongest predictor of ASD, with epilepsy - CHD comorbidity conferring the highest-risk subgroup, whereas no epilepsy but allergies and CHD represented moderate-risk groups. This framework highlights the value of advanced analytics for early identification and targeted interventions in vulnerable populations. En ligne : https://doi.org/10.1016/j.reia.2026.202826 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=581 [article] A comprehensive causal and machine learning framework for autism spectrum disorder risk prediction and high-risk subgroup identification from children with chronic health and genetic disorders in the United States [texte imprimé] / Md Roungu AHMMAD, Auteur ; Harry PANTAZOPOULOS, Auteur ; Sahil Hareshbhai KOTHIYA, Auteur . - 202826. Langues : Anglais (eng) in Research in Autism > 131 (March 2026) . - 202826 Mots-clés : Autism Spectrum Disorder  Propensity Score Matching  Machine Learning  Chronic Disease  Genetic Disorder Index. décimale : PER Périodiques Résumé : Purpose Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition often accompanied by diverse comorbidities. This study aimed to develop and evaluate a machine learning framework to identify key predictors of ASD and characterize disease–disease relationships. Methods We analyzed 2022–2023 National Survey of Children’s Health (NSCH) data. Propensity score matching (PSM) was applied to reduce confounding and approximate a randomized controlled design. Multiple machine learning algorithms were implemented to identify important predictors and assess disease–disease interactions. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration plots, and cross-validation. Results In matched cohorts, epilepsy showed the strongest association with ASD (20.0 % vs. 6.1 %; OR = 3.85, 95 % CI: 2.88–5.20). Down syndrome (DS) (14.5 % vs. 4.7 %; OR = 3.45, 95 % CI: 1.75–7.30) and congenital heart disease (CHD) (7.5 % vs. 5.4 %; OR = 1.42, 95 % CI: 1.14–1.77) were also significant, whereas diabetes, cystic fibrosis, and allergies demonstrated no consistent associations. Across machine learning analyses, epilepsy consistently emerged as the top predictor, with DS and CHD also ranked highly. Children with both epilepsy and CHD represented the highest-risk subgroup (>32 %), while those without epilepsy but with both allergies and CHD had moderate risk (>10 %). Model discrimination was good (AUC = 0.771) with satisfactory calibration. Conclusion Epilepsy is the strongest predictor of ASD, with epilepsy - CHD comorbidity conferring the highest-risk subgroup, whereas no epilepsy but allergies and CHD represented moderate-risk groups. This framework highlights the value of advanced analytics for early identification and targeted interventions in vulnerable populations. En ligne : https://doi.org/10.1016/j.reia.2026.202826 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=581