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Selection bias induced by reproductive stoppage in estimates of recurrence risk for autism spectrum disorders / Michael BEENSTOCK in Research in Autism Spectrum Disorders, 89 (November 2021)
[article]
Titre : Selection bias induced by reproductive stoppage in estimates of recurrence risk for autism spectrum disorders Type de document : Texte imprimé et/ou numérique Auteurs : Michael BEENSTOCK, Auteur Article en page(s) : 101863 Langues : Anglais (eng) Mots-clés : Recurrence risk of ASD Reproductive stoppage Selection bias Incidental truncation method Inverse probability weighting Index. décimale : PER Périodiques Résumé : Background In observational studies it is implicitly assumed that the data are representative of the general population. Self-selection by individuals may undermine this assumption, inducing sample selection bias. In studies of recurrence risk of autism spectrum disorder (ASD) it is implicitly assumed that parents who had no further children face the same recurrence risk as parents who had further children. If this assumption is false, estimates of recurrence risk may be too high or too low. Method Two canonical statistical methods for detecting and correcting for sample selection bias, including incidental truncation and inverse probability weighting, are applied to population cohort data for Israel. The data comprise 8205 younger siblings of 9117 children diagnosed with ASD, among which there were 371 recurrences. Reproductive stoppage occurred in 4216 out of 9117 families. Participants are distinguished by demographics (gender, age), ethnicity (Jews and Arabs), religiosity, birth cohort and socioeconomic status. Results Correcting for selection bias using the incidental truncation method, the average risk of recurrence is 3.83 (95 %CI: 3.41 %–4.24 %) instead of 4.53 % (95 %CI: 4.08 %–4.98 %). The bias has p-value 0.082. Since the incidental truncation method may be sensitive to parametric assumptions regarding the joint distribution of unobserved heterogeneity in stoppage and recurrence risk, robustness tests are carried out using alternative parametric assumptions, including copulas. Results using the method of inverse probability weighting were unsatisfactory because they were sensitive to hidden confounders. Conclusions Studies of recurrent risk of ASD should take account of potential selection bias induced by reproductive stoppage. En ligne : https://doi.org/10.1016/j.rasd.2021.101863 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=458
in Research in Autism Spectrum Disorders > 89 (November 2021) . - 101863[article] Selection bias induced by reproductive stoppage in estimates of recurrence risk for autism spectrum disorders [Texte imprimé et/ou numérique] / Michael BEENSTOCK, Auteur . - 101863.
Langues : Anglais (eng)
in Research in Autism Spectrum Disorders > 89 (November 2021) . - 101863
Mots-clés : Recurrence risk of ASD Reproductive stoppage Selection bias Incidental truncation method Inverse probability weighting Index. décimale : PER Périodiques Résumé : Background In observational studies it is implicitly assumed that the data are representative of the general population. Self-selection by individuals may undermine this assumption, inducing sample selection bias. In studies of recurrence risk of autism spectrum disorder (ASD) it is implicitly assumed that parents who had no further children face the same recurrence risk as parents who had further children. If this assumption is false, estimates of recurrence risk may be too high or too low. Method Two canonical statistical methods for detecting and correcting for sample selection bias, including incidental truncation and inverse probability weighting, are applied to population cohort data for Israel. The data comprise 8205 younger siblings of 9117 children diagnosed with ASD, among which there were 371 recurrences. Reproductive stoppage occurred in 4216 out of 9117 families. Participants are distinguished by demographics (gender, age), ethnicity (Jews and Arabs), religiosity, birth cohort and socioeconomic status. Results Correcting for selection bias using the incidental truncation method, the average risk of recurrence is 3.83 (95 %CI: 3.41 %–4.24 %) instead of 4.53 % (95 %CI: 4.08 %–4.98 %). The bias has p-value 0.082. Since the incidental truncation method may be sensitive to parametric assumptions regarding the joint distribution of unobserved heterogeneity in stoppage and recurrence risk, robustness tests are carried out using alternative parametric assumptions, including copulas. Results using the method of inverse probability weighting were unsatisfactory because they were sensitive to hidden confounders. Conclusions Studies of recurrent risk of ASD should take account of potential selection bias induced by reproductive stoppage. En ligne : https://doi.org/10.1016/j.rasd.2021.101863 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=458 Representativeness of autistic samples in studies recruiting through social media / Eya-Mist RØDGAARD in Autism Research, 15-8 (August 2022)
[article]
Titre : Representativeness of autistic samples in studies recruiting through social media Type de document : Texte imprimé et/ou numérique Auteurs : Eya-Mist RØDGAARD, Auteur ; Kristian JENSEN, Auteur ; Kamilla Woznica MISKOWIAK, Auteur ; Laurent MOTTRON, Auteur Article en page(s) : p.1447-1456 Langues : Anglais (eng) Mots-clés : Autism Spectrum Disorder/epidemiology Autistic Disorder Humans Intellectual Disability/epidemiology Social Media Surveys and Questionnaires autism online recruitment sampling bias selection bias Index. décimale : PER Périodiques Résumé : Survey-based research with recruitment through online channels is a convenient way to obtain large samples and has recently been increasingly used in autism research. However, sampling from online channels may be associated with a high risk of sampling bias causing findings not to be generalizable to the autism population. Here we examined autism studies that have sampled on social media for markers of sampling bias. Most samples showed one or more indicators of sampling bias, in the form of reversed sex ratio, higher employment rates, higher education level, lower fraction of individuals with intellectual disability, and later age of diagnosis than would be expected when comparing with for example population study results from published research. Findings from many of the included studies are therefore difficult to generalize to the broader autism population. Suggestions for how research strategies may be adapted to address some of the problems are discussed. LAY SUMMARY: Online surveys offer a convenient way to recruit large numbers of participants for autism research. However, the resulting samples may not fully reflect the autism population. Here we investigated the samples of 36 autism studies that recruited participants online and found that the demographic composition tended to deviate from what has been reported about the autism population in previous research. The results may thus not be generalizable to autism in general. En ligne : http://dx.doi.org/10.1002/aur.2777 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=483
in Autism Research > 15-8 (August 2022) . - p.1447-1456[article] Representativeness of autistic samples in studies recruiting through social media [Texte imprimé et/ou numérique] / Eya-Mist RØDGAARD, Auteur ; Kristian JENSEN, Auteur ; Kamilla Woznica MISKOWIAK, Auteur ; Laurent MOTTRON, Auteur . - p.1447-1456.
Langues : Anglais (eng)
in Autism Research > 15-8 (August 2022) . - p.1447-1456
Mots-clés : Autism Spectrum Disorder/epidemiology Autistic Disorder Humans Intellectual Disability/epidemiology Social Media Surveys and Questionnaires autism online recruitment sampling bias selection bias Index. décimale : PER Périodiques Résumé : Survey-based research with recruitment through online channels is a convenient way to obtain large samples and has recently been increasingly used in autism research. However, sampling from online channels may be associated with a high risk of sampling bias causing findings not to be generalizable to the autism population. Here we examined autism studies that have sampled on social media for markers of sampling bias. Most samples showed one or more indicators of sampling bias, in the form of reversed sex ratio, higher employment rates, higher education level, lower fraction of individuals with intellectual disability, and later age of diagnosis than would be expected when comparing with for example population study results from published research. Findings from many of the included studies are therefore difficult to generalize to the broader autism population. Suggestions for how research strategies may be adapted to address some of the problems are discussed. LAY SUMMARY: Online surveys offer a convenient way to recruit large numbers of participants for autism research. However, the resulting samples may not fully reflect the autism population. Here we investigated the samples of 36 autism studies that recruited participants online and found that the demographic composition tended to deviate from what has been reported about the autism population in previous research. The results may thus not be generalizable to autism in general. En ligne : http://dx.doi.org/10.1002/aur.2777 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=483