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The influence of loss to follow‐up in autism screening research: Taking stock and moving forward.

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Title: The influence of loss to follow‐up in autism screening research: Taking stock and moving forward.
Authors: Sheldrick, R. Christopher, Hooker, Jessica L., Carter, Alice S., Feinberg, Emily, Croen, Lisa A., Kuhn, Jocelyn, Slate, Elizabeth, Wetherby, Amy M.
Source: Journal of Child Psychology & Psychiatry. May2024, Vol. 65 Issue 5, p656-667. 12p.
Subjects: Diagnosis of autism, Statistical models, Secondary analysis, Research funding, Human research subjects, Descriptive statistics, Diagnostic errors, Disease prevalence, Longitudinal method, Simulation methods in education, Medical screening, Confidence intervals, Early diagnosis, Sensitivity & specificity (Statistics)
Abstract: Background: How best to improve the early detection of autism spectrum disorder (ASD) is the subject of significant controversy. Some argue that universal ASD screeners are highly accurate, whereas others argue that evidence for this claim is insufficient. Relatedly, there is no clear consensus as to the optimal role of screening for making referral decisions for evaluation and treatment. Published screening research can meaningfully inform these questions—but only through careful consideration of children who do not complete diagnostic follow‐up. Methods: We developed two simulation models that re‐analyze the results of a large‐scale validation study of the M‐CHAT‐R/F by Robins et al. (2014, Pediatrics, 133, 37). Model #1 re‐analyzes screener accuracy across six scenarios, each reflecting different assumptions regarding loss to follow‐up. Model #2 builds on this by closely examining differential attrition at each point of the multi‐step detection process. Results: Estimates of sensitivity ranged from 40% to 94% across scenarios, demonstrating that estimates of accuracy depend on assumptions regarding the diagnostic status of children who were lost to follow‐up. Across a range of plausible assumptions, data also suggest that children with undiagnosed ASD may be more likely to complete follow‐up than children without ASD, highlighting the role of clinicians and caregivers in the detection process. Conclusions: Using simulation modeling as a quantitative method to examine potential bias in screening studies, analyses suggest that ASD screening tools may be less accurate than is often reported. Models also demonstrate the critical importance of every step in a detection process—including steps that determine whether children should complete an additional evaluation. We conclude that parent and clinician decision‐making regarding follow‐up may contribute more to detection than is widely assumed. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Child Psychology & Psychiatry is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: The influence of loss to follow‐up in autism screening research: Taking stock and moving forward.
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  Data: <searchLink fieldCode="AR" term="%22Sheldrick%2C+R%2E+Christopher%22">Sheldrick, R. Christopher</searchLink><br /><searchLink fieldCode="AR" term="%22Hooker%2C+Jessica+L%2E%22">Hooker, Jessica L.</searchLink><br /><searchLink fieldCode="AR" term="%22Carter%2C+Alice+S%2E%22">Carter, Alice S.</searchLink><br /><searchLink fieldCode="AR" term="%22Feinberg%2C+Emily%22">Feinberg, Emily</searchLink><br /><searchLink fieldCode="AR" term="%22Croen%2C+Lisa+A%2E%22">Croen, Lisa A.</searchLink><br /><searchLink fieldCode="AR" term="%22Kuhn%2C+Jocelyn%22">Kuhn, Jocelyn</searchLink><br /><searchLink fieldCode="AR" term="%22Slate%2C+Elizabeth%22">Slate, Elizabeth</searchLink><br /><searchLink fieldCode="AR" term="%22Wetherby%2C+Amy+M%2E%22">Wetherby, Amy M.</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Child+Psychology+%26+Psychiatry%22">Journal of Child Psychology & Psychiatry</searchLink>. May2024, Vol. 65 Issue 5, p656-667. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Diagnosis+of+autism%22">Diagnosis of autism</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink><br /><searchLink fieldCode="DE" term="%22Secondary+analysis%22">Secondary analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22Human+research+subjects%22">Human research subjects</searchLink><br /><searchLink fieldCode="DE" term="%22Descriptive+statistics%22">Descriptive statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnostic+errors%22">Diagnostic errors</searchLink><br /><searchLink fieldCode="DE" term="%22Disease+prevalence%22">Disease prevalence</searchLink><br /><searchLink fieldCode="DE" term="%22Longitudinal+method%22">Longitudinal method</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation+methods+in+education%22">Simulation methods in education</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+screening%22">Medical screening</searchLink><br /><searchLink fieldCode="DE" term="%22Confidence+intervals%22">Confidence intervals</searchLink><br /><searchLink fieldCode="DE" term="%22Early+diagnosis%22">Early diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Sensitivity+%26+specificity+%28Statistics%29%22">Sensitivity & specificity (Statistics)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Background: How best to improve the early detection of autism spectrum disorder (ASD) is the subject of significant controversy. Some argue that universal ASD screeners are highly accurate, whereas others argue that evidence for this claim is insufficient. Relatedly, there is no clear consensus as to the optimal role of screening for making referral decisions for evaluation and treatment. Published screening research can meaningfully inform these questions—but only through careful consideration of children who do not complete diagnostic follow‐up. Methods: We developed two simulation models that re‐analyze the results of a large‐scale validation study of the M‐CHAT‐R/F by Robins et al. (2014, Pediatrics, 133, 37). Model #1 re‐analyzes screener accuracy across six scenarios, each reflecting different assumptions regarding loss to follow‐up. Model #2 builds on this by closely examining differential attrition at each point of the multi‐step detection process. Results: Estimates of sensitivity ranged from 40% to 94% across scenarios, demonstrating that estimates of accuracy depend on assumptions regarding the diagnostic status of children who were lost to follow‐up. Across a range of plausible assumptions, data also suggest that children with undiagnosed ASD may be more likely to complete follow‐up than children without ASD, highlighting the role of clinicians and caregivers in the detection process. Conclusions: Using simulation modeling as a quantitative method to examine potential bias in screening studies, analyses suggest that ASD screening tools may be less accurate than is often reported. Models also demonstrate the critical importance of every step in a detection process—including steps that determine whether children should complete an additional evaluation. We conclude that parent and clinician decision‐making regarding follow‐up may contribute more to detection than is widely assumed. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Journal of Child Psychology & Psychiatry is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1111/jcpp.13867
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      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 12
        StartPage: 656
    Subjects:
      – SubjectFull: Diagnosis of autism
        Type: general
      – SubjectFull: Statistical models
        Type: general
      – SubjectFull: Secondary analysis
        Type: general
      – SubjectFull: Research funding
        Type: general
      – SubjectFull: Human research subjects
        Type: general
      – SubjectFull: Descriptive statistics
        Type: general
      – SubjectFull: Diagnostic errors
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      – SubjectFull: Disease prevalence
        Type: general
      – SubjectFull: Longitudinal method
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      – SubjectFull: Simulation methods in education
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      – SubjectFull: Medical screening
        Type: general
      – SubjectFull: Confidence intervals
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      – SubjectFull: Early diagnosis
        Type: general
      – SubjectFull: Sensitivity & specificity (Statistics)
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      – TitleFull: The influence of loss to follow‐up in autism screening research: Taking stock and moving forward.
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              Text: May2024
              Type: published
              Y: 2024
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