What Is the Male-to-Female Ratio in Autism Spectrum Disorder? A Systematic Review and Meta-Analysis

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Abstract

To derive the first systematically calculated estimate of the relative proportion of boys and girls with autism spectrum disorder (ASD) through a meta-analysis of prevalence studies conducted since the introduction of the DSM-IV and the International Classification of Diseases, Tenth Revision.

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Assessing risk of bias in prevalence studies: modification of an existing tool and evidence of interrater agreement.

Damian Hoy, Peter Brooks, Anthony Woolf … (2012)

In the course of performing systematic reviews on the prevalence of low back and neck pain, we required a tool to assess the risk of study bias. Our objectives were to (1) modify an existing checklist and (2) test the final tool for interrater agreement. The final tool consists of 10 items addressing four domains of bias plus a summary risk of bias assessment. Two researchers tested the interrater agreement of the tool by independently assessing 54 randomly selected studies. Interrater agreement overall and for each individual item was assessed using the proportion of agreement and Kappa statistic. Raters found the tool easy to use, and there was high interrater agreement: overall agreement was 91% and the Kappa statistic was 0.82 (95% confidence interval: 0.76, 0.86). Agreement was almost perfect for the individual items on the tool and moderate for the summary assessment. We have addressed a research gap by modifying and testing a tool to assess risk of study bias. Further research may be useful for assessing the applicability of the tool across different conditions. Copyright © 2012 Elsevier Inc. All rights reserved.

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Forming inferences about some intraclass correlation coefficients.

Kenneth McGraw, Esther Wong (1996)

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A re-evaluation of random-effects meta-analysis

Julian P. T. Higgins, Simon Thompson, David J Spiegelhalter (2009)

Meta-analysis in the presence of unexplained heterogeneity is frequently undertaken by using a random-effects model, in which the effects underlying different studies are assumed to be drawn from a normal distribution. Here we discuss the justification and interpretation of such models, by addressing in turn the aims of estimation, prediction and hypothesis testing. A particular issue that we consider is the distinction between inference on the mean of the random-effects distribution and inference on the whole distribution. We suggest that random-effects meta-analyses as currently conducted often fail to provide the key results, and we investigate the extent to which distribution-free, classical and Bayesian approaches can provide satisfactory methods. We conclude that the Bayesian approach has the advantage of naturally allowing for full uncertainty, especially for prediction. However, it is not without problems, including computational intensity and sensitivity to a priori judgements. We propose a simple prediction interval for classical meta-analysis and offer extensions to standard practice of Bayesian meta-analysis, making use of an example of studies of ‘set shifting’ ability in people with eating disorders.