Tool for Converting ADHD Rating Scales Scores Based on Individual Participant Data from 53 Randomized Controlled Trials of ADHD Medications

Introduction: A variety of rating scales are currently being used to assess symptom severity and quantify symptoms change in attention-deficit/hyperactivity disorder (ADHD) research and clinical practice. This poses difficulties in interpreting scores from different scales in clinical practice and synthesizing data from studies using different scales. We aimed to develop algorithms for converting scores across the ADHD scales most often used in randomized controlled trials (RCTs) of ADHD medications in children/adolescents and adults, and to develop an online tool for implementing the algorithms. Methods: We analyzed individual participant data from RCTs of ADHD medications (32 RCTs in children/adolescents, 21 in adults), with data on at least two scales per participant at the same timepoint. We applied a series of competing models, that is, univariable and multivariable regression, random forests, and an equipercentile linkage approach, to link pairs of scales. To assess the error of the linking procedure and identify the optimal model, we calculated the median absolute error and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mrow> <mml:msup> <mml:mrow> <mml:mi>R</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> of all approaches by comparing the values predicted from the models to the observed ones. We subsequently developed a tool to implement the best algorithms. Results: We linked six commonly used ADHD scales, such as the ADHD Rating Scale (ADHD-RS-IV; investigator-rated) and the Conners’ Parent Rating Scale (CPRS-R:S). Spline models most frequently yielded the lowest prediction error, outperforming alternative conversion algorithms for absolute scores in 6 out of 12 univariable models and 8 out of 12 multivariable models. The tool for scores conversion is available at ADHD_Scale_Conversion_Tool . Conclusions: Our linkage algorithms enable the comparison and harmonization of findings across studies using different ADHD rating scales. Translating scores across scales improves the interpretability of research findings, facilitates future evidence synthesis across studies, and may support clinical practice. Our online tool supports the practical uptake of our results.