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I am doing a doctoral thesis in which I will try to use a standardized protocol to detect children with neurodevelopmental deviations in the general population (meaning children who do not have any of the known risks for this).

The research would be based on examinations that would be carried out in the 2nd, 6th, 12th, and 18th months of the child's life, that is, they would compare the results of the examination within the first year of life with the results of the examination in the 18th month.

The review would be based on the application of a standardized scale that has 3 subscales. All examination points within the scales would be scored with 1 or 0 (yes/no) and at the end the number of points achieved for each individual scale would be added up. The control point would be the examination that is done in the 18th month with another protocol. They would also compare the results with children who are at risk, but there is already data for them in the literature.

The aim of the research is to determine the predictive value of each individual scale and to determine the sensitivity and specificity of that protocol.

The number of live births in chosen city is 7,456 per year. Of this, 6% are children born prematurely (these are children who have a known risk). The percentage of neurodevelopmental deviations in the general population is 3-5%. Considering the given data, I should calculate the sample size that I need for the research. That is, how many children who have no known risk should be included in the research in order to ultimately obtain an adequate number of children with neurodevelopmental deviations in order to be able to calculate the predictive value of the scales.

Thank you in advance.

  • Please state the exact null hypothesis of your study. If there are multiple primary hypotheses, also specify the intended multiple testing correction regime. – Michael M Oct 29 '23 at 12:57
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    +1 for thinking about sample sizes before running the study. For your substantive question, you would need to make assumptions about the distribution of your predictors and how they are associated with the outcome (or, put differently: decide what effect size you would be sorry to miss). Also, sensitivity and specificity suffer from the exact same issues as accuracy, especially (but not exclusively!) for "unbalanced" data. Do consider probabilistic predictions. – Stephan Kolassa Oct 29 '23 at 14:43

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