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Studies of WES and WGS varied in size from 22 to 1745 probands; Meta-regression showed a modest relationship between study size and diagnostic utility: on average, an increase of 1000 subjects decreased the odds of diagnosis by 28% (Fig. 2a, P = 0.01).
The generated funnel plot presented no clear asymmetry upon visual inspection (Fig. 2), and the Egger's test of the intercept did not identify any significant association between study size and hospital mortality (t = 1.23; df = 10; p = 0.12).
The relation between study size and reported effect size was weak.
There was a negative correlation between study size and change in incontinence episodes/day (r = -0.25).
There was a modest negative correlation between study size and change in micturitions/day (r = -0.21).
There was no relationship between study size and change in mean voided volume (r = -0.07).
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Of note, hospital studies had an average of 84 subjects, while reference laboratory studies had an average of 558 subjects, providing a possible explanation for the inverse relationship between-study size and rate of diagnosis (Fig. 1a).
Finally, as we discuss in detail in this month's issue of the journal Perspectives on Psychological Science, we are troubled by the link between small study size and publication bias.
We note that there were no correlations between OR magnitude, or study size, and publication date, when analyzing polymorphisms individually or grouping as a whole (all p<0.1).
Prevalence across studies appeared to vary by study design, geographic region, time of assessment, and study quality scores; associations between study sample size and prevalence were not observed.
There was a positive correlation, in both obstetrics (p < 0.0001) and gynaecology (p = 0.0123), between study sample size and reporting quality.
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CEO of Professional Science Editing for Scientists @ prosciediting.com