Assuming a parametric probability model in this section, we plot the exact performance metrics of the resubstitution and leave-one-out CoD estimators, by using the analytical expressions obtained in Sections 4 and 5, under varying actual CoD, sample size, and predictor complexity (number of bins).
With current sample size, risk predictors are not useful in a clinical setting but already are a valuable research tool, for example in experimental designs comparing cases with high and low polygenic risk.
We extracted data following a pilot tested data extraction form including study characteristics (country, setting, time to follow up, sample size, and predictors), population characteristics (age, percent females, inclusion criteria), and outcome measures (type of fracture).
Using a function of the sample size as the predictor variable avoids the structural dependence between ln(OR) and var(ln(OR)) (N.B. the problem which also motivated the development of the Harbord method).
However, there is one instance (figure 4) where they clearly differ with regard to absolute bias; which can be explained by the Peters' method profiting from avoiding the structural correlation problem between outcome and standard error by using a function of sample size as the predictor variable.
If one changes the combination of earnings predictors and uses a subset of predictors, sample size increases by only nine, which frees the concern of having a smaller sample size in exchange for having more predictors.
In addition, as pointed out by Ripke et al. (37), the use of a larger sample size to develop the predictor (i.e. model from the Stage 1 sample) should produce a more accurate allele score and thus increase the amount of variance in the Stage 2 case and control status explained.
Given the small sample size and some strong predictors of variceal bleeding, it was not possible reliably to estimate multivariable models.
The variables as a set accounted for 66%and43%3% of joint attention variance at younger and older ages, respectively (59% and 31% when adjusted for the sample size and number of predictors).
The variables as a set accounted for 83% and 60% of cooperativeness variance (79%and52%2%, when adjusted for the sample size and number of predictors), for the younger and older developmental periods, respectively.
