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Because using the same data for model testing and validation leads to overfitting and deflates the estimated error rate, we used 10-fold cross-validation on a randomly selected 75% training sample for model training.
Thus, the sample for Model 1 includes all respondents in Ontario aged 18 or over.
The sample for model 2 consisted of 14 679 patients from 73 hospitals.
The final sample for model 1 included 18 101 RP patients from 225 hospitals in California during the 3-year time period.
Ideally, a determination that the reasons for discontinuation within the patient sample for Model 1 compared to Model 2 were also similar is the ideal, but such information is often not available.
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The definition of training data was realised on Kennard-Stone sampling algorithm and we selected 103 samples for model calibration and 100 samples for model validation.
We selected a random subset consisting of 70% of the samples (~500,000 samples) for model input and used the remaining 30% for model evaluation and validation.
However, the null hypothesis was rejected for 61 of 100 bootstrap samples for model 1.
It is of great value to examine the density of these estimates across samples for model diagnostics.
Repeated samples for model validation (e.g., cord/child blood concentrations) were unavailable in other cohorts in this study.
An over-fitted model has a good fit to the sample used for model building but poor generalizability to out-of-sample data.
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