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Objective: Provide guidance on sample size considerations for developingpredictive models by empirically establishing the adequate sample size, whichbalances the competing objectives of improving model performance and reducingmodel complexity as well as computational requirements.  Materials and Methods: We empirically assess the effect of sample size onprediction performance and model complexity by generating learning curves for81 prediction problems in three large observational health databases, requiringtraining of 17,248 prediction models. The adequate sample size was defined asthe sample size for which the performance of a model equalled the maximum modelperformance minus a small threshold value.  Results: The adequate sample size achieves a median reduction of the numberof observations between 9.5% and 78.5% for threshold values between 0.001 and0.02. The median reduction of the number of predictors in the models at theadequate sample size varied between 8.6% and 68.3%, respectively.  Discussion: Based on our results a conservative, yet significant, reductionin sample size and model complexity can be estimated for future predictionwork. Though, if a researcher is willing to generate a learning curve a muchlarger reduction of the model complexity may be possible as suggested by alarge outcome-dependent variability.  Conclusion: Our results suggest that in most cases only a fraction of theavailable data was sufficient to produce a model close to the performance ofone developed on the full data set, but with a substantially reduced modelcomplexity.

How little data do we need for patient-level prediction?