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Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
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Area of Science:

  • Clinical Epidemiology
  • Biostatistics

Background:

  • Variable selection is critical for developing accurate prognostic models.
  • Missing data is a common challenge in clinical research.
  • Multiple imputation is a widely adopted method for handling missing data.

Purpose of the Study:

  • To evaluate the impact of different variable selection strategies using multiply imputed data on the external performance of prognostic models.
  • To compare the performance of models derived from complete cases versus those using multiple imputation.

Main Methods:

  • Backward variable selection was employed with nine methods for handling multiply imputed data.
  • Logistic regression models were developed to predict 1-year mortality after acute myocardial infarction.
  • Models were validated using a separate, temporally distinct patient cohort.

Main Results:

  • Prognostic models developed using only complete cases showed significantly worse external performance.
  • Models developed using multiply imputed data, regardless of selection significance, performed better.
  • Eight different multiple imputation methods yielded similar model performance.

Conclusions:

  • Excluding subjects with missing data during prognostic model development can lead to suboptimal model performance.
  • Multiple imputation is recommended to effectively address missing data in prognostic model development.