| Autor: |
Menelaos Pavlou, Gareth Ambler, Chen Qu, Shaun R. Seaman, Ian R. White, Rumana Z. Omar |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
BMC Medical Research Methodology, Vol 24, Iss 1, Pp 1-13 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1471-2288 |
| DOI: |
10.1186/s12874-024-02268-5 |
| Popis: |
Abstract Background Risk prediction models are routinely used to assist in clinical decision making. A small sample size for model development can compromise model performance when the model is applied to new patients. For binary outcomes, the calibration slope (CS) and the mean absolute prediction error (MAPE) are two key measures on which sample size calculations for the development of risk models have been based. CS quantifies the degree of model overfitting while MAPE assesses the accuracy of individual predictions. Methods Recently, two formulae were proposed to calculate the sample size required, given anticipated features of the development data such as the outcome prevalence and c-statistic, to ensure that the expectation of the CS and MAPE (over repeated samples) in models fitted using MLE will meet prespecified target values. In this article, we use a simulation study to evaluate the performance of these formulae. Results We found that both formulae work reasonably well when the anticipated model strength is not too high (c-statistic |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
|
Nepřihlášeným uživatelům se plný text nezobrazuje |
K zobrazení výsledku je třeba se přihlásit.
|
