[HTML][HTML] How to develop a more accurate risk prediction model when there are few events
… Overfitted models tend to underestimate the probability of an event in low risk patients
and overestimate it in high risk patients, which could affect clinical decision making. In this
paper, we discuss the potential of penalised regression methods to alleviate this problem
and thus develop more accurate prediction models. Statistical models are often used to
predict the probability that an individual with a given set of risk factors will experience a
health outcome, usually termed an “event.” These …
and overestimate it in high risk patients, which could affect clinical decision making. In this
paper, we discuss the potential of penalised regression methods to alleviate this problem
and thus develop more accurate prediction models. Statistical models are often used to
predict the probability that an individual with a given set of risk factors will experience a
health outcome, usually termed an “event.” These …
When the number of events is low relative to the number of predictors, standard regression could produce overfitted risk models that make inaccurate predictions. Use of penalised regression may improve the accuracy of risk prediction
bmj.com
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