AbstractStepwise regression procedures are often used to identify a small set of variables that serve as important predictors of clinical outcome and to construct prediction models based on those variables. Several theoretical and practical limitations of this process are discussed and highlighted with a variety of examples from published reports. Wider appreciation of these limitations should encourage the development of more relevant models, and thereby improve the quality of clinical prediction
Introduction It is known that only a limited proportion of developed clinical prediction models (CPM...
In the 20th century, many advances in biological knowledge and evidence-based medicine were supporte...
This work seeks to develop a high quality prognostic model for the CARE-HF data; see (Richardson et ...
AbstractStepwise regression procedures are often used to identify a small set of variables that serv...
Clinical prediction models provide risk estimates for the presence of disease (diagnosis) or an even...
textabstractClinical prediction models provide risk estimates for the presence of disease (diagnosis...
<p>Prediction models play an increasingly important role in clinical and shared decision making. In ...
Objective Measurement error in predictor variables may threaten the validity of clinical prediction...
International audienceIn the 20th century, evidence-based medicine has put clinical practice on much...
Clinical prediction models play an increasingly important role in contemporary clinical care, by inf...
Massive numbers of new prediction models have been published over the past two decades and the numbe...
This thesis aims to improve methods of clinical prediction research. In clinical prediction research...
Prediction models that estimate the probabilities of developing a specific disease (diagnostic model...
Clinical prediction models estimate an individual's risk of a particular health outcome, conditional...
To evaluate limitations of common statistical modeling approaches in deriving clinical prediction mo...
Introduction It is known that only a limited proportion of developed clinical prediction models (CPM...
In the 20th century, many advances in biological knowledge and evidence-based medicine were supporte...
This work seeks to develop a high quality prognostic model for the CARE-HF data; see (Richardson et ...
AbstractStepwise regression procedures are often used to identify a small set of variables that serv...
Clinical prediction models provide risk estimates for the presence of disease (diagnosis) or an even...
textabstractClinical prediction models provide risk estimates for the presence of disease (diagnosis...
<p>Prediction models play an increasingly important role in clinical and shared decision making. In ...
Objective Measurement error in predictor variables may threaten the validity of clinical prediction...
International audienceIn the 20th century, evidence-based medicine has put clinical practice on much...
Clinical prediction models play an increasingly important role in contemporary clinical care, by inf...
Massive numbers of new prediction models have been published over the past two decades and the numbe...
This thesis aims to improve methods of clinical prediction research. In clinical prediction research...
Prediction models that estimate the probabilities of developing a specific disease (diagnostic model...
Clinical prediction models estimate an individual's risk of a particular health outcome, conditional...
To evaluate limitations of common statistical modeling approaches in deriving clinical prediction mo...
Introduction It is known that only a limited proportion of developed clinical prediction models (CPM...
In the 20th century, many advances in biological knowledge and evidence-based medicine were supporte...
This work seeks to develop a high quality prognostic model for the CARE-HF data; see (Richardson et ...