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Clinical Prediction Models

Part of the Statistics for Biology and Health Book Series

DOI: 10.1007/978-0-387-77244-8

ISBN: 978-0-387-77243-1

Publisher: Springer New York - Springer Journals

Sep 17, 2008

1 - 10 of 24 Chapters

Available

Introduction

Steyerberg, E.W.

Pages: 1 –7
Available

Applications of prediction models

Steyerberg, E.W.

Pages: 11 –31
Available

Study design for prediction models

Steyerberg, E.W.

Pages: 33 –52

[In this chapter, we consider several issues in the design of studies for prediction research. These include the selection of subjects or patients for a cohort study, strengths and limitations of case series from a single center, from registries, or prospective trials. We further discuss issues...
Available

Statistical Models for Prediction

Steyerberg, E.W.

Pages: 53 –82

[In this chapter, we consider statistical models for different types of outcomes: binary, unordered categorical, ordered categorical, continuous, and survival data. We discuss common statistical models in medical research such as the linear, logistic, and Cox regression model, and also simpler...
Available

Overfitting and optimism in prediction models

Steyerberg, E.W.

Pages: 83 –100

[If we develop a statistical model with the main aim of outcome prediction, we are primarily interested in the validity of the predictions for new subjects, outside the sample under study. A key threat to validity is overfitting, i.e. that the data under study are well described, but that...
Available

Choosing between alternative statistical models

Steyerberg, E.W.

Pages: 101 –111

[Any scientific model will have to make simplifying assumptions about reality. Nevertheless, statistical models are important tools to summarize patterns from underlying data. Statistical models can well be used to make predictions for future subjects. We consider some general issues in choosing...
Available

Dealing with missing values

Steyerberg, E.W.

Pages: 115 –137

[Missing data are a common problem in prediction research. We concentrate on missing values of predictor values (X), in the context of a prediction model for a single outcome (Y). Traditional complete case analysis suffers from inefficiency, selection bias of subjects, and other limitations. We...
Available

Case study on dealing with missing values

Steyerberg, E.W.

Pages: 139 –157

[A case study is presented on prognostic modelling in patients with moderate and severe traumatic brain injury (TBI). Individual patient data from several studies were available to quantify predictor effects and to develop and validate prognostic models. Missing values were a key issue, since few...
Available

Coding of Categorical and Continuous Predictors

Steyerberg, E.W.

Pages: 159 –173

[When developing a prediction model, an important consideration is how we code the predictors. Raw data from a study are often not in a form appropriate for entering in regression models and must first be manipulated. This is known as “coding.” As in any data analysis, we will usually start with...
Available

Restrictions on candidate predictors

Steyerberg, E.W.

Pages: 175 –189

[A major problem in predictive modelling is that we often have many candidate predictors available for the analysis, while the data set available for analysis is relatively small. A small sample size leads to problems as discussed in Chap. 5, such as limited power to test main effects of...

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