External Validation of clinical prediction models - Statswork

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External Validation of clinical prediction models

Dr. Nancy Agnes, Head, Technical Operations, Statswork info@statswork.com

Validation, particularly external validation,

II. FACTORS INFLUENCES AFFECT

is a crucial part of developing a predictive

EXTERNAL VALIDATION DATA

model. External validation is needed to ensure

that

a

prediction

model

is

generalizable to patients other than those in

the

derivative

cohort.

External

The sample size for external validation data for the implementation of the prediction model is affected by the number of events and predictors.

validation can be done by testing the model's output in data that isn't the same as

External validation of the prediction model

the data used to create the model. As a

requires a minimum of 100 events and/or

consequence, it is carried out after the

non-events,

creation of a prediction model.

studies, and a systematic analysis found

according

to

simulation

that small external validation studies are I. EXTERNAL VALIDATION

ineffective

and

inaccurate.Example:

External validation can take many forms,

Radiology imaging is often treated as

including validation in the field such as

effective

temporal, geographical and independent

researchers often validate the findings

validation. For external validation studies,

using clinical prediction model. Every

the sample size calculation estimates

prediction

based on statistical power considerations

regression analysis. The most common

have not been extensively investigated.

predictive model or the regression model

However, in order to achieve adequate

used for the clinical prediction model are

model output in the validation set, a large

linear regression if the dependent variable

sample size is needed to validate the

is continuous in nature, logistic regression

prediction model.

model if the dependent variable is binary, and

predictive

model

is

Cox-proportional

parameters

based

model

on

if

and

the

the

dependent variable is time-to-event in

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