The Wally plot approach to assess the calibration of clinical prediction models
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The Wally plot approach to assess the calibration of clinical prediction models. / Blanche, Paul; Gerds, Thomas A; Ekstrøm, Claus T.
In: Lifetime Data Analysis, Vol. 25, No. 1, 15.01.2019, p. 150-167.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - The Wally plot approach to assess the calibration of clinical prediction models
AU - Blanche, Paul
AU - Gerds, Thomas A
AU - Ekstrøm, Claus T
PY - 2019/1/15
Y1 - 2019/1/15
N2 - A prediction model is calibrated if, roughly, for any percentage x we can expect that x subjects out of 100 experience the event among all subjects that have a predicted risk of x%. Typically, the calibration assumption is assessed graphically but in practice it is often challenging to judge whether a "disappointing" calibration plot is the consequence of a departure from the calibration assumption, or alternatively just "bad luck" due to sampling variability. We propose a graphical approach which enables the visualization of how much a calibration plot agrees with the calibration assumption to address this issue. The approach is mainly based on the idea of generating new plots which mimic the available data under the calibration assumption. The method handles the common non-trivial situations in which the data contain censored observations and occurrences of competing events. This is done by building on ideas from constrained non-parametric maximum likelihood estimation methods. Two examples from large cohort data illustrate our proposal. The 'wally' R package is provided to make the methodology easily usable.
AB - A prediction model is calibrated if, roughly, for any percentage x we can expect that x subjects out of 100 experience the event among all subjects that have a predicted risk of x%. Typically, the calibration assumption is assessed graphically but in practice it is often challenging to judge whether a "disappointing" calibration plot is the consequence of a departure from the calibration assumption, or alternatively just "bad luck" due to sampling variability. We propose a graphical approach which enables the visualization of how much a calibration plot agrees with the calibration assumption to address this issue. The approach is mainly based on the idea of generating new plots which mimic the available data under the calibration assumption. The method handles the common non-trivial situations in which the data contain censored observations and occurrences of competing events. This is done by building on ideas from constrained non-parametric maximum likelihood estimation methods. Two examples from large cohort data illustrate our proposal. The 'wally' R package is provided to make the methodology easily usable.
U2 - 10.1007/s10985-017-9414-3
DO - 10.1007/s10985-017-9414-3
M3 - Journal article
C2 - 29214550
VL - 25
SP - 150
EP - 167
JO - Lifetime Data Analysis
JF - Lifetime Data Analysis
SN - 1380-7870
IS - 1
ER -
ID: 198525425