# Index of Prediction Accuracy (IPA)

## 1 Introduction

This vignette demonstrates how our software calculates the index of prediction accuracy 1. We distinguish three settings:

• uncensored binary outcome
• right censored survival outcome (no competing risks)
• right censored time to event outcome with competing risks

The Brier score is a loss type metric of prediction performance where lower values correspond to better prediction performance. The IPA formula for a model is very much the same as the formula for R2 in a standard linear regression model:

\begin{equation*} \operatorname{IPA} = 1-\frac{\text{BrierScore(Prediction model)}}{\text{BrierScore(Null model)}} \end{equation*}

## 2 Package version

data.table:> [1] ‘1.12.2’

survival:> [1] ‘2.44.1.1’

riskRegression:> [1] ‘2019.11.3’

Publish:> [1] ‘2019.11.2’


## 3 Data

For the purpose of illustrating our software we simulate data alike the data of an active surveillance prostate cancer study 2. Specifically, we generate a learning set (n=278) and a validation set (n=208). In both data sets we define a binary outcome variable for the progression status after one year. Note that smallest censored event time is larger than 1 year, and hence the event status after one year is uncensored.

set.seed(18)
astrain <- simActiveSurveillance(278)
astest <- simActiveSurveillance(208)
astrain[,Y1:=1*(event==1 & time<=1)]
astest[,Y1:=1*(event==1 & time<=1)]


## 4 IPA for a binary outcome

To illustrate the binary outome setting we analyse the 1-year progression status. We have complete 1-year followup, i.e., no dropout or otherwise censored data before 1 year. We fit two logistic regression models, one including and one excluding the biomarker erg.status:

lrfit.ex <- glm(Y1~age+lpsaden+ppb5+lmax+ct1+diaggs,data=astrain,family="binomial")
publish(lrfit.inc,org=TRUE)

Variable Units OddsRatio CI.95 p-value
age   0.98 [0.90;1.06] 0.6459
ppb5   1.09 [0.92;1.28] 0.3224
lmax   1.08 [0.83;1.41] 0.5566
ct1 cT1 Ref
cT2 1.00 [0.29;3.41] 0.9994
diaggs GNA Ref
3/3 0.60 [0.27;1.34] 0.2091
3/4 0.25 [0.05;1.30] 0.1006
erg.status neg Ref
pos 3.66 [1.90;7.02] <0.0001

Based on these models we predict the risk of progression within one year in the validation set.

astest[,risk.ex:=100*predictRisk(lrfit.ex,newdata=astest)]
astest[,risk.inc:=100*predictRisk(lrfit.inc,newdata=astest)]

age lpsaden ppb5 lmax ct1 diaggs erg.status Y1 risk.ex risk.inc
62.6 -3.2 4.9 4.6 cT1 3/3 pos 0.0 23.2 36.3
66.9 -1.7 0.7 4.1 cT1 3/3 pos 1.0 14.0 24.7
65.4 -1.5 4.0 3.9 cT1 3/3 neg 0.0 17.4 10.6
59.0 -2.8 6.8 3.3 cT2 3/4 pos 1.0 10.7 21.1
55.6 -3.5 2.8 3.0 cT1 3/3 neg 0.0 21.9 11.8
71.1 -2.6 3.3 3.7 cT1 3/3 neg 0.0 15.0 9.5

To calculate the Index of Prediction Accuracy (IPA) we call the Score function as follows on a list which includes the two logistic regression models.

X1 <- Score(list("Exclusive ERG"=lrfit.ex,"Inclusive ERG"=lrfit.inc),data=astest,
formula=Y1~1,summary="ipa",se.fit=0L,metrics="brier",contrasts=FALSE)
X1


Metric Brier:

Results by model:

model Brier IPA
1:    Null model  15.2 0.0
2: Exclusive ERG  14.8 2.7
3: Inclusive ERG  14.1 7.3

NOTE: Values are multiplied by 100 and given in % (use print(...,percent=FALSE) to avoid this.

NOTE: The lower Brier the better, the higher IPA the better.


Both logistic regression models have a lower Brier score than the Null model which ignores all predictor variables. Hence, both models have a positive IPA. The logistic regression model which excludes the ERG biomarker scores IPA=2.68% and the logistic regression model which includes the ERG biomarer scores IPA = 7.29%. The difference in IPA between the two models is 4.62%. This means that when we omit erg.status from the model, then we loose 4.62% in IPA compared to the full model. It is sometimes interesting to compare the predictor variables according to how much they contribute to the prediction performance. Generally, this is a non-trivial task which depends on the order in which the variables are entered into the model, the functional form and also on the type of model. However, we can drop one variable at a time from the full model and for each variable compute the loss in IPA as the difference between IPA of the full model and IPA of the model where the variable is omitted.

IPA(lrfit.inc,newdata=astest)

     Variable Brier IPA IPA.drop
1: Null model  15.2 0.0      7.3
2: Full model  14.1 7.3      0.0
3:        age  14.1 7.4     -0.1
5:       ppb5  14.2 6.9      0.4
6:       lmax  14.1 7.2      0.1
7:        ct1  14.1 7.3     -0.0
8:     diaggs  14.6 4.4      2.9
9: erg.status  14.8 2.7      4.6

NOTE: Values are multiplied by 100 and given in % (use print(...,percent=FALSE) to avoid this.
NOTE: IPA.drop = IPA(Full model) - IPA.


## 5 IPA for right censored survival outcome

To illustrate the survival outome setting we analyse the 3-year progression-free survival probability. So, that the combined endpoint is progression or death. We fit two Cox regression models, one including and one excluding the biomarker erg.status:

coxfit.ex <- coxph(Surv(time,event!=0)~age+lpsaden+ppb5+lmax+ct1+diaggs,data=astrain,x=TRUE)
publish(coxfit.inc,org=TRUE)

Variable Units HazardRatio CI.95 p-value
age   1.03 [0.99;1.07] 0.124
ppb5   1.21 [1.12;1.30] <0.001
lmax   1.06 [0.94;1.19] 0.359
ct1 cT1 Ref
cT2 0.97 [0.57;1.66] 0.916
diaggs GNA Ref
3/3 0.53 [0.37;0.76] <0.001
3/4 0.32 [0.18;0.58] <0.001
erg.status neg Ref
pos 1.80 [1.35;2.38] <0.001

Based on these models we predict the risk of progression or death within 3 years in the validation set.

astest[,risk.ex:=100*predictRisk(coxfit.ex,newdata=astest,times=3)]
astest[,risk.inc:=100*predictRisk(coxfit.inc,newdata=astest,times=3)]

age lpsaden ppb5 lmax ct1 diaggs erg.status Y1 risk.ex risk.inc
62.6 -3.2 4.9 4.6 cT1 3/3 pos 0.0 67.5 80.7
66.9 -1.7 0.7 4.1 cT1 3/3 pos 1.0 48.5 60.3
65.4 -1.5 4.0 3.9 cT1 3/3 neg 0.0 67.4 60.8
59.0 -2.8 6.8 3.3 cT2 3/4 pos 1.0 51.1 70.1
55.6 -3.5 2.8 3.0 cT1 3/3 neg 0.0 41.5 35.5
71.1 -2.6 3.3 3.7 cT1 3/3 neg 0.0 65.5 57.5

To calculate the Index of Prediction Accuracy (IPA) we call the Score function as follows on a list which includes the two Cox regression models.

X2 <- Score(list("Exclusive ERG"=coxfit.ex,"Inclusive ERG"=coxfit.inc),data=astest,
formula=Surv(time,event!=0)~1,summary="ipa",se.fit=0L,metrics="brier",contrasts=FALSE,times=3)
X2


Metric Brier:

Results by model:

model times Brier  IPA
1:    Null model     3  24.0  0.0
2: Exclusive ERG     3  22.4  6.4
3: Inclusive ERG     3  19.9 17.1

NOTE: Values are multiplied by 100 and given in % (use print(...,percent=FALSE) to avoid this.

NOTE: The lower Brier the better, the higher IPA the better.


It is sometimes interesting to compare the predictor variables according to how much they contribute to the prediction performance. Generally, this is a non-trivial task which depends on the order in which the variables are entered into the model, the functional form and also on the type of model. However, we can drop one variable at a time from the full model and for each variable compute the loss in IPA as the difference between IPA of the full model and IPA of the model where the variable is omitted.

IPA(coxfit.inc,newdata=astest,times=3)

     Variable times Brier  IPA IPA.drop
1: Null model     3  24.0  0.0     17.1
2: Full model     3  19.9 17.1      0.0
3:        age     3  19.7 17.6     -0.6
4:    lpsaden     3  20.1 16.2      0.8
5:       ppb5     3  21.3 11.2      5.9
6:       lmax     3  19.9 16.7      0.4
7:        ct1     3  19.9 17.0      0.1
8:     diaggs     3  20.8 13.0      4.1
9: erg.status     3  22.4  6.4     10.7

NOTE: Values are multiplied by 100 and given in % (use print(...,percent=FALSE) to avoid this.
NOTE: IPA.drop = IPA(Full model) - IPA.


## 6 IPA for right censored time to event outcome with competing risks

To illustrate the competing risk setting we analyse the 3-year risk of progression in presence of the competing risk of death without progression. We fit two sets of cause-specific Cox regression models 3, one including and one excluding the biomarker erg.status:

cscfit.ex <- CSC(Hist(time,event)~age+lpsaden+ppb5+lmax+ct1+diaggs,data=astrain)
publish(cscfit.inc)

  Variable Units                1                2
age       1.04 [1.00;1.09] 1.01 [0.95;1.07]
ppb5       1.14 [1.04;1.24] 1.39 [1.22;1.58]
lmax       1.19 [1.03;1.39] 0.82 [0.67;1.00]
ct1   cT1             Ref              Ref
cT2 1.31 [0.73;2.36] 0.31 [0.07;1.28]
diaggs   GNA             Ref              Ref
3/3 0.54 [0.35;0.84] 0.56 [0.29;1.10]
3/4 0.44 [0.22;0.88] 0.19 [0.06;0.60]
erg.status   neg             Ref              Ref
pos 2.20 [1.56;3.11] 1.20 [0.71;2.04]


Based on these models we predict the risk of progression in presence of the competing risk of death within 3 years in the validation set.

astest[,risk.ex:=100*predictRisk(cscfit.ex,newdata=astest,times=3,cause=1)]
astest[,risk.inc:=100*predictRisk(cscfit.inc,newdata=astest,times=3,cause=1)]

age lpsaden ppb5 lmax ct1 diaggs erg.status Y1 risk.ex risk.inc
62.6 -3.2 4.9 4.6 cT1 3/3 pos 0.0 49.7 65.5
66.9 -1.7 0.7 4.1 cT1 3/3 pos 1.0 45.2 60.1
65.4 -1.5 4.0 3.9 cT1 3/3 neg 0.0 50.6 42.3
59.0 -2.8 6.8 3.3 cT2 3/4 pos 1.0 46.0 69.0
55.6 -3.5 2.8 3.0 cT1 3/3 neg 0.0 26.3 19.9
71.1 -2.6 3.3 3.7 cT1 3/3 neg 0.0 51.8 42.2

To calculate the Index of Prediction Accuracy (IPA) we call the Score function as follows on a list which includes the two sets of cause-specific Cox regression models.

X3 <- Score(list("Exclusive ERG"=cscfit.ex,
"Inclusive ERG"=cscfit.inc),
data=astest, formula=Hist(time,event)~1,
summary="ipa",se.fit=0L,metrics="brier",
contrasts=FALSE,times=3,cause=1)
X3


Metric Brier:

Results by model:

model times Brier  IPA
1:    Null model     3  24.5  0.0
2: Exclusive ERG     3  23.2  5.0
3: Inclusive ERG     3  20.2 17.5

NOTE: Values are multiplied by 100 and given in % (use print(...,percent=FALSE) to avoid this.

NOTE: The lower Brier the better, the higher IPA the better.


It is sometimes interesting to compare the predictor variables according to how much they contribute to the prediction performance. Generally, this is a non-trivial task which depends on the order in which the variables are entered into the model, the functional form and also on the type of model. However, we can drop one variable at a time from the full model (here from both cause-specific Cox regression models) and for each variable compute the loss in IPA as the difference between IPA of the full model and IPA of the model where the variable is omitted.

IPA(cscfit.inc,newdata=astest,times=3)

     Variable times Brier  IPA IPA.drop
1: Null model     3  24.5  0.0     17.5
2: Full model     3  20.2 17.5      0.0
3:        age     3  20.1 18.0     -0.5
4:    lpsaden     3  20.4 16.8      0.8
5:       ppb5     3  20.4 16.5      1.1
6:       lmax     3  21.4 12.6      4.9
7:        ct1     3  19.8 18.9     -1.4
8:     diaggs     3  20.8 14.8      2.8
9: erg.status     3  23.2  5.0     12.5

NOTE: Values are multiplied by 100 and given in % (use print(...,percent=FALSE) to avoid this.
NOTE: IPA.drop = IPA(Full model) - IPA.


## Footnotes:

1

Michael W Kattan and Thomas A Gerds. The index of prediction accuracy: An intuitive measure useful for evaluating risk prediction models. Diagnostic and Prognostic Research, 2(1):7, 2018.

2

Berg KD, Vainer B, Thomsen FB, Roeder MA, Gerds TA, Toft BG, Brasso K, and Iversen P. Erg protein expression in diagnostic specimens is associated with increased risk of progression during active surveillance for prostate cancer. European urology, 66(5):851–860, 2014.

3

Brice Ozenne, Anne Lyngholm S{\o }rensen, Thomas Scheike, Christian Torp-Pedersen, and Thomas Alexander Gerds. riskregression: Predicting the risk of an event using Cox regression models. R Journal, 9(2):440–460, 2017.

Last update: 04 Nov 2019 by Thomas Alexander Gerds.