# 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 R^{2} in a
standard linear regression model:

## 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") lrfit.inc <- glm(Y1~age+lpsaden+ppb5+lmax+ct1+diaggs+erg.status,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 | |

lpsaden | 0.95 | [0.66;1.36] | 0.7747 | |

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)]
publish(head(astest[,-c(8,9)]),digits=1,org=TRUE)
```

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 4: lpsaden 14.1 7.6 -0.3 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) coxfit.inc <- coxph(Surv(time,event!=0)~age+lpsaden+ppb5+lmax+ct1+diaggs+erg.status,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 | |

lpsaden | 1.10 | [0.94;1.29] | 0.230 | |

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)]
publish(head(astest[,-c(8,9)]),digits=1,org=TRUE)
```

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

Variable Units 1 2 age 1.04 [1.00;1.09] 1.01 [0.95;1.07] lpsaden 1.13 [0.92;1.38] 1.09 [0.83;1.42] 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)]
publish(head(astest[,-c(8,9)]),digits=1,org=TRUE)
```

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.