# Estimate accuracy measures for risk prediction markers from survival data

This package provides a function to estimate the AUC, TPR(c), FPR(c), PPV(c), and NPV(c) for for a specific timepoint and marker cutoff value c using non-parametric and semi-parametric estimators. Standard errors and confidence intervals are also computed. Either analytic or bootstrap standard errors can be computed.

This R package computes non-parametric (NP) and semi-parametric (SP) estimates of common accuracy measures for risk prediction markers from survival data. It consists of the function `survAM.estimate` which estimates the AUC, TPR( c ), FPR( c ), PPV( c ), and NPV( c ) for for a specific prediction time and marker cutoff value c.

NP estimates are calculated using inverse probability weighting, while SP estimates are based on a Cox proportional hazards model. For detailed information regarding estimation methods, see references below.

Standard errors for estimates can be obtained by bootstrapping. Asymptotic standard error calculations are also available for semi-parametric Cox estimates. Confidence intervals using a normal approximation are computed.

``````##   survTime status        Y
## 1   0.1197      1  1.49310
## 2   1.0231      0 -0.73260
## 3   0.8282      0 -0.50211
## 4   2.0875      1  0.65758
## 5   4.6827      1  1.57806
## 6   0.3001      1  0.02419
``````

Estimate all measures, using the bootstrap to estimate standard errors. First we obtain non-parametric estimates using inverse probablity weighting:

``````##
## Non-Parametric IPW estimates of accuracy measures:
##    (SE's calculated using the bootstrap)
##
##         estimate     se      lower 0.95  upper 0.95
## AUC        0.786     0.034         0.712       0.845
## TPR(c)     0.769     0.050         0.658       0.852
## FPR(c)     0.410     0.025         0.363       0.460
## PPV(c)     0.231     0.026         0.184       0.285
## NPV(c)     0.941     0.017         0.897       0.967
##
##  marker cutpoint: c = 0
``````

Alternatively, we can calculate semi-parametric estimates based on a Cox proportional hazards model:

``````##
## Semi-Parametric Cox estimates of accuracy measures:
##    (SE's calculated using the bootstrap)
##
##         estimate     se      lower 0.95  upper 0.95
## coef       1.010     0.089         0.836       1.184
## AUC        0.768     0.021         0.724       0.807
## TPR(c)     0.788     0.030         0.723       0.841
## FPR(c)     0.412     0.021         0.372       0.455
## PPV(c)     0.241     0.026         0.194       0.295
## NPV(c)     0.943     0.008         0.925       0.958
##
##  marker cutpoint: c = 0
``````
``````##
## Semi-Parametric Cox estimates of accuracy measures:
##    (SE's calculated using asymptotic variance)
##
##         estimate     se      lower 0.95  upper 0.95
## coef       1.010     0.085         0.842       1.177
## AUC        0.768     0.020         0.727       0.805
## TPR(c)     0.788     0.027         0.731       0.836
## FPR(c)     0.412     0.023         0.368       0.459
## PPV(c)     0.241     0.028         0.191       0.300
## NPV(c)     0.943     0.009         0.924       0.958
##
##  marker cutpoint: c = 0
``````

For more information see `?survAM.estimate`.

### References

Liu D, Cai T, Zheng Y. Evaluating the predictive value of biomarkers with stratified case-cohort design. Biometrics 2012, 4: 1219-1227.

Pepe MS, Zheng Y, Jin Y. Evaluating the ROC performance of markers for future events. Lifetime Data Analysis. 2008, 14: 86-113.

Zheng Y, Cai T, Pepe MS, Levy, W. Time-dependent predictive values of prognostic biomarkers with failure time outcome. JASA 2008, 103: 362-368.

# survAccuracyMeasures 1.2

• initial release on CRAN, previously hosted on github.

# Reference manual

install.packages("survAccuracyMeasures")

1.2 by Marshall Brown, 5 years ago

https://github.com/mdbrown/survRpackages

Browse source code at https://github.com/cran/survAccuracyMeasures

Authors: Yingye Zheng , Tianxi Cai , and Marshall Brown

Documentation:   PDF Manual