diagnostic_assessment_binary {ThresholdROCsurvival}

Diagnostic ability assessment for binary diagnostic tests

Description

This function estimates sensitivity and specificity at a fixed time-point t for binary diagnostic tests with survival data by using two methods: 1) unknown status exclusion (USE), which excludes subjects with unknown status at t; and 2) imputation of censored times (ICT), a method based on multiple imputation. The status of the subjects at a certain time-point of interest t (the event occurred before or at t or not) is defined by the time-to-event variable.

Usage

diagnostic_assessment_binary(binary.var, time, status, predict.time,
                             method=c("USE", "ICT"), index=c("all", "sens", "spec"),
                             m=10, ci=TRUE, alpha=0.05, range=3)

Arguments

binary.var

binary variable to be used as predictor of the status. It should be a factor which two levels: - (negative, which indicates absence of the event) and + (positive, which indicates presence of the event)

time

survival time

status

censoring status codified as 0=censored, 1=event

predict.time

time-point of interest to define the subjects' status as event present or absent

method

method to be used in the estimation process. The user can choose between USE (unknown status exclusion) or ICT (imputation of censored times). Default, USE

index

indices to be estimated. The user can choose one or more of the following: sens and spec. The option all (default) estimates all two indices

m

the number of data sets to impute. Default, 10

ci

Should a confidence interval be calculated? Default, TRUE

alpha

significance level for the confidence interval. Default, 0.05

range

this value, which is passed to boxplot function from graphics package, determines the data points that are considered to be extreme in the estimates and standard errors from the multiple imputation process. We consider extreme observations those that exceed range times the interquartile range. If extreme observations are found in the estimates or standard errors from the multiple imputation process, Winsorized estimators (Wilcox, 2012) are used for the point estimate and the between and within variances. Default, 3

Details

When method is USE: First, the algorithm determines the status of the subjects at time predict.time. Those censored subjects whose status could be not be determined are excluded from the analysis. Then, diagnostic ability is assessed with standard methods in the binary setting.

When method is ICT: First, the algorithm determines the status of the subjects at time predict.time. For those subjects whose status could not be determined because their censored time is lower than t, we impute survival times using the method of Hsu et al (2006), that is implemented in the package InformativeCensoring (Ruau et al, 2020). The status of the subjects is then determined by these imputed times and is used to estimate the indices in index. Confidence intervals are calculated using the standard error proposed by Rubin (1987).

Value

An object of class diagnostic_assessment, which is a list with the following components:

sens

Sensitivity estimate and its corresponding confidence interval (if ci=TRUE), only if sensitivity has been included in index

spec

Specificity estimate and its corresponding confidence interval (if ci=TRUE), only if specificity has been included in index

method

method used in the estimation

alpha

significance level provided by the user

data

A data.frame containing the following columns previously provided by the user: cont.var, time and status, and a new column statusNA, which contains the status of the subjects at time predict.time (0=no event, 1=event, NA=unknown)

References

Heagerty PJ, Lumley T, Pepe MS. Time-Dependent ROC Curves for Censored Survival Data and a Diagnostic Marker. Biometrics 2000; 56(2): 337-344. doi: 10.1111/j.0006-341X.2000.00337.x

Heagerty PJ, Saha-Chaudhuri P (2013). survivalROC: Time-dependent ROC curve estimation from censored survival data. R package version 1.0.3. https://CRAN.R-project.org/package=survivalROC

Hsu CH, Taylor JMG, Murray S, Commenges D. Survival analysis using auxiliary variables via non-parametric multiple imputation. Statistics in Medicine 2006; 25(20): 3503-3517. doi: https://doi.org/10.1002/sim.2452

Ruau D, Burkoff N, Bartlett J, Jackson D, Jones E, Law M and Metcalfe P (2020). InformativeCensoring: Multiple Imputation for Informative Censoring. R package version 0.3.5. https://CRAN.R-project.org/package=InformativeCensoring

Rubin DB. Multiple Imputation for Nonresponse in Surveys. Wiley Series in Probability and Statistics. John Wiley & Sons (1987)

Wilcox, R. Introduction to Robust Estimation and Hypothesis Testing. 3rd Edition. Elsevier, Amsterdam (2012)

Zhou XH, Obuchowski NA and McClish DK. Statistical methods in diagnostic medicine. John Wiley and sons (2002)

See Also

diagnostic_assessment_continuous

Examples

data(NSCLC)
NSCLC$COL_cat <- factor(ifelse(NSCLC$COL>=10, "+", "-"))
set.seed(2020)
with(NSCLC, diagnostic_assessment_binary(COL_cat, OS, ST,
     1095, method="ICT", m=10, ci=TRUE))