broom (version 0.4.1)

cch_tidiers: tidiers for case-cohort data

Description

Tidiers for case-cohort analyses: summarize each estimated coefficient, or test the overall model.

Usage

"tidy"(x, conf.level = 0.95, ...)
"glance"(x, ...)

Arguments

x

a "cch" object

conf.level

confidence level for CI

...

extra arguments (not used)

Value

All tidying methods return a data.frame without rownames, whose structure depends on the method chosen.tidy returns a data.frame with one row for each term

term

name of term

estimate

estimate of coefficient

stderror

standard error

statistic

Z statistic

p.value

p-value

conf.low

low end of confidence interval

conf.high

high end of confidence interval

glance returns a one-row data.frame with the following columns:

score

score

rscore

rscore

p.value

p-value from Wald test

iter

number of iterations

n

number of predictions

nevent

number of events

Details

It is not clear what an augment method would look like, so none is provided. Nor is there currently any way to extract the covariance or the residuals.

See Also

cch

Examples

if (require("survival", quietly = TRUE)) {
    # examples come from cch documentation
    subcoh <- nwtco$in.subcohort
    selccoh <- with(nwtco, rel==1|subcoh==1)
    ccoh.data <- nwtco[selccoh,]
    ccoh.data$subcohort <- subcoh[selccoh]
    ## central-lab histology 
    ccoh.data$histol <- factor(ccoh.data$histol,labels=c("FH","UH"))
    ## tumour stage
    ccoh.data$stage <- factor(ccoh.data$stage,labels=c("I","II","III" ,"IV"))
    ccoh.data$age <- ccoh.data$age/12 # Age in years
    
    fit.ccP <- cch(Surv(edrel, rel) ~ stage + histol + age, data = ccoh.data,
                   subcoh = ~subcohort, id= ~seqno, cohort.size = 4028)
    
    tidy(fit.ccP)
    
    # coefficient plot
    library(ggplot2)
    ggplot(tidy(fit.ccP), aes(x = estimate, y = term)) + geom_point() +
        geom_errorbarh(aes(xmin = conf.low, xmax = conf.high), height = 0) +
        geom_vline(xintercept = 0)
    
    # compare between methods
    library(dplyr)
    fits <- data_frame(method = c("Prentice", "SelfPrentice", "LinYing")) %>%
        group_by(method) %>%
        do(tidy(cch(Surv(edrel, rel) ~ stage + histol + age, data = ccoh.data,
                    subcoh = ~subcohort, id= ~seqno, cohort.size = 4028,
                    method = .$method)))
    
    # coefficient plots comparing methods
    ggplot(fits, aes(x = estimate, y = term, color = method)) + geom_point() +
        geom_errorbarh(aes(xmin = conf.low, xmax = conf.high)) +
        geom_vline(xintercept = 0)
}