PwePred (version 1.0.0)

pwexpm_fit: Fit the Piecewise Exponential Distribution

Description

Fit the piecewise exponential distribution with right censoring data. User can specifity all breakpoints, some of the breakpoints or let the function estimate the breakpoints.

Usage

pwexpm_fit(time, event, breakpoint=NULL, nbreak=0, exclude_int=NULL, min_pt_tail=5,
        max_set=10000, seed=1818, trace=FALSE, optimizer='mle', tol=1e-4)

Value

A object of class "pwexpm" is a list containing the following components:

brk

estimated breakpoints.

lam

estimated piecewise hazard rates.

logLik

the log-likelihood of the model.

AIC

the Akaike information criterion of the model.

BIC

the Bayesian information criterion of the model.

para

the parameters used to estimate the model.

The generic accessor functions AIC, BIC, logLik can be used to extract various useful statistics from pwexpm. The plot function can be used to make a simple plot for pwexpm.

Arguments

time

observed time from randomization. For right censored data, this is the follow-up time.

event

the status indicator, normally 0=censor, 1=event. Other choices are TRUE/FALSE (TRUE = event).

breakpoint

fixed breakpoints. Pre-specifity some breakpionts. The maximum value must be earlier than the last event time.

nbreak

total number of breakpoints in the model. This number includes the points specified in breakpoint. If nbreak=NULL, then nbreak=ceiling(8*(# unique events)^0.2).

exclude_int

an interval that excludes any estimated breakpoints (e.g., exclude_int=c(10,Inf) will exclude any estimated breakpoints after t=10). See details.

min_pt_tail

the minimum number of events used for estimating the tail (the hazard rate of the last piece). See details.

max_set

maximum estimated combination of breakpoints.

seed

a random seed. Do not set seed if seed=NULL.

trace

(internal use) logical; if TRUE, the returned data frame contains the log-likelihood of all possible breakpoints instead of the one with maximum likelihood.

optimizer

one of the optimizers: mle, ols, or hybrid.

tol

the minimum allowed gap between two breakpoints. The gap is calculated as (max(time)-min(time))*tol. Keep it as default in most cases.

Author

Tianchen Xu

Details

See webpage https://zjph602xtc.github.io/PWEXP/ for a detailed description of the model and optimizers.

If user specifies breakpoint, we will check the values to make the model identifiable. Any breakpoints after the last event time will be removed; Any breakpoints before the first event time will be removed; a mid-point will be used if there are NO events between any two concesutive breakpoints. A warning will be given.

If user sets nbreak=NULL, then the function will automatically apply nbreak=ceiling(8*(# unique events)^0.2). This empirical number of breakpoints is for the reference below, and it may be too large in many cases.

Argument exclude_int is a vector of two values such as exclude_int=c(a, b) (b can be Inf). It defines an interval that excludes any estimated breakpoints. It is helpful when excluding breakpoints that are too close to the tail.

In order to obtain a more robust hazard rate estimation of the tail, user can set min_pt_tail to the minimum number of events for estimating the tail (last piece of the piecewise exponential). It only works for optimizer='mle'.

References

Muller, H. G., & Wang, J. L. (1994). Hazard rate estimation under random censoring with varying kernels and bandwidths. Biometrics, 61-76.

See Also

pwexpm, boot.pwexpm, cv.pwexpm

Examples

event_dist <- function(n)rpwexpm(n, rate=c(0.1, 0.01, 0.2), breakpoint=c(5,14))
dat <- simdata(rand_rate = 20, total_sample = 1000, drop_rate = 0.03,
               advanced_dist = list(event_dist=event_dist),
               add_column = c('censor_reason','event','followT','followT_abs'))
cut <- quantile(dat$randT, 0.8)
train <- cut_dat(var_randT = 'randT', cut = cut, data = dat,
                 var_followT = 'followT', var_followT_abs = 'followT_abs',
                 var_event = 'event', var_censor_reason = 'censor_reason')

fit_a0 <- pwexpm_fit(train$followT, train$event, breakpoint = c(5,14))
fit_a1 <- pwexpm_fit(train$followT, train$event, nbreak = 2, breakpoint = c(14))
fit_b0 <- pwexpm_fit(train$followT, train$event, nbreak = 0)
fit_b1 <- pwexpm_fit(train$followT, train$event, nbreak = 1)
fit_b2 <- pwexpm_fit(train$followT, train$event, nbreak = 2)