Fit a Mixture Cure Rate (MCR) Survival Model

Description

Fits a cure rate model using a flexible link function and a variety of survival distributions. The model accounts for a cured fraction through a logistic-type link and estimates the model via an EM-like algorithm.

Usage

MCRfit(
  formula,
  data,
  dist = "weibull",
  link = "logit",
  tau = 1,
  maxit = 1000,
  tol = 1e-05
)

Arguments

Value

An object of class "MCR", which is a list containing:

Examples

require(EMGCR)

data(liver2)
names(liver2)
liver2$sex <- factor(liver2$sex)
liver2$grade <- factor(liver2$grade)
liver2$radio <- factor(liver2$radio)
liver2$chemo <- factor(liver2$chemo)
str(liver2)
model <- MCRfit(
  survival::Surv(time, status) ~ age + sex + grade + radio + chemo |
    age + medh + grade + radio + chemo,
  dist = "loglogistic",
  link = "plogit",
  tau = 0.15,
  data = liver2
)
model

Liver Cancer Data

Description

A sample of 2,766 patients diagnosed with liver cancer between 2012 and 2016, whose cancer grades were well identified. Available individual-level covariates include age at diagnosis, sex, pathological grade of the liver cancer, number of relapses, and median household income.

Usage

data(liver)

Format

A data frame with 2,766 observations and 10 variables:

ID

Unique patient identifier

age

Age as a factor (e.g., age group)

ageNumeric

Age as a numeric variable

grade

Pathological grade of liver cancer (I, II, III, IV)

medh

Median household income as factor (possibly grouped)

medhNumeric

Median household income as numeric

relapse

Number of relapses after first diagnosis

sex

Sex of patient: 1 = male, 0 = female

status

Event indicator: 1 = death, 0 = censored

time

Survival time in months

Details

The grade of the disease is categorized into four levels:

1. Grade I – Well differentiated

2. Grade II – Moderately differentiated

3. Grade III – Poorly differentiated

4. Grade IV – Undifferentiated/anaplastic

Examples

data(liver)
head(liver)

Liver Cancer Data 2

Description

A sample of 1,736 patients who have been diagnosed with liver cancer between 2012 and 2016, whose cancer grades are well identified. Available individual-level covariates include age at diagnosis, sex, race, grade of liver cancer, median household income, time to treatment, tumor size, radiation indicator, and chemotherapy indicator. The grade of diseas is categorized into four levels: well-differentiated (Grade I), moderately differentiated (Grade II), poorly differentiated (Grade III), and undifferentiated/anaplastic (Grade IV).

Usage

data(liver2)

Format

A data frame with 1,736 observations and 10 variables:

ID

Unique patient identifier

time

Survival time in months

status

censored = 0, dead due to liver cancer = 1

sex

Sex of patient: Male=1, Female=0

age

Scaled age of diagnosis

medh

Scaled median household income of the subject

race

While = 1, Other = 0

grade

Pathological grade of liver cancer

chemo

chemotherapy = 1, non = 0

radio

radiation = 1, non = 0

Examples

data(liver2)
head(liver2)

Plot multiple MCR model fits against Kaplan-Meier curve

Description

Plot multiple MCR model fits against Kaplan-Meier curve

Usage

## S3 method for class 'MCR'
plot(...)

Arguments

Value

A ggplot object with Kaplan-Meier and survival curves for each model.

QQ-Plot of Residuals for MCR Model

Description

Produces a Q-Q plot of residuals from a Mixture Cure Rate (MCR) model fitted via MCRfit. Optionally, a simulation envelope can be included for Cox-Snell residuals.

Usage

qqMCR(
  object,
  type = c("cox-snell", "quantile"),
  envelope = FALSE,
  nsim = 100,
  censor = NULL,
  ...
)

Arguments

Details

The function generates QQ-plots of either Cox-Snell or quantile residuals. When envelope = TRUE and type = "cox-snell", a simulation envelope is added using Monte Carlo replications.

Value

A QQ-plot is produced as a side effect. Nothing is returned.

See Also

MCRfit, residuals.MCR

Examples

data(liver)
fit <- MCRfit(survival::Surv(time, status) ~ age + medh + relapse + grade | sex + grade,
              data = liver, dist = "weibull", link = "logit")
qqMCR(fit, type = "quantile", envelope = TRUE, nsim = 50, censor = liver$status)

Generate Random Samples for Mixture Cure Rate (MCR) Model

Description

Simulates survival data from a mixture cure rate model with covariates and user-defined link and latency distributions. Censoring is applied randomly.

Usage

rMCM(
  n,
  x,
  w,
  censor,
  alpha,
  beta,
  eta,
  dist = "weibull",
  link = "logit",
  tau = 1
)

Arguments

Value

A list with elements:

time

Observed (possibly censored) survival time.

status

Event indicator (1 = event, 0 = censored).

x

Covariate matrix for the latency component.

w

Covariate matrix for the cure component.

pCcensur

Percentage of cured individuals.

pUCcensur

Percentage of censored cases among the uncured.

Examples

# Example: Simulating survival data using the inverse Gaussian distribution
library(EMGCR)

n <- 500
beta <- c(1, -1, -2)
eta <- c(0.5, -0.5)
alpha <- 1.5

p <- length(beta)
q <- length(eta)

set.seed(10)
X <- matrix(rnorm(n*(p-1),0,1),n,p-1)
X <- cbind(1,X)

set.seed(20)
W <- matrix(runif(n*q,-1,1),n,q)
W <- scale(W)

max_censoring <- 10

set.seed(1234)
sim_data <- rMCM(n=n, x = X, w = W,
                 censor = max_censoring,
                 beta = beta, eta = eta,
                 alpha = alpha,
                 link = "logit", dist = "invgauss", tau = 1)

names(sim_data)
head(sim_data)
attributes(sim_data)
attr(sim_data, "pCcensur")
attr(sim_data, "pUCcensur")

Compute residuals for MCR model

Description

This function computes Global Cox-Snell and randomized quantile residuals for objects of class MCR.

Usage

## S3 method for class 'MCR'
residuals(object, type = c("cox-snell", "quantile"), ...)

Arguments

Value

A numeric vector of residuals.

Examples

data(liver)
names(liver)

model <- MCRfit(
 survival::Surv(time, status) ~ age + medh + relapse + grade | sex + age + medh + grade,
 data = liver
)
summary(residuals(model,type="quantile"))