Confidence interval

Description

This is a confint method for class "stdCoxph".

Usage

## S3 method for class 'stdCoxph'
confint(object, parm, level = 0.95, fun, type="plain", ...)

Arguments

Details

confint.stdCoxph extracts the est element from object, and inputs this to fun. It then uses the delta method to compute a confidence interval for the output of fun.

Value

a matrix with q rows and 2 columns, containing the computed confidence interval.

Author(s)

Arvid Sjolander.

Confidence interval

Description

This is a confint method for class "stdGee".

Usage

## S3 method for class 'stdGee'
confint(object, parm, level = 0.95, fun, type="plain", ...)

Arguments

Details

confint.stdGee extracts the est element from object, and inputs this to fun. It then uses the delta method to compute a confidence interval for the output of fun.

Value

a matrix with 1 row and 2 columns, containing the computed confidence interval.

Author(s)

Arvid Sjolander.

Confidence interval

Description

This is a confint method for class "stdGlm".

Usage

## S3 method for class 'stdGlm'
confint(object, parm, level = 0.95, fun, type="plain", ...)

Arguments

Details

confint.stdGlm extracts the est element from object, and inputs this to fun. It then uses the delta method to compute a confidence interval for the output of fun.

Value

a matrix with 1 row and 2 columns, containing the computed confidence interval.

Author(s)

Arvid Sjolander.

Confidence interval

Description

This is a confint method for class "stdParfrailty".

Usage

## S3 method for class 'stdParfrailty'
confint(object, parm, level = 0.95, fun, type="plain", ...)

Arguments

Details

confint.stdParfrailty extracts the est element from object, and inputs this to fun. It then uses the delta method to compute a confidence interval for the output of fun.

Value

a matrix with q rows and 2 columns, containing the computed confidence interval.

Author(s)

Arvid Sjolander.

Fits shared frailty gamma-Weibull models

Description

parfrailty fits shared frailty gamma-Weibull models. It is specifically designed to work with the function stdParfrailty, which performs regression standardization in shared frailty gamma-Weibull models.

Usage

parfrailty(formula, data, clusterid, init)

Arguments

Details

parfrailty fits the shared frailty gamma-Weibull model

\lambda(t_{ij}|C_{ij})=\lambda(t_{ij};\alpha,\eta)U_iexp\{h(C_{ij};\beta)\},

where t_{ij} and C_{ij} are the survival time and covariate vector for subject j in cluster i, respectively. \lambda(t;\alpha,\eta) is the Weibull baseline hazard function

\eta t^{\eta-1}\alpha^{-\eta},

where \eta is the shape parameter and \alpha is the scale parameter. U_i is the unobserved frailty term for cluster i, which is assumed to have a gamma distribution with scale = 1/shape = \phi. h(X;\beta) is the regression function as specified by the formula argument, parametrized by a vector \beta. The ML estimates \{log(\hat{\alpha}),log(\hat{\eta}),log(\hat{\phi}),\hat{\beta}\} are obtained by maximizing the marginal (over U) likelihood.

Value

An object of class "parfrailty" is a list containing:

Note

If left truncation is present, it is assumed that it is strong left truncation. This means that, even if the truncation time may be subject-specific, the whole cluster is unobserved if at least one subject in the cluster dies before his/her truncation time. If all subjects in the cluster survive beyond their subject-specific truncation times, then the whole cluster is observed (Van den Berg and Drepper, 2016).

Author(s)

Arvid Sjolander and Elisabeth Dahlqwist.

References

Dahlqwist E., Pawitan Y., Sjolander A. (2019). Regression standardization and attributable fraction estimation with between-within frailty models for clustered survival data. Statistical Methods in Medical Research 28(2), 462-485.

Van den Berg G.J., Drepper B. (2016). Inference for shared frailty survival models with left-truncated data. Econometric Reviews, 35(6), 1075-1098.

Examples

## Not run: 
require(survival)

#simulate data
n <- 1000
m <- 3
alpha <- 1.5
eta <- 1
phi <- 0.5
beta <- 1
id <- rep(1:n, each=m)
U <- rep(rgamma(n, shape=1/phi,scale=phi), each=m)
X <- rnorm(n*m)
#reparametrize scale as in rweibull function
weibull.scale <- alpha/(U*exp(beta*X))^(1/eta)
T <- rweibull(n*m, shape=eta, scale=weibull.scale)

#right censoring
C <- runif(n*m, 0,10)
D <- as.numeric(T<C)
T <- pmin(T, C)
             
#strong left-truncation
L <- runif(n*m, 0, 2)
incl <- T>L
incl <- ave(x=incl, id, FUN=sum)==m
dd <- data.frame(L, T, D, X, id)
dd <- dd[incl, ]  
 
fit <- parfrailty(formula=Surv(L, T, D)~X, data=dd, clusterid="id")
print(summary(fit))


## End(Not run)

Plots Cox regression standardization fit

Description

This is a plot method for class "stdCoxph".

Usage

## S3 method for class 'stdCoxph'
plot(x, plot.CI = TRUE, CI.type = "plain", CI.level = 0.95,
  transform = NULL, contrast = NULL, reference = NULL, legendpos="bottomleft", ...)

Arguments

Author(s)

Arvid Sjolander

See Also

stdCoxph

Examples

##See documentation for stdCoxph

Plots GEE regression standardization fit

Description

This is a plot method for class "stdGee".

Usage

## S3 method for class 'stdGee'
plot(x, CI.type = "plain", CI.level = 0.95,
  transform = NULL, contrast = NULL, reference = NULL, ...)

Arguments

Author(s)

Arvid Sjolander

See Also

stdGee

Examples

##See documentation for stdGee

Plots GLM regression standardization fit

Description

This is a plot method for class "stdGlm".

Usage

## S3 method for class 'stdGlm'
plot(x, CI.type = "plain", CI.level = 0.95,
  transform = NULL, contrast = NULL, reference = NULL, ...)

Arguments

Author(s)

Arvid Sjolander

See Also

stdGlm

Examples

##See documentation for stdGlm

Plots parfrailty standardization fit

Description

This is a plot method for class "stdParfrailty".

Usage

## S3 method for class 'stdParfrailty'
plot(x, plot.CI = TRUE, CI.type = "plain", CI.level = 0.95,
  transform = NULL, contrast = NULL, reference = NULL, legendpos="bottomleft", ...)

Arguments

Author(s)

Arvid Sjolander

See Also

stdParfrailty

Examples

##See documentation for stdParfrailty

Prints summary of parfrailty fit

Description

This is a print method for class "summary.parfrailty".

Usage

## S3 method for class 'summary.parfrailty'
print(x, digits = max(3L, getOption("digits") - 3L),
                             ...)

Arguments

Author(s)

Arvid Sjolander and Elisabeth Dahlqwist

See Also

parfrailty

Examples

##See documentation for frailty

Prints summary of Cox regression standardization fit

Description

This is a print method for class "summary.stdCoxph".

Usage

## S3 method for class 'summary.stdCoxph'
print(x, ...)

Arguments

Author(s)

Arvid Sjolander

See Also

stdCoxph

Examples

##See documentation for stdCoxph

Prints summary of GEE regression standardization fit

Description

This is a print method for class "summary.stdGee".

Usage

## S3 method for class 'summary.stdGee'
print(x, ...)

Arguments

Author(s)

Arvid Sjolander

See Also

stdGee

Examples

##See documentation for stdGee

Prints summary of GLM regression standardization fit

Description

This is a print method for class "summary.stdGlm".

Usage

## S3 method for class 'summary.stdGlm'
print(x, ...)

Arguments

Author(s)

Arvid Sjolander

See Also

stdGlm

Examples

##See documentation for stdGlm

Prints summary of Frailty standardization fit

Description

This is a print method for class "summary.stdParfrailty".

Usage

## S3 method for class 'summary.stdParfrailty'
print(x, ...)

Arguments

Author(s)

Arvid Sjolander

See Also

stdParfrailty

Examples

##See documentation for stdParfrailty

Regression standardization in Cox proportional hazards models

Description

stdCoxph performs regression standardization in Cox proportional hazards models, at specified values of the exposure, over the sample covariate distribution. Let T, X, and Z be the survival outcome, the exposure, and a vector of covariates, respectively. stdCoxph uses a fitted Cox proportional hazards model to estimate the standardized survival function \theta(t,x)=E\{S(t|X=x,Z)\}, where t is a specific value of T, x is a specific value of X, and the expectation is over the marginal distribution of Z.

Usage

stdCoxph(fit, data, X, x, t, clusterid, subsetnew)

Arguments

Details

stdCoxph assumes that a Cox proportional hazards model

\lambda(t|X,Z)=\lambda_0(t)exp\{h(X,Z;\beta)\}

has been fitted. Breslow's estimator of the cumulative baseline hazard \Lambda_0(t)=\int_0^t\lambda_0(u)du is used together with the partial likelihood estimate of \beta to obtain estimates of the survival function S(t|X=x,Z):

\hat{S}(t|X=x,Z)=exp[-\hat{\Lambda}_0(t)exp\{h(X=x,Z;\hat{\beta})\}].

For each t in the t argument and for each x in the x argument, these estimates are averaged across all subjects (i.e. all observed values of Z) to produce estimates

\hat{\theta}(t,x)=\sum_{i=1}^n \hat{S}(t|X=x,Z_i)/n,

where Z_i is the value of Z for subject i, i=1,...,n. The variance for \hat{\theta}(t,x) is obtained by the sandwich formula.

Value

An object of class "stdCoxph" is a list containing

Note

Standardized survival functions are sometimes referred to as (direct) adjusted survival functions in the literature.

stdCoxph does not currently handle time-varying exposures or covariates.

stdCoxph internally loops over all values in the t argument. Therefore, the function will usually be considerably faster if length(t) is small.

The variance calculation performed by stdCoxph does not condition on the observed covariates \bar{Z}=(Z_1,...,Z_n). To see how this matters, note that

var\{\hat{\theta}(t,x)\}=E[var\{\hat{\theta}(t,x)|\bar{Z}\}]+var[E\{\hat{\theta}(t,x)|\bar{Z}\}].

The usual parameter \beta in a Cox proportional hazards model does not depend on \bar{Z}. Thus, E(\hat{\beta}|\bar{Z}) is independent of \bar{Z} as well (since E(\hat{\beta}|\bar{Z})=\beta), so that the term var[E\{\hat{\beta}|\bar{Z}\}] in the corresponding variance decomposition for var(\hat{\beta}) becomes equal to 0. However, \theta(t,x) depends on \bar{Z} through the average over the sample distribution for Z, and thus the term var[E\{\hat{\theta}(t,x)|\bar{Z}\}] is not 0, unless one conditions on \bar{Z}. The variance calculation by Gail and Byar (1986) ignores this term, and thus effectively conditions on \bar{Z}.

Author(s)

Arvid Sjolander

References

Chang I.M., Gelman G., Pagano M. (1982). Corrected group prognostic curves and summary statistics. Journal of Chronic Diseases 35, 669-674.

Gail M.H. and Byar D.P. (1986). Variance calculations for direct adjusted survival curves, with applications to testing for no treatment effect. Biometrical Journal 28(5), 587-599.

Makuch R.W. (1982). Adjusted survival curve estimation using covariates. Journal of Chronic Diseases 35, 437-443.

Sjolander A. (2016). Regression standardization with the R-package stdReg. European Journal of Epidemiology 31(6), 563-574.

Sjolander A. (2016). Estimation of causal effect measures with the R-package stdReg. European Journal of Epidemiology 33(9), 847-858.

Examples

require(survival)

n <- 1000
Z <- rnorm(n)
X <- rnorm(n, mean=Z)
T <- rexp(n, rate=exp(X+Z+X*Z)) #survival time
C <- rexp(n, rate=exp(X+Z+X*Z)) #censoring time
U <- pmin(T, C) #time at risk
D <- as.numeric(T < C) #event indicator
dd <- data.frame(Z, X, U, D)
fit <- coxph(formula=Surv(U, D)~X+Z+X*Z, data=dd, method="breslow")
fit.std <- stdCoxph(fit=fit, data=dd, X="X", x=seq(-1,1,0.5), t=1:5)
print(summary(fit.std, t=3))
plot(fit.std)

Regression standardization in conditional generalized estimating equations

Description

stdGee performs regression standardization in linear and log-linear fixed effects models, at specified values of the exposure, over the sample covariate distribution. Let Y, X, and Z be the outcome, the exposure, and a vector of covariates, respectively. It is assumed that data are clustered with a cluster indicator i. stdGee uses fitted fixed effects model, with cluster-specific intercept a_i (see details), to estimate the standardized mean \theta(x)=E\{E(Y|i,X=x,Z)\}, where x is a specific value of X, and the outer expectation is over the marginal distribution of (a_i,Z).

Usage

stdGee(fit, data, X, x, clusterid, subsetnew)

Arguments

Details

stdGee assumes that a fixed effects model

\eta\{E(Y|i,X,Z)\}=a_i+h(X,Z;\beta)

has been fitted. The link function \eta is assumed to be the identity link or the log link. The conditional generalized estimating equation (CGGE) estimate of \beta is used to obtain estimates of the cluster-specific means:

\hat{a}_i=\sum_{j=1}^{n_i}r_{ij}/n_i,

where

r_{ij}=Y_{ij}-h(X_{ij},Z_{ij};\hat{\beta})

if \eta is the identity link, and

r_{ij}=Y_{ij}exp\{-h(X_{ij},Z_{ij};\hat{\beta})\}

if \eta is the log link, and (X_{ij},Z_{ij}) is the value of (X,Z) for subject j in cluster i, j=1,...,n_i, i=1,...,n. The CGEE estimate of \beta and the estimate of a_i are used to estimate the mean E(Y|i,X=x,Z):

\hat{E}(Y|i,X=x,Z)=\eta^{-1}\{\hat{a}_i+h(X=x,Z;\hat{\beta})\}.

For each x in the x argument, these estimates are averaged across all subjects (i.e. all observed values of Z and all estimated values of a_i) to produce estimates

\hat{\theta}(x)=\sum_{i=1}^n \sum_{j=1}^{n_i} \hat{E}(Y|i,X=x,Z_i)/N,

where N=\sum_{i=1}^n n_i. The variance for \hat{\theta}(x) is obtained by the sandwich formula.

Value

An object of class "stdGee" is a list containing

Note

The variance calculation performed by stdGee does not condition on the observed covariates \bar{Z}=(Z_{11},...,Z_{nn_i}). To see how this matters, note that

var\{\hat{\theta}(x)\}=E[var\{\hat{\theta}(x)|\bar{Z}\}]+var[E\{\hat{\theta}(x)|\bar{Z}\}].

The usual parameter \beta in a generalized linear model does not depend on \bar{Z}. Thus, E(\hat{\beta}|\bar{Z}) is independent of \bar{Z} as well (since E(\hat{\beta}|\bar{Z})=\beta), so that the term var[E\{\hat{\beta}|\bar{Z}\}] in the corresponding variance decomposition for var(\hat{\beta}) becomes equal to 0. However, \theta(x) depends on \bar{Z} through the average over the sample distribution for Z, and thus the term var[E\{\hat{\theta}(x)|\bar{Z}\}] is not 0, unless one conditions on \bar{Z}.

Author(s)

Arvid Sjolander.

References

Goetgeluk S. and Vansteelandt S. (2008). Conditional generalized estimating equations for the analysis of clustered and longitudinal data. Biometrics 64(3), 772-780.

Martin R.S. (2017). Estimation of average marginal effects in multiplicative unobserved effects panel models. Economics Letters 160, 16-19.

Sjolander A. (2019). Estimation of marginal causal effects in the presence of confounding by cluster. Biostatistics doi: 10.1093/biostatistics/kxz054

Examples

require(drgee)

n <- 1000
ni <- 2
id <- rep(1:n, each=ni)
ai <- rep(rnorm(n), each=ni)
Z <- rnorm(n*ni)
X <- rnorm(n*ni, mean=ai+Z)
Y <- rnorm(n*ni, mean=ai+X+Z+0.1*X^2)
dd <- data.frame(id, Z, X, Y)
fit <- gee(formula=Y~X+Z+I(X^2), data=dd, clusterid="id", link="identity",
  cond=TRUE)
fit.std <- stdGee(fit=fit, data=dd, X="X", x=seq(-3,3,0.5), clusterid="id")
print(summary(fit.std, contrast="difference", reference=2))
plot(fit.std)

Regression standardization in generalized linear models

Description

stdGlm performs regression standardization in generalized linear models, at specified values of the exposure, over the sample covariate distribution. Let Y, X, and Z be the outcome, the exposure, and a vector of covariates, respectively. stdGlm uses a fitted generalized linear model to estimate the standardized mean \theta(x)=E\{E(Y|X=x,Z)\}, where x is a specific value of X, and the outer expectation is over the marginal distribution of Z.

Usage

stdGlm(fit, data, X, x, clusterid, case.control = FALSE, subsetnew)

Arguments

Details

stdGlm assumes that a generalized linear model

\eta\{E(Y|X,Z)\}=h(X,Z;\beta)

has been fitted. The maximum likelihood estimate of \beta is used to obtain estimates of the mean E(Y|X=x,Z):

\hat{E}(Y|X=x,Z)=\eta^{-1}\{h(X=x,Z;\hat{\beta})\}.

For each x in the x argument, these estimates are averaged across all subjects (i.e. all observed values of Z) to produce estimates

\hat{\theta}(x)=\sum_{i=1}^n \hat{E}(Y|X=x,Z_i)/n,

where Z_i is the value of Z for subject i, i=1,...,n. The variance for \hat{\theta}(x) is obtained by the sandwich formula.

Value

An object of class "stdGlm" is a list containing

Note

The variance calculation performed by stdGlm does not condition on the observed covariates \bar{Z}=(Z_1,...,Z_n). To see how this matters, note that

var\{\hat{\theta}(x)\}=E[var\{\hat{\theta}(x)|\bar{Z}\}]+var[E\{\hat{\theta}(x)|\bar{Z}\}].

The usual parameter \beta in a generalized linear model does not depend on \bar{Z}. Thus, E(\hat{\beta}|\bar{Z}) is independent of \bar{Z} as well (since E(\hat{\beta}|\bar{Z})=\beta), so that the term var[E\{\hat{\beta}|\bar{Z}\}] in the corresponding variance decomposition for var(\hat{\beta}) becomes equal to 0. However, \theta(x) depends on \bar{Z} through the average over the sample distribution for Z, and thus the term var[E\{\hat{\theta}(x)|\bar{Z}\}] is not 0, unless one conditions on \bar{Z}.

Author(s)

Arvid Sjolander.

References

Rothman K.J., Greenland S., Lash T.L. (2008). Modern Epidemiology, 3rd edition. Lippincott, Williams and Wilkins.

Sjolander A. (2016). Regression standardization with the R-package stdReg. European Journal of Epidemiology 31(6), 563-574.

Sjolander A. (2016). Estimation of causal effect measures with the R-package stdReg. European Journal of Epidemiology 33(9), 847-858.

Examples

##Example 1: continuous outcome
n <- 1000
Z <- rnorm(n)
X <- rnorm(n, mean=Z)
Y <- rnorm(n, mean=X+Z+0.1*X^2)
dd <- data.frame(Z, X, Y)
fit <- glm(formula=Y~X+Z+I(X^2), data=dd)
fit.std <- stdGlm(fit=fit, data=dd, X="X", x=seq(-3,3,0.5))
print(summary(fit.std))
plot(fit.std)

##Example 2: binary outcome
n <- 1000
Z <- rnorm(n)
X <- rnorm(n, mean=Z)
Y <- rbinom(n, 1, prob=(1+exp(X+Z))^(-1))
dd <- data.frame(Z, X, Y)
fit <- glm(formula=Y~X+Z+X*Z, family="binomial", data=dd)
fit.std <- stdGlm(fit=fit, data=dd, X="X", x=seq(-3,3,0.5))
print(summary(fit.std))
plot(fit.std)

Regression standardization in shared frailty gamma-Weibull models

Description

stdParfrailty performs regression standardization in shared frailty gamma-Weibull models, at specified values of the exposure, over the sample covariate distribution. Let T, X, and Z be the survival outcome, the exposure, and a vector of covariates, respectively. stdParfrailty uses a fitted Cox proportional hazards model to estimate the standardized survival function \theta(t,x)=E\{S(t|X=x,Z)\}, where t is a specific value of T, x is a specific value of X, and the expectation is over the marginal distribution of Z.

Usage

stdParfrailty(fit, data, X, x, t, clusterid, subsetnew)

Arguments

Details

stdParfrailty assumes that a shared frailty gamma-Weibull model

\lambda(t_{ij}|X_{ij},Z_{ij})=\lambda(t_{ij};\alpha,\eta)U_iexp\{h(X_{ij},Z_{ij};\beta)\}

has been fitted, with parametrization as descibed in the help section for parfrailty. Integrating out the gamma frailty gives the survival function

S(t|X,Z)=[1+\phi\Lambda_0(t;\alpha,\eta)exp\{h(X,Z;\beta)\}]^{-1/\phi},

where \Lambda_0(t;\alpha,\eta) is the cumulative baseline hazard

(t/\alpha)^{\eta}.

The ML estimates of (\alpha,\eta,\phi,\beta) are used to obtain estimates of the survival function S(t|X=x,Z):

\hat{S}(t|X=x,Z)=[1+\hat{\phi}\Lambda_0(t;\hat{\alpha},\hat{\eta})exp\{h(X,Z;\hat{\beta})\}]^{-1/\hat{\phi}}.

For each t in the t argument and for each x in the x argument, these estimates are averaged across all subjects (i.e. all observed values of Z) to produce estimates

\hat{\theta}(t,x)=\sum_{i=1}^n \hat{S}(t|X=x,Z_i)/n.

The variance for \hat{\theta}(t,x) is obtained by the sandwich formula.

Value

An object of class "stdParfrailty" is a list containing

Note

Standardized survival functions are sometimes referred to as (direct) adjusted survival functions in the literature.

stdParfrailty does not currently handle time-varying exposures or covariates.

stdParfrailty internally loops over all values in the t argument. Therefore, the function will usually be considerably faster if length(t) is small.

The variance calculation performed by stdParfrailty does not condition on the observed covariates \bar{Z}=(Z_1,...,Z_n). To see how this matters, note that

var\{\hat{\theta}(t,x)\}=E[var\{\hat{\theta}(t,x)|\bar{Z}\}]+var[E\{\hat{\theta}(t,x)|\bar{Z}\}].

The usual parameter \beta in a Cox proportional hazards model does not depend on \bar{Z}. Thus, E(\hat{\beta}|\bar{Z}) is independent of \bar{Z} as well (since E(\hat{\beta}|\bar{Z})=\beta), so that the term var[E\{\hat{\beta}|\bar{Z}\}] in the corresponding variance decomposition for var(\hat{\beta}) becomes equal to 0. However, \theta(t,x) depends on \bar{Z} through the average over the sample distribution for Z, and thus the term var[E\{\hat{\theta}(t,x)|\bar{Z}\}] is not 0, unless one conditions on \bar{Z}. The variance calculation by Gail and Byar (1986) ignores this term, and thus effectively conditions on \bar{Z}.

Author(s)

Arvid Sjolander

References

Chang I.M., Gelman G., Pagano M. (1982). Corrected group prognostic curves and summary statistics. Journal of Chronic Diseases 35, 669-674.

Dahlqwist E., Pawitan Y., Sjolander A. (2019). Regression standardization and attributable fraction estimation with between-within frailty models for clustered survival data. Statistical Methods in Medical Research 28(2), 462-485.

Gail M.H. and Byar D.P. (1986). Variance calculations for direct adjusted survival curves, with applications to testing for no treatement effect. Biometrical Journal 28(5), 587-599.

Makuch R.W. (1982). Adjusted survival curve estimation using covariates. Journal of Chronic Diseases 35, 437-443.

Examples

## Not run: 

require(survival)

#simulate data
n <- 1000
m <- 3
alpha <- 1.5
eta <- 1
phi <- 0.5
beta <- 1
id <- rep(1:n, each=m)
U <- rep(rgamma(n, shape=1/phi, scale=phi), each=m)
X <- rnorm(n*m)
#reparametrize scale as in rweibull function
weibull.scale <- alpha/(U*exp(beta*X))^(1/eta)
T <- rweibull(n*m, shape=eta, scale=weibull.scale)

#right censoring
C <- runif(n*m, 0, 10)
D <- as.numeric(T<C)
T <- pmin(T, C)
  
#strong left-truncation
L <- runif(n*m, 0, 2)
incl <- T>L
incl <- ave(x=incl, id, FUN=sum)==m
dd <- data.frame(L, T, D, X, id)
dd <- dd[incl, ]  
 
fit <- parfrailty(formula=Surv(L, T, D)~X, data=dd, clusterid="id")
fit.std <- stdParfrailty(fit=fit, data=dd, X="X", x=seq(-1,1,0.5), t=1:5, clusterid="id")
print(summary(fit.std, t=3))
plot(fit.std)


## End(Not run)

Summarizes parfrailty fit

Description

This is a summary method for class "parfrailty".

Usage

## S3 method for class 'parfrailty'
summary(object, CI.type = "plain", CI.level = 0.95, 
  digits=max(3L, getOption("digits") - 3L), ...)

Arguments

Author(s)

Arvid Sjolander and Elisabeth Dahlqwist.

See Also

parfrailty

Examples

##See documentation for frailty

Summarizes Cox regression standardization fit

Description

This is a summary method for class "stdCoxph".

Usage

## S3 method for class 'stdCoxph'
summary(object, t, CI.type = "plain", CI.level = 0.95,
  transform = NULL, contrast = NULL, reference = NULL, ...)

Arguments

Author(s)

Arvid Sjolander

See Also

stdCoxph

Examples

##See documentation for stdCoxph

Summarizes GEE regression standardization fit

Description

This is a summary method for class "stdGee".

Usage

## S3 method for class 'stdGee'
summary(object, CI.type = "plain", CI.level = 0.95,
  transform = NULL, contrast = NULL, reference = NULL, ...)

Arguments

Author(s)

Arvid Sjolander

See Also

stdGee

Examples

##See documentation for stdGee

Summarizes GLM regression standardization fit

Description

This is a summary method for class "stdGlm".

Usage

## S3 method for class 'stdGlm'
summary(object, CI.type = "plain", CI.level = 0.95,
  transform = NULL, contrast = NULL, reference = NULL, ...)

Arguments

Author(s)

Arvid Sjolander

See Also

stdGlm

Examples

##See documentation for stdGlm

Summarizes Frailty standardization fit

Description

This is a summary method for class "stdParfrailty".

Usage

## S3 method for class 'stdParfrailty'
summary(object, t, CI.type = "plain", CI.level = 0.95,
  transform = NULL, contrast = NULL, reference = NULL, ...)

Arguments

Author(s)

Arvid Sjolander

See Also

stdParfrailty

Examples

##See documentation for stdParfrailty