standsurv: Marginal survival and hazards of fitted flexsurvreg models

In flexsurv: Flexible Parametric Survival and Multi-State Models

Marginal survival and hazards of fitted flexsurvreg models

Description

Returns a tidy data.frame of marginal survival probabilities or the hazards of the marginal survival at user-defined time points and covariate patterns. Standardization is performed over any undefined covariates in the model. The user provides the data to standardize over. Contrasts can be calculated resulting in estimates of the average treatment effect or the average treatment effect in the treated if a treated subset of the data are supplied.

Usage

standsurv(
  object,
  newdata = NULL,
  at = list(list()),
  atreference = 1,
  type = "survival",
  t = NULL,
  ci = FALSE,
  se = FALSE,
  boot = FALSE,
  B = NULL,
  cl = 0.95,
  trans = "log",
  contrast = NULL,
  trans.contrast = NULL,
  seed = NULL,
  rmap,
  ratetable,
  scale.ratetable = 365.25,
  n.gauss.quad = 100
)

Arguments

object	Output from flexsurvreg or flexsurvspline, representing a fitted survival model object.
newdata	Data frame containing covariate values to produce marginal values for. If not specified then the fitted model data.frame is used. There must be a column for every covariate in the model formula for which the user wishes to standardize over. These are in the same format as the original data, with factors as a single variable, not 0/1 contrasts. Any covariates that are to be fixed should be specified in at. There should be one row for every combination of covariates in which to standardize over. If newdata contains a variable named '(weights)' then a weighted mean will be used to create the standardized estimates. This is the default behaviour if the fitted model contains case weights, which are stored in the fitted model data.frame.
at	A list of scenarios in which specific covariates are fixed to certain values. Each element of at must itself be a list. For example, for a covariate group with levels "Good", "Medium" and "Poor", the standardized survival plots for each group averaging over all other covariates is specified using at=list(list(group="Good"), list(group="Medium"), list(group="Poor")).
atreference	The reference scenario for making contrasts. Default is 1 (i.e. the first element of at).
type	"survival" for marginal survival probabilities. In a relative survival framework this returns the marginal all-cause survival (see details). "hazard" for the hazard of the marginal survival probability. In a relative survival framework this returns the marginal all-cause hazard (see details). "rmst" for standardized restricted mean survival. "relsurvival" for marginal relative survival (can only be specified if a relative survival model has been fitted in flexsurv). "excesshazard" for marginal excess hazards (can only be specified if a relative survival model has been fitted in flexsurv).
t	Times to calculate marginal values at.
ci	Should confidence intervals be calculated? Defaults to FALSE
se	Should standard errors be calculated? Defaults to FALSE
boot	Should bootstrapping be used to calculate standard error and confidence intervals? Defaults to FALSE, in which case the delta method is used
B	Number of bootstrap simulations from the normal asymptotic distribution of the estimates used to calculate confidence intervals or standard errors. Decrease for greater speed at the expense of accuracy. Only specify if boot = TRUE
cl	Width of symmetric confidence intervals, relative to 1.
trans	Transformation to apply when calculating standard errors via the delta method to obtain confidence intervals. The default transformation is "log". Other possible names are "none", "loglog", "logit".
contrast	Contrasts between standardized measures defined by at scenarios. Options are "difference" and "ratio". There will be n-1 new columns created where n is the number of at scenarios. Default is NULL (i.e. no contrasts are calculated).
trans.contrast	Transformation to apply when calculating standard errors for contrasts via the delta method to obtain confidence intervals. The default transformation is "none" for differences in survival, hazard or RMST, and "log" for ratios of survival, hazard or RMST.
seed	The random seed to use (for bootstrapping confidence intervals)
rmap	An list that maps data set names to expected ratetable names. This must be specified if all-cause survival and hazards are required after fitting a relative survival model. This can also be specified if expected rates are required for plotting purposes. See the details section below.
ratetable	A table of expected event rates (see ratetable)
scale.ratetable	Transformation from the time scale of the fitted flexsurv model to the time scale in ratetable. For example, if the analysis time of the fitted model is in years and the ratetable is in units/day then we should use scale.ratetable = 365.25. This is the default as often the ratetable will be in units/day (see example).
n.gauss.quad	Number of Gaussian quadrature points used for integrating the all-cause survival function when calculating RMST in a relative survival framework (default = 100)

Details

The syntax of standsurv follows closely that of Stata's standsurv command written by Paul Lambert and Michael Crowther. The function calculates standardized (marginal) measures including standardized survival functions, standardized restricted mean survival times and the hazard of standardized survival. The standardized survival is defined as

S(t|X=x) = E[S(t|X=x,Z)] = 1/N * sum(S(t|X=x,Z=z_i))

The hazard of the standardized survival is a weighted average of individual hazard functions at time t, weighted by the survival function at this time:

h(t|X=x) = sum(S(t|X=x,Z=z_i) * h(t|X=x,Z=z_i)) / sum(S(t|X=x,Z=z_i))

Marginal expected survival and hazards can be calculated by providing a population-based lifetable of class ratetable in ratetable and a mapping between stratification factors in the lifetable and the user dataset using rmap. If these stratification factors are not in the fitted survival model then the user must specify them in newdata along with the covariates of the model. The marginal expected survival is calculated using the "Ederer" method that assumes no censoring as this is most relevant approach for forecasting (see survexp). A worked example is given below.

Marginal all-cause survival and hazards can be calculated after fitting a relative survival model, which utilise the expected survival from a population ratetable. See Rutherford et al. (Chapter 6) for further details.

Value

A tibble containing one row for each time-point. The column naming convention is at{i} for the ith scenario with corresponding confidence intervals (if specified) named at{i}_lci and at{i}_uci. Contrasts are named contrast{k}_{j} for the comparison of the kth versus the jth at scenario.

In addition tidy long-format data.frames are returned in the attributes standsurv_at and standsurv_contrast. These can be passed to ggplot for plotting purposes (see plot.standsurv).

Author(s)

Michael Sweeting <[e-mail]>

References

Paul Lambert, 2021. "STANDSURV: Stata module to compute standardized (marginal) survival and related functions," Statistical Software Components S458991, Boston College Department of Economics. https://ideas.repec.org/c/boc/bocode/s458991.html

Rutherford, MJ, Lambert PC, Sweeting MJ, Pennington B, Crowther MJ, Abrams KR, Latimer NR. 2020. "NICE DSU Technical Support Document 21: Flexible Methods for Survival Analysis" https://nicedsu.sites.sheffield.ac.uk/tsds/flexible-methods-for-survival-analysis-tsd

Examples

## mean age is higher in those with smaller observed survival times 
newbc <- bc
set.seed(1)
newbc$age <- rnorm(dim(bc)[1], mean = 65-scale(newbc$recyrs, scale=FALSE),
 sd = 5)

## Fit a Weibull flexsurv model with group and age as covariates
weib_age <- flexsurvreg(Surv(recyrs, censrec) ~ group+age, data=newbc, 
                       dist="weibull")
                       
## Calculate standardized survival and the difference in standardized survival
## for the three levels of group across a grid of survival times                        
standsurv_weib_age <- standsurv(weib_age, 
                                           at = list(list(group="Good"), 
                                                     list(group="Medium"), 
                                                     list(group="Poor")), 
                                           t=seq(0,7, length.out=100),
                                           contrast = "difference", ci=FALSE)
standsurv_weib_age

## Calculate hazard of standardized survival and the marginal hazard ratio
## for the three levels of group across a grid of survival times
## 10 bootstraps for confidence intervals (this should be larger)
## Not run:           
haz_standsurv_weib_age <- standsurv(weib_age, 
                                           at = list(list(group="Good"), 
                                                     list(group="Medium"), 
                                                     list(group="Poor")), 
                                           t=seq(0,7, length.out=100),
                                           type="hazard",
                                           contrast = "ratio", boot = TRUE,
                                           B=10, ci=TRUE)
haz_standsurv_weib_age                                            
plot(haz_standsurv_weib_age, ci=TRUE)
## Hazard ratio plot shows a decreasing marginal HR 
## Whereas the conditional HR is constant (model is a PH model)
plot(haz_standsurv_weib_age, contrast=TRUE, ci=TRUE)

## Calculate standardized survival from a Weibull model together with expected
## survival matching to US lifetables

# age at diagnosis in days. This is required to match to US ratetable, whose
# timescale is measured in days
newbc$agedays <- floor(newbc$age * 365.25)  
## Create some random diagnosis dates centred on 01/01/2010 with SD=1 year
## These will be used to match to expected rates in the lifetable
newbc$diag <- as.Date(floor(rnorm(dim(newbc)[1], 
                     mean = as.Date("01/01/2010", "%d/%m/%Y"), sd=365)), 
                     origin="1970-01-01")
## Create sex (assume all are female)
newbc$sex <- factor("female")
standsurv_weib_expected <- standsurv(weib_age, 
                                           at = list(list(group="Good"), 
                                                     list(group="Medium"), 
                                                     list(group="Poor")), 
                                           t=seq(0,7, length.out=100),
                                           rmap=list(sex = sex,
                                                     year = diag,
                                                     age = agedays),
                                           ratetable = survival::survexp.us,
                                           scale.ratetable = 365.25,
                                           newdata = newbc)
## Plot marginal survival with expected survival superimposed                                            
plot(standsurv_weib_expected, expected=TRUE)

## End(Not run)

flexsurv documentation built on Feb. 16, 2023, 5:07 p.m.