adjustedCurves (version 0.11.3)

adjusted_curve_diff: Estimate the difference between or the ratio of two Confounder-Adjusted Survival Curves or CIFs

Description

Given a previously created adjustedsurv or adjustedcif object, calculate the difference between or the ratio of two of the variable specific curves. Can either calculate the whole difference / ratio curve or estimates at specified points in time.

Usage

adjusted_curve_diff(adj, group_1=NULL, group_2=NULL,
                    times=NULL, conf_int=FALSE, conf_level=0.95,
                    use_boot=FALSE, interpolation="steps")
adjusted_curve_ratio(adj, group_1=NULL, group_2=NULL,
                     times=NULL, conf_int=FALSE, conf_level=0.95,
                     use_boot=FALSE, interpolation="steps")

Value

Returns a data.frame containing the columns time (the points in time where the difference or ratios were estimated) and diff or ratio (the estimated difference or ratio).

If conf_int=TRUE was used in the function call, it additionally contains the columns se (the estimated standard error of the difference, not included for ratios), ci_lower (lower limit of the confidence interval of the difference/ratio), ci_upper (upper limit of the confidence interval of the difference/ratio) and p_value (the p-value for the mentioned test).

Arguments

adj

An adjustedsurv object created using the adjustedsurv function, or a adjustedcif object created using the adjustedcif function.

group_1

Optional argument to get a specific difference or ratio. This argument takes a single character string specifying one of the levels of the variable used in the original adjustedsurv or adjustedcif function call. This group will be subtracted from. For example if group_1="A" and group_2="B" the difference A - B or ratio A / B will be used. If NULL, the order of the factor levels in the original data determines the order. If not NULL, the group_2 argument also needs to be specified.

group_2

Also a single character string specifying one of the levels of variable. This corresponds to the right side of the difference equation. See argument group_1.

times

An optional numeric vector of points in time at which the difference or ratio should be estimated. If NULL (default) the differences or ratios are estimated for the whole curve.

conf_int

Whether standard errors, confidence intervals and p-values should be calculated. Only possible when either conf_int=TRUE or bootstap=TRUE was used in the original function call. See details for how those are estimated.

conf_level

A number specifying the confidence level of the confidence intervals.

use_boot

Whether to use the standard errors estimated using bootstrapping for the confidence interval and p-value calculation. Can only be used if bootstrap=TRUE was used in the original adjustedsurv or adjustedcif function call. Ignored if conf_int=FALSE.

interpolation

Either "steps" (default) or "linear". This parameter controls how interpolation is performed. If this argument is set to "steps", the curves will be treated as step functions. If it is set to "linear", the curves wil be treated as if there are straight lines between the point estimates instead. Points that lie between estimated points will be interpolated accordingly. Should usually be kept at "steps". See Details.

Author

Robin Denz

Details

Confidence Intervals & P-Values

For differences, the standard error of the difference is estimated using the pooled standard error of the two probability estimates, given by: $$SE_{group_1 - group_2} = \sqrt{SE_{group_1}^2 + SE_{group_2}^2}$$

Confidence intervals are then calculated using this pooled standard error and the normal approximation. The P-Values are also obtained using this standard error combined with a two-sided one-sample t-test. The null-hypothesis is that the difference is equal to 0, and the alternative hypothesis is that the difference is not equal to 0.

For ratios, the confidence intervals are calculated according to the method given by Fieller (1954), assuming the probabilities to be independent. P-values are calculated using a one-sample two-sided t-test with the test-statistic of Fieller (1954).

If p-values are calculated for multiple points in time simultaneously, the user should adjust those. See ?p.adjust for more information.

Overall Difference Test

This function does not perform a test of the overall difference between two functions. To calculate the integral of the difference in a given interval the plot_curve_diff function can be used. Additionally, to test whether that integral is equal to zero the adjusted_curve_test function can be used. No such test is available for ratios, as it is unclear what that would entail.

More than Two Groups

If more than two groups are present in variable, all other comparisons except for group_1 vs. group_2 are ignored. If multiple comparisons are desired, the user needs to call this function multiple times and adjust the group_1 and group_2 arguments accordingly.

Graphical Displays

There is no directly associated plot method for this function. However, this function is used internally when calling the plot_curve_diff function. In order to get a plot of the difference curve or point estimates, that function can be used.

Multiple Imputation

This function works exactly the same way for adjusted survival curves or adjusted CIFs estimated using multiple imputation as it does without any missing values. If multiple imputation was used previously, this function simply uses the pooled estimates to calculate the differences or ratios.

Computational Details

When estimating the difference or ratios at some point in time at which no direct point estimates are available, this function needs to interpolate the curves. The interpolation method can be controlled using the interpolation function. In most cases, the estimated curves are step functions and the default (interpolation="steps") is therefore appropriate. However, when parametric survival models where used in the estimation process it might be preferable to use linear interpolation instead.

References

John P. Klein, Brent Logan, Mette Harhoff, and Per Kragh Andersen (2007). "Analyzing Survival Curves at a Fixed Point in Time". In: Statistics in Medicine 26, pp. 4505-4519

Michael Coory, Karen E. Lamb, and Michael Sorich (2014). "Risk-Difference Curves can be used to Communicate Time-Dependent Effects of Adjuvant Therapies for Early Stage Cancer". In: Journal of Clinical Epidemiology 67, pp. 966-972

Edgar C. Fieller (1954). "Some Problems in Interval Estimation". In: Journal of the Royal Statistical Society, Series B 16.2, pp. 175-185

See Also

plot_curve_diff, adjustedsurv, adjustedcif

Examples

library(adjustedCurves)
library(survival)

#### Simple Survival Case with adjusted survival curves ####

# simulate some data as example
set.seed(42)
sim_dat <- sim_confounded_surv(n=30, max_t=1.2)
sim_dat$group <- as.factor(sim_dat$group)

# propensity score model
ps_mod <- glm(group ~ x1 + x2 + x4 + x5, data=sim_dat, family="binomial")

# use it to estimate adjusted survival curves with bootstrapping
adjsurv <- adjustedsurv(data=sim_dat,
                        variable="group",
                        ev_time="time",
                        event="event",
                        method="iptw_km",
                        treatment_model=ps_mod,
                        conf_int=TRUE,
                        bootstrap=TRUE,
                        n_boot=10) # n_boot should be much higher in reality

# calculate the whole difference curve
adjdiff <- adjusted_curve_diff(adjsurv)
adjratio <- adjusted_curve_ratio(adjsurv)

# only some points in time
adjdiff <- adjusted_curve_diff(adjsurv, times=c(0.2, 0.4))
adjratio <- adjusted_curve_ratio(adjsurv, times=c(0.2, 0.4))

# with confidence intervals, p-values
adjdiff <- adjusted_curve_diff(adjsurv, times=c(0.2, 0.4), conf_int=TRUE)
adjratio <- adjusted_curve_ratio(adjsurv, times=c(0.2, 0.4), conf_int=TRUE)

# using bootstrapping
adjdiff <- adjusted_curve_diff(adjsurv, times=c(0.2, 0.4), conf_int=TRUE,
                               use_boot=TRUE)

#### Competing Risks Case with adjusted CIFs ####
if (requireNamespace("cmprsk") & requireNamespace("riskRegression") &
    requireNamespace("prodlim")) {

library(cmprsk)
library(riskRegression)
library(prodlim)

set.seed(1)

# simulate some data as example
sim_dat <- sim_confounded_crisk(n=100, max_t=1.2)
sim_dat$group <- as.factor(sim_dat$group)

# estimate a cause-specific cox-regression for the outcome
csc_mod <- CSC(Hist(time, event) ~ x1 + x2 + x3 + x4 + x5 + x6 + group,
               data=sim_dat)

# use it to calculate adjusted CIFs for cause = 1 with bootstrapping
adjcif <- adjustedcif(data=sim_dat,
                      variable="group",
                      ev_time="time",
                      event="event",
                      method="direct",
                      outcome_model=csc_mod,
                      conf_int=TRUE,
                      bootstrap=TRUE,
                      n_boot=10,
                      cause=1,
                      product.limit=FALSE)

# calculate the whole difference curve
adjdiff <- adjusted_curve_diff(adjcif)
adjratio <- adjusted_curve_ratio(adjcif)

# with confidence intervals
adjdiff <- adjusted_curve_diff(adjcif, conf_int=TRUE)
adjratio <- adjusted_curve_ratio(adjcif, conf_int=TRUE)

# only at specific points in time
adjdiff <- adjusted_curve_diff(adjcif, times=c(0.2, 0.4), conf_int=TRUE)
adjratio <- adjusted_curve_ratio(adjcif, times=c(0.2, 0.4), conf_int=TRUE)
}