nphparams: Simultaneous Inference For Parameters Quantifying Differences...

In nph: Planning and Analysing Survival Studies under Non-Proportional Hazards

Simultaneous Inference For Parameters Quantifying Differences Between Two Survival Functions

Description

Hypothesis tests with parametric multiple testing adjustment and simultaneous confidence intervals for a set of parameters, which quantify differences between two survival functions. Eligible parameters are differences in survival probabilities, log survival probabilities, complementary log log (cloglog) transformed survival probabilities, quantiles of the survival functions, log transformed quantiles, restricted mean survival times, as well as an average hazard ratio, the Cox model score statistic (logrank statistic), and the Cox-model hazard ratio.

Usage

nphparams(
  time,
  event,
  group,
  data = parent.frame(),
  param_type,
  param_par = NA,
  param_alternative = NA,
  lvl = 0.95,
  closed_test = FALSE,
  alternative_test = "two.sided",
  alternative_CI = "two.sided",
  haz_method = "local",
  rhs = 0,
  perturb = FALSE,
  Kpert = 500
)

Arguments

time	vector of observed event/censored times.
event	Vector with entries 0 or 1 (or FALSE/TRUE) indicating if an event was observed (1) or the time is censored (0).
group	group indicator, must be a vector with entries 0 or 1 indicating the allocation of a subject to one of two groups. Group 0 is regarded as reference group when calculating parameters.
data	an optional data frame containing the time, event and group data.
param_type	character vector defining the set of parameters that should be analysed. Possible entries are "S","logS","cloglogS", "Q","logQ","RMST","avgHR","score" and "HR", representing differences in survival probabilities, log survival probabilities, complementary log log (cloglog) transformed survival probabilities, quantiles of the survival functions, log transformed quantiles, restricted mean survival times, as well as an average hazard ratio, the Cox model score statistic (logrank statistic), and the Cox-model hazard ratio.
param_par	numeric vector which contains the time points at which the requested parameters are evaluated (e.g. x-year survival or RMST after x-years), or, in case of analysing quantiles, the according probability. May be NA for parameter types "RMST","avgHR","score" or "HR". In this case, the minimum of the largest event times of the two groups is used. Also, times greater than this minimum are replaced by this minumum for "RMST","avgHR","score" or "HR".
param_alternative	optional character vector with entries "less" or "greater", defining the alternative for each parameter. Only required if one-sided tests or one-sided confidence intervals are requested. Note that group 0 is regarded as reference group when calculating parameters and therefore whether "greater" or "less" corresponds to a benefit may depend on the type of parameter. In general, to show larger survival in group 1 compared to group 0, alternatives would be "greater" for parameter types "S", "logS", "Q", "logQ" and "RMST" and would be "less" for parameters types "cloglogS", "avgHR","HR", and "score". (The score test is defined here such that alternative "less" corresponds to smaller hazard (and better survival) in group 1 compared to group 0.)
lvl	Confidence level. Applies to, both, unadjusted and multiplicity adjusted (simultaneous) confidence intervals.
closed_test	logical indicating whether p-values should be adjusted using a closed testing procedure. Default is FALSE, and in this case p-values will be adjusted by a single step procedure. With k hypotheses this involves the computation of 2^k tests, which may require considerable computation time.
alternative_test	character with possible values "tow.sided" (default) or "one-sided". Specifies whether hypothesis tests should be two-sided or one-sided. In the #' latter case, param_alternative must be defined.
alternative_CI	character with possible values "tow.sided" (default) or "one-sided". Specifies whether confidence intervals should be two-sided or one-sided. In the latter case, param_alternative must be defined.
haz_method	character with possible values "local" or "muhaz". Specifies whether local hazard should be calculated under a local constant hazard assumption (default) #' or using the function muhaz from the muhaz package. Only relevant when median or log(median) survival times are analysed.
rhs	right-hand side vector of null hypotheses. Refers to log-scaled difference for ratios. Default is to consider for all null hypothesis a difference of 0.
perturb	logical, indicating whether the perturbation based estiamte should be used instead of the asymptotic estimate to calculate the covariance matrix. Defaults to FALSE.
Kpert	The number of perturbation samples to be used with the perturbation approach for covariance estimation.

Value

A list of class nphparams with elements:

est

Estimated differences (at log-scale in case of ratios).

V

Estimated covariance matrix of differences.

tab

A data frame with analysis results. Contains the parameter type (Parameter) and settings (Time_or_which_quantile), the estimated difference (Estimate), its standard error (SE), unadjusted confidence interval lower and upper bounds (lwr_unadjusted, upr_unadjusted), unadjusted p-values (p_unadj), mulitplicity adjusted confidence interval lower and upper bounds (lwr_adjusted, upr_adjusted), single-step multiplcity adjusted p-values (p_adj), closed-test adjusted p-values, if requested (p_adjusted_closed_test) and for comparison Bonferroni-Holm adjusted p-values (p_Holm).

param

The used parameter settings. If param_par was NA for "HR","avgHR" or "RMST", it is replaced by minmaxt here.

paramin

The parameter settings as provided to the function. The only difference to param is in param_par, as NA is not replaced here.

dat0

A data frame with information on all observed events in group 0. Contains time (t), number of events (ev), Nelson-Aalen estimate (NAsurv) and Kaplan-Meier estimate (KMsurv) of survival, and the number at risk (atrisk).

dat1

A data frame with information on all observed events in group 1. Contains time (t), number of events (ev), Nelson-Aalen estimate (NAsurv) and Kaplan-Meier estimate (KMsurv) of survival, and the number at risk (atrisk).

minmaxt

Minimum of the largest event times of the two groups.

est0

Estimated parameter values in group 0.

est1

Estimated parameter values in group 1.

V0

Estimated covariance matrix of parameter estimates in group 0.

V1

Estimated covariance matrix of parameter estimates in group 1.

Author(s)

Robin Ristl, [e-mail]

See Also

print.nphparams, plot.nphparams

Examples

data(pembro)
set1<-nphparams(time=time, event=event, group=group,data=pembro,
param_type=c("score","S"),
param_par=c(3.5,2),
param_alternative=c("less","greater"),
closed_test=TRUE,alternative_test="one.sided")
print(set1)
plot(set1,trt_name="Pembrolizumab",ctr_name="Cetuximab")

set2<-nphparams(time=time, event=event, group=group, data=pembro,  
param_type=c("S","S","S","Q","RMST"),
param_par=c(0.5,1,2,0.5,3.5))
print(set2)
plot(set2,showlines=TRUE,show_rmst_diff=TRUE)

#Create a summary table for set2, showing parameter estimates for each group and the
#estimated differences between groups. Also show unadjusted and multiplicity adjusted
#confidence intervals using the multivariate normal method and, for comparison,
#Bonferroni adjusted confidence intervals:

set2Bonf<-nphparams(time=time, event=event, group=group, data=pembro,  
param_type=c("S","S","S","Q","RMST"),
param_par=c(0.5,1,2,0.5,3.5),
lvl=1-0.05/5)
KI_paste<-function(x,r) {
x<-round(x,r)
paste("[",x[,1],", ",x[,2],"]",sep="")
}
r<-3
tab<-data.frame(
Parameter=paste(set2$tab[,1],set2$tab[,2]),
Pembrolizumab=round(set2$est1,r),
Cetuximab=round(set2$est0,r),
Difference=round(set2$tab$Estimate,r),
CI_undadj=KI_paste(set2$tab[,5:6],r),
CI_adj=KI_paste(set2$tab[,8:9],r),
CI_Bonf=KI_paste(set2Bonf$tab[,c(5:6)],r))
tab

nph documentation built on May 17, 2022, 1:06 a.m.