effectsize_tte {CompAREdesign}R Documentation

Effect for composite time-to-event endpoints

Description

This function calculates different effect measures for time-to-event composite outcomes. The composite endpoint is assumed to be a time-to-event endpoint formed by a combination of two events (E1 and E2). The effect size is calculated on the basis of anticipated information on the composite components and the correlation between them. Marginal distributions are assumed for m The function allows to compute the effect size in terms of the geometric average hazard ratio, the average hazard ratio, the ratio of restricted mean survival times and the median survival time ratio.

Usage

effectsize_tte(
  p0_e1,
  p0_e2,
  HR_e1,
  HR_e2,
  beta_e1 = 1,
  beta_e2 = 1,
  case,
  copula = "Frank",
  rho = 0.3,
  rho_type = "Spearman",
  subdivisions = 1000,
  plot_HR = FALSE
)

Arguments

p0_e1

numeric parameter between 0 and 1, expected proportion of observed events for the endpoint E1

p0_e2

numeric parameter between 0 and 1, expected proportion of observed events for the endpoint E2

HR_e1

numeric parameter between 0 and 1, expected cause specific hazard Ratio the endpoint E1

HR_e2

numeric parameter between 0 and 1, expected cause specific hazard Ratio the endpoint E2

beta_e1

numeric positive parameter, shape parameter (β_1) for a Weibull distribution for the endpoint E1 in the control group. See details for more info.

beta_e2

numeric positive parameter, shape parameter (β_2) for a Weibull distribution for the endpoint E2 in the control group. See details for more info.

case

integer parameter in 1,2,3,4 1: none of the endpoints is death 2: endpoint 2 is death 3: endpoint 1 is death 4: both endpoints are death by different causes

copula

character indicating the copula to be used: "Frank" (default), "Gumbel" or "Clayton". See details for more info.

rho

numeric parameter between -1 and 1, Spearman's correlation coefficient o Kendall Tau between the marginal distribution of the times to the two events E1 and E2. See details for more info.

rho_type

character indicating the type of correlation to be used: "Spearman" (default) or "Tau". See details for more info.

subdivisions

integer parameter greater than or equal to 10. Number of subintervals to estimate the effect size. The default is 1000.

plot_HR

logical. If the HR over time should be displayed. The default is FALSE

Details

Some parameters might be difficult to anticipate, especially the shape parameters of Weibull distributions and those referred to the relationship between the marginal distributions. For the shape parameters (beta_e1, beta_e2) of the Weibull distribution, we recommend to use β_j=0.5, β_j=1 or β_j=2 if a decreasing, constant or increasing rates over time are expected, respectively. For the correlation (rho) between both endpoints, generally a positive value is expected as it has no sense to design an study with two endpoints negatively correlated. We recommend to use ρ=0.1, ρ=0.3 or ρ=0.5 for weak, mild and moderate correlations, respectively. For the type of correlation (rho_type), although two different type of correlations are implemented, we recommend the use of the Spearman's correlation. In any case, if no information is available on these parameters, we recommend to use the default values provided by the function.

All returned expected effect sizes for the composite endpoint should be interpreted in relative terms (treated to control). gAHR and AHR represent the risk reduction that will be achieved with the new therapy, while RMST_ratio and Median_ratio represent the gain in time gain terms until the event.

Value

A list formed by two lists: effect_size, which contains the expected treatment effect measures and measures_by_group, which contains some measures for each group

effect_size list:

gAHR

geometric Average Hazard Ratio

AHR

Average Hazard Ratio

RMST_ratio

Restricted Mean Survival Time Ratio

Median_ratio

Median Survival Time Ratio

measures_by_group list:

pstar

array with the probability of observing the composite event for each group

p1

array with the probability of observing the first event for each group

p2

array with the probability of observing the second event for each group

RMST

array with the restricted mean survival time for each group

Median

array with the median surival time for each group

References

Schemper, M., Wakounig, S., Heinze, G. (2009). The estimation of average hazard ratios by weighted Cox regression. Stat. in Med. 28(19): 2473–2489. doi:10.1002/sim.3623


[Package CompAREdesign version 1.9 Index]