MetaAnalyticSurvCont: Compute surrogacy measures for a continuous...

In Surrogate: Evaluation of Surrogate Endpoints in Clinical Trials

Compute surrogacy measures for a continuous (normally-distributed) surrogate and a time-to-event true endpoint in the meta-analytic multiple-trial setting.

Description

The function 'MetaAnalyticSurvCont()' fits the model for a continuous surrogate and time-to-event true endpoint described by Alonso et al. (2016) in the meta-analytic multiple-trial setting.

Usage

MetaAnalyticSurvCont(
  data,
  true,
  trueind,
  surrog,
  trt,
  center,
  trial,
  patientid,
  copula,
  adjustment
)

Arguments

data	A data frame with the correct columns (See Data Format).
true	Observed time-to-event for true endpoint.
trueind	Time-to-event indicator for the true endpoint.
surrog	Continuous surrogate endpoint.
trt	Treatment indicator.
center	Center indicator (equal to trial if there are no different centers). This is the unit for which specific treatment effects are estimated.
trial	Trial indicator. This is the unit for which common baselines are to be used.
patientid	Patient indicator.
copula	The copula that is used, either "Clayton", "Hougaard" or "Plackett"
adjustment	The adjustment that should be made for the trial-level surrogacy, either "unadjusted", "weighted" or "adjusted"

Value

Returns an object of class "MetaAnalyticSurvCont" that can be used to evaluate surrogacy and contains the following elements:

• Indiv.Surrogacy: a data frame that contains the measure for the individual level surrogacy and 95% confidence interval.

• Trial.R2: a data frame that contains the R^2_{trial} and 95% confidence interval to evaluate surrogacy at the trial level.

• EstTreatEffects: a data frame that contains the estimated treatment effects and sample size for each trial.

• nlm.output: output of the maximization procedure (nlm) to maximize the likelihood.

Model

In the model, a copula-based model is used for the true time-to-event endpoint and the surrogate continuous, normally distributed endpoint. More specifically, three copulas can be used: the Clayton copula, Hougaard copula and Plackett copula. The marginal model for the true endpoint is the proportional hazard model. The marginal model for the surrogate endpoint is the classical linear regression model. The quality of the surrogate at the individual level can be evaluated by either Kendall's \tau or Spearman's \rho, depending on which copula function is used. The quality of the surrogate at the trial level can be evaluated by considering the R^2_{trial} between the estimated treatment effects.

Data Format

The data frame must contains the following columns:

• a column with the observed time-to-event for the true endpoint

• a column with the time-to-event indicator for the true endpoint: 1 if the event is observed, 0 otherwise

• a column with the continuous surrogate endpoint

• a column with the treatment indicator: 0 or 1

• a column with the trial indicator

• a column with the center indicator. If there are no different centers within each trial, the center indicator is equal to the trial indicator

• a column with the patient indicator

Author(s)

Dries De Witte

References

Alonso A, Bigirumurame T, Burzykowski T, Buyse M, Molenberghs G, Muchene L, Perualila NJ, Shkedy Z, Van der Elst W, et al. (2016). Applied surrogate endpoint evaluation methods with SAS and R. CRC Press New York

Examples

## Not run: 
data("prostate")
fit <- MetaAnalyticSurvCont(data = prostate, true = SURVTIME, trueind = SURVIND, surrog = PSA,
trt = TREAT, center = TRIAL, trial = TRIAL, patientid = PATID,
copula = "Hougaard", adjustment = "weighted")
summary(fit)
print(fit)
plot(fit)

## End(Not run)

Surrogate documentation built on June 22, 2024, 9:16 a.m.