ste: Surrogate threshold effect for the one-step Joint surrogate...
In frailtypack: Shared, Joint (Generalized) Frailty Models; Surrogate Endpoints
ste R Documentation
Surrogate threshold effect for the one-step Joint surrogate model for the evaluation of a
canditate surrogate endpoint.
Description
This function compute the surrogate threshold effect (STE) from the one-step joint frailty
model or joint frailty-copula
model. The STE is defined as the minimum treament effect
on surrogate endpoint, necessary to predict a non-zero effect on
true endpoint (Burzykowski et al., 2006).
Usage
ste(object, var.used = "error.estim", alpha. = 0.05,
pred.int.use = "up")
Arguments
object
An object inheriting from jointSurroPenal class
(output from calling the jointSurroPenal or jointSurroCopPenal function ).
var.used
This argument takes two values. The first one is "error.estim"
and indicates if the prediction error takes into account
the estimation error of the estimates of the parameters. If the estimates
are supposed to be known or if the dataset includes a high number of trials with
a high number of subject per trial, value No.error can be used.
The default is error.estim, which is highly recommended in practice.
alpha.
The confidence level for the prediction interval. The default is 0.05
pred.int.use
A character string that indicates the bound of the prediction interval
to use to compute the STE. Possible values are up for the upper bound (the default)
or lw for the lower bound. up when we have a protective treatment effect and lw
when we have a deleterious treatment effect (see details).
Details
The STE is obtained by solving the equation
l(\alpha0) = 0
(resp.
u(\alpha0) = 0), where
\alpha0 represents
the corresponding STE, and
l(\alpha0) (resp.
u(\alpha0)) is the lower (resp. upper) bound of the prediction interval
of the treatment effect on the true endpoint (\beta + b0) . Thereby,
where 
represents the set of estimates for the fixed-effects and the
variance-covariance parameters of the random effects obtained from the joint surrogate
model
(Sofeu et al., 2019).
If the previous equations gives two solutions, STE can be the
minimum (resp. the maximum) value or both of them, according to the shape of the function.
If the concavity of the function is turned upwards, STE is the first value and
the second value represents the maximum (res. the minimum) treament value observable
on the surrogate that can predict a nonzero treatment effect on true endpoint.
If the concavity of the function is turned down, both of the solutions
represent the STE and the interpretation is such that accepted values of the
treatment effects on S predict a nonzero treatment effects on T
Given that negative values of treatment effect indicate a reduction of the risk
of failure and are considered beneficial, STE is recommended to be computed from
the upper prediction
limit
u(\alpha0).
The details on the computation of STE are described in
Burzykowski et al. (2006).
Value
Returns and displays the STE.
Author(s)
Casimir Ledoux Sofeu casimir.sofeu@u-bordeaux.fr, scl.ledoux@gmail.com and
Virginie Rondeau virginie.rondeau@inserm.fr
References
Burzykowski T, Buyse M (2006). "Surrogate threshold effect: an alternative
measure for meta-analytic surrogate endpoint validation." Pharmaceutical
Statistics, 5(3), 173-186.ISSN 1539-1612.
Sofeu, C. L., Emura, T., and Rondeau, V. (2019). One-step validation method for surrogate
endpoints using data from multiple randomized cancer clinical trials with failure-time endpoints.
Statistics in Medicine 38, 2928-2942.
Sofeu, C. L. and Rondeau, V. (2020). How to use frailtypack for validating failure-time surrogate
endpoints using individual patient data from meta-analyses of randomized controlled trials.
PLOS ONE; 15, 1-25.
See Also
jointSurroPenal, jointSurroCopPenal, predict
Examples
###--- Joint surrogate model ---###
###---evaluation of surrogate endpoints---###
data(dataOvarian)
joint.surro.ovar <- jointSurroPenal(data = dataOvarian, n.knots = 8,
init.kappa = c(2000,1000), indicator.alpha = 0,
nb.mc = 200, scale = 1/365)
# ======STE=====
# Assuming errors on the estimates
ste(joint.surro.ovar, var.used = "error.estim")
# Assuming no errors on the estimates
ste(joint.surro.ovar, var.used = "No.error", pred.int.use = "up")
frailtypack documentation built on Oct. 20, 2024, 1:08 a.m.
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| ste | R Documentation |
Surrogate threshold effect for the one-step Joint surrogate model for the evaluation of a canditate surrogate endpoint.
Description
This function compute the surrogate threshold effect (STE) from the one-step joint frailty
model or joint frailty-copula
model. The STE is defined as the minimum treament effect
on surrogate endpoint, necessary to predict a non-zero effect on
true endpoint (Burzykowski et al., 2006).
Usage
ste(object, var.used = "error.estim", alpha. = 0.05,
pred.int.use = "up")
Arguments
object |
An object inheriting from |
var.used |
This argument takes two values. The first one is |
alpha. |
The confidence level for the prediction interval. The default is |
pred.int.use |
A character string that indicates the bound of the prediction interval
to use to compute the STE. Possible values are |
Details
The STE is obtained by solving the equation
l(\alpha0) = 0
(resp.
u(\alpha0) = 0), where
\alpha0 represents
the corresponding STE, and
l(\alpha0) (resp.
u(\alpha0)) is the lower (resp. upper) bound of the prediction interval
of the treatment effect on the true endpoint (\beta + b0) . Thereby,
where
represents the set of estimates for the fixed-effects and the
variance-covariance parameters of the random effects obtained from the joint surrogate
model
(Sofeu et al., 2019).
If the previous equations gives two solutions, STE can be the
minimum (resp. the maximum) value or both of them, according to the shape of the function.
If the concavity of the function is turned upwards, STE is the first value and
the second value represents the maximum (res. the minimum) treament value observable
on the surrogate that can predict a nonzero treatment effect on true endpoint.
If the concavity of the function is turned down, both of the solutions
represent the STE and the interpretation is such that accepted values of the
treatment effects on S predict a nonzero treatment effects on T
Given that negative values of treatment effect indicate a reduction of the risk
of failure and are considered beneficial, STE is recommended to be computed from
the upper prediction
limit
u(\alpha0).
The details on the computation of STE are described in Burzykowski et al. (2006).
Value
Returns and displays the STE.
Author(s)
Casimir Ledoux Sofeu casimir.sofeu@u-bordeaux.fr, scl.ledoux@gmail.com and Virginie Rondeau virginie.rondeau@inserm.fr
References
Burzykowski T, Buyse M (2006). "Surrogate threshold effect: an alternative measure for meta-analytic surrogate endpoint validation." Pharmaceutical Statistics, 5(3), 173-186.ISSN 1539-1612.
Sofeu, C. L., Emura, T., and Rondeau, V. (2019). One-step validation method for surrogate endpoints using data from multiple randomized cancer clinical trials with failure-time endpoints. Statistics in Medicine 38, 2928-2942.
Sofeu, C. L. and Rondeau, V. (2020). How to use frailtypack for validating failure-time surrogate endpoints using individual patient data from meta-analyses of randomized controlled trials. PLOS ONE; 15, 1-25.
See Also
jointSurroPenal, jointSurroCopPenal, predict
Examples
###--- Joint surrogate model ---###
###---evaluation of surrogate endpoints---###
data(dataOvarian)
joint.surro.ovar <- jointSurroPenal(data = dataOvarian, n.knots = 8,
init.kappa = c(2000,1000), indicator.alpha = 0,
nb.mc = 200, scale = 1/365)
# ======STE=====
# Assuming errors on the estimates
ste(joint.surro.ovar, var.used = "error.estim")
# Assuming no errors on the estimates
ste(joint.surro.ovar, var.used = "No.error", pred.int.use = "up")
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