| Type: | Package |
| Title: | Longitudinal Surrogate Marker Analysis |
| Version: | 1.0 |
| Description: | Assess the proportion of treatment effect explained by a longitudinal surrogate marker as described in Agniel D and Parast L (2021) <doi:10.1111/biom.13310>. |
| License: | GPL-2 | GPL-3 [expanded from: GPL] |
| Imports: | stringr, splines, mgcv, Rsurrogate, dplyr, here, tidyr, fs, KernSmooth, stats, fdapace, grf, lme4, mvnfast, plyr, tibble, magrittr, glue, purrr, readr, refund, fda, fda.usc |
| NeedsCompilation: | no |
| Packaged: | 2022-09-28 00:37:31 UTC; parastlm |
| Author: | Layla Parast [aut, cre], Denis Agniel [aut] |
| Maintainer: | Layla Parast <parast@austin.utexas.edu> |
| Depends: | R (≥ 3.5.0) |
| Repository: | CRAN |
| Date/Publication: | 2022-09-29 10:00:02 UTC |
Estimate the surrogate value of a longitudinal marker
Description
Estimate the surrogate value of a longitudinal marker
Usage
estimate_surrogate_value(y_t, y_c, X_t, X_c, method = c("gam", "linear",
"kernel"), k = 3, var = FALSE, bootstrap_samples = 50, alpha = 0.05)
Arguments
y_t |
vector of n1 outcome measurements for treatment group |
y_c |
vector of n0 outcome measurements for control or reference group |
X_t |
n1 x T matrix of longitudinal surrogate measurements for treatment group, where T is the number of time points |
X_c |
n0 x T matrix of longitudinal surrogate measurements for control or reference group, where T is the number of time points |
method |
method for dimension-reduction of longitudinal surrogate, either 'gam', 'linear', or 'kernel' |
k |
number of eigenfunctions to use in semimetric |
var |
logical, if TRUE then standard error estimates and confidence intervals are provided |
bootstrap_samples |
number of bootstrap samples to use for standard error estimation, used if var = TRUE, default is 50 |
alpha |
alpha level, default is 0.05 |
Value
a tibble containing estimates of the treatment effect (Deltahat), the residual treatment effect (Deltahat_S), and the proportion of treatment effect explained (R); if var = TRUE, then standard errors of Deltahat_S and R are also provided (Deltahat_S_se and R_se), and quantile-based 95% confidence intervals for Deltahat_S and R are provided (Deltahat_S_ci_l [lower], Deltahat_S_ci_h [upper], R_ci_l [lower], R_ci_u [upper])
References
Agniel D and Parast L (2021). Evaluation of Longitudinal Surrogate Markers. Biometrics, 77(2): 477-489.
Examples
library(dplyr)
data(full_data)
wide_ds <- full_data %>%
dplyr::select(id, a, tt, x, y) %>%
tidyr::spread(tt, x)
wide_ds_0 <- wide_ds %>% filter(a == 0)
wide_ds_1 <- wide_ds %>% filter(a == 1)
X_t <- wide_ds_1 %>% dplyr::select(`-1`:`1`) %>% as.matrix
y_t <- wide_ds_1 %>% pull(y)
X_c <- wide_ds_0 %>% dplyr::select(`-1`:`1`) %>% as.matrix
y_c <- wide_ds_0 %>% pull(y)
estimate_surrogate_value(y_t = y_t, y_c = y_c, X_t = X_t, X_c = X_c,
method = 'gam', var = FALSE)
estimate_surrogate_value(y_t = y_t, y_c = y_c, X_t = X_t, X_c = X_c,
method = 'linear', var = TRUE, bootstrap_sample = 50)
Example data to illustrate functions
Description
Simulated nonsmooth data to illustrate functions
Usage
data("full_data")Format
A data frame with 10100 observations on the following 5 variables.
ida unique person ID
atreatment group, 0 or 1
tttime
xsurrogate marker value
yprimary outcome
Pre-smooth sparse longitudinal data
Description
Pre-smooth sparse longitudinal data
Usage
presmooth_data(obs_data, ...)
Arguments
obs_data |
data.frame or tibble containing the observed data, with columns |
... |
additional arguments passed on to |
Value
list containing matrices X_t and X_c, which are the smoothed surrogate values for the treated and control groups, respectively, for use in downstream analyses
Examples
library(dplyr)
data(full_data)
obs_ds <- group_by(full_data, id)
obs_data <- sample_n(obs_ds, 5)
obs_data <- ungroup(obs_data)
head(obs_data)
presmooth_X <- presmooth_data(obs_data)