Package 'longsurr'

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.

id

a unique person ID

a

treatment group, 0 or 1

tt

time

x

surrogate marker value

y

primary 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 id identifying the individual measured, tt identifying the time of the observation, x the value of the surrogate at time tt, and a indicating 1 for treatment arm and 0 for control arm.
...	additional arguments passed on to fpca

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)

---