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#' @importFrom dplyr mutate group_by summarise arrange n
#' @exportS3Method
adjust.LogRank <- function(model, data, ...){
# Creates data
df <- create.tte.df(model, data)
# Risk set and failure time times
df <- process.tte.df(df, ref_arm=model$ref_arm)
df <- df %>% mutate(lin_pred=0)
# Get ordered data
df <- get.ordered.data(df, ref_arm=model$ref_arm)
df <- get.tte.adjustment(df, model, data)
df <- df %>%
dplyr::mutate(
uu_cl = .data$trt1 * (.data$O.hat - .data$adjust1) -
.data$trt0 * (.data$O.hat - .data$adjust0),
ssig_l = .data$event * .data$Y0 * .data$Y1 / .data$Y^2
)
# Summarize by car_strata (if CL, then single car_strata)
ss <- df %>%
dplyr::filter(!is.na(.data$uu_cl)) %>%
dplyr::group_by(.data$car_strata) %>%
dplyr::summarise(
U_SL_z = sum(.data$uu_cl),
var_adj = model$p_trt * (1 - model$p_trt) * unique(.data$bsigb) * dplyr::n(),
.groups = "drop"
) %>%
dplyr::arrange(.data$car_strata)
# Final quantities for the C(S)L statistic
U_CSL <- mean(df$uu_cl)
var_CSL <- mean(df$ssig_l) - sum(ss$var_adj) / data$n
se <- sqrt(var_CSL / data$n)
statistic <- U_CSL / se
result <- list(
strata_sum=ss,
U=U_CSL,
se=se,
statistic=statistic
)
return(
structure(
class="TTEResult",
list(result=result, settings=model, data=data)
)
)
}
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