Merge pull request #228 from Merck/spelling

Fix spelling and run styler

R/counting_process.R

@@ -20,9 +20,9 @@
 #'
 #' Produces a data frame that is sorted by stratum and time.
 #' Included in this is only the times at which one or more event occurs.
-#' The output dataset contains stratum, tte (time-to-event),
-#' at risk count and count of events at the specified tte
-#' sorted by stratum and tte.
+#' The output dataset contains stratum, TTE (time-to-event),
+#' at risk count, and count of events at the specified TTE
+#' sorted by stratum and TTE.
 #'
 #' @details
 #' The function only considered two group situation.

R/early_zero_weight.R

@@ -29,8 +29,6 @@
 #' @importFrom data.table ":=" as.data.table fifelse merge.data.table setDF
 #' @noRd
 early_zero_weight <- function(x, early_period = 4, fail_rate = NULL) {
-
-
   ans <- as.data.table(x)
   n_stratum <- length(unique(ans$stratum))
 

R/fh_weight.R

@@ -38,10 +38,8 @@ fh_weight <- function(
       counting_process(arm = "experimental"),
     rho = 0,
     gamma = 1) {
-
-
   # Input checking ----
-  if(length(rho) != 1 || length(gamma) != 1) {
+  if (length(rho) != 1 || length(gamma) != 1) {
     stop("fh_weight: please input single numerical values of rho and gamma.")
   }
 

R/maxcombo.R

@@ -16,12 +16,12 @@
 #  You should have received a copy of the GNU General Public License
 #  along with this program.  If not, see <http://www.gnu.org/licenses/>.
 
-#' Maxcombo test
+#' MaxCombo test
 #'
 #' WARNING: This experimental function is a work-in-progress. The function
 #' arguments will change as we add additional features.
 #'
-#' @param data a tte dataset
+#' @param data A TTE dataset.
 #' @param rho Numeric vector. Must be greater
 #'   than or equal to zero. Must be the same length as `gamma`.
 #' @param gamma Numeric vector. Must be
@@ -37,9 +37,10 @@
 #' parameters of this test method (`parameter`),
 #' point estimation of the treatment effect (`estimation`),
 #' standardized error of the treatment effect (`se`),
-#' Z-score of each test of the Maxcombo (`z`),
+#' Z-score of each test of the MaxCombo (`z`),
 #' p-values (`p_value`)
-#' and the correlation matrix of each tests in Maxcombo (begin with `v`)
+#' and the correlation matrix of each tests in MaxCombo (begin with `v`)
+#'
 #' @export
 #'
 #' @seealso [wlr()], [rmst()], [milestone()]
@@ -48,15 +49,16 @@
 #' sim_pw_surv(n = 200) |>
 #'   cut_data_by_event(150) |>
 #'   maxcombo(rho = c(0, 0), gamma = c(0, 1), return_corr = TRUE)
-maxcombo <- function(data = sim_pw_surv(n = 200) |> cut_data_by_event(150),
-                     rho = c(0, 0, 1),
-                     gamma = c(0, 1, 1),
-                     return_variance = FALSE,
-                     return_corr = FALSE) {
+maxcombo <- function(
+    data = sim_pw_surv(n = 200) |> cut_data_by_event(150),
+    rho = c(0, 0, 1),
+    gamma = c(0, 1, 1),
+    return_variance = FALSE,
+    return_corr = FALSE) {
   # Input checking ----
   n_weight <- length(rho)
 
-  if(any(!is.numeric(rho)) || any(!is.numeric(gamma)) || any(rho < 0) || any(gamma < 0)) {
+  if (any(!is.numeric(rho)) || any(!is.numeric(gamma)) || any(rho < 0) || any(gamma < 0)) {
     stop("maxcombo: please input positive values of rho and gamma.")
   }
 
@@ -80,10 +82,10 @@ maxcombo <- function(data = sim_pw_surv(n = 200) |> cut_data_by_event(150),
   # We want ave_rho[i,j]   = (rho[i] + rho[j])/2
   # and     ave_gamma[i,j] = (gamma[i] + gamma[j])/2
   ave_rho <- (matrix(rho, nrow = n_weight, ncol = n_weight, byrow = FALSE) +
-                matrix(rho, nrow = n_weight, ncol = n_weight, byrow = TRUE)
+    matrix(rho, nrow = n_weight, ncol = n_weight, byrow = TRUE)
   ) / 2
   ave_gamma <- (matrix(gamma, nrow = n_weight, ncol = n_weight) +
-                  matrix(gamma, nrow = n_weight, ncol = n_weight, byrow = TRUE)
+    matrix(gamma, nrow = n_weight, ncol = n_weight, byrow = TRUE)
   ) / 2
 
   # Convert to all original and average rho/gamma into a data.table ----
@@ -117,11 +119,12 @@ maxcombo <- function(data = sim_pw_surv(n = 200) |> cut_data_by_event(150),
     y = res |> as.data.frame(),
     by = c("rho", "gamma"),
     all.x = TRUE,
-    sort = FALSE)
+    sort = FALSE
+  )
 
   # Tidy outputs ----
   ans <- list()
-  ans$method <- "Maxcombo"
+  ans$method <- "MaxCombo"
   temp <- data.frame(rho = rho, gamma = gamma)
   temp$x <- paste0("FH(", temp$rho, ", ", temp$gamma, ")")
   ans$parameter <- paste(temp$x, collapse = " + ")

R/sim_fixed_n.R

@@ -94,7 +94,8 @@
 #' x <- sim_fixed_n(
 #'   n_sim = 10,
 #'   timing_type = 2,
-#'   rho_gamma = data.frame(rho = 0, gamma = c(0, 1)))
+#'   rho_gamma = data.frame(rho = 0, gamma = c(0, 1))
+#' )
 #'
 #' # Get power approximation
 #' x |>
@@ -135,7 +136,6 @@ sim_fixed_n <- function(
     # Default is to to logrank testing, but one or more Fleming-Harrington tests
     # can be specified
     rho_gamma = data.frame(rho = 0, gamma = 0)) {
-
   # Check input values ----
   # Check input enrollment rate assumptions
   if (!("duration" %in% names(enroll_rate))) {
@@ -264,7 +264,7 @@ sim_fixed_n <- function(
       dropout_rate = dr,
       block = block
     )
-  #for (i in 1:n_sim) {
+    # for (i in 1:n_sim) {
 
     # Calculate possible cutting dates ----
     # Date 1: Study date that targeted event rate achieved
@@ -383,21 +383,25 @@ doAnalysis <- function(d, rho_gamma, n_stratum) {
     res <- d |>
       wlr(weight = fh(rho = rho_gamma$rho, gamma = rho_gamma$gamma))
 
-    ans <- data.frame(method = res$method,
-                      parameter = res$parameter,
-                      estimation = res$estimation,
-                      se = res$se,
-                      z = res$z)
+    ans <- data.frame(
+      method = res$method,
+      parameter = res$parameter,
+      estimation = res$estimation,
+      se = res$se,
+      z = res$z
+    )
   } else {
     res <- d |>
       maxcombo(rho = rho_gamma$rho, gamma = rho_gamma$gamma, return_corr = TRUE)
 
-    ans <- data.frame(method = rep(res$method, nrow(rho_gamma)),
-                      parameter = rep(res$parameter, nrow(rho_gamma)),
-                      estimation = rep("-", nrow(rho_gamma)),
-                      se = rep("-", nrow(rho_gamma)),
-                      z = res$z,
-                      p_value = rep(res$p_value, nrow(rho_gamma)))
+    ans <- data.frame(
+      method = rep(res$method, nrow(rho_gamma)),
+      parameter = rep(res$parameter, nrow(rho_gamma)),
+      estimation = rep("-", nrow(rho_gamma)),
+      se = rep("-", nrow(rho_gamma)),
+      z = res$z,
+      p_value = rep(res$p_value, nrow(rho_gamma))
+    )
     ans <- cbind(ans, res$corr |> as.data.frame())
   }
 

R/wlr.R

@@ -18,14 +18,14 @@
 
 #' Weighted logrank test
 #'
-#' @param data cutted dataset generated by sim_pw_surv
-#' @param weight weighting functions, such as [fh()], [mb()], and
+#' @param data Dataset that has been cut, generated by [sim_pw_surv()].
+#' @param weight Weighting functions, such as [fh()], [mb()], and
 #'   [early_zero()].
 #' @param return_variance A logical flag that, if `TRUE`, adds columns
 #'   estimated variance for weighted sum of observed minus expected;
 #'   see details; Default: `FALSE`.
 #'
-#' @return A list contraining the test method (`method`),
+#' @return A list containing the test method (`method`),
 #' parameters of this test method (`parameter`),
 #' point estimation of the treatment effect (`estimation`),
 #' standardized error of the treatment effect (`se`),
@@ -34,6 +34,7 @@
 #' @importFrom data.table setDF setDT
 #'
 #' @export
+#'
 #' @details
 #' - \eqn{z} - Standardized normal Fleming-Harrington weighted logrank test.
 #' - \eqn{i} - Stratum index.
@@ -97,23 +98,20 @@ wlr <- function(data, weight, return_variance = FALSE) {
     ans$estimation <- sum(x$weight * x$o_minus_e)
     ans$se <- sqrt(sum(x$weight^2 * x$var_o_minus_e))
     ans$z <- ans$estimation / ans$se
-
   } else if (inherits(weight, "mb")) {
     x <- x |> mb_weight(delay = weight$delay, w_max = weight$w_max)
 
     ans$parameter <- paste0("MB(delay = ", weight$delay, ", max_weight = ", weight$w_max, ")")
     ans$estimate <- sum(x$o_minus_e * x$mb_weight)
     ans$se <- sqrt(sum(x$var_o_minus_e * x$mb_weight^2))
     ans$z <- ans$estimate / ans$se
-
   } else if (inherits(weight, "early_period")) {
     x <- x |> early_zero_weight(early_period = weight$early_period, fail_rate = weight$fail_rate)
 
     ans$parameter <- paste0("Xu 2017 with first ", weight$early_period, " months of 0 weights")
     ans$estimate <- sum(x$o_minus_e * x$weight)
     ans$se <- sqrt(sum(x$var_o_minus_e * x$weight^2))
     ans$z <- ans$estimate / ans$se
-
   }
   return(ans)
 }

inst/WORDLIST

@@ -0,0 +1,82 @@
+batchtools
+biomarker
+biometrical
+biopharmaceutical
+blazingly
+documentable
+doi
+downweighting
+durations
+errantly
+frac
+geq
+gsDesign
+immuno
+ldots
+logrank
+magrittr
+modestWLRT
+multisession
+nonproportional
+operatorname
+parallelization
+parallelize
+pharma
+pkgdown
+reproducibility
+survRM
+tidyverse
+topologies
+unadjusted
+unstratified
+widehat
+ANCOVA
+Bo
+Breslow's
+Burman
+CMD
+Codecov
+Foulkes
+Fredrik
+Freidlin
+Guo
+Harhoff
+HPC
+Hu
+Iacona
+Ji
+Jian
+Jing
+Kaplan
+Korn
+Lachin
+Liao
+Liu
+Luo
+Magirr
+MaxCombo
+Mukhopadhyay
+MWLRT
+Pralay
+Qin
+Rahway
+RMST
+RMTL
+Rong
+Roychoudhury
+RStudio
+Rui
+Satrajit
+Tai
+Tatsuoka
+Tianle
+Tsang
+Tsiatis
+TTE
+WPGSD
+Xiaodong
+Xu
+Xuejing
+YAML
+Zhen
+Zifang