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Merge pull request #105 from keaven/almostIntegerIssue101
Add `gsSurvCalendar()`, fix `toInteger()`, and fix `toBinomialExact()`
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| Package: gsDesign | ||
| Version: 3.5.0 | ||
| Version: 3.6.0 | ||
| Title: Group Sequential Design | ||
| Authors@R: person(given = "Keaven", family = "Anderson", email = | ||
| "[email protected]", role = c('aut','cre')) | ||
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| #' Time-to-event endpoint design with calendar timing of analyses | ||
| #' | ||
| #' @param test.type Test type. See \code{\link{gsSurv}}. | ||
| #' @param alpha Type I error rate. Default is 0.025 since 1-sided | ||
| #' testing is default. | ||
| #' @param sided \code{1} for 1-sided testing, \code{2} for 2-sided testing. | ||
| #' @param beta Type II error rate. Default is 0.10 | ||
| #' (90\% power); \code{NULL} if power is to be computed based on | ||
| #' other input values. | ||
| #' @param astar Normally not specified. If \code{test.type = 5} | ||
| #' or \code{6}, \code{astar} specifies the total probability | ||
| #' of crossing a lower bound at all analyses combined. This | ||
| #' will be changed to \code{1 - alpha} when default value of | ||
| #' \code{0} is used. Since this is the expected usage, | ||
| #' normally \code{astar} is not specified by the user. | ||
| #' @param sfu A spending function or a character string | ||
| #' indicating a boundary type (that is, \code{"WT"} for | ||
| #' Wang-Tsiatis bounds, \code{"OF"} for O'Brien-Fleming bounds and | ||
| #' \code{"Pocock"} for Pocock bounds). For one-sided and symmetric | ||
| #' two-sided testing is used to completely specify spending | ||
| #' (\code{test.type = 1}, \code{2}), \code{sfu}. The default value is | ||
| #' \code{sfHSD} which is a Hwang-Shih-DeCani spending function. | ||
| #' @param sfupar Real value, default is \code{-4} which is an | ||
| #' O'Brien-Fleming-like conservative bound when used with the | ||
| #' default Hwang-Shih-DeCani spending function. This is a | ||
| #' real-vector for many spending functions. The parameter | ||
| #' \code{sfupar} specifies any parameters needed for the spending | ||
| #' function specified by \code{sfu}; this will be ignored for | ||
| #' spending functions (\code{sfLDOF}, \code{sfLDPocock}) | ||
| #' or bound types (\code{"OF"}, \code{"Pocock"}) | ||
| #' that do not require parameters. | ||
| #' @param sfl Specifies the spending function for lower | ||
| #' boundary crossing probabilities when asymmetric, | ||
| #' two-sided testing is performed | ||
| #' (\code{test.type = 3}, \code{4}, \code{5}, or \code{6}). | ||
| #' Unlike the upper bound, | ||
| #' only spending functions are used to specify the lower bound. | ||
| #' The default value is \code{sfHSD} which is a | ||
| #' Hwang-Shih-DeCani spending function. The parameter | ||
| #' \code{sfl} is ignored for one-sided testing | ||
| #' (\code{test.type = 1}) or symmetric 2-sided testing | ||
| #' (\code{test.type = 2}). | ||
| #' @param sflpar Real value, default is \code{-2}, which, with the | ||
| #' default Hwang-Shih-DeCani spending function, specifies a | ||
| #' less conservative spending rate than the default for the | ||
| #' upper bound. | ||
| #' @param calendarTime Vector of increasing positive numbers | ||
| #' with calendar times of analyses. Time 0 is start of | ||
| #' randomization. | ||
| #' @param spending Select between calendar-based spending and | ||
| #' information-based spending. | ||
| #' @param lambdaC Scalar, vector or matrix of event hazard | ||
| #' rates for the control group; rows represent time periods while | ||
| #' columns represent strata; a vector implies a single stratum. | ||
| #' @param hr Hazard ratio (experimental/control) under the | ||
| #' alternate hypothesis (scalar). | ||
| #' @param hr0 Hazard ratio (experimental/control) under the null | ||
| #' hypothesis (scalar). | ||
| #' @param eta Scalar, vector or matrix of dropout hazard rates | ||
| #' for the control group; rows represent time periods while | ||
| #' columns represent strata; if entered as a scalar, rate is | ||
| #' constant across strata and time periods; if entered as a | ||
| #' vector, rates are constant across strata. | ||
| #' @param etaE Matrix dropout hazard rates for the experimental | ||
| #' group specified in like form as \code{eta}; if \code{NULL}, | ||
| #' this is set equal to \code{eta}. | ||
| #' @param gamma A scalar, vector or matrix of rates of entry by | ||
| #' time period (rows) and strata (columns); if entered as a | ||
| #' scalar, rate is constant across strata and time periods; | ||
| #' if entered as a vector, rates are constant across strata. | ||
| #' @param R A scalar or vector of durations of time periods for | ||
| #' recruitment rates specified in rows of \code{gamma}. Length is the | ||
| #' same as number of rows in \code{gamma}. Note that when variable | ||
| #' enrollment duration is specified (input \code{T = NULL}), the final | ||
| #' enrollment period is extended as long as needed. | ||
| #' @param S A scalar or vector of durations of piecewise constant | ||
| #' event rates specified in rows of \code{lambda}, \code{eta} and \code{etaE}; | ||
| #' this is \code{NULL} if there is a single event rate per stratum | ||
| #' (exponential failure) or length of the number of rows in \code{lambda} | ||
| #' minus 1, otherwise. | ||
| #' @param minfup A non-negative scalar less than the maximum value | ||
| #' in \code{calendarTime}. Enrollment will be cut off at the | ||
| #' difference between the maximum value in \code{calendarTime} | ||
| #' and \code{minfup}. | ||
| #' @param ratio Randomization ratio of experimental treatment | ||
| #' divided by control; normally a scalar, but may be a vector with | ||
| #' length equal to number of strata. | ||
| #' @param r Integer value controlling grid for numerical | ||
| #' integration as in Jennison and Turnbull (2000); default is 18, | ||
| #' range is 1 to 80. Larger values provide larger number of grid | ||
| #' points and greater accuracy. Normally \code{r} will not be changed by | ||
| #' the user. | ||
| #' @param tol Tolerance for error passed to the \code{\link{gsDesign}} function. | ||
| #' | ||
| #' @export | ||
| #' | ||
| #' @rdname gsSurvCalendar | ||
| #' | ||
| #' @examples | ||
| #' # First example: while timing is calendar-based, spending is event-based | ||
| #' x <- gsSurvCalendar() %>% toInteger() | ||
| #' gsBoundSummary(x) | ||
| #' | ||
| #' # Second example: both timing and spending are calendar-based | ||
| #' # This results in less spending at interims and leaves more for final analysis | ||
| #' y <- gsSurvCalendar(spending = "calendar") %>% toInteger() | ||
| #' gsBoundSummary(y) | ||
| #' | ||
| #' # Note that calendar timing for spending relates to planned timing for y | ||
| #' # rather than timing in y after toInteger() conversion | ||
| #' | ||
| #' # Values plugged into spending function for calendar time | ||
| #' y$usTime | ||
| #' # Actual calendar fraction from design after toInteger() conversion | ||
| #' y$T / max(y$T) | ||
| gsSurvCalendar <- function( | ||
| test.type = 4, alpha = 0.025, sided = 1, beta = 0.1, astar = 0, | ||
| sfu = gsDesign::sfHSD, sfupar = -4, | ||
| sfl = gsDesign::sfHSD, sflpar = -2, | ||
| calendarTime = c(12, 24, 36), | ||
| spending = c("information", "calendar"), | ||
| lambdaC = log(2) / 6, hr = .6, hr0 = 1, eta = 0, etaE = NULL, | ||
| gamma = 1, R = 12, S = NULL, minfup = 18, ratio = 1, | ||
| r = 18, tol = 1e-06) { | ||
| checkVector(calendarTime, isType = "numeric") | ||
| if (min(diff(calendarTime)) < 0) stop(paste("calendarTime must be an increasing vector of positive numbers, including final analysis\n input calendarTime = ", | ||
| paste(calendarTime, collapse = ", "))) | ||
| x <- nSurv( | ||
| lambdaC = lambdaC, hr = hr, hr0 = hr0, eta = eta, etaE = etaE, | ||
| gamma = gamma, R = R, S = S, T = max(calendarTime), | ||
| minfup = minfup, ratio = ratio, | ||
| alpha = alpha, beta = beta, sided = sided | ||
| ) | ||
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| # Get interim expected event counts and sample size based on | ||
| # input gamma, eta, lambdaC, R, S, minfup | ||
| eDC <- NULL | ||
| eDE <- NULL | ||
| eNC <- NULL | ||
| eNE <- NULL | ||
| T <- NULL | ||
| k <- length(calendarTime) | ||
| for (i in 1:k) { | ||
| xx <- nEventsIA(tIA = calendarTime[i], x = x, simple = FALSE) | ||
| eDC <- rbind(eDC, xx$eDC) | ||
| eDE <- rbind(eDE, xx$eDE) | ||
| eNC <- rbind(eNC, xx$eNC) | ||
| eNE <- rbind(eNE, xx$eNE) | ||
| } | ||
| timing <- rowSums(eDC) + rowSums(eDE) | ||
| timing <- timing / max(timing) | ||
| # if calendar spending, set usTime, lsTime | ||
| if (spending[1] == "calendar") { | ||
| lsTime <- calendarTime / max(calendarTime) | ||
| } else { | ||
| lsTime <- NULL | ||
| } | ||
| usTime <- lsTime | ||
|
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| # Now inflate events to get targeted power | ||
| y <- gsDesign::gsDesign( | ||
| k = k, test.type = test.type, alpha = alpha / sided, | ||
| beta = beta, astar = astar, n.fix = x$d, timing = timing, | ||
| sfu = sfu, sfupar = sfupar, sfl = sfl, sflpar = sflpar, tol = tol, | ||
| delta1 = log(hr), delta0 = log(hr0), | ||
| usTime = usTime, lsTime = lsTime | ||
| ) | ||
| y$hr <- hr | ||
| y$hr0 <- hr0 | ||
| y$R <- x$R | ||
| y$S <- x$S | ||
| y$minfup <- x$minfup | ||
| # Inflate fixed design enrollment to get targeted events | ||
| inflate <- max(y$n.I) / x$d | ||
| y$gamma <- x$gamma * inflate | ||
| y$eDC <- inflate * eDC | ||
| y$eDE <- inflate * eDE | ||
| y$eNC <- inflate * eNC | ||
| y$eNE <- inflate * eNE | ||
| y$ratio <- ratio | ||
| y$lambdaC <- x$lambdaC | ||
| y$etaC <- x$etaC | ||
| y$etaE <- x$etaE | ||
| y$variable <- x$variable | ||
| y$tol <- tol | ||
| y$T <- calendarTime | ||
| class(y) <- c("gsSurv", "gsDesign") | ||
|
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| nameR <- nameperiod(cumsum(y$R)) | ||
| stratnames <- paste("Stratum", 1:ncol(y$lambdaC)) | ||
| if (is.null(y$S)) { | ||
| nameS <- "0-Inf" | ||
| } else { | ||
| nameS <- nameperiod(cumsum(c(y$S, Inf))) | ||
| } | ||
| rownames(y$lambdaC) <- nameS | ||
| colnames(y$lambdaC) <- stratnames | ||
| rownames(y$etaC) <- nameS | ||
| colnames(y$etaC) <- stratnames | ||
| rownames(y$etaE) <- nameS | ||
| colnames(y$etaE) <- stratnames | ||
| rownames(y$gamma) <- nameR | ||
| colnames(y$gamma) <- stratnames | ||
| return(y) | ||
| } |
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