From: Grant Izmirlian <izmirlian_at_nih.gov>

Date: Tue, 8 Jan 2008 14:33:13 -0500

R-packages_at_r-project.org

https://stat.ethz.ch/mailman/listinfo/r-packages Received on Fri 11 Jan 2008 - 00:47:33 EST

Date: Tue, 8 Jan 2008 14:33:13 -0500

Hello List:

Please find uploaded to CRAN a new package, PwrGSD

The package is intended for the design and analysis of group sequential trials
There are two main functions,

(1) GrpSeqBnds: computes group sequential stopping boundaries for interim

analysis of a sequential trial based upon a normally distributed test statistic. This can be done via the Lan-Demets procedure with Obrien-Fleming, Pocock or Wang-Tsiatis spending. This can also be done via boundaries created to correspond with the stochastic curtailment procedure. (2) PwrGSD (same as the package name) which computes operating characteristics such as power, expected duration, weighted avergage relative risks at the boundaries among other things, that correspond to a user supplied hypothetical two arm trial under a user supplied choice of monitoring scheme and choice of test statistic within the weighted log-rank class of test statistics. Computations are done either via aysmptotic methods or via simulation. Note: another feature is the flexible provision for time dependent non-compliance. The function has a nice calling interface based upon the specification of boundary methods via functional forms. There are alot of nice summarization methods which allow the user to explore the space of hypothetical trial scenarios and boundary construction methods, such as a compound object class for linking individuals calls to PwrGSD to components of a list that are linked to an indexing dataframe for reference purpose. I appologize for a lengthy note, but attach a quite informative example below. Best Regards, Grant Izmirlian Mathematical Statistician Division of Cancer Prevention US National Cancer Institute 1-(301)496-7519 izmirlig_at_mail.nih.gov Հրանտ Իզմիրլյան --------------------------------------------------------------------------------- tlook <- c(7.14, 8.14, 9.14, 10.14, 10.64, 11.15, 12.14, 13.14, 14.14, 15.14, 16.14, 17.14, 18.14, 19.14, 20.14) t0 <- 0:19 h0 <- c(rep(3.73e-04, 2), rep(7.45e-04, 3), rep(1.49e-03, 15)) rhaz <-c(1, 0.9125, 0.8688, 0.7814, 0.6941, 0.6943, 0.6072, 0.5202, 0.4332, 0.652, 0.6524, 0.6527, 0.653, 0.6534, 0.6537, 0.6541, 0.6544, 0.6547, 0.6551, 0.6554) hc <- c(rep(1.05e-02, 2), rep(2.09e-02, 3), rep(4.19e-02, 15)) hd1B <- c(0.1109, 0.1381, 0.1485, 0.1637, 0.2446, 0.2497, 0) test.example <- PwrGSD( EfficacyBoundary=LanDemets(alpha=0.05, spending= ObrienFleming), FutilityBoundary=LanDemets(alpha=0.1,spending=ObrienFleming), RR.Futility = 0.82, sided="<",method="A",accru =7.73, accrat=9818.65, tlook =tlook, tcut0 =t0, h0=h0, tcut1=t0, rhaz=rhaz, tcutc0=t0, hc0=hc, tcutc1=t0, hc1=hc, tcutd0B =c(0, 13), hd0B =c(0.04777, 0), tcutd1B =0:6, hd1B =hd1B, noncompliance =crossover, gradual =TRUE, WtFun =c("FH", "SFH", "Ramp"), ppar =c(0, 1, 0, 1, 10, 10))R-packages mailing list

## we will construct a variety of alternate hypotheses relative to the

## base case specified above

max.effect <- 0.80 + 0.05*(0:8) n.me <- length(max.effect)

## we will also vary extent of censoring relative to the base case

## specified above

cens.amt <- 0.75 + 0.25*(0:2) n.ca <- length(cens.amt)

## we may also wish to compare the Lan-Demets/O'Brien-Fleming efficacy

## boundary with a Pocock efficacy boundary

Eff.bound.choice <- 1:2 ebc.nms <- c("LanDemets(alpha=0.05, spending=ObrienFleming)", "SC(alpha=0.05, crit=0.90)") n.ec <- length(Eff.bound.choice)

## The following line creates the indexing dataframe, `descr', with one

## line for each possible combination of the selection variables we've

## created.

descr <- as.data.frame( cbind(Eff.bound.choice=rep(Eff.bound.choice, each=n.ca*n.me), cens.amt=rep(rep(cens.amt, each=n.me), n.ec), max.effect=rep(max.effect, n.ec*n.ca))) descr$Eff.bound.choice <- ebc.nms[descr$Eff.bound.choice]

## Now descr contains one row for each combination of the levels of

## the user defined selection variables, `Eff.bound.choice',

## `max.effect' and `cens.amt'. Keep in mind that the names and number

## of these variables is arbitrary. Next we create a skeleton

## `cpd.PwrGSD' object with a call to the function `cpd.PwrGSD' with

## argument `descr'

test.example.set <- cpd.PwrGSD(descr)

## Now, the newly created object, of class `cpd.PwrGSD', contains

## an element `descr', a component `date', the date created

## and a component `Elements', an empty list of length equal

## to the number of rows in `descr'. Next we do the computation in

## a loop over the rows of `descr'.

n.descr <- nrow(descr) for(k in 1:n.descr){ ## First, we copy the original call to the current call, ## `Elements[[k]]$call' test.example.set$Elements[[k]]$call <- test.example$call ## Use the efficacy boundary choice in the kth row of `descr' ## to set the efficacy boundary choice in the current call test.example.set$Elements[[k]]$call$EfficacyBoundary <- parse(text=as.character(descr[k,"Eff.bound.choice"]))[[1]] ## Derive the `rhaz' defined by the selection variable "max.effect" ## in the kth row of `descr' and use this to set the `rhaz' ## components of the current call test.example.set$Elements[[k]]$call$rhaz <- exp(descr[k,"max.effect"] * log(rhaz)) ## Derive the censoring components from the selection variable ## "cens.amt" in the kth row of `descr' and place that result ## into the current call test.example.set$Elements[[k]]$call$hc0 <- test.example.set$Elements[[k]]$call$hc1 <- exp(descr[k, "cens.amt"] * log(hc)) ## Now the current call corresponds exactly to the selection ## variable values in row `k' of `descr'. The computation is ## done by calling `update' test.example.set$Elements[[k]] <- update(test.example.set$Elements[[k]]) cat(k/n.descr, "\r") } ## We can plot the results -- see the help under `plot.cpd.PwrGSD' plot(test.example.set, formula = ~ max.effect | stat * cens.amt, subset=(substring(Eff.bound.choice, 1,9)=="LanDemets")) plot(test.example.set, formula = ~ max.effect | stat * cens.amt, subset=(substring(Eff.bound.choice, 1,2)=="SC")) ## Notice the appearance of the selection variable `stat' which was ## not defined in the dataset `descr'. _______________________________________________

R-packages_at_r-project.org

https://stat.ethz.ch/mailman/listinfo/r-packages Received on Fri 11 Jan 2008 - 00:47:33 EST

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