# User input estimates of sample size and power for group sequential designs with early outcomes Provides sample sizes and power for group sequential designs with early outcomes defined by a matrix of fixed numbers of participants with data for all outcomes (early and primary) at each interim time-point or a function that gives a similarly structured matrix for all sample sizes in a pre-set range and a correlation model or correlation matrix, recruitment period and interim analysis time-points. ```r gsearlyUser(trecruit, s, tfu, tinterims, ninterims, pow=0.9, n=NULL, tref=c(1,2), vphi=0.5, cmodel="uniform", sd=1, rho=0.5, theta, fp, tn, treatnames=c("control","treat"), sopt=list(r=18, bisect=list(min=20, max=10000, niter=1000, tol=0.001))) ``` ## Arguments - `trecruit`: As for `gsearlyModel`. - `s`: As for `gsearlyModel`. - `tfu`: As for `gsearlyModel`. - `tinterims`: As for `gsearlyModel`. - `ninterims`: A matrix with `s + 1` columns and `length(interims)` rows giving the numbers of participants providing data or present at each interim analysis (row) at recruitment (first column) and at each follow-up time point (`tfu`) in columns 2 to `s + 1`. Alternatively, a function fn(x) that gives a matrix of these dimensions for sample sizes x in the range `(sopt$bisect$min, sopt$bisect$max)`. Currently this is only implemented and tested for functions based on fixed proportions. - `pow`: As for `gsearlyModel`. - `n`: As for `gsearlyModel`. - `tref`: As for `gsearlyModel`. - `vphi`: As for `gsearlyModel`. - `cmodel`: Either a correlation model, `c("uniform", "exponential")`, defined by correlation parameter `rho`, or a user supplied correlation matrix of dimensions `s` x `s`. - `sd`: As for `gsearlyModel`. - `rho`: As for `gsearlyModel` if `cmodel` is set to `"uniform"` or `"exponential"`, otherwise ignored. - `theta`: As for `gsearlyModel`. - `fp`: As for `gsearlyModel`. - `tn`: As for `gsearlyModel`. - `treatnames`: As for `gsearlyModel`. - `sopt`: As for `gsearlyModel`. ## Returns An object of class gsearly is a list containing the following components - **title**: Package title and version number. - **call**: Call to `gsearlyUser`. - **rdata**: A list of same structure as for `gsearlyModel` with the recruitment model set to `"none"`, the recruitment model parameter (`m`) set to `NA` and the numbers of study participants recruited and with outcome data at each of the interims set to `ninterims`. - **idata**: A list of same structure as for `gsearlyModel` with the correlation model type (`cmodel$type`) set to `"none"` and the parameter (`cmodel$rho`) set to `NA`, if either `"uniform"` or `"exponential"` are not selected. - **power**: A list of same structure as for `gsearlyModel`. In addition for designs based on fixed numbers of participants at interim, where `n` is unset, the minimum and maximum attainable power (`rangepow`). ## See Also `gsearlyModel` ## Examples ```r # For 90 percent power (pow), a call to gsearlyModel provides a feasible design fp <- c(0.0000,0.0010,0.0250) tn <- c(0.2400,0.7200,0.9750) modeldesign <- gsearlyModel(rmodel="dilin", trecruit=36, s=3, tfu=c(3,6,12), tinterims=c(18,30), pow=0.9, vphi=0.5, m=2, cmodel="uniform", sd=20, rho=0.5, theta=8, fp=fp, tn=tn) modeldesign # This design can be replicated using gsearlyUser n <- modeldesign$rdata$n["total"] ninterims <- modeldesign$rdata$interims cmodel <- modeldesign$idata$cmodel$corrmat userdesign <- gsearlyUser(trecruit=36, s=3, tfu=c(3,6,12), tinterims=c(18,30), ninterims=ninterims, n=n, vphi=0.5, cmodel=cmodel, sd=20, theta=8, fp=fp, tn=tn) userdesign # Expected numbers of participants at interim analyses modeldesign$rdata$interims userdesign$rdata$interims # Information at these interims and final analysis modeldesign$idata$interims userdesign$idata$interims # Upper and lower stopping boundaries and probabilities rbind(modeldesign$power$lowerror, modeldesign$power$upperror) rbind(userdesign$power$lowerror, userdesign$power$upperror) # Change correlation matrix and interim numbers cmodel <- matrix(c(1,0.2,0.1, 0.2,1,0.1, 0.1,0.1,1), nrow=3, byrow=TRUE) ninterims <- matrix(c(130,110,90,45, 200,175,160,120), nrow=2, byrow=TRUE) # For 90 percent power (pow), a call to gsearlyUser provides a feasible design nuserdesign <- gsearlyUser(trecruit=36, s=3, tfu=c(3,6,12), tinterims=c(18,30), ninterims=ninterims, vphi=0.5, pow=0.9, cmodel=cmodel, sd=20, theta=8, fp=fp, tn=tn) nuserdesign ```

gsearlyUser function