PwrGSD {PwrGSD}

Calculate Power in a Group Sequential Design

Description

Derives power in a two arm clinical trial under a group sequential design. Allows for arbitrary number of interim analyses, arbitrary specification of arm-0/arm-1 time to event distributions (via survival or hazard), arm-0/arm-1 censoring distribution, provisions for two types of continuous time non-compliance according to arm-0/arm-1 rate followed by switch to new hazard rate. Allows for analyses using (I) weighted log-rank statistic, with weighting function (a) a member of the Flemming-Harrington G-Rho class, or (b) a stopped version thereof, or (c) the ramp-plateau deterministic weights, or (II) the integrated survival distance (currently under method=="S" without futility only). Stopping boundaries are computed via the Lan-Demets method, Haybittle method, converted from the stochastic curtailment procedure, or be completely specified by the user. The Lan-Demets boundaries can be constructed usign either O'Brien-Flemming, Pocock or Wang-Tsiatis power alpha-spending. The C kernel is readily extensible, and further options will become available in the near future.

Usage

PwrGSD(EfficacyBoundary = LanDemets(alpha = 0.05, spending = ObrienFleming),
    FutilityBoundary = LanDemets(alpha = 0.1, spending = ObrienFleming),
    NonBindingFutility = TRUE, sided = c("2>", "2<", "1>", "1<"),
    method = c("S", "A"), accru, accrat, tlook,
    tcut0 = NULL, h0 = NULL, s0 = NULL, tcut1 = NULL,
    rhaz = NULL, h1 = NULL, s1 = NULL, tcutc0 = NULL, hc0 = NULL,
    sc0 = NULL, tcutc1 = NULL, hc1 = NULL, sc1 = NULL, tcutd0A = NULL,
    hd0A = NULL, sd0A = NULL, tcutd0B = NULL, hd0B = NULL, sd0B = NULL,
    tcutd1A = NULL, hd1A = NULL, sd1A = NULL, tcutd1B = NULL,
    hd1B = NULL, sd1B = NULL, tcutx0A = NULL, hx0A = NULL, sx0A = NULL,
    tcutx0B = NULL, hx0B = NULL, sx0B = NULL, tcutx1A = NULL,
    hx1A = NULL, sx1A = NULL, tcutx1B = NULL, hx1B = NULL, sx1B = NULL,
    noncompliance = c("none", "crossover", "mixed", "user"),
    gradual = FALSE, WtFun = c("FH", "SFH", "Ramp"), ppar = cbind(c(0, 0)), 
    Spend.Info = c("Variance", "Events", "Hybrid(k)", "Calendar"), RR.Futility = NULL, 
    qProp.one.or.Q = c("one", "Q"), Nsim = NULL, detail = FALSE, StatType = c("WLR",
        "ISD"), doProj=FALSE)

Arguments

EfficacyBoundary

This specifies the method used to construct the efficacy boundary. The available choices are:

‘⁠(i) ⁠’Lan-Demets(alpha=<total type I error>, spending =<spending function>). The Lan-Demets method is based upon a error probability spending approach. The spending function can be set to ObrienFleming, Pocock, or Power(rho), where rho is the the power argument for the power spending function: rho=3 is roughly equivalent to the O'Brien-Fleming spending function and smaller powers result in a less conservative spending function.

‘⁠(ii) ⁠’Haybittle(alpha=<total type I error>, b.Haybittle=<user specified boundary point>). The Haybittle approach is conceptually the simplest of all methods for efficacy boundary construction. However, as it spends nearly no alpha until the end, is for all practical purposes equivalent to a single analysis design and to be considered overly conservative. This method sets all the boundary points equal to b.Haybittle, a user specified value (try 3) for all analyses except the last, which is calculated so as to result in the total type I error, set with the argument alpha.

‘⁠(iii) ⁠’SC(be.end=<efficacy boundary point at trial end>, prob=<threshold for conditional type I error for efficacy stopping>). The stochastic curtailment method is based upon the conditional probability of type I error given the current value of the statistic. Under this method, a sequence of boundary points on the standard normal scale (as are boundary points under all other methods) is calculated so that the total probability of type I error is maintained. This is done by considering the joint probabilities of continuing to the current analysis and then exceeding the threshold at the current analysis. A good value for the threshold value for the conditional type I error, prob is 0.90 or greater.

‘⁠(iv) ⁠’User supplied boundary points in the form c(b1, b2, b3, ..., b_m), where m is the number of looks.

FutilityBoundary

This specifies the method used to construct the futility boundary. The available choices are:

‘⁠(i) ⁠’Lan-Demets(alpha=<total type II error>, spending= <spending function>). The Lan-Demets method is based upon a error probability spending approach. The spending function can be set to ObrienFleming, Pocock, or Power(rho), where rho is the the power argument for the power spending function: rho=3 is roughly equivalent to the O'Brien-Fleming spending function and smaller powers result in a less conservative spending function.

‘⁠NOTE: ⁠’there is no implementation of the Haybittle method for futility boundary construction. Given that the futility boundary depends upon values of the drift function, this method doesn't apply.

‘⁠(ii) ⁠’SC(be.end=<efficacy boundary point at trial end>, prob=<threshold for conditional type II error for futility stopping>, drift.end=<projected drift at end of trial>). The stochastic curtailment method is based upon the conditional probability of type II error given the current value of the statistic. Under this method, a sequence of boundary points on the standard normal scale (as are boundary points under all other methods) is calculated so that the total probability of type II error, is maintained. This is done by considering the joint probabilities of continuing to the current analysis and then exceeding the threshold at the current analysis. A good value for the threshold value for the conditional type I error, prob is 0.90 or greater.

‘⁠(iii) ⁠’User supplied boundary points in the form c(b1, b2, b3, ..., b_m), where m is the number of looks.

NonBindingFutility

When using a futility boundary and this is set to 'TRUE', the efficacy boundary will be constructed in the absence of the futility boundary, and then the futility boundary will be constructed given the resulting efficacy boundary. This results in a more conservative efficacy boundary with true type I error less than the nominal level. This is recommended due to the fact that futility crossings are viewed by DSMB's with much less gravity than an efficacy crossing and as such, the consensus is that efficacy bounds should not be discounted towards the null hypothesis because of paths which cross a futility boundary. Default value is 'TRUE'.

sided

Set to “2>” (quoted) for two sided tests of the null hypothesis when a positive drift corresponds to efficacy. Set to “2<” (quoted) for two sided tests of the null hypothesis when a negative drift corresponds to efficacy. Set to “1>” or “1<” for one sided tests of H0 when efficacy corresponds to a positive or negative drift, respectively. If method==“S” then this must be of the same length as StatType because the interpretation of sided is different depending upon whether StatType==“WLR” (negative is benefit) or StatType==“ISD” (positive is benefit)

method

Determines how to calculate the power. Set to “A” (Asymptotic method, the default) or “S” (Simulation method)

accru

The upper endpoint of the accrual period beginning with time 0.

accrat

The rate of accrual per unit of time.

tlook

The times of planned interim analyses.

tcut0

Left hand endpoints for intervals upon which the arm-0 specific mortality is constant. The last given component is the left hand endpoint of the interval having right hand endpoint infinity.

h0

A vector of the same length as tcut0 which specifies the piecewise constant arm-0 mortality rate.

s0

Alternatively, the arm-0 mortality distribution can be supplied via this argument, in terms of of the corresponding survival function values at the times given in the vector tcut0. If s0 is supplied, then h0is derived internally, assuming the piecewise exponential distrubiton. If you specify s0, the first element must be 1, and correspondingly, the first component of tcut0 will be the lower support point of the distribution. You must supply either h0 or s0 but not both.

tcut1

Left hand endpoints for intervals upon which the arm-1 specific mortality is constant. The last given component is the left hand endpoint of the interval having right hand endpoint infinity.

rhaz

A vector of piecewise constant arm-1 versus arm-0 mortality rate ratios. If tcut1 and tcut0 are not identical, then tcut1, h0, and rhaz are internally rederived at the union of the sequences tcut0 and tcut1. In all cases the arm-1 mortality rate is then derived at the time cutpoints tcut1 as rhaz timesh0.

h1

Alternatively, the arm-1 mortality distribution can be supplied via this argument by specifying the piecewise constant arm-1 mortality rate. See the comments above.

s1

Alternatively, the arm-1 mortality distribution can be supplied via this argument, in terms of of the corresponding survival function values at the times given in the vector tcut1. Comments regarding s0 above apply here as well. You must supply exactly one of the following: h1, rhaz, or s1.

tcutc0

Left hand endpoints for intervals upon which the arm-0 specific censoring distribution hazard function is constant. The last given component is the left hand endpoint of the interval having right hand endpoint infinity.

hc0

A vector of the same length as tcutc0 which specifies the arm-0 censoring distribution in terms of a piecewise constant hazard function.

sc0

Alternatively, the arm-0 censoring distribution can be supplied via this argument, in terms of of the corresponding survival function values at the times given in the vector tcutc0. See comments above. You must supply either hc0 or sc0 but not both.

tcutc1

Left hand endpoints for intervals upon which the arm-1 specific censoring distribution hazard function is constant. The last given component is the left hand endpoint of the interval having right hand endpoint infinity.

hc1

A vector of the same length as tcutc1 which specifies the arm-1 censoring distribution in terms of a piecewise constant hazard function.

sc1

Alternatively, the arm-1 censoring distribution can be supplied via this argument, in terms of of the corresponding survival function values at the times given in the vector tcutc1. See comments above. You must supply either hc1 or sc1 but not both.

noncompliance

(i) Seting noncompliance to “none” for no non-compliance will automatically set the non-compliance arguments, below, to appropriate values for no compliance. This requires no additional user specification of non-compliance parameters. (ii) Setting noncompliance to “crossover” will automatically set crossover values in the arm 0/1 specific post-cause-B-delay-mortality for cross-over, i.e. simple interchange of the arm 0 and arm 1 mortalities. The user is required to specify all parameters corresponding to the arm 0/1 specific cause-B-delay distributions. The cause-A-delay and post-cause-A-delay-mortality are automatically set so as not to influence the calculations. Setting noncompliance to “mixed” will set the arm 0/1 specific post-cause-B-delay-mortality distributions for crossover as defined above. The user specifies the arm 0/1 specific cause-B-delay distribution as above, and in addition, all parameters related to the arm 0/1 specific cause-A-delay distributions and corresponding arm 0/1 specific post-cause-A-delay-mortality distributions. (iii) Setting noncompliance to “user” requires the user to specify all non-compliance distribution parameters.

tcutd0A

Left hand endpoints for intervals upon which the arm-0 specific cause-A delay distribution hazard function is constant. The last given component is the left hand endpoint of the interval having right hand endpoint infinity. Required only when noncompliance is set to “mixed” or “user”.

hd0A

A vector of the same length as tcutd0A containing peicewise constant hazard rates for the arm-0 cause-A delay distribution. Required only when noncompliance is set to “mixed” or “user”.

sd0A

When required, the arm-0 cause-A-delay distribution is alternately specified via a survival function. A vector of the same length as tcutd0A.

tcutd0B

Left hand endpoints for intervals upon which the arm-0 specific cause-B delay distribution hazard function is constant. The last given component is the left hand endpoint of the interval having right hand endpoint infinity. Always required when noncompliance is set to any value other than “none”.

hd0B

A vector of the same length as tcutd0B containing peicewise constant hazard rates for the arm-0 cause-B delay distribution. Always required when noncompliance is set to any value other than “none”.

sd0B

When required, the arm-0 cause-B-delay distribution is alternately specified via a survival function. A vector of the same length as tcutd0B.

tcutd1A

Left hand endpoints for intervals upon which the arm-1 specific cause-A delay distribution hazard function is constant. The last given component is the left hand endpoint of the interval having right hand endpoint infinity. Required only when noncompliance is set to “mixed” or “user”.

hd1A

A vector of the same length as tcutd1A containing peicewise constant hazard rates for the arm-1 cause-A delay distribution. Required only when noncompliance is set to “mixed” or “user”.

sd1A

When required, the arm-1 cause-A-delay distribution is alternately specified via a survival function. A vector of the same length as tcutd1A.

tcutd1B

Left hand endpoints for intervals upon which the arm-1 specific cause-B delay distribution hazard function is constant. The last given component is the left hand endpoint of the interval having right hand endpoint infinity. Always required when noncompliance is set to any value other than “none”.

hd1B

A vector of the same length as tcutd1B containing peicewise constant hazard rates for the arm-1 cause-B delay distribution. Always required when noncompliance is set to any value other than “none”.

sd1B

When required, the arm-1 cause-A-delay distribution is alternately specified via a survival function. A vector of the same length as tcutd1A.

tcutx0A

Left hand endpoints for intervals upon which the arm-0 specific post-cause-A-delay-mortality rate is constant. The last given component is the left hand endpoint of the interval having right hand endpoint infinity. Required only when noncompliance is set to “mixed” or “user”.

hx0A

A vector of the same length as tcutx0A containing the arm-0 post-cause-A-delay mortality rates. Required only when noncompliance is set to “mixed” or “user”.

sx0A

When required, the arm-0 post-cause-A-delay mortality distribution is alternately specified via a survival function. A vector of the same length as tcutx0A.

tcutx0B

Left hand endpoints for intervals upon which the arm-0 specific post-cause-B-delay-mortality rate is constant. The last given component is the left hand endpoint of the interval having right hand endpoint infinity. Always required when noncompliance is set to any value other than “none”.

hx0B

A vector of the same length as tcutx0B containing the arm-0 post-cause-B-delay mortality rates. Always required when noncompliance is set to any value other than “none”.

sx0B

When required, the arm-0 post-cause-B-delay mortality distribution is alternately specified via a survival function. A vector of the same length as tcutx0B.

tcutx1A

Left hand endpoints for intervals upon which the arm-1 specific post-cause-A-delay-mortality rate is constant. The last given component is the left hand endpoint of the interval having right hand endpoint infinity. Required only when noncompliance is set to “mixed” or “user”.

hx1A

A vector of the same length as tcutx1A containing the arm-1 post-cause-A-delay mortality rates. Required only when noncompliance is set to “mixed” or “user”.

sx1A

When required, the arm-1 post-cause-A-delay mortality distribution is alternately specified via a survival function. A vector of the same length as tcutx1A.

tcutx1B

Left hand endpoints for intervals upon which the arm-1 specific post-cause-B-delay-mortality rate is constant. The last given component is the left hand endpoint of the interval having right hand endpoint infinity. Always required when noncompliance is set to any value other than “none”.

hx1B

A vector of the same length as tcutx1B containing the arm-1 post-cause-B-delay mortality rates. Always required when noncompliance is set to any value other than “none”.

sx1B

When required, the arm-1 post-cause-B-delay mortality distribution is alternately specified via a survival function. A vector of the same length as tcutx1B.

gradual

Should the conversion to post-noncompliance mortality be gradual. Under the default behavior, gradual=FALSE, there is an immediate conversion to the post-noncompliance mortality rate function. If gradual is set to TRUE then this conversion is done “gradually”. In truth, at the individual level what is done is that the new mortality rate function is a convex combination of the pre-noncompliance mortality and the post-noncompliance mortality, with the weighting in proportion to the time spent in compliance with the study arm protocal.

WtFun

Specifies the name of a weighting function (of time) for assigning relative weights to events according to the times at which they occur. The default, “FH”, a two parameter weight function, specifies the ‘Fleming-Harrington’ g-rho family of weighting functions defined as the pooled arm survival function (Kaplan-Meier estimate) raised to the g times its complement raised to the rho. Note that g=rho=0 corresponds to the unweighted log-rank statistic. A second choice is the “SFH” function, (for ‘Stopped Fleming-Harrington’), meaning that the “FH” weights are capped at their value at a user specified time, which has a total of 3 parameters. A third choice is Ramp(tcut). Under this choice, weights are assigned in a linearly manner from time 0 until a user specified cut-off time, tcut, after which events are weighted equally. It is possible to conduct computations on nstat candidate statistics within a single run. In this case, WtFun should be a character vector of length nstat having components set from among the available choices.

ppar

A vector containing all the weight function parameters, in the order determined by that of “WtFun”. For example, if WtFun is set to c("FH","SFH","Ramp") then ppar should be a vector of length six, with the “FH” parameters in the first two elements, “SFH” parameters in the next 3 elements, and “Ramp” parameter in the last element.

RR.Futility

The relative risk corresponding to the alternative alternative hypothesis that is required in the construction of the futility boundary. Required if Boundary.Futility is set to a non-null value.

Spend.Info

When the test statistic is something other than the unweighted log-rank statistic, the variance information, i.e. the ratio of variance at interim analysis to variance at the end of trial, is something other than the ratio of events at interim analysis to the events at trial end. The problem is that in practice one doesn't necessarily have a good idea what the end of trial variance should be. In this case the user may wish to spend the type I and type II error probabilities according to a different time scale. Possible choices are “Variance”, (default), which just uses the variance ratio scale, “Events”, which uses the events ratio scale, “Hybrid(k)”, which makes a linear transition from the “Variance” scale to the “Events” scale beginning with analysis number k. The last choice, “Calendar”, uses the calendar time scale

qProp.one.or.Q

If a futility boundary is specified, what assumption should be made about the drift function (the mean value of the weighted log-rank statistic at analysis j normalized by the square root of the variance function at analysis k). In practice we don't presume to know the shape of the drift function. Set to “one” or “Q”. The choice “one” results in a more conservative boundary.

Nsim

If you specify method==“S”, then you must specify the number of simulations. 1000 should be sufficient.

detail

If you specify method==“S”, and want to see the full level of detail regarding arguments returned from the C level code, specify detail==TRUE

StatType

If you specify method==“S”, then the available choices are “WLR” (weighted log-rank) and “ISD” (integrated survival difference).

doProj

Works only when method==“S”. If a weighted log-rank statistic is specified without maximum information having been stipulated in the design then certain functionals, the Q first and second moments, must be projected. Setting this argument to TRUE includes this projection into the simulation runs.

Value

Returns a value of class PwrGSD which has components listed below. Note that the print method will display a summary table of estimated powers and type I errors as a nstat by 2 matrix. The summary method returns the same object invisibly, but after computing the summary table mentioned above, and it is included in the returned value as a commponent TBL. See examples below.

dPower

A length(tlook) by nstat matrix containing in each column, an increment in power that resulted at that analysis time for the given statistic.

dErrorI

A length(tlook) by nstat matrix containing in each column, an increment in type I error that resulted at that analysis time for the given statistic. Always sums to the total alpha specified in alphatot

detail

A list with components equal to the arguments of the C-call, which correspond in a natural way to the arguments specified in the R call, along with the computed results in palpha0vec, palpha1vec, inffrac, and mu. The first two are identical to dErrorI and dPower, explained above. The last two are length(tlook) by nstat matrices. For each statistic specified in par, the corresponding columns of pinffrac and mu contain the information fraction and drift at each of the analysis times.

call

the call

Author(s)

Grant Izmirlian [e-mail]

References

Gu, M.-G. and Lai, T.-L. “Determination of power and sample size in the design of clinical trials with failure-time endpoints and interim analyses.” Controlled Clinical Trials 20 (5): 423-438. 1999

Izmirlian, G. “The PwrGSD package.” NCI Div. of Cancer Prevention Technical Report. 2004

Jennison, C. and Turnbull, B.W. (1999) Group Sequential Methods: Applications to Clinical Trials Chapman & Hall/Crc, Boca Raton FL

Proschan, M.A., Lan, K.K.G., Wittes, J.T. (2006), corr 2nd printing (2008) Statistical Monitoring of Clinical Trials A Unified Approach Springer Verlag, New York

Izmirlian G. (2014). Estimation of the relative risk following group sequential procedure based upon the weighted log-rank statistic. Statistics and its Interface 7(1), 27-42

See Also

cpd.PwrGSD

Examples

library(PwrGSD)

test.example <-
  PwrGSD(EfficacyBoundary = LanDemets(alpha = 0.05, spending = ObrienFleming),
         FutilityBoundary = LanDemets(alpha = 0.1, spending = ObrienFleming),
	 RR.Futility = 0.82, sided="1<",method="A",accru =7.73, accrat =9818.65,
         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),
         tcut0 =0:19, h0 =c(rep(3.73e-04, 2), rep(7.45e-04, 3),
                            rep(1.49e-03, 15)),
         tcut1 =0:19, rhaz =c(1, 0.9125, 0.8688, 0.7814, 0.6941,
                              0.6943, 0.6072, 0.5202, 0.4332, 0.6520,
                              0.6524, 0.6527, 0.6530, 0.6534, 0.6537,
                              0.6541, 0.6544, 0.6547, 0.6551, 0.6554),
         tcutc0 =0:19, hc0 =c(rep(1.05e-02, 2), rep(2.09e-02, 3),
                              rep(4.19e-02, 15)),
         tcutc1 =0:19, hc1 =c(rep(1.05e-02, 2), rep(2.09e-02, 3),
                              rep(4.19e-02, 15)),
         tcutd0B =c(0, 13), hd0B =c(0.04777, 0),
         tcutd1B =0:6, hd1B =c(0.1109, 0.1381, 0.1485, 0.1637, 0.2446,
                               0.2497, 0),
         noncompliance =crossover, gradual =TRUE,
         WtFun =c("FH", "SFH", "Ramp"),
         ppar =c(0, 1, 0, 1, 10, 10))