SPCDPower: Calculate the power, sample size for a Sequential Parallel...

In SPCDAnalyze: Design and Analyze Studies using the Sequential Parallel Comparison Design

Description

Patients are randomized in three groups, patients who receive placebo in phase 1 and again in phase 2 of the study, patients who receive placebo in phase 1 and active in phase 2 and patients who receive active therapy in phase 1 and are not included in phase 2. A response criteria is determined and the phase 2 data of patients who respond in phase 1 is eliminated. Each phase is analyzed separately and the results are pooled. Calculates power or sample size as a function of the alternative hypothesis, posed in terms of response rates or effect sizes, for both binary and continuous outcomes.

Usage

SPCDPower(n=NULL, power=NULL, p, w=0.5, placeboProp=.66, drop = 0, alpha = 0.025,
         effect_size = rep(NULL, 2))

Arguments

n	Total sample size of the study, leave as null if you want the sample size computed.
power	Power of the study, leave as null if you want the power computed.
p	A 2 by 2 matrix, matrix(c(Phase1.response.drug,phase1.response.placebo,phase2.response.drug,phase2.response.placebo),2,2) indicating the alternative hypothesis
w	Weight for the first phase in the combined test
placeboProp	Proportion of patients randomized to placebo in the first phase
drop	The proportion of placebo non-responders that drop after the first phase
alpha	Significance level
effect_size	This is an alternative method of specifying the alternative. If it is used only p[2,1] needs to be specified. This is useful in the situation where a continuous endpoint is used and treatment response is not defined as the endpoint being greater than a constant.

Details

This program considers the situation in which response rates are supplied by the investigator, response is judged as by whether or not a continuous variable is greater than a constant, and the continuous variable is analyzed rather than the response variable. In this case it turns out the effect size for a comparison, where the response rates are p and q for placebo and active drug is qnorm(1-p)-qnorm(1-q).

Value

A numeric vector with the following fields, sample size n, Power for for the SPCD when using a dichotomous response outcome, Power for the SPCD using a continuous outcome where response is judged as a continuous variable being greater than a fixed constant, Power for a conventional design for a dichotomous variable and a continuous variable, Power for a SPCD design where the null is rejected if either the first phase or the second phase shows a significant difference. The first value is not corrected for multiple comparisons while the second uses a bonferroni correction.

Author(s)

David A. Schoenfeld [e-mail]

References

Fava, M., Evins, A. E., Dorer, D. J., and Schoenfeld, D. A. (2003). The problem of the placebo response in clinical trials for psychiatric disorders: culprits, possible remedies, and a novel study design approach. Psychotherapy and psychosomatics, 72,3, 115–127.

Tamura, R. N., & Huang, X. (2007). An examination of the efficiency of the sequential parallel design in psychiatric clinical trials. Clinical Trials, 4,4, 309-31.

See Also

SPCDbinary,SPCDcontinuous

Examples

SPCDPower(n=150, power=NULL, p=matrix(c(.6,.3,.5,.3),2,2), w=0.5,
placeboProp=.66, drop = .1, alpha = 0.025,effect_size = rep(NULL, 2))

Example output

Loading required package: nlme
Loading required package: lme4
Loading required package: Matrix

Attaching package: ‘lme4’

The following object is masked from ‘package:nlme’:

    lmList

Loading required package: plyr
                     n          SPCD_Response        SPCD_Continuous 
           150.0000000              0.9256103              0.9890109 
 Conventional_Response Conventional_Continous            uncorrected 
             0.9600764              0.9974665              0.9975607 
             corrected 
             0.9934268 

SPCDAnalyze documentation built on May 2, 2019, 4 p.m.