CompSurvMod: Conditional Power (Comparison)

In CP: Conditional Power Calculations

Conditional Power (Comparison)

Description

Calculates the conditional power within the exponential model and the non-mixture models with exponential, Weibull type and Gamma type survival.

Usage

 CompSurvMod(data, cont.time, new.pat = c(0, 0),
             theta.0 = 1, alpha = 0.05,
             disp.data = FALSE, plot.km = FALSE)

Arguments

data	Data frame which consists of at least three columns with the group (two different expressions) in the first, status (1 = event, 0 = censored) in the second and event time in the third column.
cont.time	Period of time of continuing the trial.
new.pat	2-dimensional vector which consists of numbers of new patients who will be recruited each time unit (first component = group 1, second component = group 2) with default at (0, 0).
theta.0	Originally postulated clinically relevant difference (hazard ratio = hazard of group 2 / hazard of group 1) with default at 1.
alpha	Significance level for conditional power calculations with default at 0.05.
disp.data	Logical value indicating if all calculated data should be displayed with default at FALSE.
plot.km	Logical value indicating if Kaplan-Meier curves and estimated survival curves according to the four mentioned models should be plotted with default at FALSE.

Details

This function calculates the conditional power within the exponential model and the non-mixture models with exponential, Weibull type and Gamma type survival and plots the conditional power curves.

Optionally, further data will be displayed. This includes data from interim analysis, log-likelihoods, AICs, calculated estimators and further patient times.

Moreover, it is possible to plot the Kaplan-Meier curves and the estimated survival curves according to the four mentioned models.

Value

See Details.

Returns a list which consists of the following components:

lambda1.hat.exp	estimated hazard of group 1 within the exponential model
lambda2.hat.exp	estimated hazard of group 2 within the exponential model
theta.hat.exp	estimated hazard ratio = estimated hazard of group 2 / estimated hazard of group 1 within the exponential model
gamma.theta.0.exp	conditional power within the exponential model
lambda1.hat.nm.exp	estimated rate parameter of group 1 within the non-mixture model with exponential survival
c1.hat.nm.exp	estimated survival fraction of group 1 within the non-mixture model with exponential survival
lambda2.hat.nm.exp	estimated rate parameter of group 2 within the non-mixture model with exponential survival
c2.hat.nm.exp	estimated survival fraction of group 2 within the non-mixture model with exponential survival
theta.hat.nm.exp	estimated hazard ratio = log(estimated survival fraction of group 2) / log(estimated survival fraction of group 1) within the non-mixture model with exponential survival
gamma.theta.0.nm.exp	conditional power within the non-mixture model with exponential survival
lambda1.hat.nm.wei	estimated scale parameter of group 1 within the non-mixture model with Weibull type survival
k1.hat.nm.wei	estimated shape parameter of group 1 within the non-mixture model with Weibull type survival
c1.hat.nm.wei	estimated survival fraction of group 1 within the non-mixture model with Weibull type survival
lambda2.hat.nm.wei	estimated scale parameter of group 2 within the non-mixture model with Weibull type survival
k2.hat.nm.wei	estimated shape parameter of group 2 within the non-mixture model with Weibull type survival
c2.hat.nm.wei	estimated survival fraction of group 2 within the non-mixture model with Weibull type survival
theta.hat.nm.wei	estimated hazard ratio = log(estimated survival fraction of group 2) / log(estimated survival fraction of group 1) within the non-mixture model with Weibull type survival
gamma.theta.0.nm.wei	conditional power within the non-mixture model with Weibull type survival
a1.hat.nm.gamma	estimated shape parameter of group 1 within the non-mixture model with Gamma type survival
b1.hat.nm.gamma	estimated rate parameter of group 1 within the non-mixture model with Gamma type survival
c1.hat.nm.gamma	estimated survival fraction of group 1 within the non-mixture model with Gamma type survival
a2.hat.nm.gamma	estimated shape parameter of group 2 within the non-mixture model with Gamma type survival
b2.hat.nm.gamma	estimated rate parameter of group 2 within the non-mixture model with Gamma type survival
c2.hat.nm.gamma	estimated survival fraction of group 2 within the non-mixture model with Gamma type survival
theta.hat.nm.gamma	estimated hazard ratio = log(estimated survival fraction of group 2) / log(estimated survival fraction of group 1) within the non-mixture model with Gamma type survival
gamma.theta.0.nm.gamma	conditional power within the non-mixture model with Gamma type survival

Note

There are several mechanisms to ensure that no illegal operations will be done and maximum likelihood calculations will be executed stable. That is why there should not be too less data in the data frame, for example one patient of each group and both being censored.

Author(s)

Andreas Kuehnapfel

References

Kuehnapfel, A. (2013). Die bedingte Power in der Ueberlebenszeitanalyse.

See Also

CP
ConPwrExp
ConPwrNonMixExp
ConPwrNonMixWei
ConPwrNonMixGamma
ConPwrExpAndersen
GenerateDataFrame
test

Examples

 # data frame 'test' generated by 'GenerateDataFrame'
 
 # conditional power calculations
 # within the four mentioned models
 CompSurvMod(data = test, cont.time = 12, new.pat = c(2.5, 2.5),
             theta.0 = 0.75, alpha = 0.05,
             disp.data = TRUE, plot.km = TRUE)

CP documentation built on May 31, 2023, 7:48 p.m.