CompSurvMod {CP}

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)