Calculates the conditional power within the exponential model and the non-mixture models with exponential, Weibull type and Gamma type survival.
CompSurvMod(data, cont.time, new.pat = c(0, 0),
theta.0 = 1, alpha = 0.05,
disp.data = FALSE, plot.km = FALSE)See Details.
Returns a list which consists of the following components:
estimated hazard of group 1 within the exponential model
estimated hazard of group 2 within the exponential model
estimated hazard ratio = estimated hazard of group 2 / estimated hazard of group 1 within the exponential model
conditional power within the exponential model
estimated rate parameter of group 1 within the non-mixture model with exponential survival
estimated survival fraction of group 1 within the non-mixture model with exponential survival
estimated rate parameter of group 2 within the non-mixture model with exponential survival
estimated survival fraction of group 2 within the non-mixture model with exponential survival
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
conditional power within the non-mixture model with exponential survival
estimated scale parameter of group 1 within the non-mixture model with Weibull type survival
estimated shape parameter of group 1 within the non-mixture model with Weibull type survival
estimated survival fraction of group 1 within the non-mixture model with Weibull type survival
estimated scale parameter of group 2 within the non-mixture model with Weibull type survival
estimated shape parameter of group 2 within the non-mixture model with Weibull type survival
estimated survival fraction of group 2 within the non-mixture model with Weibull type survival
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
conditional power within the non-mixture model with Weibull type survival
estimated shape parameter of group 1 within the non-mixture model with Gamma type survival
estimated rate parameter of group 1 within the non-mixture model with Gamma type survival
estimated survival fraction of group 1 within the non-mixture model with Gamma type survival
estimated shape parameter of group 2 within the non-mixture model with Gamma type survival
estimated rate parameter of group 2 within the non-mixture model with Gamma type survival
estimated survival fraction of group 2 within the non-mixture model with Gamma type survival
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
conditional power within the non-mixture model with Gamma type survival
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.
Period of time of continuing the trial.
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).
Originally postulated clinically relevant difference (hazard ratio = hazard of group 2 / hazard of group 1) with default at 1.
Significance level for conditional power calculations with default at 0.05.
Logical value indicating if all calculated data should be displayed with default at FALSE.
Logical value indicating if Kaplan-Meier curves and estimated survival curves according to the four mentioned models should be plotted with default at FALSE.
Andreas Kuehnapfel
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.
Kuehnapfel, A. (2013). Die bedingte Power in der Ueberlebenszeitanalyse.
CP
ConPwrExp
ConPwrNonMixExp
ConPwrNonMixWei
ConPwrNonMixGamma
ConPwrExpAndersen
GenerateDataFrame
test
# 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)
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