dropout_weibull | R Documentation |
Used as input to the dropout
-argument in study_parameters
dropout_weibull(proportion, rate)
proportion |
Total proportion of subjects that have dropped out at the last time point. Must be less than 1. |
rate |
Indicates the "shape" of the dropout process, if > 1 then dropout is
concentrated at the end of the study, if |
N.B a constant (rate = 1) hazard of dropout does not mean dropout is linear over time. It means that the risk of dropping out at the next time point is constant over the study period.
A plcp_weibull
named list
, with the first element containing the
dropout function
.
Galbraith, S., Stat, M., & Marschner, I. C. (2002). Guidelines for the design of clinical trials with longitudinal outcomes. Controlled clinical trials, 23(3), 257-273.
dropout_manual
, per_treatment
p <- study_parameters(n1 = 11,
n2 = 5,
n3 = 6,
T_end = 10,
icc_pre_subject = 0.5,
icc_pre_cluster = 0,
var_ratio = 0.03,
icc_slope = 0.05,
dropout = dropout_weibull(proportion = 0.3, rate = 3),
cohend = -0.8)
get_dropout(p)
plot(p, plot = 2)
# Different per treatment
tx <- dropout_weibull(proportion = 0.3, rate = 3)
cc <- dropout_weibull(proportion = 0.3, rate = 1/3)
dropout <- per_treatment(control = cc,
treatment = tx)
p <- study_parameters(n1 = 11,
n2 = 5,
n3 = 6,
T_end = 10,
icc_pre_subject = 0.5,
icc_pre_cluster = 0,
var_ratio = 0.03,
icc_slope = 0.05,
dropout = dropout,
cohend = -0.8)
plot(p, plot = 2)
# Compare power for different dropout amounts
dropout <- c(dropout_weibull(proportion = 0.3, rate = 3),
dropout_weibull(proportion = 0.5, rate = 3),
dropout_weibull(proportion = 0.5, rate = 1/3))
p <- study_parameters(n1 = 11,
n2 = 5,
n3 = 6,
T_end = 10,
icc_pre_subject = 0.5,
icc_pre_cluster = 0,
var_ratio = 0.03,
icc_slope = 0.05,
dropout = dropout,
cohend = -0.8)
get_power(p)
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