Clinical Trial Example
This trial evaluated whether oral adjuvant chemotherapy with tegaful and uracil (UFT) and leucovorin (LV) reduces the recurrence after resection of liver metastasis from colorectal carcinoma as compared with no adjuvant therapy in Japan (UFT/LV trial) (Hasegawa et al. PLoS One 2016;11:e0162400.). The null hypothesis \(log(HR) = 0\) was tested with the one-sided significance level of 0.025. The minimum of clinically important effect size was hypothesized as HR = 0.65. The test statistic was a stratified log-rank score. Suppose that four interim analyses and one final analysis were planned to be performed but when to perform was not fixed in advance.
The result of the interim analyses were as follows. * Fisher information at analyses: (5.67, 9.18, 14.71, 20.02) * Score statistic = (3.40, 4.35, 7.75, 11.11)
Locally efficient adaptive design
The initial working test (SPRT) is prepared as a basis of conditional error function. Its stopping boundary is \(-\log(\alpha) / \rho + 1 / 2 \rho t\), where the significance level \(\alpha = 0.025\) and the minimum of clinically important effect size \(\rho = -log(0.65)\) will be substituted and \(t\) is the Fisher information. This stopping boundary is depicted below.
# Working test: sequential probability ratio test (SPRT)
plot(1, 1, type="n", xlim=c(0, 25), ylim=c(0, 15), xlab="Fisher Inf.", ylab = "Score Stat.")
alpha <- 0.025
rho <- -log(0.65)
abline(-log(alpha) / rho, 1/2 * rho)
The four interim analyses can be performed by the function
adaptive_analysis_norm_local. Designating
FALSE to the argument final_analysis indicates
that the latest analysis is not the final, i.e., the overall
significance level must not be exhausted at this time.
# Final interim analysis
interim_analysis_4 <- adaptive_analysis_norm_local(
overall_sig_level = 0.025,
min_effect_size = -log(0.65),
times = c(5.67, 9.18, 14.71, 20.02),
stats = c(3.40, 4.35, 7.75, 11.11),
final_analysis = FALSE
)The result is summarized as follows:
# Summary
print( with(interim_analysis_4, data.frame(analysis=0:par$analyses, time=par$times,
intercept=char$intercept, stat=par$stats, boundary=char$boundary,
pr_cond_err=char$cond_type_I_err, reject_H0=char$rej_H0)) )
#> analysis time intercept stat boundary pr_cond_err reject_H0
#> 1 0 0.00 8.563198 0.00 8.563198 0.02500000 FALSE
#> 2 1 5.67 8.562666 3.40 9.783935 0.06392209 FALSE
#> 3 2 9.18 8.562085 4.35 10.539378 0.06951043 FALSE
#> 4 3 14.71 8.551346 7.75 11.719755 0.18084726 FALSE
#> 5 4 20.02 8.456860 11.11 12.768997 0.48935479 FALSEAt the forth (final) interim analysis, the null hypothesis is not
rejected. Then, the maximum sample size (here, the maximum Fisher
information level) is calculated. The alternative hypothesis for which
an adequate level of power will be ensured can be determined arbitrarily
referring to all available data including the interim results but not
correlates of future data. Here, the maximum likelihood estimate \(11.11 / 20.02\) at the forth interim
analysis is chosen as the alternative hypothesis. The maximum
information level to obtaine the marginal power of 0.75 can be
calculated by the function sample_size_norm_local.
# Sample size calculation
sample_size_norm_local(
overall_sig_level = 0.025,
min_effect_size = -log(0.65),
effect_size = 11.11 / 20.02, # needs not be MLE
time = 20.02,
target_power = 0.75,
sample_size = TRUE
)
#> [1] 24.44479Finally, suppose that the final analysis is performed at \(t = 24.44\). The same function used at
interim analyses, adaptive_analysis_norm_local, can be used
with setting final_analysis = TRUE.
# Final analysis
final_analysis <- adaptive_analysis_norm_local(
overall_sig_level = 0.025,
min_effect_size = -log(0.65),
times = c(5.67, 9.18, 14.71, 20.02, 24.44),
stats = c(3.40, 4.35, 7.75, 11.11, 14.84),
final_analysis = TRUE
)Again, the result is summarized as:
# Summary
print( with(final_analysis, data.frame(analysis=0:par$analyses, time=par$times,
intercept=char$intercept, stat=par$stats, boundary=char$boundary,
pr_cond_err=char$cond_type_I_err, reject_H0=char$rej_H0)) )
#> analysis time intercept stat boundary pr_cond_err reject_H0
#> 1 0 0.00 8.563198 0.00 8.563198 0.02500000 FALSE
#> 2 1 5.67 8.562666 3.40 9.783935 0.06392209 FALSE
#> 3 2 9.18 8.562085 4.35 10.539378 0.06951043 FALSE
#> 4 3 14.71 8.551346 7.75 11.719755 0.18084726 FALSE
#> 5 4 20.02 8.456860 11.11 12.768997 0.48935479 FALSE
#> 6 5 24.44 NA 14.84 11.166106 1.00000000 TRUEAs indicated at the final row, the null hypothesis is rejected.
Globally efficient adaptive design
Globally efficient adaptive design can be performed in a similar way by using the functions for globally efficient functions.
The initial working test, a group sequential design with 50 analyses,
is prepared as a basis of conditional error function. Its stopping
boundary can be constructed by the function
work_test_norm_global.
init_work_test <- work_test_norm_global(min_effect_size = -log(0.65), cost_type_1_err = 0)Here, cost_type_1_err = 0 means the value of loss caused
by erroneous rejection of the null hypothesis is calculated to make the
constructed working test have exactly the type I error probability of
\(\alpha\). The default value of
cost_type_1_err is 0 and thus can be omitted.
The boundary of the working test just constructed is displayed by the
next code.
with(init_work_test, plot(par$U_0, char$boundary, xlim=range(0, par$U_0),
ylim=range(0, char$boundary[-1]), pch=16, cex=0.5) )
The four interim analyses can be performed by the function
adaptive_analysis_norm_global. Designating
FALSE to the argument final_analysis indicates
that the latest analysis is not the final, i.e., the overall
significance level must not be exhausted at this time.
# Final interim analysis
interim_analysis_4 <- adaptive_analysis_norm_global(
initial_test = init_work_test,
times = c(5.67, 9.18, 14.71, 20.02),
stats = c(3.40, 4.35, 7.75, 11.11),
final_analysis = FALSE,
estimate = FALSE
)The result is:
# Summary
print( with(interim_analysis_4, data.frame(analysis=0:par$analyses, time=par$times,
cost=char$cost0, stat=par$stats, boundary=char$boundary, pr_cond_err=char$cond_type_I_err,
reject_H0=char$rej_H0)) )
#> analysis time cost stat boundary pr_cond_err reject_H0
#> 1 0 0.00 1683.458 0.00 Inf 0.02500000 FALSE
#> 2 1 5.67 1555.020 3.40 7.004168 0.06006569 FALSE
#> 3 2 9.18 1545.278 4.35 8.690863 0.06007655 FALSE
#> 4 3 14.71 1528.397 7.75 10.724362 0.15229716 FALSE
#> 5 4 20.02 1471.727 11.11 12.239176 0.39095697 FALSEAt the forth (final) interim analysis, the null hypothesis is not
rejected. Then, the maximum Fisher information level is calculated. The
maximum likelihood estimate \(11.11 /
20.02\) at the forth interim analysis is chosen as the
alternative hypothesis, though this is not compelling. The maximum
information level to obtaine the marginal power of \(0.75\) can be calculated by the function
sample_size_norm_global.
# Sample size calculation
sample_size_norm_global(
initial_test = init_work_test,
effect_size = 11.11 / 20.02, # needs not be MLE
time = 20.02,
target_power = 0.75,
sample_size = TRUE
)
#> [1] 25.88426Finally, suppose that the final analysis is performed at \(t = 25.88\). The same function used at
interim analyses can be used, with setting
final_analysis = TRUE.
# Final analysis
final_analysis <- adaptive_analysis_norm_global(
initial_test = init_work_test,
times = c(5.67, 9.18, 14.71, 20.02, 25.88),
stats = c(3.40, 4.35, 7.75, 11.11, 14.84),
costs = interim_analysis_4$char$cost0[-1], # Omited element is for time = 0
final_analysis = TRUE,
estimate = FALSE
)
# Summary
print( with(final_analysis, data.frame(analysis=0:par$analyses, time=par$times,
cost=char$cost0, stat=par$stats, boundary=char$boundary, pr_cond_err=char$cond_type_I_err,
reject_H0=char$rej_H0)) )
#> analysis time cost stat boundary pr_cond_err reject_H0
#> 1 0 0.00 1683.458 0.00 Inf 0.02500000 FALSE
#> 2 1 5.67 1555.020 3.40 7.004168 0.06006569 FALSE
#> 3 2 9.18 1545.278 4.35 8.690863 0.06007655 FALSE
#> 4 3 14.71 1528.397 7.75 10.724362 0.15229716 FALSE
#> 5 4 20.02 1471.727 11.11 12.239176 0.39095697 FALSE
#> 6 5 25.88 NA 14.84 11.780124 1.00000000 TRUEAs indicated by the final row, the null hypothesis is rejected.
Note that, if estimate = TRUE, additionally exact
P-value, median unbiased estimate, and confidence limits can be
calculated. These results will be extracted by:
# Estimte (P-value, median unbiased estimate, and confidence limits)
print( final_analysis$est )