splinetrials_analysis objectncs_analysis() returns an object of class splinetrials_analysis: a 32-column
tibble with one row per unique combination of
data[[arm]] and data[[time_scheduled_label]] (see the arguments of
ncs_analysis()).
arm: values of data[[arm]].
time: values of data[[time_scheduled_label]].
n: number of times the combination appears in data.
est: mean of data[[response]].
sd: standard deviation of data[[response]].
se: standard error of data[[response]] (i.e., sd / sqrt(n)).
lower: lower bound of confidence interval.
upper: upper bound of confidence interval.
response_est: estimated marginal mean.
response_se: standard error of response_est.
response_df: degrees of freedom used for calculating the confidence
interval for response_est.
response_lower: lower bound of confidence interval for response_est.
response_upper: upper bound of confidence interval for response_est.
change_est: estimated change from baseline.
change_se: standard error of change_est.
change_df: degrees of freedom used for calculating the confidence
interval for and testing the significance of change_est.
change_lower: lower bound of confidence interval for change_est.
change_upper: upper bound of confidence interval for change_est.
change_test_statistic: test statistic measuring the significance of
change_est.
change_p_value: p-value for the significance of change_est.
diff_est: treatment effect.
diff_se: standard error of diff_est.
diff_df: degrees of freedom used for calculating the confidence interval
for and testing the significance of diff_est.
diff_lower: lower bound of confidence interval for diff_est.
diff_upper: upper bound of confidence interval for diff_est.
diff_test_statistic: test statistic measuring the significance of
diff_est.
diff_p_value: p-value for the significance of diff_est.
percent_slowing_est: estimated percent slowing.
percent_slowing_lower: lower bound of confidence interval for
percent_slowing_est.
percent_slowing_upper: upper bound of confidence interval for
percent_slowing_est.
correlation: the covariance structure of the analysis model. This is the
same value repeated for each row.
optimizer: invariably mmrm+tmb to indicate that mmrm::mmrm() (which
uses the TMB package) was used to fit the model.
If ncs_analysis() had return_models = TRUE, then the analysis model, an
mmrm object, will be included as the analysis_model attribute.
The function ncs_analysis(), which produces objects of this
class.