splinetrials_subgroup_analysis object

ncs_analysis_subgroup() returns an object of class splinetrials_subgroup_analysis: a named list with three to seven elements.

between and within

These are each tibbles, and they share many of the same columns and values but are sorted in a different order. Each contains one row per unique combination of arm, time_scheduled_label, and subgroup found in the data (see the arguments of ncs_analysis_subgroup()). The values in columns arm through change_p_value as well as correlation and optimizer are identical. The two tables' treatment effect analysis results columns differ in name and content, with between's columns bearing the prefix diff_subgroup_ and within's columns bearing the prefix diff_arm_ (see the Treatment effects section of ncs_analysis_subgroup()). Lastly, only within contains the percent slowing analysis results.

between

A 30-column tibble sorted by time, then by arm, then by subgroup.

Columns:

1. arm: values of data[[arm]].

2. time: values of data[[time_scheduled_label]].

3. subgroup: values of data[[subgroup]].

4. n: number of times the combination appears in data.

5. est: mean of data[[response]].

6. sd: standard deviation of data[[response]].

7. se: standard error of data[[response]] (i.e., sd / sqrt(n)).

8. lower: lower bound of confidence interval.

9. upper: upper bound of confidence interval.

10. response_est: estimated marginal mean.

11. response_se: standard error of response_est.

12. response_df: degrees of freedom used to calculate the confidence interval for response_est.

13. response_lower: lower bound of confidence interval for response_est.

14. response_upper: upper bound of confidence interval for response_est.

15. change_est: estimated change from baseline.

16. change_se: standard error of change_est.

17. change_df: degrees of freedom used for calculating the confidence interval for and testing the significance of change_est.

18. change_lower: lower bound of confidence interval for change_est.

19. change_upper: upper bound of confidence interval for change_est.

20. change_test_statistic: test statistic measuring the significance of change_est.

21. change_p_value: p-value for the significance of change_est.

22. diff_subgroup_est: treatment effect of subgroup within arm.

23. diff_subgroup_se: standard error of diff_subgroup_est.

24. diff_subgroup_df: degrees of freedom used for calculating the confidence interval for and testing the significance of diff_subgroup_est.

25. diff_subgroup_lower: lower bound of confidence interval for diff_subgroup_est.

26. diff_subgroup_upper: upper bound of confidence interval for diff_subgroup_est.

27. diff_subgroup_test_statistic: test statistic measuring the significance of diff_subgroup_est.

28. diff_subgroup_p_value: p-value for the significance of diff_subgroup_est.

29. correlation: the covariance structure of the analysis model. This is the same value repeated for each row.

30. optimizer: invariably mmrm+tmb to indicate that mmrm::mmrm() (which uses the TMB package) was used to fit the model.

within

A 33-column tibble sorted by subgroup, then by arm, then by time.

Columns:

1. arm: values of data[[arm]].

2. time: values of data[[time_scheduled_label]].

3. subgroup: values of data[[subgroup]].

4. n: number of times the combination appears in data.

5. est: mean of data[[response]].

6. sd: standard deviation of data[[response]].

7. se: standard error of data[[response]] (i.e., sd / sqrt(n)).

8. lower: lower bound of confidence interval.

9. upper: upper bound of confidence interval.

10. response_est: estimated marginal mean.

11. response_se: standard error of response_est.

12. response_df: degrees of freedom used for calculating the confidence interval for response_est.

13. response_lower: lower bound of confidence interval for response_est.

14. response_upper: upper bound of confidence interval for response_est.

15. change_est: estimated change from baseline.

16. change_se: standard error of change_est.

17. change_df: degrees of freedom for calculating the confidence interval for and estimating the significance of change_est.

18. change_lower: lower bound of confidence interval for change_est.

19. change_upper: upper bound of confidence interval for change_est.

20. change_test_statistic: test statistic measuring the significance of change_est.

21. change_p_value: p-value for the significance of change_est.

22. diff_arm_est: treatment effect of arm within subgroup.

23. diff_arm_se: standard error of diff_arm_est.

24. diff_arm_df: degrees of freedom for calculating the confidence interval for and testing the significance of diff_arm_est.

25. diff_arm_lower: lower bound of confidence interval for diff_arm_est.

26. diff_arm_upper: upper bound of confidence interval for diff_arm_est.

27. diff_arm_test_statistic: test statistic measuring the significance of diff_arm_est.

28. diff_arm_p_value: p-value for the significance of diff_arm_est.

29. percent_slowing_est: estimated percent slowing.

30. percent_slowing_lower: lower bound of confidence interval for percent_slowing_est.

31. percent_slowing_upper: upper bound of confidence interval for percent_slowing_est.

32. correlation: the covariance structure of the analysis model. This is the same value repeated for each row.

33. optimizer: invariably mmrm+tmb to indicate that mmrm::mmrm() (which uses the TMB package) was used to fit the model.

type3

A tibble with a row for each term in the model (not counting any intercepts). Contains the following six columns:

1. effect: the name of the model term.

2. chisquare_test_statistic: the Chi-squared test statistic measuring the significance of the model term.

3. df: the degrees of freedom used for testing the significance of the model term.

4. p_value: the p-value for the significance of the model term.

5. correlation: the covariance structure of the analysis model. This is the same value repeated for each row.

6. optimizer: invariably mmrm+tmb to indicate that mmrm::mmrm() (which uses the TMB package) was used to fit the model.

interaction

This element is only present if subgroup_interaction_test = TRUE.

A 2 by 10 data frame with class anova.mmrm. The first row represents the "reduced" model and the second row represents the "full" model. The columns are as follows:

1. model: c("reduced model", "full model"), identifying the model associated with each row.

2. aic: the AIC of the model.

3. bic: the BIC of the model.

4. loglik: the log likelihood of the model.

5. -2*log(l): equal to -2 * loglik.

6. test_statistic: the test statistic used for testing the significance of the second-order interaction term(s) between the spline time, subgroup, and arm. This value is the second element of the column; the first element is always a missing value.

7. df: the degrees of freedom used for testing the significance of the second-order interaction term(s) between the spline term, subgroup, and arm. This value is the second element of the column; the first element is always a missing value.

8. p_value: the p-value for the significance of the second-order interaction term(s) between the spline term, subgroup, and arm. This value is the second element of the column; the first element is always a missing value.

9. correlation: the covariance structure of the analysis model. This is the same value repeated for each row.

10. optimizer: invariably mmrm+tmb to indicate that mmrm::mmrm() (which uses the TMB package) was used to fit the model.

analysis_model

This element is only present if return_models = TRUE.

An mmrm object: the fitted model used to perform analyses that produced the between, within, and type3 results.

full and reduced

These elements are only present if subgroup_interaction_test = TRUE and return_models = TRUE.

Both are mmrm objects: the two maximum-likelihood-estimated models used to perform the subgroup interaction test whose results are in the interaction element. See the Subgroup interaction test section of ncs_analysis_subgroup().

See also

The function ncs_analysis_subgroup(), which produces objects of this class.