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tests/testthat/_snaps/logistic_regression.md
In tern: Create Common TLGs Used in Clinical Trials
tidy.glm works as expected for simple case
Code
res
Output
variable variable_label term term_label interaction
1 ARMCD Planned Arm Code ARM A Reference ARM A, n = 64
2 ARMCD Planned Arm Code ARM B ARM B, n = 68
3 ARMCD Planned Arm Code ARM C ARM C, n = 52
4 AGE Age AGE Age
5 SEX Sex F Reference F, n = 100
6 SEX Sex M M, n = 84
interaction_label reference reference_label estimate std_error df
1 2
2 -1.973924 1.14659 1
3 16.01132 2300.5 1
4 0.1674111 0.08943489 1
5
6 0.6291583 0.9193781 1
pvalue is_variable_summary is_term_summary odds_ratio lcl ucl
1 0.2272022 TRUE FALSE
2 0.0851491 FALSE TRUE 0.1389107 0.00724572 2.663113
3 0.9944468 FALSE TRUE 8987294 0 Inf
4 0.06122358 FALSE TRUE 1.18224 0.938983 1.488517
5 TRUE FALSE
6 0.4937666 FALSE TRUE 1.876031 0.1756955 20.03177
ci
1
2 0.00724572, 2.66311277
3 0, Inf
4 0.938983, 1.488517
5
6 0.1756955, 20.0317685
tidy.glm works as expected for interaction case
Code
res
Output
variable term interaction reference estimate std_error
1 SEX F
2 SEX M 0.7437057 0.9406595
3 ARMCD ARM A
4 ARMCD ARM B -11.53157 6.443318
5 ARMCD ARM B AGE 35 NA NA
6 ARMCD ARM C 14.72268 13103.52
7 ARMCD ARM C AGE 35 NA NA
8 AGE AGE -0.0511774 0.1462201
9 AGE AGE ARMCD ARM A NA NA
10 AGE AGE ARMCD ARM B NA NA
11 AGE AGE ARMCD ARM C NA NA
12 ARMCD:AGE ARM A
13 ARMCD:AGE ARM B 0.3040042 0.1882037
14 ARMCD:AGE ARM C 0.04825293 356.2659
logistic_regression_cols works as expected
Code
res
Output
Degrees of Freedom Parameter Estimate Standard Error Odds Ratio Wald 75% CI p-value
————————————————————————————————————————————————————————————————————————————————————————————————
df estimate se or ci p
summarize_logistic works as expected for interaction model with continuous variable
Code
res
Output
Degrees of Freedom Parameter Estimate Standard Error Odds Ratio Wald 99% CI p-value
—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Sex
Reference F, n = 100
M, n = 84 1 0.744 0.941 2.10 (0.19, 23.73) 0.4292
Planned Arm Code 2 0.2016
Reference ARM A, n = 64
ARM B, n = 68 1 -11.532 6.443 0.0735
Age
35 0.41 (0.01, 11.60)
ARM C, n = 52 1 14.723 13103.521 0.9991
Age
35 >999.99 (0.00, >999.99)
Age
Age 1 -0.051 0.146 0.7263
Planned Arm Code
ARM A 0.95 (0.65, 1.38)
ARM B 1.29 (0.95, 1.75)
ARM C 1.00 (0.00, >999.99)
Interaction of Planned Arm Code * Age 2 0.2713
Reference ARM A, n = 64
ARM B, n = 68 1 0.304 0.188 0.1062
ARM C, n = 52 1 0.048 356.266 0.9999
summarize_logistic works as expected for interaction model with categorical variable
Code
res
Output
Degrees of Freedom Parameter Estimate Standard Error Odds Ratio Wald 99% CI p-value
—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Age
Age 1 0.186 0.093 1.21 (0.95, 1.53) 0.0461
Planned Arm Code 2 1.0000
Reference ARM A, n = 64
ARM B, n = 68 1 -19.927 4655.091 0.9966
Sex
F 0.00 (0.00, >999.99)
M 1.05 (0.02, 45.12)
ARM C, n = 52 1 -0.485 6977.551 0.9999
Sex
F 0.62 (0.00, >999.99)
M >999.99 (0.00, >999.99)
Sex
Reference F, n = 100
M, n = 84 1 -18.467 4655.091 0.9968
Planned Arm Code
ARM A 0.00 (0.00, >999.99)
ARM B 4.52 (0.22, 94.89)
ARM C 1.20 (0.00, >999.99)
Interaction of Planned Arm Code * Sex 2 1.0000
Reference ARM A or F, n = 129
ARM B * M, n = 31 1 19.975 4655.091 0.9966
ARM C * M, n = 24 1 18.649 8840.154 0.9983
summarize_logistic works as expected for simple model without interactions
Code
res
Output
Degrees of Freedom Parameter Estimate Standard Error Odds Ratio Wald 99% CI p-value
—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Planned Arm Code 2 0.2435
Reference ARM A, n = 64
ARM B, n = 68 1 -1.905 1.134 0.15 (<0.01, 2.76) 0.0928
ARM C, n = 52 1 16.089 2306.294 >999.99 (0.00, >999.99) 0.9944
Age
Age 1 0.165 0.090 1.18 (0.94, 1.49) 0.0665
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tidy.glm works as expected for simple case
Code
res
Output
variable variable_label term term_label interaction
1 ARMCD Planned Arm Code ARM A Reference ARM A, n = 64
2 ARMCD Planned Arm Code ARM B ARM B, n = 68
3 ARMCD Planned Arm Code ARM C ARM C, n = 52
4 AGE Age AGE Age
5 SEX Sex F Reference F, n = 100
6 SEX Sex M M, n = 84
interaction_label reference reference_label estimate std_error df
1 2
2 -1.973924 1.14659 1
3 16.01132 2300.5 1
4 0.1674111 0.08943489 1
5
6 0.6291583 0.9193781 1
pvalue is_variable_summary is_term_summary odds_ratio lcl ucl
1 0.2272022 TRUE FALSE
2 0.0851491 FALSE TRUE 0.1389107 0.00724572 2.663113
3 0.9944468 FALSE TRUE 8987294 0 Inf
4 0.06122358 FALSE TRUE 1.18224 0.938983 1.488517
5 TRUE FALSE
6 0.4937666 FALSE TRUE 1.876031 0.1756955 20.03177
ci
1
2 0.00724572, 2.66311277
3 0, Inf
4 0.938983, 1.488517
5
6 0.1756955, 20.0317685
tidy.glm works as expected for interaction case
Code
res
Output
variable term interaction reference estimate std_error
1 SEX F
2 SEX M 0.7437057 0.9406595
3 ARMCD ARM A
4 ARMCD ARM B -11.53157 6.443318
5 ARMCD ARM B AGE 35 NA NA
6 ARMCD ARM C 14.72268 13103.52
7 ARMCD ARM C AGE 35 NA NA
8 AGE AGE -0.0511774 0.1462201
9 AGE AGE ARMCD ARM A NA NA
10 AGE AGE ARMCD ARM B NA NA
11 AGE AGE ARMCD ARM C NA NA
12 ARMCD:AGE ARM A
13 ARMCD:AGE ARM B 0.3040042 0.1882037
14 ARMCD:AGE ARM C 0.04825293 356.2659
logistic_regression_cols works as expected
Code
res
Output
Degrees of Freedom Parameter Estimate Standard Error Odds Ratio Wald 75% CI p-value
————————————————————————————————————————————————————————————————————————————————————————————————
df estimate se or ci p
summarize_logistic works as expected for interaction model with continuous variable
Code
res
Output
Degrees of Freedom Parameter Estimate Standard Error Odds Ratio Wald 99% CI p-value
—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Sex
Reference F, n = 100
M, n = 84 1 0.744 0.941 2.10 (0.19, 23.73) 0.4292
Planned Arm Code 2 0.2016
Reference ARM A, n = 64
ARM B, n = 68 1 -11.532 6.443 0.0735
Age
35 0.41 (0.01, 11.60)
ARM C, n = 52 1 14.723 13103.521 0.9991
Age
35 >999.99 (0.00, >999.99)
Age
Age 1 -0.051 0.146 0.7263
Planned Arm Code
ARM A 0.95 (0.65, 1.38)
ARM B 1.29 (0.95, 1.75)
ARM C 1.00 (0.00, >999.99)
Interaction of Planned Arm Code * Age 2 0.2713
Reference ARM A, n = 64
ARM B, n = 68 1 0.304 0.188 0.1062
ARM C, n = 52 1 0.048 356.266 0.9999
summarize_logistic works as expected for interaction model with categorical variable
Code
res
Output
Degrees of Freedom Parameter Estimate Standard Error Odds Ratio Wald 99% CI p-value
—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Age
Age 1 0.186 0.093 1.21 (0.95, 1.53) 0.0461
Planned Arm Code 2 1.0000
Reference ARM A, n = 64
ARM B, n = 68 1 -19.927 4655.091 0.9966
Sex
F 0.00 (0.00, >999.99)
M 1.05 (0.02, 45.12)
ARM C, n = 52 1 -0.485 6977.551 0.9999
Sex
F 0.62 (0.00, >999.99)
M >999.99 (0.00, >999.99)
Sex
Reference F, n = 100
M, n = 84 1 -18.467 4655.091 0.9968
Planned Arm Code
ARM A 0.00 (0.00, >999.99)
ARM B 4.52 (0.22, 94.89)
ARM C 1.20 (0.00, >999.99)
Interaction of Planned Arm Code * Sex 2 1.0000
Reference ARM A or F, n = 129
ARM B * M, n = 31 1 19.975 4655.091 0.9966
ARM C * M, n = 24 1 18.649 8840.154 0.9983
summarize_logistic works as expected for simple model without interactions
Code
res
Output
Degrees of Freedom Parameter Estimate Standard Error Odds Ratio Wald 99% CI p-value
—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Planned Arm Code 2 0.2435
Reference ARM A, n = 64
ARM B, n = 68 1 -1.905 1.134 0.15 (<0.01, 2.76) 0.0928
ARM C, n = 52 1 16.089 2306.294 >999.99 (0.00, >999.99) 0.9944
Age
Age 1 0.165 0.090 1.18 (0.94, 1.49) 0.0665
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