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tests/testthat/_snaps/summarize_glm_count.md
In tern: Create Common TLGs Used in Clinical Trials
h_glm_poisson glm-fit works with healthy input
Code
res
Output
Estimate SE z_value Pr coefs
1 2.10487843 0.07035975 29.9159442 1.220818e-196 (Intercept)
2 0.10946932 0.09613709 1.1386793 2.548369e-01 ARMB: Placebo
3 -0.04371457 0.10265030 -0.4258591 6.702105e-01 ARMC: Combination
h_glm_poisson emmeans-fit works with healthy input
Code
res
Output
ARMCD rate std.error df null statistic p.value
1 ARM A 8.206105 0.5773795 Inf 1 29.91594 1.220818e-196
2 ARM B 9.155436 0.5997925 Inf 1 33.80055 1.935734e-250
3 ARM C 7.855107 0.5871181 Inf 1 27.57650 2.129731e-167
h_glm_poisson glm-fit works with healthy input with covariates
Code
res
Output
Estimate SE z_value Pr coefs
1 2.01065582 0.18541942 10.8438255 2.133586e-27 (Intercept)
2 0.07631174 0.17896220 0.4264126 6.698072e-01 REGION1Asia
3 0.64425750 0.22389462 2.8775033 4.008358e-03 REGION1Eurasia
4 2.13096720 0.36521976 5.8347533 5.387022e-09 REGION1Europe
5 -0.07449500 0.20314837 -0.3667024 7.138410e-01 REGION1North America
6 0.38101695 0.21554753 1.7676703 7.711605e-02 REGION1South America
7 0.11047866 0.09872549 1.1190490 2.631192e-01 ARMB: Placebo
8 -0.17694419 0.10873176 -1.6273459 1.036637e-01 ARMC: Combination
h_glm_poisson emmeans-fit works with healthy input with covariates
Code
res
Output
ARMCD rate std.error df null statistic p.value
1 ARM A 12.64167 1.2378669 Inf 1 25.90902 5.270655e-148
2 ARM B 14.11838 1.2848735 Inf 1 29.09088 4.682722e-186
3 ARM C 10.59153 0.9708089 Inf 1 25.74821 3.375733e-146
h_glm_quasipoisson glm-fit works with healthy input
Code
res
Output
Estimate SE z_value Pr coefs
1 2.01065582 0.7781805 2.5837912 0.01051488 (Intercept)
2 0.07631174 0.7510804 0.1016026 0.91917812 REGION1Asia
3 0.64425750 0.9396557 0.6856314 0.49377257 REGION1Eurasia
4 2.13096720 1.5327784 1.3902643 0.16605816 REGION1Europe
5 -0.07449500 0.8525865 -0.0873753 0.93046426 REGION1North America
6 0.38101695 0.9046241 0.4211881 0.67408881 REGION1South America
7 0.11047866 0.4143377 0.2666392 0.79003312 ARMB: Placebo
8 -0.17694419 0.4563327 -0.3877526 0.69862863 ARMC: Combination
h_glm_quasipoisson emmeans-fit works with healthy input
Code
res
Output
ARM rate std.error df null statistic p.value
1 A: Drug X 12.64167 5.195162 Inf 1 6.173420 6.682841e-10
2 B: Placebo 14.11838 5.392442 Inf 1 6.931571 4.161914e-12
3 C: Combination 10.59153 4.074355 Inf 1 6.135104 8.510352e-10
h_glm_negbin glm-fit works with healthy input
Code
res
Output
Estimate SE z_value Pr coefs
1 1.005041594 0.1992268 5.04471149 4.542062e-07 (Intercept)
2 0.007741431 0.1919877 0.04032253 9.678360e-01 REGION1Asia
3 0.317703043 0.2360653 1.34582686 1.783584e-01 REGION1Eurasia
4 0.591541717 0.4058327 1.45759983 1.449509e-01 REGION1Europe
5 0.117240049 0.2196300 0.53380718 5.934749e-01 REGION1North America
6 0.139971334 0.2348685 0.59595610 5.512046e-01 REGION1South America
7 0.113082781 0.1056295 1.07056107 2.843668e-01 ARMB: Placebo
8 0.026817451 0.1131811 0.23694292 8.127011e-01 ARMC: Combination
h_glm_negbin emmeans-fit works with healthy input
Code
res
Output
ARM response std.error df null statistic p.value
1 A: Drug X 3.322579 0.3367532 Inf 1 11.84712 2.227054e-32
2 B: Placebo 3.720373 0.3782682 Inf 1 12.92183 3.390023e-38
3 C: Combination 3.412887 0.3424577 Inf 1 12.23369 2.054037e-34
h_glm_count glm-fit works with healthy input
Code
res
Output
Estimate SE z_value Pr coefs
1 2.10487843 0.07035975 29.9159442 1.220818e-196 (Intercept)
2 0.10946932 0.09613709 1.1386793 2.548369e-01 ARMCDARM B
3 -0.04371457 0.10265030 -0.4258591 6.702105e-01 ARMCDARM C
h_glm_count emmeans-fit works with healthy input
Code
res
Output
ARMCD rate std.error df null statistic p.value
1 ARM A 8.206105 0.5773795 Inf 1 29.91594 1.220818e-196
2 ARM B 9.155436 0.5997925 Inf 1 33.80055 1.935734e-250
3 ARM C 7.855107 0.5871181 Inf 1 27.57650 2.129731e-167
h_ppmeans works with healthy input
Code
fits
Output
$glm_fit
Call: stats::glm(formula = formula, family = stats::poisson(link = "log"),
data = .df_row, offset = offset)
Coefficients:
(Intercept) REGION1Asia REGION1Eurasia
2.01066 0.07631 0.64426
REGION1Europe REGION1North America REGION1South America
2.13097 -0.07450 0.38102
ARMCDARM B ARMCDARM C
0.11048 -0.17694
Degrees of Freedom: 199 Total (i.e. Null); 192 Residual
Null Deviance: 983.8
Residual Deviance: 939 AIC: 1498
$emmeans_fit
ARMCD rate SE df asymp.LCL asymp.UCL
ARM A 12.6 1.238 Inf 10.43 15.3
ARM B 14.1 1.285 Inf 11.81 16.9
ARM C 10.6 0.971 Inf 8.85 12.7
Results are averaged over the levels of: REGION1
Confidence level used: 0.95
Intervals are back-transformed from the log scale
Code
fits2
Output
$glm_fit
Call: stats::glm(formula = formula, family = stats::quasipoisson(link = "log"),
data = .df_row, offset = offset)
Coefficients:
(Intercept) REGION1Asia REGION1Eurasia
2.01066 0.07631 0.64426
REGION1Europe REGION1North America REGION1South America
2.13097 -0.07450 0.38102
ARMCDARM B ARMCDARM C
0.11048 -0.17694
Degrees of Freedom: 199 Total (i.e. Null); 192 Residual
Null Deviance: 983.8
Residual Deviance: 939 AIC: NA
$emmeans_fit
ARMCD rate SE df asymp.LCL asymp.UCL
ARM A 12.6 5.20 Inf 5.65 28.3
ARM B 14.1 5.39 Inf 6.68 29.8
ARM C 10.6 4.07 Inf 4.98 22.5
Results are averaged over the levels of: REGION1
Confidence level used: 0.95
Intervals are back-transformed from the log scale
s_glm_count works with healthy input
Code
res
Output
$n
[1] 73
$rate
[1] 14.11838
attr(,"label")
[1] "Adjusted Rate"
$rate_ci
[1] 11.81189 16.87525
attr(,"label")
[1] "95% CI"
$rate_ratio
character(0)
attr(,"label")
[1] "Adjusted Rate Ratio"
$rate_ratio_ci
character(0)
attr(,"label")
[1] "95% CI"
$pval
character(0)
attr(,"label")
[1] "p-value"
s_glm_count (negative binomial) works with healthy input
Code
res
Output
$n
[1] 73
$rate
[1] 3.720373
attr(,"label")
[1] "Adjusted Rate"
$rate_ci
[1] 3.048181 4.540799
attr(,"label")
[1] "95% CI"
$rate_ratio
character(0)
attr(,"label")
[1] "Adjusted Rate Ratio"
$rate_ratio_ci
character(0)
attr(,"label")
[1] "95% CI"
$pval
character(0)
attr(,"label")
[1] "p-value"
s_glm_count works with no reference group selected.
Code
res
Output
$n
[1] 73
$rate
[1] 14.11838
attr(,"label")
[1] "Adjusted Rate"
$rate_ci
[1] 11.81189 16.87525
attr(,"label")
[1] "95% CI"
$rate_ratio
[1] 0.8954054 0.7501944
attr(,"label")
[1] "Adjusted Rate Ratio"
$rate_ratio_ci
[1] 0.7378778 0.6062152 1.0865633 0.9283695
attr(,"label")
[1] "95% CI"
$pval
[1] 0.263119218 0.008203621
attr(,"label")
[1] "p-value"
s_glm_count (negative binomial) works with no reference group selected.
Code
res
Output
$n
[1] 73
$rate
[1] 3.720373
attr(,"label")
[1] "Adjusted Rate"
$rate_ci
[1] 3.048181 4.540799
attr(,"label")
[1] "95% CI"
$rate_ratio
[1] 0.8930767 0.9173508
attr(,"label")
[1] "Adjusted Rate Ratio"
$rate_ratio_ci
[1] 0.7260672 0.7381034 1.0985017 1.1401282
attr(,"label")
[1] "95% CI"
$pval
[1] 0.2843668 0.4367453
attr(,"label")
[1] "p-value"
summarize_glm_count works with healthy inputs
Code
res
Output
B: Placebo A: Drug X C: Combination
(N=73) (N=69) (N=58)
————————————————————————————————————————————————————————————————————————————————————
Number of exacerbations per patient
0 8 (10.96%) 3 (4.35%) 6 (10.34%)
1 9 (12.33%) 11 (15.94%) 6 (10.34%)
2 15 (20.55%) 18 (26.09%) 9 (15.52%)
3 11 (15.07%) 14 (20.29%) 15 (25.86%)
4 9 (12.33%) 10 (14.49%) 9 (15.52%)
5 9 (12.33%) 7 (10.14%) 8 (13.79%)
6 4 (5.48%) 4 (5.80%) 4 (6.90%)
7 8 (10.96%) 2 (2.90%) 0 (0.00%)
10 0 (0.00%) 0 (0.00%) 1 (1.72%)
Unadjusted exacerbation rate (per year)
Rate 9.1554 8.2061 7.8551
summarize_glm_count (negative binomial) works with healthy inputs
Code
res
Output
B: Placebo A: Drug X C: Combination
(N=73) (N=69) (N=58)
————————————————————————————————————————————————————————————————————————————————————
Number of exacerbations per patient
0 8 (10.96%) 3 (4.35%) 6 (10.34%)
1 9 (12.33%) 11 (15.94%) 6 (10.34%)
2 15 (20.55%) 18 (26.09%) 9 (15.52%)
3 11 (15.07%) 14 (20.29%) 15 (25.86%)
4 9 (12.33%) 10 (14.49%) 9 (15.52%)
5 9 (12.33%) 7 (10.14%) 8 (13.79%)
6 4 (5.48%) 4 (5.80%) 4 (6.90%)
7 8 (10.96%) 2 (2.90%) 0 (0.00%)
10 0 (0.00%) 0 (0.00%) 1 (1.72%)
Unadjusted exacerbation rate (per year)
Rate 3.1918 2.9275 3.0862
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h_glm_poisson glm-fit works with healthy input
Code
res
Output
Estimate SE z_value Pr coefs
1 2.10487843 0.07035975 29.9159442 1.220818e-196 (Intercept)
2 0.10946932 0.09613709 1.1386793 2.548369e-01 ARMB: Placebo
3 -0.04371457 0.10265030 -0.4258591 6.702105e-01 ARMC: Combination
h_glm_poisson emmeans-fit works with healthy input
Code
res
Output
ARMCD rate std.error df null statistic p.value
1 ARM A 8.206105 0.5773795 Inf 1 29.91594 1.220818e-196
2 ARM B 9.155436 0.5997925 Inf 1 33.80055 1.935734e-250
3 ARM C 7.855107 0.5871181 Inf 1 27.57650 2.129731e-167
h_glm_poisson glm-fit works with healthy input with covariates
Code
res
Output
Estimate SE z_value Pr coefs
1 2.01065582 0.18541942 10.8438255 2.133586e-27 (Intercept)
2 0.07631174 0.17896220 0.4264126 6.698072e-01 REGION1Asia
3 0.64425750 0.22389462 2.8775033 4.008358e-03 REGION1Eurasia
4 2.13096720 0.36521976 5.8347533 5.387022e-09 REGION1Europe
5 -0.07449500 0.20314837 -0.3667024 7.138410e-01 REGION1North America
6 0.38101695 0.21554753 1.7676703 7.711605e-02 REGION1South America
7 0.11047866 0.09872549 1.1190490 2.631192e-01 ARMB: Placebo
8 -0.17694419 0.10873176 -1.6273459 1.036637e-01 ARMC: Combination
h_glm_poisson emmeans-fit works with healthy input with covariates
Code
res
Output
ARMCD rate std.error df null statistic p.value
1 ARM A 12.64167 1.2378669 Inf 1 25.90902 5.270655e-148
2 ARM B 14.11838 1.2848735 Inf 1 29.09088 4.682722e-186
3 ARM C 10.59153 0.9708089 Inf 1 25.74821 3.375733e-146
h_glm_quasipoisson glm-fit works with healthy input
Code
res
Output
Estimate SE z_value Pr coefs
1 2.01065582 0.7781805 2.5837912 0.01051488 (Intercept)
2 0.07631174 0.7510804 0.1016026 0.91917812 REGION1Asia
3 0.64425750 0.9396557 0.6856314 0.49377257 REGION1Eurasia
4 2.13096720 1.5327784 1.3902643 0.16605816 REGION1Europe
5 -0.07449500 0.8525865 -0.0873753 0.93046426 REGION1North America
6 0.38101695 0.9046241 0.4211881 0.67408881 REGION1South America
7 0.11047866 0.4143377 0.2666392 0.79003312 ARMB: Placebo
8 -0.17694419 0.4563327 -0.3877526 0.69862863 ARMC: Combination
h_glm_quasipoisson emmeans-fit works with healthy input
Code
res
Output
ARM rate std.error df null statistic p.value
1 A: Drug X 12.64167 5.195162 Inf 1 6.173420 6.682841e-10
2 B: Placebo 14.11838 5.392442 Inf 1 6.931571 4.161914e-12
3 C: Combination 10.59153 4.074355 Inf 1 6.135104 8.510352e-10
h_glm_negbin glm-fit works with healthy input
Code
res
Output
Estimate SE z_value Pr coefs
1 1.005041594 0.1992268 5.04471149 4.542062e-07 (Intercept)
2 0.007741431 0.1919877 0.04032253 9.678360e-01 REGION1Asia
3 0.317703043 0.2360653 1.34582686 1.783584e-01 REGION1Eurasia
4 0.591541717 0.4058327 1.45759983 1.449509e-01 REGION1Europe
5 0.117240049 0.2196300 0.53380718 5.934749e-01 REGION1North America
6 0.139971334 0.2348685 0.59595610 5.512046e-01 REGION1South America
7 0.113082781 0.1056295 1.07056107 2.843668e-01 ARMB: Placebo
8 0.026817451 0.1131811 0.23694292 8.127011e-01 ARMC: Combination
h_glm_negbin emmeans-fit works with healthy input
Code
res
Output
ARM response std.error df null statistic p.value
1 A: Drug X 3.322579 0.3367532 Inf 1 11.84712 2.227054e-32
2 B: Placebo 3.720373 0.3782682 Inf 1 12.92183 3.390023e-38
3 C: Combination 3.412887 0.3424577 Inf 1 12.23369 2.054037e-34
h_glm_count glm-fit works with healthy input
Code
res
Output
Estimate SE z_value Pr coefs
1 2.10487843 0.07035975 29.9159442 1.220818e-196 (Intercept)
2 0.10946932 0.09613709 1.1386793 2.548369e-01 ARMCDARM B
3 -0.04371457 0.10265030 -0.4258591 6.702105e-01 ARMCDARM C
h_glm_count emmeans-fit works with healthy input
Code
res
Output
ARMCD rate std.error df null statistic p.value
1 ARM A 8.206105 0.5773795 Inf 1 29.91594 1.220818e-196
2 ARM B 9.155436 0.5997925 Inf 1 33.80055 1.935734e-250
3 ARM C 7.855107 0.5871181 Inf 1 27.57650 2.129731e-167
h_ppmeans works with healthy input
Code
fits
Output
$glm_fit
Call: stats::glm(formula = formula, family = stats::poisson(link = "log"),
data = .df_row, offset = offset)
Coefficients:
(Intercept) REGION1Asia REGION1Eurasia
2.01066 0.07631 0.64426
REGION1Europe REGION1North America REGION1South America
2.13097 -0.07450 0.38102
ARMCDARM B ARMCDARM C
0.11048 -0.17694
Degrees of Freedom: 199 Total (i.e. Null); 192 Residual
Null Deviance: 983.8
Residual Deviance: 939 AIC: 1498
$emmeans_fit
ARMCD rate SE df asymp.LCL asymp.UCL
ARM A 12.6 1.238 Inf 10.43 15.3
ARM B 14.1 1.285 Inf 11.81 16.9
ARM C 10.6 0.971 Inf 8.85 12.7
Results are averaged over the levels of: REGION1
Confidence level used: 0.95
Intervals are back-transformed from the log scale
Code
fits2
Output
$glm_fit
Call: stats::glm(formula = formula, family = stats::quasipoisson(link = "log"),
data = .df_row, offset = offset)
Coefficients:
(Intercept) REGION1Asia REGION1Eurasia
2.01066 0.07631 0.64426
REGION1Europe REGION1North America REGION1South America
2.13097 -0.07450 0.38102
ARMCDARM B ARMCDARM C
0.11048 -0.17694
Degrees of Freedom: 199 Total (i.e. Null); 192 Residual
Null Deviance: 983.8
Residual Deviance: 939 AIC: NA
$emmeans_fit
ARMCD rate SE df asymp.LCL asymp.UCL
ARM A 12.6 5.20 Inf 5.65 28.3
ARM B 14.1 5.39 Inf 6.68 29.8
ARM C 10.6 4.07 Inf 4.98 22.5
Results are averaged over the levels of: REGION1
Confidence level used: 0.95
Intervals are back-transformed from the log scale
s_glm_count works with healthy input
Code
res
Output
$n
[1] 73
$rate
[1] 14.11838
attr(,"label")
[1] "Adjusted Rate"
$rate_ci
[1] 11.81189 16.87525
attr(,"label")
[1] "95% CI"
$rate_ratio
character(0)
attr(,"label")
[1] "Adjusted Rate Ratio"
$rate_ratio_ci
character(0)
attr(,"label")
[1] "95% CI"
$pval
character(0)
attr(,"label")
[1] "p-value"
s_glm_count (negative binomial) works with healthy input
Code
res
Output
$n
[1] 73
$rate
[1] 3.720373
attr(,"label")
[1] "Adjusted Rate"
$rate_ci
[1] 3.048181 4.540799
attr(,"label")
[1] "95% CI"
$rate_ratio
character(0)
attr(,"label")
[1] "Adjusted Rate Ratio"
$rate_ratio_ci
character(0)
attr(,"label")
[1] "95% CI"
$pval
character(0)
attr(,"label")
[1] "p-value"
s_glm_count works with no reference group selected.
Code
res
Output
$n
[1] 73
$rate
[1] 14.11838
attr(,"label")
[1] "Adjusted Rate"
$rate_ci
[1] 11.81189 16.87525
attr(,"label")
[1] "95% CI"
$rate_ratio
[1] 0.8954054 0.7501944
attr(,"label")
[1] "Adjusted Rate Ratio"
$rate_ratio_ci
[1] 0.7378778 0.6062152 1.0865633 0.9283695
attr(,"label")
[1] "95% CI"
$pval
[1] 0.263119218 0.008203621
attr(,"label")
[1] "p-value"
s_glm_count (negative binomial) works with no reference group selected.
Code
res
Output
$n
[1] 73
$rate
[1] 3.720373
attr(,"label")
[1] "Adjusted Rate"
$rate_ci
[1] 3.048181 4.540799
attr(,"label")
[1] "95% CI"
$rate_ratio
[1] 0.8930767 0.9173508
attr(,"label")
[1] "Adjusted Rate Ratio"
$rate_ratio_ci
[1] 0.7260672 0.7381034 1.0985017 1.1401282
attr(,"label")
[1] "95% CI"
$pval
[1] 0.2843668 0.4367453
attr(,"label")
[1] "p-value"
summarize_glm_count works with healthy inputs
Code
res
Output
B: Placebo A: Drug X C: Combination
(N=73) (N=69) (N=58)
————————————————————————————————————————————————————————————————————————————————————
Number of exacerbations per patient
0 8 (10.96%) 3 (4.35%) 6 (10.34%)
1 9 (12.33%) 11 (15.94%) 6 (10.34%)
2 15 (20.55%) 18 (26.09%) 9 (15.52%)
3 11 (15.07%) 14 (20.29%) 15 (25.86%)
4 9 (12.33%) 10 (14.49%) 9 (15.52%)
5 9 (12.33%) 7 (10.14%) 8 (13.79%)
6 4 (5.48%) 4 (5.80%) 4 (6.90%)
7 8 (10.96%) 2 (2.90%) 0 (0.00%)
10 0 (0.00%) 0 (0.00%) 1 (1.72%)
Unadjusted exacerbation rate (per year)
Rate 9.1554 8.2061 7.8551
summarize_glm_count (negative binomial) works with healthy inputs
Code
res
Output
B: Placebo A: Drug X C: Combination
(N=73) (N=69) (N=58)
————————————————————————————————————————————————————————————————————————————————————
Number of exacerbations per patient
0 8 (10.96%) 3 (4.35%) 6 (10.34%)
1 9 (12.33%) 11 (15.94%) 6 (10.34%)
2 15 (20.55%) 18 (26.09%) 9 (15.52%)
3 11 (15.07%) 14 (20.29%) 15 (25.86%)
4 9 (12.33%) 10 (14.49%) 9 (15.52%)
5 9 (12.33%) 7 (10.14%) 8 (13.79%)
6 4 (5.48%) 4 (5.80%) 4 (6.90%)
7 8 (10.96%) 2 (2.90%) 0 (0.00%)
10 0 (0.00%) 0 (0.00%) 1 (1.72%)
Unadjusted exacerbation rate (per year)
Rate 3.1918 2.9275 3.0862
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