tests/testthat/_snaps/mmrm-methods.md
In openpharma/mmrm: Mixed Models for Repeated Measures
h_print_call works as expected
Formula: FEV1 ~ RACE + SEX + ARMCD * AVISIT + us(AVISIT | USUBJID)
Data: fev_data (used 1 observations from 2 subjects with maximum 3
timepoints)
h_print_call works as expected for weighted fits
Formula: FEV1 ~ RACE + SEX + ARMCD * AVISIT + us(AVISIT | USUBJID)
Data: fev_data (used 1 observations from 2 subjects with maximum 3
timepoints)
Weights: .mmrm_weights
h_print_cov works as expected
Covariance: Toeplitz (3 variance parameters)
Covariance: Toeplitz (6 variance parameters of 2 groups)
h_print_aic_list works as expected
AIC BIC logLik deviance
234.2 234.2 -252.2 345235.2
print.summary.mmrm works as expected
mmrm fit
Formula: FEV1 ~ RACE + SEX + ARMCD * AVISIT + us(AVISIT | USUBJID)
Data: fev_data (used 537 observations from 197 subjects with maximum 4
timepoints)
Covariance: unstructured (10 variance parameters)
Method: Satterthwaite
Vcov Method: Asymptotic
Inference: REML
Model selection criteria:
AIC BIC logLik deviance
3406.4 3439.3 -1693.2 3386.4
Coefficients:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 30.78 0.89 219.00 34.7 <2e-16 ***
RACEBlack or African American 1.53 0.62 169.00 2.5 0.02 *
RACEWhite 5.64 0.67 157.00 8.5 2e-14 ***
SEXFemale 0.33 0.53 166.00 0.6 0.54
ARMCDTRT 3.77 1.07 146.00 3.5 6e-04 ***
AVISITVIS2 4.84 0.80 144.00 6.0 1e-08 ***
AVISITVIS3 10.34 0.82 156.00 12.6 <2e-16 ***
AVISITVIS4 15.05 1.31 138.00 11.5 <2e-16 ***
ARMCDTRT:AVISITVIS2 -0.04 1.13 139.00 0.0 0.97
ARMCDTRT:AVISITVIS3 -0.69 1.19 158.00 -0.6 0.56
ARMCDTRT:AVISITVIS4 0.62 1.85 130.00 0.3 0.74
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Covariance estimate:
VIS1 VIS2 VIS3 VIS4
VIS1 40.6 14.4 5.0 13.4
VIS2 14.4 26.6 2.8 7.5
VIS3 5.0 2.8 14.9 0.9
VIS4 13.4 7.5 0.9 95.6
print.summary.mmrm works as expected for weighted models
mmrm fit
Formula: FEV1 ~ RACE + SEX + ARMCD * AVISIT + us(AVISIT | USUBJID)
Data: fev_data (used 537 observations from 197 subjects with maximum 4
timepoints)
Weights: .mmrm_weights
Covariance: unstructured (10 variance parameters)
Method: Satterthwaite
Vcov Method: Asymptotic
Inference: REML
Model selection criteria:
AIC BIC logLik deviance
3446.0 3478.8 -1713.0 3426.0
Coefficients:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 30.34 0.91 222.00 33.5 <2e-16 ***
RACEBlack or African American 1.91 0.61 180.00 3.1 0.002 **
RACEWhite 6.07 0.65 163.00 9.3 <2e-16 ***
SEXFemale 0.56 0.52 175.00 1.1 0.281
ARMCDTRT 3.67 1.09 146.00 3.4 1e-03 ***
AVISITVIS2 4.86 0.83 144.00 5.8 4e-08 ***
AVISITVIS3 10.48 0.85 159.00 12.3 <2e-16 ***
AVISITVIS4 15.58 1.29 128.00 12.1 <2e-16 ***
ARMCDTRT:AVISITVIS2 -0.03 1.15 140.00 0.0 0.977
ARMCDTRT:AVISITVIS3 -0.65 1.21 163.00 -0.5 0.596
ARMCDTRT:AVISITVIS4 0.02 1.82 120.00 0.0 0.990
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Covariance estimate:
VIS1 VIS2 VIS3 VIS4
VIS1 251.6 88.5 27.9 87.7
VIS2 88.5 159.5 13.4 48.7
VIS3 27.9 13.4 90.7 2.3
VIS4 87.7 48.7 2.3 542.6
print.summary.mmrm works as expected for rank deficient fits
mmrm fit
Formula: FEV1 ~ RACE + SEX + SEX2 + ARMCD * AVISIT + us(AVISIT | USUBJID)
Data: .mmrm_dat_rank_deficient (used 537 observations from 197
subjects with maximum 4 timepoints)
Covariance: unstructured (10 variance parameters)
Method: Satterthwaite
Vcov Method: Asymptotic
Inference: REML
Model selection criteria:
AIC BIC logLik deviance
3406.4 3439.3 -1693.2 3386.4
Coefficients: (1 not defined because of singularities)
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 30.78 0.89 219.00 34.7 <2e-16 ***
RACEBlack or African American 1.53 0.62 169.00 2.5 0.02 *
RACEWhite 5.64 0.67 157.00 8.5 2e-14 ***
SEXFemale 0.33 0.53 166.00 0.6 0.54
SEX2Female NA NA NA NA NA
ARMCDTRT 3.77 1.07 146.00 3.5 6e-04 ***
AVISITVIS2 4.84 0.80 144.00 6.0 1e-08 ***
AVISITVIS3 10.34 0.82 156.00 12.6 <2e-16 ***
AVISITVIS4 15.05 1.31 138.00 11.5 <2e-16 ***
ARMCDTRT:AVISITVIS2 -0.04 1.13 139.00 0.0 0.97
ARMCDTRT:AVISITVIS3 -0.69 1.19 158.00 -0.6 0.56
ARMCDTRT:AVISITVIS4 0.62 1.85 130.00 0.3 0.74
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Covariance estimate:
VIS1 VIS2 VIS3 VIS4
VIS1 40.6 14.4 5.0 13.4
VIS2 14.4 26.6 2.8 7.5
VIS3 5.0 2.8 14.9 0.9
VIS4 13.4 7.5 0.9 95.6
print.summary.mmrm works as expected for grouped fits
mmrm fit
Formula: FEV1 ~ ARMCD + us(AVISIT | ARMCD/USUBJID)
Data: fev_data (used 537 observations from 197 subjects with maximum 4
timepoints)
Covariance: unstructured (20 variance parameters of 2 groups)
Method: Satterthwaite
Vcov Method: Asymptotic
Inference: REML
Model selection criteria:
AIC BIC logLik deviance
3702.7 3768.3 -1831.3 3662.7
Coefficients:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 41.2 0.4 94.0 101 <2e-16 ***
ARMCDTRT 3.5 0.6 147.0 6 2e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Covariance estimate:
Group: PBO
VIS1 VIS2 VIS3 VIS4
VIS1 110.7 49.2 -7.0 -47.0
VIS2 49.2 40.1 -2.7 -22.4
VIS3 -7.0 -2.7 23.5 17.7
VIS4 -47.0 -22.4 17.7 132.0
Group: TRT
VIS1 VIS2 VIS3 VIS4
VIS1 106.8 42.6 2.8 -46.3
VIS2 42.6 40.7 4.6 -4.8
VIS3 2.8 4.6 26.0 20.5
VIS4 -46.3 -4.8 20.5 172.9
print.summary.mmrm works as expected for spatial fits
mmrm fit
Formula: FEV1 ~ ARMCD + sp_exp(VISITN | USUBJID)
Data: fev_data (used 537 observations from 197 subjects with maximum 4
timepoints)
Covariance: spatial exponential (2 variance parameters)
Method: Satterthwaite
Vcov Method: Asymptotic
Inference: REML
Model selection criteria:
AIC BIC logLik deviance
3859.1 3865.7 -1927.6 3855.1
Coefficients:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 40.3 0.7 194.0 60 <2e-16 ***
ARMCDTRT 4.2 1.0 188.0 4 2e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Covariance estimate:
0 1
0 84.4 33.0
1 33.0 84.4
openpharma/mmrm documentation built on April 14, 2025, 2:10 a.m.
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h_print_call works as expected
Formula: FEV1 ~ RACE + SEX + ARMCD * AVISIT + us(AVISIT | USUBJID)
Data: fev_data (used 1 observations from 2 subjects with maximum 3
timepoints)
h_print_call works as expected for weighted fits
Formula: FEV1 ~ RACE + SEX + ARMCD * AVISIT + us(AVISIT | USUBJID)
Data: fev_data (used 1 observations from 2 subjects with maximum 3
timepoints)
Weights: .mmrm_weights
h_print_cov works as expected
Covariance: Toeplitz (3 variance parameters)
Covariance: Toeplitz (6 variance parameters of 2 groups)
h_print_aic_list works as expected
AIC BIC logLik deviance
234.2 234.2 -252.2 345235.2
print.summary.mmrm works as expected
mmrm fit
Formula: FEV1 ~ RACE + SEX + ARMCD * AVISIT + us(AVISIT | USUBJID)
Data: fev_data (used 537 observations from 197 subjects with maximum 4
timepoints)
Covariance: unstructured (10 variance parameters)
Method: Satterthwaite
Vcov Method: Asymptotic
Inference: REML
Model selection criteria:
AIC BIC logLik deviance
3406.4 3439.3 -1693.2 3386.4
Coefficients:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 30.78 0.89 219.00 34.7 <2e-16 ***
RACEBlack or African American 1.53 0.62 169.00 2.5 0.02 *
RACEWhite 5.64 0.67 157.00 8.5 2e-14 ***
SEXFemale 0.33 0.53 166.00 0.6 0.54
ARMCDTRT 3.77 1.07 146.00 3.5 6e-04 ***
AVISITVIS2 4.84 0.80 144.00 6.0 1e-08 ***
AVISITVIS3 10.34 0.82 156.00 12.6 <2e-16 ***
AVISITVIS4 15.05 1.31 138.00 11.5 <2e-16 ***
ARMCDTRT:AVISITVIS2 -0.04 1.13 139.00 0.0 0.97
ARMCDTRT:AVISITVIS3 -0.69 1.19 158.00 -0.6 0.56
ARMCDTRT:AVISITVIS4 0.62 1.85 130.00 0.3 0.74
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Covariance estimate:
VIS1 VIS2 VIS3 VIS4
VIS1 40.6 14.4 5.0 13.4
VIS2 14.4 26.6 2.8 7.5
VIS3 5.0 2.8 14.9 0.9
VIS4 13.4 7.5 0.9 95.6
print.summary.mmrm works as expected for weighted models
mmrm fit
Formula: FEV1 ~ RACE + SEX + ARMCD * AVISIT + us(AVISIT | USUBJID)
Data: fev_data (used 537 observations from 197 subjects with maximum 4
timepoints)
Weights: .mmrm_weights
Covariance: unstructured (10 variance parameters)
Method: Satterthwaite
Vcov Method: Asymptotic
Inference: REML
Model selection criteria:
AIC BIC logLik deviance
3446.0 3478.8 -1713.0 3426.0
Coefficients:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 30.34 0.91 222.00 33.5 <2e-16 ***
RACEBlack or African American 1.91 0.61 180.00 3.1 0.002 **
RACEWhite 6.07 0.65 163.00 9.3 <2e-16 ***
SEXFemale 0.56 0.52 175.00 1.1 0.281
ARMCDTRT 3.67 1.09 146.00 3.4 1e-03 ***
AVISITVIS2 4.86 0.83 144.00 5.8 4e-08 ***
AVISITVIS3 10.48 0.85 159.00 12.3 <2e-16 ***
AVISITVIS4 15.58 1.29 128.00 12.1 <2e-16 ***
ARMCDTRT:AVISITVIS2 -0.03 1.15 140.00 0.0 0.977
ARMCDTRT:AVISITVIS3 -0.65 1.21 163.00 -0.5 0.596
ARMCDTRT:AVISITVIS4 0.02 1.82 120.00 0.0 0.990
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Covariance estimate:
VIS1 VIS2 VIS3 VIS4
VIS1 251.6 88.5 27.9 87.7
VIS2 88.5 159.5 13.4 48.7
VIS3 27.9 13.4 90.7 2.3
VIS4 87.7 48.7 2.3 542.6
print.summary.mmrm works as expected for rank deficient fits
mmrm fit
Formula: FEV1 ~ RACE + SEX + SEX2 + ARMCD * AVISIT + us(AVISIT | USUBJID)
Data: .mmrm_dat_rank_deficient (used 537 observations from 197
subjects with maximum 4 timepoints)
Covariance: unstructured (10 variance parameters)
Method: Satterthwaite
Vcov Method: Asymptotic
Inference: REML
Model selection criteria:
AIC BIC logLik deviance
3406.4 3439.3 -1693.2 3386.4
Coefficients: (1 not defined because of singularities)
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 30.78 0.89 219.00 34.7 <2e-16 ***
RACEBlack or African American 1.53 0.62 169.00 2.5 0.02 *
RACEWhite 5.64 0.67 157.00 8.5 2e-14 ***
SEXFemale 0.33 0.53 166.00 0.6 0.54
SEX2Female NA NA NA NA NA
ARMCDTRT 3.77 1.07 146.00 3.5 6e-04 ***
AVISITVIS2 4.84 0.80 144.00 6.0 1e-08 ***
AVISITVIS3 10.34 0.82 156.00 12.6 <2e-16 ***
AVISITVIS4 15.05 1.31 138.00 11.5 <2e-16 ***
ARMCDTRT:AVISITVIS2 -0.04 1.13 139.00 0.0 0.97
ARMCDTRT:AVISITVIS3 -0.69 1.19 158.00 -0.6 0.56
ARMCDTRT:AVISITVIS4 0.62 1.85 130.00 0.3 0.74
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Covariance estimate:
VIS1 VIS2 VIS3 VIS4
VIS1 40.6 14.4 5.0 13.4
VIS2 14.4 26.6 2.8 7.5
VIS3 5.0 2.8 14.9 0.9
VIS4 13.4 7.5 0.9 95.6
print.summary.mmrm works as expected for grouped fits
mmrm fit
Formula: FEV1 ~ ARMCD + us(AVISIT | ARMCD/USUBJID)
Data: fev_data (used 537 observations from 197 subjects with maximum 4
timepoints)
Covariance: unstructured (20 variance parameters of 2 groups)
Method: Satterthwaite
Vcov Method: Asymptotic
Inference: REML
Model selection criteria:
AIC BIC logLik deviance
3702.7 3768.3 -1831.3 3662.7
Coefficients:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 41.2 0.4 94.0 101 <2e-16 ***
ARMCDTRT 3.5 0.6 147.0 6 2e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Covariance estimate:
Group: PBO
VIS1 VIS2 VIS3 VIS4
VIS1 110.7 49.2 -7.0 -47.0
VIS2 49.2 40.1 -2.7 -22.4
VIS3 -7.0 -2.7 23.5 17.7
VIS4 -47.0 -22.4 17.7 132.0
Group: TRT
VIS1 VIS2 VIS3 VIS4
VIS1 106.8 42.6 2.8 -46.3
VIS2 42.6 40.7 4.6 -4.8
VIS3 2.8 4.6 26.0 20.5
VIS4 -46.3 -4.8 20.5 172.9
print.summary.mmrm works as expected for spatial fits
mmrm fit
Formula: FEV1 ~ ARMCD + sp_exp(VISITN | USUBJID)
Data: fev_data (used 537 observations from 197 subjects with maximum 4
timepoints)
Covariance: spatial exponential (2 variance parameters)
Method: Satterthwaite
Vcov Method: Asymptotic
Inference: REML
Model selection criteria:
AIC BIC logLik deviance
3859.1 3865.7 -1927.6 3855.1
Coefficients:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 40.3 0.7 194.0 60 <2e-16 ***
ARMCDTRT 4.2 1.0 188.0 4 2e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Covariance estimate:
0 1
0 84.4 33.0
1 33.0 84.4
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