# 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