Baseline characteristics

PAM_clean %>% 
  dplyr::select(AGE50, GENDERF, ibddx, ModSevere,current_meds___1, pamtotal, SESIBD, PROImp) -> baseline

baseline %>% tbl_summary(label = list(AGE50 ~ "Age > 50", GENDERF~ "Female", ibddx ~ "IBD Diagnosis", ModSevere ~ "Moderate to Severe Disease",current_meds___1 ~ "Active Steroid Use", pamtotal ~ "PAM Score", SESIBD ~ "SES Score", PROImp ~ "Daily Impact Score"),
        statistic = list(all_continuous() ~ "{mean} ({sd})"),
        missing_text = "(Missing)")

Characteristic	N = 148¹
Age > 50	78 (53%)
Female	80 (54%)
IBD Diagnosis
CD	86 (58%)
UC	54 (36%)
IC	3 (2.0%)
Unknown	5 (3.4%)
Moderate to Severe Disease	112 (76%)
Active Steroid Use	16 (11%)
PAM Score	65 (11)
SES Score	218 (40)
Daily Impact Score	7 (9)
¹ n (%); Mean (SD)

Bivariate Analysis

tbl_uv_proimp <- 
  tbl_uvregression(
    PAM_clean[c("PROImp", "AGE50", "GENDERF", "ibddx", "ModSevere", "current_meds___1", "pamtotal", "SESIBD")],
    method = lm,
    y = PROImp,
    label = list(AGE50 ~ "Age > 50", GENDERF~ "Female", ibddx ~ "IBD Diagnosis", ModSevere ~ "Moderate to Severe Disease", pamtotal ~ "PAM Score", SESIBD ~ "SES Score", current_meds___1 ~ "Active Steroid Use")
  )
print(tbl_uv_proimp, method = render)

`...` must be empty.
✖ Problematic argument:
• method = render

Characteristic	N	Beta	95% CI¹	p-value
Age > 50	148	0.62	-2.2, 3.4	0.7
Female	148	3.0	0.27, 5.8	0.032
IBD Diagnosis	148
CD		—	—
UC		-6.1	-8.9, -3.3	<0.001
IC		-9.2	-19, 0.27	0.057
Unknown		-4.9	-12, 2.5	0.2
Moderate to Severe Disease	148	3.8	0.61, 7.0	0.020
Active Steroid Use	148	8.4	4.0, 13	<0.001
PAM Score	148	-0.18	-0.30, -0.06	0.005
SES Score	148	-0.12	-0.15, -0.10	<0.001
¹ CI = Confidence Interval

NULL

Multivariable Models

PAM -> daily life impact

impact3 <- lm(PROImp ~ AGE50 + GENDERF + ibddx + ModSevere + current_meds___1 + pamtotal,
              data = PAM_clean)
summary(impact3 )


Call:
lm(formula = PROImp ~ AGE50 + GENDERF + ibddx + ModSevere + current_meds___1 + 
    pamtotal, data = PAM_clean)

Residuals:
    Min      1Q  Median      3Q     Max 
-18.899  -4.591  -1.235   3.638  22.351 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)      15.27104    4.47618   3.412 0.000846 ***
AGE50             0.50919    1.30751   0.389 0.697551    
GENDERF           2.86242    1.26667   2.260 0.025389 *  
ibddxUC          -5.13531    1.42453  -3.605 0.000434 ***
ibddxIC          -9.16832    4.56839  -2.007 0.046699 *  
ibddxUnknown     -4.46756    3.60267  -1.240 0.217040    
ModSevere         0.77830    1.61173   0.483 0.629930    
current_meds___1  7.11258    2.05781   3.456 0.000727 ***
pamtotal         -0.14351    0.05791  -2.478 0.014407 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 7.585 on 139 degrees of freedom
Multiple R-squared:  0.2691,    Adjusted R-squared:  0.227 
F-statistic: 6.397 on 8 and 139 DF,  p-value: 4.607e-07

broom::glance(impact3 )

broom::tidy(impact3)

model_performance(impact3)

# Indices of model performance

AIC      |      BIC |    R2 | R2 (adj.) |  RMSE | Sigma
-------------------------------------------------------
1030.452 | 1060.424 | 0.269 |     0.227 | 7.350 | 7.585

tbl_regression(impact3, label = list(AGE50 ~ "Age > 50", GENDERF~ "Female", ibddx ~ "IBD Diagnosis", ModSevere ~ "Moderate to Severe Disease", pamtotal ~ "PAM Score", current_meds___1 ~ "Active Steroid Use"))

Characteristic	Beta	95% CI¹	p-value
Age > 50	0.51	-2.1, 3.1	0.7
Female	2.9	0.36, 5.4	0.025
IBD Diagnosis
CD	—	—
UC	-5.1	-8.0, -2.3	<0.001
IC	-9.2	-18, -0.14	0.047
Unknown	-4.5	-12, 2.7	0.2
Moderate to Severe Disease	0.78	-2.4, 4.0	0.6
Active Steroid Use	7.1	3.0, 11	<0.001
PAM Score	-0.14	-0.26, -0.03	0.014
¹ CI = Confidence Interval

SES –> Daily life impact

PROImp2 <- lm(PROImp ~ AGE50 + GENDERF + ibddx + ModSevere + current_meds___1 + SESIBD,
              data = PAM_clean)
summary(PROImp2 )


Call:
lm(formula = PROImp ~ AGE50 + GENDERF + ibddx + ModSevere + current_meds___1 + 
    SESIBD, data = PAM_clean)

Residuals:
     Min       1Q   Median       3Q      Max 
-18.5542  -4.0577  -0.5539   3.0497  18.6023 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)      30.15776    3.67583   8.204 1.40e-13 ***
AGE50             0.51444    1.11300   0.462 0.644655    
GENDERF           2.50757    1.08897   2.303 0.022781 *  
ibddxUC          -4.34910    1.22537  -3.549 0.000528 ***
ibddxIC          -7.10943    3.92738  -1.810 0.072422 .  
ibddxUnknown     -5.97394    3.10103  -1.926 0.056090 .  
ModSevere        -0.24791    1.38992  -0.178 0.858697    
current_meds___1  5.28236    1.78415   2.961 0.003610 ** 
SESIBD           -0.10707    0.01408  -7.602 3.94e-12 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 6.514 on 139 degrees of freedom
Multiple R-squared:  0.4609,    Adjusted R-squared:  0.4299 
F-statistic: 14.86 on 8 and 139 DF,  p-value: 1.402e-15

broom::glance(PROImp2 )

broom::tidy(PROImp2)

model_performance(PROImp2)

# Indices of model performance

AIC     |      BIC |    R2 | R2 (adj.) |  RMSE | Sigma
------------------------------------------------------
985.397 | 1015.369 | 0.461 |     0.430 | 6.313 | 6.514

tbl_regression(PROImp2, label = list(AGE50 ~ "Age > 50", GENDERF~ "Female", ibddx ~ "IBD Diagnosis", ModSevere ~ "Moderate to Severe Disease", SESIBD ~ "SES Score", current_meds___1 ~ "Active Steroid Use"))

Characteristic	Beta	95% CI¹	p-value
Age > 50	0.51	-1.7, 2.7	0.6
Female	2.5	0.35, 4.7	0.023
IBD Diagnosis
CD	—	—
UC	-4.3	-6.8, -1.9	<0.001
IC	-7.1	-15, 0.66	0.072
Unknown	-6.0	-12, 0.16	0.056
Moderate to Severe Disease	-0.25	-3.0, 2.5	0.9
Active Steroid Use	5.3	1.8, 8.8	0.004
SES Score	-0.11	-0.13, -0.08	<0.001
¹ CI = Confidence Interval

NA
NA

PAM + SES –> Daily life impact

PROImp3 <- lm(PROImp ~ AGE50 + GENDERF + ibddx + ModSevere + current_meds___1 + pamtotal + SESIBD,
              data = PAM_clean)
summary(PROImp3 )


Call:
lm(formula = PROImp ~ AGE50 + GENDERF + ibddx + ModSevere + current_meds___1 + 
    pamtotal + SESIBD, data = PAM_clean)

Residuals:
     Min       1Q   Median       3Q      Max 
-18.5583  -4.0652  -0.5632   3.0320  18.5940 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)      30.095441   4.399968   6.840 2.36e-10 ***
AGE50             0.518316   1.126952   0.460 0.646292    
GENDERF           2.507389   1.092929   2.294 0.023289 *  
ibddxUC          -4.346810   1.232961  -3.526 0.000574 ***
ibddxIC          -7.115468   3.948416  -1.802 0.073711 .  
ibddxUnknown     -5.972715   3.112596  -1.919 0.057063 .  
ModSevere        -0.246027   1.396834  -0.176 0.860449    
current_meds___1  5.284626   1.792727   2.948 0.003759 ** 
pamtotal          0.001404   0.054030   0.026 0.979303    
SESIBD           -0.107222   0.015301  -7.008 9.79e-11 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 6.537 on 138 degrees of freedom
Multiple R-squared:  0.4609,    Adjusted R-squared:  0.4258 
F-statistic: 13.11 on 9 and 138 DF,  p-value: 5.633e-15

broom::glance(PROImp3 )

broom::tidy(PROImp3)

model_performance(PROImp3)

# Indices of model performance

AIC     |      BIC |    R2 | R2 (adj.) |  RMSE | Sigma
------------------------------------------------------
987.396 | 1020.365 | 0.461 |     0.426 | 6.313 | 6.537

tbl_regression(PROImp3, label = list(AGE50 ~ "Age > 50", GENDERF~ "Female", ibddx ~ "IBD Diagnosis", ModSevere ~ "Moderate to Severe Disease", pamtotal ~ "PAM Score", SESIBD ~ "SES Score", current_meds___1 ~ "Active Steroid Use"))

Characteristic	Beta	95% CI¹	p-value
Age > 50	0.52	-1.7, 2.7	0.6
Female	2.5	0.35, 4.7	0.023
IBD Diagnosis
CD	—	—
UC	-4.3	-6.8, -1.9	<0.001
IC	-7.1	-15, 0.69	0.074
Unknown	-6.0	-12, 0.18	0.057
Moderate to Severe Disease	-0.25	-3.0, 2.5	0.9
Active Steroid Use	5.3	1.7, 8.8	0.004
PAM Score	0.00	-0.11, 0.11	>0.9
SES Score	-0.11	-0.14, -0.08	<0.001
¹ CI = Confidence Interval

NA

Mediation analysis

library(mediation)

Impact_M <- lm(SESIBD ~ AGE50 + GENDERF + type_of_inflammatory + ModSevere + current_meds___1 + pamtotal + PROImp,
              data = PAM_clean)
summary(Impact_M )


Call:
lm(formula = SESIBD ~ AGE50 + GENDERF + type_of_inflammatory + 
    ModSevere + current_meds___1 + pamtotal + PROImp, data = PAM_clean)

Residuals:
    Min      1Q  Median      3Q     Max 
-84.429 -20.701   2.285  20.494  83.602 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)          170.4572    18.9047   9.017 1.32e-15 ***
AGE50                  0.9518     5.3592   0.178    0.859    
GENDERF                3.0930     5.3050   0.583    0.561    
type_of_inflammatory  -2.0528     3.9988  -0.513    0.609    
ModSevere             -4.1555     6.2843  -0.661    0.510    
current_meds___1       1.1120     8.7488   0.127    0.899    
pamtotal               1.0436     0.2427   4.301 3.17e-05 ***
PROImp                -2.3181     0.3348  -6.923 1.47e-10 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 31.32 on 140 degrees of freedom
Multiple R-squared:  0.4127,    Adjusted R-squared:  0.3833 
F-statistic: 14.05 on 7 and 140 DF,  p-value: 9.971e-14

broom::glance(Impact_M )

tbl_regression(Impact_M, label = list(AGE50 ~ "Age > 50", GENDERF~ "Female", type_of_inflammatory ~ "IBD Diagnosis", ModSevere ~ "Moderate to Severe Disease", current_meds___1 ~ "Active Steroid Use"))

Characteristic	Beta	95% CI¹	p-value
Age > 50	0.95	-9.6, 12	0.9
Female	3.1	-7.4, 14	0.6
IBD Diagnosis	-2.1	-10, 5.9	0.6
Moderate to Severe Disease	-4.2	-17, 8.3	0.5
Active Steroid Use	1.1	-16, 18	0.9
PAM total	1.0	0.56, 1.5	<0.001
PROImp	-2.3	-3.0, -1.7	<0.001
¹ CI = Confidence Interval



impact1 <- lm(PROImp ~ AGE50 + GENDERF + type_of_inflammatory + ModSevere + current_meds___1 + pamtotal + SESIBD,
              data = PAM_clean)
summary(impact1 )


Call:
lm(formula = PROImp ~ AGE50 + GENDERF + type_of_inflammatory + 
    ModSevere + current_meds___1 + pamtotal + SESIBD, data = PAM_clean)

Residuals:
     Min       1Q   Median       3Q      Max 
-21.1583  -4.5813  -0.5136   3.5836  19.2667 

Coefficients:
                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)          25.413500   4.711529   5.394 2.86e-07 ***
AGE50                 0.684121   1.166216   0.587  0.55841    
GENDERF               2.352209   1.139909   2.064  0.04091 *  
type_of_inflammatory  0.963520   0.868168   1.110  0.26897    
ModSevere             1.566099   1.364787   1.148  0.25313    
current_meds___1      5.583644   1.846726   3.024  0.00297 ** 
pamtotal              0.005888   0.056245   0.105  0.91677    
SESIBD               -0.110016   0.015892  -6.923 1.47e-10 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 6.823 on 140 degrees of freedom
Multiple R-squared:  0.4042,    Adjusted R-squared:  0.3744 
F-statistic: 13.57 on 7 and 140 DF,  p-value: 2.586e-13

broom::glance(impact1 )

broom::tidy(impact1)

tbl_regression(impact1, label = list(AGE50 ~ "Age > 50", GENDERF~ "Female", type_of_inflammatory ~ "IBD Diagnosis", ModSevere ~ "Moderate to Severe Disease", pamtotal ~ "PAM Score", SESIBD ~ "SES Score", current_meds___1 ~ "Active Steroid Use"))

Characteristic	Beta	95% CI¹	p-value
Age > 50	0.68	-1.6, 3.0	0.6
Female	2.4	0.10, 4.6	0.041
IBD Diagnosis	0.96	-0.75, 2.7	0.3
Moderate to Severe Disease	1.6	-1.1, 4.3	0.3
Active Steroid Use	5.6	1.9, 9.2	0.003
PAM Score	0.01	-0.11, 0.12	>0.9
SES Score	-0.11	-0.14, -0.08	<0.001
¹ CI = Confidence Interval


# Step 3: Mediation analysis
results <- mediate(Impact_M, impact1, treat="pamtotal", mediator="SESIBD",
                   boot=TRUE, sims=500)

Running nonparametric bootstrap

summary(results)


Causal Mediation Analysis 

Nonparametric Bootstrap Confidence Intervals with the Percentile Method

               Estimate 95% CI Lower 95% CI Upper p-value    
ACME           -0.11481     -0.17027        -0.06  <2e-16 ***
ADE             0.00589     -0.08285         0.09   0.920    
Total Effect   -0.10892     -0.18562        -0.04   0.008 ** 
Prop. Mediated  1.05406      0.48845         2.67   0.008 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Sample Size Used: 148 


Simulations: 500

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PAM_Clean

2022-11-18