Fatigue Analysis

Packages

library(tidyverse)
library(gtsummary)
library(ggplot2)
library(broom) 
library(performance)
library(glmtoolbox)
library(DescTools)
library(here)
library(corrplot)
library(broom)
library(pROC)
library(expss)

Upload data

load("~/University of Michigan Dropbox/Jessica Sheehan/R-Code/Fatigue Survey/cd_final.rda") 
load("~/University of Michigan Dropbox/Jessica Sheehan/R-Code/Fatigue Survey/uc_final.rda") 

Data Cleaning

Join Data

rbind(cd_final, uc_final) -> ibd_all

Filter out gender responses with very small n

ibd_all %>% filter(gender != "Other") %>% 
            filter(gender != "Diclose") -> ibd_pts 

Make variable for clinically significant fatigue based on IBD-F

ibd_pts %>% 
mutate(ibd_f_fatigue = case_when(ibd_f_total >= 12 ~ '1', TRUE ~ "0")) ->ibd_pts

ibd_pts$ibd_f_fatigue <- as.numeric(ibd_pts$ibd_f_fatigue)

Labels

ibd_pts = apply_labels(ibd_pts,
                      sbj_id = "Subject ID",
                      ibd_dx = "IBD Diagnosis",
                      age = "Age",
                      gender = "Sex",
                      race_6 = "Race",
                      ethnicity = "Ethnicity",
                      severe_dx = "Moderate-to-Severe IBD",
                      active_steroids = "Active Corticosteroid Use",
                      prednisone_last_year = "Prednisone in the Last year",
                      steroid_months_yr = "Total Months of Steroids in the Last Year",
                      steroid_month_life = "Total Months of Steroids in Lifetime",
                      meds_class = "IBD Medications",
                      anxiety = "Self-Reported Anxiety Diagnosis",
                      depression = "Self-Reported Depression Diagnosis",
                      osa = "Obstructive Sleep Apnea",
                      pro_fatigue = "Fatigue PRO Score",
                      pro_sleep = "Sleep PRO Score", 
                      pro_concentrate = "Concentration PRO Score",
                      facit_total = "FACIT-F Score",
                      facit_fatigue = "FACIT-F < 30",
                      ibd_f_total = "IBD-F Score",
                      ibd_f_fatigue = "IBD-F > 12",
                      stop_bang_total = "STOPBANG Score",
                      osa_mod_high = "Intermediate to High Risk for OSA",
                      osa_high = "High Risk of OSA")

Demographics

ibd_pts %>% dplyr::select(ibd_dx, age, gender, race_6, ethnicity, BMI, severe_dx, active_steroids, prednisone_last_year, steroid_months_yr, steroid_month_life, meds_class, anxiety, depression, osa, pro_fatigue, pro_sleep, pro_concentrate, facit_total, facit_fatigue, ibd_f_total, ibd_f_fatigue, bmi_sb, stop_bang_total, osa_mod_high, osa_high, pro_concentrate) -> ibd_demo 

na.omit(ibd_demo) -> ibd_demo_all

ibd_demo_all$severe_dx <- as.numeric(ibd_demo_all$severe_dx)
ibd_demo_all$osa_mod_high <- as.numeric(ibd_demo_all$osa_mod_high)
ibd_demo_all$osa_high <- as.numeric(ibd_demo_all$osa_high)
ibd_demo_all$facit_fatigue <- as.numeric(ibd_demo_all$facit_fatigue)
ibd_demo_all$steroid_months_yr <- as.numeric(ibd_demo_all$steroid_months_yr)
ibd_demo_all$steroids_month_life <- as.numeric(ibd_demo_all$steroid_month_life)
ibd_demo_all$pro_fatigue <- as.numeric(ibd_demo_all$pro_fatigue)
ibd_demo_all$pro_sleep <- as.numeric(ibd_demo_all$pro_sleep)
ibd_demo_all$pro_concentrate <- as.numeric(ibd_demo_all$pro_concentrate)
ibd_demo_all$BMI <- as.numeric(ibd_demo_all$BMI)

ibd_demo_all %>% tbl_summary(
        statistic = list(all_continuous() ~ "{mean} ({sd})"),
        type = list(steroid_months_yr ~ "continuous"),
        missing_text = "(Missing)")

Characteristic	N = 2561
IBD Diagnosis
Crohn's disease	150 (59%)
Ulcerative colitis	106 (41%)
Indeterminant colitis	0 (0%)
Age	48 (16)
Sex
Male	119 (46%)
Female	137 (54%)
Other	0 (0%)
Diclose	0 (0%)
Race
American Indian or Alaska Native	2 (0.8%)
Asian	6 (2.3%)
Black or African American	7 (2.7%)
Native Hawaiian or other Pacific Islander	0 (0%)
Unknown	2 (0.8%)
White	229 (89%)
Other	6 (2.3%)
Multiracial	4 (1.6%)
Ethnicity
Hispanic or Latino	5 (2.0%)
Not Hispanic or Latino	235 (92%)
Choose not to disclose	8 (3.1%)
Unknown	8 (3.1%)
BMI	28 (6)
Moderate-to-Severe IBD	188 (73%)
Active Corticosteroid Use	15 (5.9%)
Prednisone in the Last year	38 (15%)
Total Months of Steroids in the Last Year	0.58 (1.80)
Total Months of Steroids in Lifetime	22 (44)
IBD Medications
Biologic	111 (43%)
ASA	33 (13%)
Immunomodulator	12 (4.7%)
Small	13 (5.1%)
none	31 (12%)
Dual	56 (22%)
Self-Reported Anxiety Diagnosis	81 (32%)
Self-Reported Depression Diagnosis	63 (25%)
Obstructive Sleep Apnea	32 (13%)
Fatigue PRO Score
0	54 (21%)
1	77 (30%)
2	74 (29%)
3	41 (16%)
4	10 (3.9%)
Sleep PRO Score
0	150 (59%)
1	49 (19%)
2	25 (9.8%)
3	23 (9.0%)
4	9 (3.5%)
Concentration PRO Score
0	154 (60%)
1	57 (22%)
2	24 (9.4%)
3	15 (5.9%)
4	6 (2.3%)
FACIT-F Score	35 (12)
FACIT-F < 30	84 (33%)
IBD-F Score	8 (5)
IBD-F > 12	64 (25%)
bmi_sb	30 (12%)
STOPBANG Score
0	33 (13%)
1	73 (29%)
2	62 (24%)
3	39 (15%)
4	28 (11%)
5	12 (4.7%)
6	5 (2.0%)
8	4 (1.6%)
Intermediate to High Risk for OSA	88 (34%)
High Risk of OSA	21 (8.2%)
Total Months of Steroids in Lifetime	22 (44)
1 n (%); Mean (SD)

Look at subgroups based on risk stratification

# high risk 
ibd_demo_all %>% filter(osa_high != "0") %>% 
  dplyr::select(ibd_dx, age, gender, race_6, ethnicity, BMI, severe_dx, active_steroids, steroid_months_yr, steroid_month_life, meds_class, anxiety, depression, osa, pro_fatigue, facit_total, facit_fatigue, ibd_f_total, stop_bang_total, osa_mod_high, osa_high) -> osa_high_risk 
osa_high_risk %>% tbl_summary(
        statistic = list(all_continuous() ~ "{mean} ({sd})"),
        missing_text = "(Missing)")

Characteristic	N = 211
IBD Diagnosis
Crohn's disease	10 (48%)
Ulcerative colitis	11 (52%)
Indeterminant colitis	0 (0%)
Age	60 (10)
Sex
Male	19 (90%)
Female	2 (9.5%)
Other	0 (0%)
Diclose	0 (0%)
Race
American Indian or Alaska Native	0 (0%)
Asian	1 (4.8%)
Black or African American	0 (0%)
Native Hawaiian or other Pacific Islander	0 (0%)
Unknown	0 (0%)
White	20 (95%)
Other	0 (0%)
Multiracial	0 (0%)
Ethnicity
Hispanic or Latino	0 (0%)
Not Hispanic or Latino	18 (86%)
Choose not to disclose	1 (4.8%)
Unknown	2 (9.5%)
BMI	34 (8)
Moderate-to-Severe IBD	16 (76%)
Active Corticosteroid Use	2 (9.5%)
Total Months of Steroids in the Last Year
0	13 (62%)
1	3 (14%)
2	1 (4.8%)
4	1 (4.8%)
5	1 (4.8%)
12	2 (9.5%)
Total Months of Steroids in Lifetime	18 (33)
IBD Medications
Biologic	10 (48%)
ASA	3 (14%)
Immunomodulator	1 (4.8%)
Small	1 (4.8%)
none	1 (4.8%)
Dual	5 (24%)
Self-Reported Anxiety Diagnosis	3 (14%)
Self-Reported Depression Diagnosis	4 (19%)
Obstructive Sleep Apnea	12 (57%)
Fatigue PRO Score
0	3 (14%)
1	9 (43%)
2	4 (19%)
3	4 (19%)
4	1 (4.8%)
FACIT-F Score	33 (14)
FACIT-F < 30	7 (33%)
IBD-F Score	8 (4)
STOPBANG Score
5	12 (57%)
6	5 (24%)
8	4 (19%)
Intermediate to High Risk for OSA	21 (100%)
High Risk of OSA	21 (100%)
1 n (%); Mean (SD)

# moderate risk
ibd_demo_all$mod_osa <- 0; ibd_demo_all[ibd_demo_all$stop_bang_total > 2 & ibd_demo_all$stop_bang_total < 5, "mod_osa"] <- 1

ibd_demo_all %>% filter(mod_osa != "0") %>% 
  dplyr::select(ibd_dx, age, gender, race_6, ethnicity, BMI, severe_dx, active_steroids, steroid_months_yr, steroid_month_life, meds_class, anxiety, depression, osa, pro_fatigue, facit_total, facit_fatigue, ibd_f_total, stop_bang_total, osa_mod_high, osa_high) -> osa_mod_high_risk 
osa_mod_high_risk %>% tbl_summary(
        statistic = list(all_continuous() ~ "{mean} ({sd})"),
        missing_text = "(Missing)")

Characteristic	N = 671
IBD Diagnosis
Crohn's disease	39 (58%)
Ulcerative colitis	28 (42%)
Indeterminant colitis	0 (0%)
Age	57 (15)
Sex
Male	51 (76%)
Female	16 (24%)
Other	0 (0%)
Diclose	0 (0%)
Race
American Indian or Alaska Native	1 (1.5%)
Asian	1 (1.5%)
Black or African American	3 (4.5%)
Native Hawaiian or other Pacific Islander	0 (0%)
Unknown	2 (3.0%)
White	58 (87%)
Other	1 (1.5%)
Multiracial	1 (1.5%)
Ethnicity
Hispanic or Latino	2 (3.0%)
Not Hispanic or Latino	59 (88%)
Choose not to disclose	3 (4.5%)
Unknown	3 (4.5%)
BMI	30 (8)
Moderate-to-Severe IBD	47 (70%)
Active Corticosteroid Use	6 (9.0%)
Total Months of Steroids in the Last Year
0	50 (75%)
0.5	1 (1.5%)
1	6 (9.0%)
2	5 (7.5%)
3	3 (4.5%)
6	1 (1.5%)
10	1 (1.5%)
Total Months of Steroids in Lifetime	26 (55)
IBD Medications
Biologic	24 (36%)
ASA	13 (19%)
Immunomodulator	6 (9.0%)
Small	4 (6.0%)
none	7 (10%)
Dual	13 (19%)
Self-Reported Anxiety Diagnosis	22 (33%)
Self-Reported Depression Diagnosis	20 (30%)
Obstructive Sleep Apnea	13 (19%)
Fatigue PRO Score
0	15 (22%)
1	23 (34%)
2	19 (28%)
3	9 (13%)
4	1 (1.5%)
FACIT-F Score	36 (12)
FACIT-F < 30	19 (28%)
IBD-F Score	7 (5)
STOPBANG Score
3	39 (58%)
4	28 (42%)
Intermediate to High Risk for OSA	67 (100%)
High Risk of OSA	0 (0%)
1 n (%); Mean (SD)

## Look at what proportion of these people actually got sleep studies 

# low risk 
ibd_demo_all %>% filter(osa_mod_high != "1") %>% 
  dplyr::select(ibd_dx, age, gender, race_6, ethnicity, BMI, severe_dx, active_steroids, steroid_months_yr, steroid_month_life, meds_class, anxiety, depression, osa, pro_fatigue, facit_total, facit_fatigue, ibd_f_total, stop_bang_total, osa_mod_high, osa_high) -> osa_low_risk
osa_low_risk %>% tbl_summary(
        statistic = list(all_continuous() ~ "{mean} ({sd})"),
        missing_text = "(Missing)")

Characteristic	N = 1681
IBD Diagnosis
Crohn's disease	101 (60%)
Ulcerative colitis	67 (40%)
Indeterminant colitis	0 (0%)
Age	43 (15)
Sex
Male	49 (29%)
Female	119 (71%)
Other	0 (0%)
Diclose	0 (0%)
Race
American Indian or Alaska Native	1 (0.6%)
Asian	4 (2.4%)
Black or African American	4 (2.4%)
Native Hawaiian or other Pacific Islander	0 (0%)
Unknown	0 (0%)
White	151 (90%)
Other	5 (3.0%)
Multiracial	3 (1.8%)
Ethnicity
Hispanic or Latino	3 (1.8%)
Not Hispanic or Latino	158 (94%)
Choose not to disclose	4 (2.4%)
Unknown	3 (1.8%)
BMI	26.7 (5.0)
Moderate-to-Severe IBD	125 (74%)
Active Corticosteroid Use	7 (4.2%)
Total Months of Steroids in the Last Year
0	139 (83%)
0.5	1 (0.6%)
1	14 (8.3%)
2	7 (4.2%)
3	3 (1.8%)
4	2 (1.2%)
12	2 (1.2%)
Total Months of Steroids in Lifetime	21 (40)
IBD Medications
Biologic	77 (46%)
ASA	17 (10%)
Immunomodulator	5 (3.0%)
Small	8 (4.8%)
none	23 (14%)
Dual	38 (23%)
Self-Reported Anxiety Diagnosis	56 (33%)
Self-Reported Depression Diagnosis	39 (23%)
Obstructive Sleep Apnea	7 (4.2%)
Fatigue PRO Score
0	36 (21%)
1	45 (27%)
2	51 (30%)
3	28 (17%)
4	8 (4.8%)
FACIT-F Score	35 (13)
FACIT-F < 30	58 (35%)
IBD-F Score	8 (5)
STOPBANG Score
0	33 (20%)
1	73 (43%)
2	62 (37%)
Intermediate to High Risk for OSA	0 (0%)
High Risk of OSA	0 (0%)
1 n (%); Mean (SD)

NA

Now look at people who have intermediate to high risk for OSA but DO NOT have a diagnosis of OSA

Demographics

ibd_demo_all$osa_mod_high <- as.numeric(ibd_demo_all$osa_mod_high )

# make variable for OSA risk without a diagnosis 
ibd_demo_all$mod_osa_no_dx <- 0; ibd_demo_all[ibd_demo_all$osa_mod_high >= 1 & ibd_demo_all$osa < 1, "mod_osa_no_dx"] <- 1

ibd_demo_all %>% dplyr::select(osa, osa_mod_high, mod_osa_no_dx) -> osa_variables

ibd_demo_all$mod_osa_no_dx <- as.numeric(ibd_demo_all$mod_osa_no_dx)

ibd_demo_all %>% 
mutate(pro_fatigue_2 = case_when(pro_fatigue > 1 ~ '1', TRUE ~ "0")) ->ibd_demo_all

ibd_demo_all$pro_fatigue_2 <- as.numeric(ibd_demo_all$pro_fatigue_2)

# Demographics 
ibd_demo_all %>% filter(mod_osa_no_dx != "0") %>% 
  dplyr::select(ibd_dx, age, gender, race_6, ethnicity, severe_dx, active_steroids, steroid_months_yr, steroid_month_life, meds_class, anxiety, depression, osa, facit_total, facit_fatigue, ibd_f_total, pro_fatigue, pro_fatigue_2,  pro_sleep, pro_concentrate, stop_bang_total) -> osa_mod_high_risk 
osa_mod_high_risk %>% tbl_summary(
        statistic = list(all_continuous() ~ "{mean} ({sd})"),
        missing_text = "(Missing)")

Characteristic	N = 631
IBD Diagnosis
Crohn's disease	35 (56%)
Ulcerative colitis	28 (44%)
Indeterminant colitis	0 (0%)
Age	57 (14)
Sex
Male	52 (83%)
Female	11 (17%)
Other	0 (0%)
Diclose	0 (0%)
Race
American Indian or Alaska Native	1 (1.6%)
Asian	1 (1.6%)
Black or African American	3 (4.8%)
Native Hawaiian or other Pacific Islander	0 (0%)
Unknown	2 (3.2%)
White	54 (86%)
Other	1 (1.6%)
Multiracial	1 (1.6%)
Ethnicity
Hispanic or Latino	2 (3.2%)
Not Hispanic or Latino	54 (86%)
Choose not to disclose	3 (4.8%)
Unknown	4 (6.3%)
Moderate-to-Severe IBD	47 (75%)
Active Corticosteroid Use	7 (11%)
Total Months of Steroids in the Last Year
0	43 (68%)
0.5	1 (1.6%)
1	7 (11%)
2	5 (7.9%)
3	3 (4.8%)
5	1 (1.6%)
6	1 (1.6%)
10	1 (1.6%)
12	1 (1.6%)
Total Months of Steroids in Lifetime	27 (56)
IBD Medications
Biologic	25 (40%)
ASA	11 (17%)
Immunomodulator	4 (6.3%)
Small	5 (7.9%)
none	5 (7.9%)
Dual	13 (21%)
Self-Reported Anxiety Diagnosis	15 (24%)
Self-Reported Depression Diagnosis	15 (24%)
Obstructive Sleep Apnea	0 (0%)
FACIT-F Score	38 (11)
FACIT-F < 30	13 (21%)
IBD-F Score	7 (4)
Fatigue PRO Score
0	14 (22%)
1	26 (41%)
2	16 (25%)
3	6 (9.5%)
4	1 (1.6%)
pro_fatigue_2	23 (37%)
Sleep PRO Score
0	39 (62%)
1	13 (21%)
2	6 (9.5%)
3	3 (4.8%)
4	2 (3.2%)
Concentration PRO Score
0	40 (63%)
1	15 (24%)
2	4 (6.3%)
3	2 (3.2%)
4	2 (3.2%)
STOPBANG Score
3	36 (57%)
4	18 (29%)
5	5 (7.9%)
6	3 (4.8%)
8	1 (1.6%)
1 n (%); Mean (SD)

Univeriate Analysis (excludes people who already have OSA diagnosis)

ibd_demo_all %>% filter(osa == "0") %>% tbl_summary(
        by = "mod_osa_no_dx", 
        statistic = list(all_continuous() ~ "{mean} ({sd})"),
        type = list(steroid_months_yr ~ "continuous",
                    pro_fatigue ~ "continuous",
                    pro_concentrate ~ "continuous",
                    pro_sleep ~ "continuous"),
        missing_text = "(Missing)") %>% 
        add_p()

There was an error in 'add_p()/add_difference()' for variable 'osa', p-value omitted:
Error in stats::chisq.test(x = structure(c(0L, 0L, 0L, 0L, 0L, 0L, 0L, : 'x' and 'y' must have at least 2 levels

Characteristic	0, N = 1611	1, N = 631	p-value2
IBD Diagnosis			0.7
Crohn's disease	95 (59%)	35 (56%)
Ulcerative colitis	66 (41%)	28 (44%)
Indeterminant colitis	0 (0%)	0 (0%)
Age	43 (15)	57 (14)	<0.001
Sex			<0.001
Male	47 (29%)	52 (83%)
Female	114 (71%)	11 (17%)
Other	0 (0%)	0 (0%)
Diclose	0 (0%)	0 (0%)
Race			0.3
American Indian or Alaska Native	1 (0.6%)	1 (1.6%)
Asian	4 (2.5%)	1 (1.6%)
Black or African American	4 (2.5%)	3 (4.8%)
Native Hawaiian or other Pacific Islander	0 (0%)	0 (0%)
Unknown	0 (0%)	2 (3.2%)
White	144 (89%)	54 (86%)
Other	5 (3.1%)	1 (1.6%)
Multiracial	3 (1.9%)	1 (1.6%)
Ethnicity			0.11
Hispanic or Latino	3 (1.9%)	2 (3.2%)
Not Hispanic or Latino	152 (94%)	54 (86%)
Choose not to disclose	3 (1.9%)	3 (4.8%)
Unknown	3 (1.9%)	4 (6.3%)
BMI	26.5 (4.9)	29.1 (5.8)	<0.001
Moderate-to-Severe IBD	119 (74%)	47 (75%)	>0.9
Active Corticosteroid Use	7 (4.3%)	7 (11%)	0.071
Prednisone in the Last year	21 (13%)	14 (22%)	0.089
Total Months of Steroids in the Last Year	0.42 (1.50)	0.94 (2.21)	0.011
Total Months of Steroids in Lifetime	21 (41)	27 (56)	0.8
IBD Medications			0.4
Biologic	72 (45%)	25 (40%)
ASA	17 (11%)	11 (17%)
Immunomodulator	5 (3.1%)	4 (6.3%)
Small	8 (5.0%)	5 (7.9%)
none	22 (14%)	5 (7.9%)
Dual	37 (23%)	13 (21%)
Self-Reported Anxiety Diagnosis	53 (33%)	15 (24%)	0.2
Self-Reported Depression Diagnosis	36 (22%)	15 (24%)	0.8
Obstructive Sleep Apnea	0 (0%)	0 (0%)
Fatigue PRO Score	1.58 (1.13)	1.27 (0.97)	0.061
Sleep PRO Score	0.81 (1.16)	0.67 (1.05)	0.5
Concentration PRO Score	0.70 (1.02)	0.59 (0.98)	0.5
FACIT-F Score	35 (12)	38 (11)	0.2
FACIT-F < 30	57 (35%)	13 (21%)	0.032
IBD-F Score	8 (5)	7 (4)	0.2
IBD-F > 12	47 (29%)	8 (13%)	0.010
bmi_sb	8 (5.0%)	10 (16%)	0.007
STOPBANG Score			<0.001
0	33 (20%)	0 (0%)
1	69 (43%)	0 (0%)
2	59 (37%)	0 (0%)
3	0 (0%)	36 (57%)
4	0 (0%)	18 (29%)
5	0 (0%)	5 (7.9%)
6	0 (0%)	3 (4.8%)
8	0 (0%)	1 (1.6%)
Intermediate to High Risk for OSA	0 (0%)	63 (100%)	<0.001
High Risk of OSA	0 (0%)	9 (14%)	<0.001
Total Months of Steroids in Lifetime	21 (41)	27 (56)	0.8
mod_osa	0 (0%)	54 (86%)	<0.001
pro_fatigue_2	84 (52%)	23 (37%)	0.035
1 n (%); Mean (SD)
2 Fisher’s exact test; Wilcoxon rank sum test; Pearson’s Chi-squared test

ibd_demo_all %>% filter(osa == "0") %>% tbl_uvregression(
    method = glm,
    y = mod_osa_no_dx,
    include = c(ibd_dx, age, gender, severe_dx, active_steroids, prednisone_last_year, steroid_months_yr, steroid_month_life, meds_class, anxiety, depression, facit_fatigue, ibd_f_total, ibd_f_fatigue, pro_fatigue, pro_sleep, pro_concentrate),
    method.args = list(family = binomial),
    exponentiate = TRUE,
  ) 

Characteristic	N	OR1	95% CI1	p-value
IBD Diagnosis	224
Crohn's disease		—	—
Ulcerative colitis		1.15	0.64, 2.07	0.6
Age	224	1.06	1.04, 1.09	<0.001
Sex	224
Male		—	—
Female		0.09	0.04, 0.18	<0.001
Moderate-to-Severe IBD	224	1.04	0.54, 2.06	>0.9
Active Corticosteroid Use	224	2.75	0.90, 8.37	0.069
Prednisone in the Last year	224	1.90	0.88, 4.01	0.092
Total Months of Steroids in the Last Year	224	1.17	1.00, 1.40	0.062
Total Months of Steroids in Lifetime	224	1.00	1.00, 1.01	0.4
IBD Medications	224
Biologic		—	—
ASA		1.86	0.76, 4.49	0.2
Immunomodulator		2.30	0.53, 9.38	0.2
Small		1.80	0.50, 5.92	0.3
none		0.65	0.20, 1.80	0.4
Dual		1.01	0.46, 2.18	>0.9
Self-Reported Anxiety Diagnosis	224	0.64	0.32, 1.22	0.2
Self-Reported Depression Diagnosis	224	1.09	0.53, 2.13	0.8
Fatigue PRO Score	224	0.76	0.57, 1.01	0.059
Sleep PRO Score	224	0.89	0.67, 1.16	0.4
Concentration PRO Score	224	0.89	0.65, 1.20	0.5
FACIT-F < 30	224	0.47	0.23, 0.92	0.034
IBD-F Score	224	0.96	0.90, 1.02	0.2
IBD-F > 12	224	0.35	0.15, 0.76	0.012
1 OR = Odds Ratio, CI = Confidence Interval

Multviariable analysis

ibd_demo_all %>% filter(osa == "0") -> ibd_no_osa

set_label(ibd_no_osa$mod_osa_no_dx) <- "Predictors of Increased Risk for OSA"

osa_no_dx <- glm(mod_osa_no_dx ~ age + gender +  BMI + active_steroids + facit_fatigue,
              family = "binomial", 
              data = ibd_no_osa)


summary(osa_no_dx )

Call:
glm(formula = mod_osa_no_dx ~ age + gender + BMI + active_steroids + 
    facit_fatigue, family = "binomial", data = ibd_no_osa)

Coefficients:
                Estimate Std. Error z value Pr(>|z|)    
(Intercept)     -9.20199    1.61263  -5.706 1.16e-08 ***
age              0.08466    0.01572   5.385 7.23e-08 ***
genderFemale    -3.13482    0.52098  -6.017 1.77e-09 ***
BMI              0.18280    0.04294   4.257 2.07e-05 ***
active_steroids  1.88471    0.80187   2.350   0.0188 *  
facit_fatigue    0.11655    0.50189   0.232   0.8164    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 266.17  on 223  degrees of freedom
Residual deviance: 151.19  on 218  degrees of freedom
AIC: 163.19

Number of Fisher Scoring iterations: 6

broom::glance(osa_no_dx )

broom::tidy(osa_no_dx , exponentiate = TRUE)

tbl_regression(osa_no_dx, exponentiate = TRUE)

Characteristic	OR1	95% CI1	p-value
Age	1.09	1.06, 1.12	<0.001
Sex
Male	—	—
Female	0.04	0.01, 0.11	<0.001
BMI	1.20	1.11, 1.31	<0.001
Active Corticosteroid Use	6.58	1.42, 34.1	0.019
FACIT-F < 30	1.12	0.42, 3.03	0.8
1 OR = Odds Ratio, CI = Confidence Interval

# C-Statistic/AUROC 
Cstat(osa_no_dx)

[1] 0.9035788

# Model performance 
model_performance(osa_no_dx)

# Indices of model performance

AIC     |    AICc |     BIC | Tjur's R2 |  RMSE | Sigma | Log_loss | Score_log | Score_spherical |   PCP
--------------------------------------------------------------------------------------------------------
163.191 | 163.578 | 183.661 |     0.478 | 0.324 | 1.000 |    0.337 |   -31.542 |           0.033 | 0.789

performance::check_model(osa_no_dx)

Plot model

library(sjPlot)
library(sjlabelled)
library(sjmisc)
library(ggplot2)

plot_model(osa_no_dx) + scale_y_log10(limits = c(0.01, 50)) 

Scale for y is already present.
Adding another scale for y, which will replace the existing scale.

Now look at whether we can predict clinically significant fatigue (FACIT-F < 30 and IBD-F > 12) using PROs

Correlations

correlation1 <- cor(ibd_demo_all$facit_total, ibd_demo_all$ibd_f_total, method = 'pearson')
correlation1

[1] -0.889915

correlation2 <- cor(ibd_demo_all$facit_total, ibd_demo_all$pro_fatigue, method = 'pearson')
correlation2

[1] -0.799167

correlation3 <- cor(ibd_demo_all$ibd_f_total, ibd_demo_all$pro_fatigue, method = 'pearson')
correlation3

[1] 0.8087237

correlation4 <- cor(ibd_demo_all$facit_fatigue, ibd_demo_all$pro_fatigue, method = 'pearson')
correlation4

[1] 0.6512549

correlation5 <- cor(ibd_demo_all$ibd_f_fatigue, ibd_demo_all$pro_fatigue, method = 'pearson')
correlation5

[1] 0.6517001

correlation6 <- cor(ibd_demo_all$ibd_f_fatigue, ibd_demo_all$facit_fatigue, method = 'pearson')
correlation6

[1] 0.7300945

Pro_fatigue and FACIT-F

roc_facit_f <- roc(ibd_demo_all$facit_fatigue, ibd_demo_all$pro_fatigue)

Setting levels: control = 0, case = 1
Setting direction: controls < cases

roc_facit_f

Call:
roc.default(response = ibd_demo_all$facit_fatigue, predictor = ibd_demo_all$pro_fatigue)

Data: ibd_demo_all$pro_fatigue in 172 controls (ibd_demo_all$facit_fatigue 0) < 84 cases (ibd_demo_all$facit_fatigue 1).
Area under the curve: 0.8837

coords(roc_facit_f)

plot.roc(roc_facit_f)

coords(roc_facit_f, "best", ret=c("threshold", "sens", "spec", "ppv", "npv"))

coords(roc_facit_f, "local maximas", ret=c("threshold", "sens", "spec", "ppv", "npv"))

ci(roc_facit_f, of = "thresholds", thresholds = 1)

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95% CI (2000 stratified bootstrap replicates):

ci(roc_facit_f, of = "thresholds", thresholds = 2)

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95% CI (2000 stratified bootstrap replicates):

ci(roc_facit_f, of = "thresholds", thresholds = 3)

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95% CI (2000 stratified bootstrap replicates):

roc_facit_f_plot <- plot.roc(ibd_demo_all$facit_fatigue, ibd_demo_all$pro_fatigue,
                   main = "FACIT-F", 
                   percent=TRUE,
                   ci = TRUE,                  # compute AUC (of AUC by default)
                   print.auc = TRUE)           # print the AUC (will contain the CI)

Setting levels: control = 0, case = 1
Setting direction: controls < cases

ciobj <- ci.se(roc_facit_f_plot,                         # CI of sensitivity
               specificities = seq(0, 100, 5)) # over a select set of specificities

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plot(ciobj, type = "shape", col = "#1c61b6AA")     # plot as a blue shape

plot(ci(roc_facit_f_plot, of = "thresholds", thresholds = "best")) # add one threshold

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PRO-fatigue and IBD-F

roc_ibd_f <- roc(ibd_demo_all$ibd_f_fatigue, ibd_demo_all$pro_fatigue)

Setting levels: control = 0, case = 1
Setting direction: controls < cases

roc_ibd_f

Call:
roc.default(response = ibd_demo_all$ibd_f_fatigue, predictor = ibd_demo_all$pro_fatigue)

Data: ibd_demo_all$pro_fatigue in 192 controls (ibd_demo_all$ibd_f_fatigue 0) < 64 cases (ibd_demo_all$ibd_f_fatigue 1).
Area under the curve: 0.9073

coords(roc_ibd_f)

plot.roc(roc_ibd_f)

coords(roc_ibd_f, "best", ret=c("threshold", "sens", "spec", "ppv", "npv"))

coords(roc_ibd_f, "local maximas", ret=c("threshold", "sens", "spec", "ppv", "npv"))

ci(roc_ibd_f, of = "thresholds", thresholds = 1)

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95% CI (2000 stratified bootstrap replicates):

ci(roc_ibd_f, of = "thresholds", thresholds = 2)

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95% CI (2000 stratified bootstrap replicates):

ci(roc_ibd_f, of = "thresholds", thresholds = 3)

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95% CI (2000 stratified bootstrap replicates):

roc_ibd_f_plot <- plot.roc(ibd_demo_all$ibd_f_fatigue, ibd_demo_all$pro_fatigue,
                   main = "IBD-F", 
                   percent=TRUE,
                   ci = TRUE,                  # compute AUC (of AUC by default)
                   print.auc = TRUE)           # print the AUC (will contain the CI)

Setting levels: control = 0, case = 1
Setting direction: controls < cases

ciobj <- ci.se(roc_facit_f_plot,                         # CI of sensitivity
               specificities = seq(0, 100, 5)) # over a select set of specificities

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plot(ciobj, type = "shape", col = "#1c61b6AA")     # plot as a blue shape

plot(ci(roc_facit_f_plot, of = "thresholds", thresholds = "best")) # add one threshold

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factors associated with fatigue (FACIT-F)

fatigue_1 <- glm(facit_fatigue ~ ibd_dx + age + gender + severe_dx + active_steroids + anxiety + depression,
              family = "binomial", 
              data = ibd_demo_all)


summary(fatigue_1 )

Call:
glm(formula = facit_fatigue ~ ibd_dx + age + gender + severe_dx + 
    active_steroids + anxiety + depression, family = "binomial", 
    data = ibd_demo_all)

Coefficients:
                         Estimate Std. Error z value Pr(>|z|)   
(Intercept)               -2.3205     0.7278  -3.188  0.00143 **
ibd_dxUlcerative colitis  -0.3894     0.3255  -1.196  0.23161   
age                        0.0028     0.0101   0.277  0.78165   
genderFemale               1.0079     0.3293   3.060  0.00221 **
severe_dx                  0.3880     0.3819   1.016  0.30966   
active_steroids            1.3863     0.5959   2.326  0.02001 * 
anxiety                    0.8636     0.3865   2.234  0.02546 * 
depression                 1.1701     0.4031   2.902  0.00370 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 324.02  on 255  degrees of freedom
Residual deviance: 262.46  on 248  degrees of freedom
AIC: 278.46

Number of Fisher Scoring iterations: 4

broom::glance(fatigue_1 )

broom::tidy(fatigue_1 , exponentiate = TRUE)

tbl_regression(fatigue_1, exponentiate = TRUE) -> facit_f_table
print(facit_f_table)

Characteristic	OR1	95% CI1	p-value
IBD Diagnosis
Crohn's disease	—	—
Ulcerative colitis	0.68	0.35, 1.28	0.2
Age	1.00	0.98, 1.02	0.8
Sex
Male	—	—
Female	2.74	1.45, 5.30	0.002
Moderate-to-Severe IBD	1.47	0.70, 3.17	0.3
Active Corticosteroid Use	4.00	1.23, 13.2	0.020
Self-Reported Anxiety Diagnosis	2.37	1.10, 5.06	0.025
Self-Reported Depression Diagnosis	3.22	1.46, 7.15	0.004
1 OR = Odds Ratio, CI = Confidence Interval

NULL

# C-Statistic/AUROC 
Cstat(fatigue_1)

[1] 0.792428

# Model performance 
model_performance(fatigue_1)

# Indices of model performance

AIC     |    AICc |     BIC | Tjur's R2 |  RMSE | Sigma | Log_loss | Score_log | Score_spherical |   PCP
--------------------------------------------------------------------------------------------------------
278.458 | 279.041 | 306.820 |     0.231 | 0.413 | 1.000 |    0.513 |   -39.230 |           0.015 | 0.661

performance::check_model(fatigue_1)

factors predicting fatigue on IBD-F

ibd_demo_all$ibd_f_fatigue <- as.numeric(ibd_demo_all$ibd_f_fatigue)
fatigue_2 <- glm(ibd_f_fatigue ~ ibd_dx + age + gender + severe_dx + active_steroids + anxiety + depression,
              family = "binomial", 
              data = ibd_demo_all)


summary(fatigue_2 )

Call:
glm(formula = ibd_f_fatigue ~ ibd_dx + age + gender + severe_dx + 
    active_steroids + anxiety + depression, family = "binomial", 
    data = ibd_demo_all)

Coefficients:
                          Estimate Std. Error z value Pr(>|z|)    
(Intercept)              -2.926860   0.809549  -3.615  0.00030 ***
ibd_dxUlcerative colitis -0.009747   0.346603  -0.028  0.97757    
age                      -0.005868   0.010927  -0.537  0.59126    
genderFemale              0.970983   0.362487   2.679  0.00739 ** 
severe_dx                 1.054037   0.455741   2.313  0.02073 *  
active_steroids           1.203510   0.626871   1.920  0.05487 .  
anxiety                   0.442700   0.419197   1.056  0.29094    
depression                1.355883   0.420178   3.227  0.00125 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 287.92  on 255  degrees of freedom
Residual deviance: 235.30  on 248  degrees of freedom
AIC: 251.3

Number of Fisher Scoring iterations: 5

broom::glance(fatigue_2 )

broom::tidy(fatigue_2 , exponentiate = TRUE)

tbl_regression(fatigue_2, exponentiate = TRUE) -> ibd_f_table
print(ibd_f_table)

Characteristic	OR1	95% CI1	p-value
IBD Diagnosis
Crohn's disease	—	—
Ulcerative colitis	0.99	0.50, 1.95	>0.9
Age	0.99	0.97, 1.02	0.6
Sex
Male	—	—
Female	2.64	1.31, 5.49	0.007
Moderate-to-Severe IBD	2.87	1.22, 7.44	0.021
Active Corticosteroid Use	3.33	0.93, 11.3	0.055
Self-Reported Anxiety Diagnosis	1.56	0.67, 3.51	0.3
Self-Reported Depression Diagnosis	3.88	1.71, 8.97	0.001
1 OR = Odds Ratio, CI = Confidence Interval

NULL

# C-Statistic/AUROC 
Cstat(fatigue_2)

[1] 0.7871094

# Model performance 
model_performance(fatigue_2)

# Indices of model performance

AIC     |    AICc |     BIC | Tjur's R2 |  RMSE | Sigma | Log_loss | Score_log | Score_spherical |   PCP
--------------------------------------------------------------------------------------------------------
251.304 | 251.887 | 279.665 |     0.205 | 0.387 | 1.000 |    0.460 |   -20.648 |           0.019 | 0.702

performance::check_model(fatigue_2)