-
Notifications
You must be signed in to change notification settings - Fork 0
/
NHANES2017_2018_PA_DM.R
427 lines (348 loc) · 17.3 KB
/
NHANES2017_2018_PA_DM.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
library(nhanesA)
library(knitr)
library(tidyverse)
library(plyr)
library(survey)
library(ggplot2)
library(readxl)
library(car)
library(Rmisc)
library(ggpubr)
library(caret)
library(tableone)
library(odds.n.ends)
library(pROC)
library(Deducer)
library(broom)
#DEMORAPHIC, EXAMINATION, LAB, QUESTIONNAIRE DATA
# Research aim- We aimed to quantify the risk of diabetes and determine whether a relationship is present between physical activity (PA)levels and diabetes.
#Population- USA Non-institutionalized citizens >= 20 years old, Outcome-Diabetes, exposure- PA, Time- 2017 - 2018
#Covariates- gender, age, race, educational level, BMI, marital status
#DOWNLOAD NHANES DATA DIRECTLY TO R
#Explore the NHANES DATA 2018
kable(head(nhanesTables('DEMO', 2018)))
kable(head(nhanesTables('EXAM', 2018)))
kable(head(nhanesTables('Q', 2018)))
#further explore NHANES 2018
kable(head(nhanesTableVars(data_group = 'DEMO', nh_table = 'DEMO_J', namesonly = FALSE)))
kable(head(nhanesTableVars(data_group = 'EXAM', nh_table = 'BMX_J', namesonly = FALSE)))
kable(head(nhanesTableVars(data_group = 'LAB', nh_table = 'GHB_J', namesonly = FALSE)))
kable(head(nhanesTableVars(data_group = 'Q', nh_table = 'PAQ_J', namesonly = FALSE)))
#Download data 2017 - 2018 demographic data (id, age, gender, educational level, race)
demo_2018<-nhanes('DEMO_J')
names(demo_2018)
demo1<- demo_2018[c("SEQN", "RIAGENDR", "RIDAGEYR", "DMDMARTL", "DMDEDUC2", "RIDRETH3")]
demo_vars<- names(demo1)
demo_2<-nhanesTranslate('DEMO_J', demo_vars, data = demo1)
kable(head(demo_2, n=5))
#Download data diabetes data (id, hba1c)
#labs-hbaic
db_lab_2018<- nhanes('GHB_J')
names(db_lab_2018)
hbaic_lab<- db_lab_2018[c("SEQN","LBXGH")]
hbaic_lab_vars<- names(hbaic_lab)
hbaic_lab_2<-nhanesTranslate('GHB_J', hbaic_lab_vars, data = hbaic_lab)
kable(head(hbaic_lab_2, n=5))
#Download data PA
db_PA_2018<-nhanes('PAQ_J')
names(db_PA_2018)
db_PA_qs<- db_PA_2018[c("SEQN","PAD615","PAD630", "PAD645", "PAD660", "PAD675","PAQ610", "PAQ625", "PAQ640", "PAQ655", "PAQ670")]
db_PA_qs_vars<- names(db_PA_qs)
have_PA_qs_2<-nhanesTranslate('PAQ_J', db_PA_qs_vars, data = db_PA_qs)
kable(head(have_PA_qs_2))
# multiply the METs by the CDC recommended factors
have_PA_qs_2$PAQ610_score <- have_PA_qs_2$PAQ610 *8
have_PA_qs_2$PAQ625_score <- have_PA_qs_2$PAQ625 *4
have_PA_qs_2$PAQ640_score <- have_PA_qs_2$PAQ640 *4
have_PA_qs_2$PAQ655_score <- have_PA_qs_2$PAQ655 *8
have_PA_qs_2$PAQ670_score <- have_PA_qs_2$PAQ670 *8
#PA duration in minute per day
have_PA_qs_2$minutes<-rowSums(cbind(have_PA_qs_2$PAQ610_score , have_PA_qs_2$PAQ625_score ,have_PA_qs_2$PAQ640_score , have_PA_qs_2$PAQ655_score , have_PA_qs_2$PAQ670_score), na.rm=TRUE)
#PA duration in days per week
have_PA_qs_2$days<-rowSums(cbind(have_PA_qs_2$PAD615 , have_PA_qs_2$PAD630 ,have_PA_qs_2$PAD645 , have_PA_qs_2$PAD660 , have_PA_qs_2$PAD675), na.rm=TRUE)
have_PA_qs_2<-have_PA_qs_2 %>%
mutate(met_1 = PAQ610_score * PAD615,
met_2 = PAQ625_score * PAD630,
met_3 = PAQ640_score * PAD645,
met_4 = PAQ655_score * PAD660,
met_5 = PAQ670_score * PAD675)
#cbind- merge PA variables importanat for our analysis
have_PA_qs_2$combined_MET_Scores<- rowSums(cbind(have_PA_qs_2$met_1 , have_PA_qs_2$met_2 ,have_PA_qs_2$met_3 , have_PA_qs_2$met_4 , have_PA_qs_2$met_5), na.rm=TRUE)
have_PA_qs_2$combined_MET_Scores_hrs <- have_PA_qs_2$combined_MET_Scores /60
head(have_PA_qs_2)
PA_Level_MET_Score <- have_PA_qs_2 %>%
dplyr::select("SEQN", "combined_MET_Scores_hrs")
kable(head(PA_Level_MET_Score))
#Download data BMI
db_BMI_2018<-nhanes('BMX_J')
db_BMI_vars<-names(db_BMI_2018)
db_BMI_2018_qs<- db_BMI_2018[c("SEQN","BMXHT","BMXWT")]
db_BMI_2018_2<-nhanesTranslate('BMX_J', db_BMI_vars, data = db_BMI_2018_qs)
kable(head(db_BMI_2018_2))
#merge all datasets
stat_project_analytic_data<- join_all(list(demo_2, hbaic_lab_2, PA_Level_MET_Score, db_BMI_2018_2), by = "SEQN", type = "full")
kable(head(stat_project_analytic_data))
dim(stat_project_analytic_data)
#Data cleaning
#filter age 20-80
stat_project_analytic_data_cleaned <-stat_project_analytic_data %>%
dplyr::filter(RIDAGEYR >= 20 & RIDAGEYR <= 80)
#mutate to rename variables and create BMI variable
stat_project_analytic_data_cleaned <-stat_project_analytic_data %>%
dplyr::filter(RIDAGEYR >= 20 & RIDAGEYR<= 80)%>%
mutate(DMDMARTL = recode_factor(DMDMARTL, "Married" = "Married",
"Widowed" = "Other",
"Refused" = "NA",
"Divorced" = "Other",
"Separated"= "Other",
"Living with partner" = "Other",
"Never married" = "Unmarried"),
DMDEDUC2 = recode_factor(DMDEDUC2, "Don't Know" = "NA",
"Refused" = "NA",
"9-11th grade (Includes 12th grad" = "9-11th grade ",
"High school graduate/GED or equi" = "College graduate or higher",
"Some college or AA degree"= "College graduate or higher",
"College graduate or above" = "College graduate or higher"),
BMXHT = BMXHT/100,
BMI = BMXWT/BMXHT^2,
age.cat = cut(x = RIDAGEYR,
breaks = c(20, 44, 64, 79, Inf),
labels = c("20-44", "45-64", "65-79", ">=80")),
BMI.cat = cut(x = BMI,
breaks = c(0, 18.5, 25, 30, +Inf),
labels = c("<18.5", "18.5-24.9", "25-29.9", ">=30"))
)
#Create hbaic category a
stat_project_analytic_data_cleaned$LBXGH<- ifelse(stat_project_analytic_data_cleaned$LBXGH < 6.5, 0, 1)
stat_project_analytic_data_cleaned$LBXGH<- as.factor(stat_project_analytic_data_cleaned$LBXGH)
#create diabetes status category
stat_project_analytic_data_cleaned$db_status[stat_project_analytic_data_cleaned$LBXGH == "0"]<- "Non_diabetic"
stat_project_analytic_data_cleaned$db_status[stat_project_analytic_data_cleaned$LBXGH == "1"]<- "Diabetic"
stat_project_analytic_data_cleaned$db_status<-as.factor(stat_project_analytic_data_cleaned$db_status)
# EDA
table(stat_project_analytic_data_cleaned$RIAGENDR)
table(stat_project_analytic_data_cleaned$age.cat)
table(stat_project_analytic_data_cleaned$DMDMARTL)
table(stat_project_analytic_data_cleaned$DMDEDUC2)
table(stat_project_analytic_data_cleaned$LBXGH)
table(stat_project_analytic_data_cleaned$BMI.cat)
table(stat_project_analytic_data_cleaned$RIDRETH3)
summary(stat_project_analytic_data_cleaned)
#EDA
par(mfrow = c(2, 3))
hist(stat_project_analytic_data_cleaned$RIDAGEYR)#AGE
hist(stat_project_analytic_data_cleaned$BMI)#BMI
plot(stat_project_analytic_data_cleaned$RIDRETH3)#RACE
plot(stat_project_analytic_data_cleaned$RIAGENDR)#Gender
plot(stat_project_analytic_data_cleaned$LBXGH)#DM status
#select diabetes status, PA levels, and covariates that have been shown to be associated with increased prevalence of diabetes (age, gender, race, bmi).We had pre-determined covariates, so we did not perform correlation analysis.
NHANES_DB_PA_DATA_ANALYSIS<- stat_project_analytic_data_cleaned%>%
dplyr::select(SEQN, RIAGENDR, age.cat, DMDMARTL, DMDEDUC2, RIDRETH3, combined_MET_Scores_hrs, BMI.cat, db_status)
kable(head(NHANES_DB_PA_DATA_ANALYSIS))
#RENAME VARIABLES
NHANES_DB_PA_DATA_ANALYSIS$Age<- NHANES_DB_PA_DATA_ANALYSIS$age.cat
NHANES_DB_PA_DATA_ANALYSIS$marital_status<- NHANES_DB_PA_DATA_ANALYSIS$DMDMARTL
NHANES_DB_PA_DATA_ANALYSIS$education<- NHANES_DB_PA_DATA_ANALYSIS$DMDEDUC2
NHANES_DB_PA_DATA_ANALYSIS$Gender<-NHANES_DB_PA_DATA_ANALYSIS$RIAGENDR
NHANES_DB_PA_DATA_ANALYSIS$race <- NHANES_DB_PA_DATA_ANALYSIS$RIDRETH3
NHANES_DB_PA_DATA_ANALYSIS_2<- NHANES_DB_PA_DATA_ANALYSIS%>%
dplyr::select(SEQN,combined_MET_Scores_hrs, BMI.cat, db_status, Age, Gender, marital_status, education, race)
kable(head(NHANES_DB_PA_DATA_ANALYSIS_2))
#Remove NA's
complete_NHANES_DB_PA_DATA_ANALYSIS_3<-na.omit(NHANES_DB_PA_DATA_ANALYSIS_2)
#subset
#remove rows where column matital status & education is equal to "NA"
complete_NHANES_DB_PA_DATA_ANALYSIS_3 <- subset(complete_NHANES_DB_PA_DATA_ANALYSIS_3, complete_NHANES_DB_PA_DATA_ANALYSIS_3$education != "NA")
complete_NHANES_DB_PA_DATA_ANALYSIS_3 <- subset(complete_NHANES_DB_PA_DATA_ANALYSIS_3, complete_NHANES_DB_PA_DATA_ANALYSIS_3$marital_status != "NA")
summary(complete_NHANES_DB_PA_DATA_ANALYSIS_3)
dim(complete_NHANES_DB_PA_DATA_ANALYSIS_3)
kable(head(complete_NHANES_DB_PA_DATA_ANALYSIS_3))
#write data as a table
write.table(complete_NHANES_DB_PA_DATA_ANALYSIS_3, file = 'complete_NHANES_DB_PA_DATA_ANALYSIS_33.txt', quote = FALSE,sep='\t', col.names = NA, row.names = TRUE)
#Load cleaned data
NHANES_COMPLETE<- read_excel("complete_NHANES_DB_PA_DATA_ANALYSIS_33.xlsx")
typeof(NHANES_COMPLETE)
NHANES_COMPLETE<- as.data.frame(NHANES_COMPLETE)
#EDA
summary(NHANES_COMPLETE)
str(NHANES_COMPLETE)
#convert to variables to factor
NHANES_COMPLETE$BMI.cat <- as.factor(NHANES_COMPLETE$BMI.cat)
NHANES_COMPLETE$db_status <- as.factor(NHANES_COMPLETE$db_status)
NHANES_COMPLETE$Age <- as.factor(NHANES_COMPLETE$Age)
NHANES_COMPLETE$Gender <- as.factor(NHANES_COMPLETE$Gender)
NHANES_COMPLETE$marital_status <- as.factor(NHANES_COMPLETE$marital_status)
NHANES_COMPLETE$race <- as.factor(NHANES_COMPLETE$race)
NHANES_COMPLETE$education <- as.factor(NHANES_COMPLETE$education)
str(NHANES_COMPLETE)
#rename variables again
NHANES_COMPLETE <-NHANES_COMPLETE%>%
dplyr::select(combined_MET_Scores_hrs, BMI.cat, db_status, Age, Gender, marital_status, education, race)%>%
mutate(race = recode_factor(race, "Mexican American" = "Hispanic",
"Other Hispanic" = "Hispanic",
"Other Race - Including Multi-Rac" = "Other/Multiracial",
"Non-Hispanic Asian" = "Asian",
"Non-Hispanic Black" = "Black",
"Non-Hispanic White" = "White"))
reorder_size <- function(x) {
factor(x, levels = names(sort(table(x), decreasing = TRUE)))
}
#create Mets score factor
NHANES_COMPLETE<-NHANES_COMPLETE%>%
mutate(MET.cat = cut(x = combined_MET_Scores_hrs,
breaks = c(-Inf,0, 1000, +Inf),
labels = c("No_physical_activity", "mod_PA_level", "High_PA_level"))
)
summary(NHANES_COMPLETE)
# More EDA
ggplot(NHANES_COMPLETE, aes(x = reorder_size(BMI.cat)))+
geom_bar(color = "blue", fill="blue")+
facet_grid(~ db_status)+
theme_minimal()+
xlab("BMI Category")+
scale_x_discrete( guide = guide_axis(angle = 70))
ggplot(NHANES_COMPLETE, aes(x = reorder_size(Age)))+
geom_bar(color = "blue", fill="blue")+
facet_grid(~ db_status)+
theme_minimal()+
xlab("Age Category")+
scale_x_discrete( guide = guide_axis(angle = 70))
ggplot(NHANES_COMPLETE, aes(x = reorder_size(db_status)))+
geom_bar(color = "blue", fill="blue")+
theme_minimal()+
xlab("DM Status")+
scale_x_discrete( guide = guide_axis(angle = 70))
ggplot(NHANES_COMPLETE, aes(x = reorder_size(MET.cat)))+
geom_bar(color = "blue", fill="blue")+
facet_grid(~ db_status)+
theme_minimal()+
xlab("METscore Category")+
scale_x_discrete( guide = guide_axis(angle = 70))
ggplot(NHANES_COMPLETE, aes(marital_status))+
geom_bar(color = "blue", fill="blue")+
facet_grid(~ db_status)+
theme_minimal()+
xlab("Marital status")+
scale_x_discrete( guide = guide_axis(angle = 70))
ggplot(NHANES_COMPLETE, aes(x = reorder_size(race)))+
geom_bar(color = "blue", fill="blue")+
facet_grid(~ db_status)+
theme_minimal()+
xlab("Race")+
scale_x_discrete( guide = guide_axis(angle = 70))
ggplot(NHANES_COMPLETE, aes(x = reorder_size(Gender)))+
geom_bar(color = "blue", fill="blue")+
facet_grid(~ db_status)+
theme_minimal()+
xlab("Gender")+
scale_x_discrete( guide = guide_axis(angle = 70))
#normality assumption
##shapiro-wilks and qqplots
#shapiro
shapiro.test(NHANES_COMPLETE$combined_MET_Scores_hrs)
#qqplots
ggqqplot(NHANES_COMPLETE$combined_MET_Scores_hrs)
#other variables are categorical so normality testing was not performed. the METs scores were not normally distributed as seen by the shapiro-wilks test (p , 0.05) and qq-plots results
#Ho= there is no significant association between PA level and diabetes status
#Ha= there is a significant association between PA level and diabetes status
# chi-square for categorical variables
chisq.test(NHANES_COMPLETE$BMI.cat, NHANES_COMPLETE$db_status)
chisq.test(NHANES_COMPLETE$Age, NHANES_COMPLETE$db_status)
chisq.test(NHANES_COMPLETE$Gender, NHANES_COMPLETE$db_status)
chisq.test(NHANES_COMPLETE$marital_status, NHANES_COMPLETE$db_status)
chisq.test(NHANES_COMPLETE$education, NHANES_COMPLETE$db_status)
chisq.test(NHANES_COMPLETE$race, NHANES_COMPLETE$db_status)
chisq.test(NHANES_COMPLETE$MET.cat, NHANES_COMPLETE$db_status)
#T-test
t.test( NHANES_COMPLETE$combined_MET_Scores_hrs ~ NHANES_COMPLETE$db_status)
#create table one. Categorical Chisquare, continous t-test
CreateTableOne(data= NHANES_COMPLETE, strata = "db_status", vars = c("Age", "Gender", "BMI.cat","marital_status", "education", "race", "MET.cat", "combined_MET_Scores_hrs" ))
#Model1- Model I only comparing diabetes to physical activity
logit1<- glm(formula= db_status~MET.cat, data= NHANES_COMPLETE, family='binomial')
summary(logit1)
odds.n.ends(logit1)
rocplot(logit1)
#Model2 comparing diabetes to physical activity, adjusted for baseline gender, age, and race
logit2<- glm(formula= db_status~MET.cat+Gender+Age+race, data = NHANES_COMPLETE, family='binomial'(link=logit))
summary(logit2)
odds.n.ends(logit2)
rocplot(logit2)
# Model3 comparing diabetes to physical activity,adjusted for baseline gender, age, race, education level, marital status, BMI
logit3<- glm(formula = db_status~ MET.cat+Gender+Age+race+marital_status+education+BMI.cat, data= NHANES_COMPLETE, family='binomial')
summary(logit3)
odds.n.ends(logit3)
rocplot(logit3)
#females subgroup
female_NHANES_COMPLETE<- NHANES_COMPLETE%>%
filter(Gender == "Female")
female_NHANES_COMPLETE <-female_NHANES_COMPLETE%>%
dplyr::select( combined_MET_Scores_hrs, BMI.cat, db_status, Age, Gender, marital_status, education, race)%>%
mutate(race = recode_factor(race, "Mexican American" = "Hispanic",
"Other Hispanic" = "Hispanic",
"Other Race - Including Multi-Rac" = "Other/Multiracial",
"Non-Hispanic Asian" = "Asian",
"Non-Hispanic Black" = "Black",
"Non-Hispanic White" = "White"))
reorder_size <- function(x) {
factor(x, levels = names(sort(table(x), decreasing = TRUE)))
}
#create Mets score factor
female_NHANES_COMPLETE<-female_NHANES_COMPLETE%>%
mutate(MET.cat = cut(x = combined_MET_Scores_hrs,
breaks = c(-Inf,0, 1000, +Inf),
labels = c("No_physical_activity", "mod_PA_level", "High_PA_level"))
)
summary(female_NHANES_COMPLETE)
#Female log regression
#model1
Flogit1<- glm(formula= db_status~MET.cat, data= female_NHANES_COMPLETE, family='binomial')
summary(Flogit1)
odds.n.ends(Flogit1)
rocplot(Flogit1)
#Model2 comparing diabetes to physical activity, adjusted for baseline age, and race
GFlogit2<- glm(formula= db_status~MET.cat+Age+race, data = female_NHANES_COMPLETE, family='binomial'(link=logit))
summary(GFlogit2)
odds.n.ends(GFlogit2)
rocplot(GFlogit2)
# Model3 comparing diabetes to physical activity,adjusted for baseline age, race, education level, marital status, BMI
Flogit3<- glm(formula = db_status~ MET.cat+Age+race+marital_status+education+BMI.cat, data= female_NHANES_COMPLETE, family='binomial')
summary(Flogit3)
odds.n.ends(Flogit3)
rocplot(Flogit3)
#Males subgroup
male_NHANES_COMPLETE<- NHANES_COMPLETE%>%
filter(Gender == "Male")
male_NHANES_COMPLETE <-male_NHANES_COMPLETE%>%
dplyr::select(combined_MET_Scores_hrs, BMI.cat, db_status, Age, Gender, marital_status, education, race)%>%
mutate(race = recode_factor(race, "Mexican American" = "Hispanic",
"Other Hispanic" = "Hispanic",
"Other Race - Including Multi-Rac" = "Other/Multiracial",
"Non-Hispanic Asian" = "Asian",
"Non-Hispanic Black" = "Black",
"Non-Hispanic White" = "White"))
reorder_size <- function(x) {
factor(x, levels = names(sort(table(x), decreasing = TRUE)))
}
#create Mets score factor
male_NHANES_COMPLETE<-male_NHANES_COMPLETE%>%
mutate(MET.cat = cut(x = combined_MET_Scores_hrs,
breaks = c(-Inf,0, 1000, +Inf),
labels = c("No_physical_activity", "mod_PA_level", "High_PA_level"))
)
summary(male_NHANES_COMPLETE)
#Males log regression
#Model1
Mlogit1<- glm(formula= db_status~MET.cat, data= male_NHANES_COMPLETE, family='binomial')
summary(Mlogit1)
odds.n.ends(Mlogit1)
rocplot(Mlogit1)
#Model2 comparing diabetes to physical activity, adjusted for baseline gender, age, and race
GFlogit2<- glm(formula= db_status~MET.cat+Age+race, data = male_NHANES_COMPLETE, family='binomial'(link=logit))
summary(GFlogit2)
odds.n.ends(GFlogit2)
rocplot(GFlogit2)
# Model3 comparing diabetes to physical activity,adjusted for baseline gender, age, race, education level, marital status, BMI
Mlogit3<- glm(formula = db_status~ MET.cat+Age+race+marital_status+education+BMI.cat, data= male_NHANES_COMPLETE, family='binomial')
summary(Mlogit3)
odds.n.ends(Mlogit3)
rocplot(Mlogit3)