R Notebook
Concept of study
Theoretical concept for study
data_clean <- read_sav("C:/Users/binht/Dropbox/Hue/data/2022_data_consultant/01 DD 1/data_clean.sav")- There are some specific variables using in this analysis
names(data_clean) [1] "ID" "C1.Name" "C2.Medical_record_code"
[4] "C3.Gender" "C4.1Address" "C4.2Phone_number"
[7] "C5.Age" "NHOMTUOI" "C6.Education_level"
[10] "C7.Occupation" "C8.Living_with" "C9.1Caregiver_myself"
[13] "C9.2Caregiver_spouse" "C9.3.Caregiver_children" "C10.1First_year_diagnosis"
[16] "C10.2Number_of_year_disease" "PHANLOAINAMMACBENH" "C11.Comobidity"
[19] "C11.1What_comobidity" "C11.TANGHUYETAP" "C11SUYTIM"
[22] "C11VIEMDADAY" "C11DTD" "C11KHAC"
[25] "C12.1.metered_dose_inhaler" "C12.2.Turberhaler" "C12.3.Accuhaler"
[28] "C13.Number_year_use_medicine" "C14.Received_instruction" "Phanloainamdungthuoc"
[31] "C15.1.Instructors_health_care_worker" "C15.2.Instructors_radio_television" "C15.3.Instructors_Internet"
[34] "C15.4.Instructors_reading_guideline" "C15.5.Instructors_Poster" "C15.5.Instructors_Another_person"
[37] "C15.5.Instructors_other" "C16.Smoking_past" "C17.Smoking_present"
[40] "sogoi" "phanloai" "solannhapvien"
[43] "phanloaisolannhapvien" "sodotcap" "phanloaidotcap"
[46] "Mucdotacnghen" "II.Knowledge.1" "II.2"
[49] "II.3" "II.4" "II.5"
[52] "II.6" "II.7" "II.8"
[55] "II.9" "II.10" "II.11"
[58] "II.12" "II.13" "TONG_KIENTHUCCHUNG"
[61] "III.1" "III.2" "III.3"
[64] "III.4" "III.5" "III.6"
[67] "III.7" "III.8" "III.9"
[70] "III.10" "III.11" "III.12"
[73] "III.13" "TONG_LYTHUYET" "TONGYDINH"
[76] "TONGATB" "TONGSN" "TONGPBC"
[79] "IV.1.1" "IV.1.2" "IV.1.3"
[82] "IV.1.4" "IV.1.5" "IV.1.6"
[85] "IV.2.1" "IV.2.2" "IV.2.3"
[88] "IV.2.4" "IV.2.5" "IV.2.6"
[91] "IV.3.1" "IV.3.2" "IV.3.3"
[94] "IV.3.4" "IV.3.5" "IV.3.6"
[97] "DIEMTHUCHANH" "PL_THUCHANH" "V.1.MMAS8"
[100] "V.2" "V.3" "V.4"
[103] "V.5" "V.6" "V.7"
[106] "V.8" "TONGMMAS8" "PL_MMAS8"
[109] "VI.1TAI" "VI.2" "VI.3"
[112] "VI.4" "VI.5" "VI.6"
[115] "VI.7" "VI.8" "VI.9"
[118] "VI.10" "TONGTAI10ITEMS" "phantai3new"
[121] "VI.11" "VI.12" "TONGTAI15"
[124] "PHANLAOI15" "TONGTAI610" "PHANLOAI610"
[127] "TONGTAI1112" "PHANLOAI1112" "PL_TAI3GROUP"
[130] "TONGmMRC" "PHANLOAIMmrc" "CAT1"
[133] "CAT2" "CAT3" "CAT4"
[136] "CAT5" "CAT6" "CAT7"
[139] "CAT8" "TONGCAT" "PHANCAT"
[142] "MMASgroup2" "TAI2group" "TUANTHUKHAM" 1.1. Personal factors:
+ Current smoking: STT 39 C17.Smoking_present
+ Knowledge : STT: 60 TONG_KIENTHUCCHUNG
+ Smoking technique STT: 98 PL_THUCHANH
1.2. Independent variables
Covars 1: Social Supportive: STT 12,13,1,4 C9.1Caregiver_myself; C9.2Caregiver_spouse; C9.3.Caregiver_children
Covars 2: mMRC: 130 TONGCAT
CAT: STT 140 PHANLOAIMmrc
Group 1: TONGYDINH ; TONGATB ; TONGSN
1.3. Mediators: Intention : 74 TONG_LYTHUYET
1.4. Outcome Treatment adherence : Tuân thủ điều trị:143 TAI2group
data <- subset(data_clean, select = c("C9.1Caregiver_myself", "C9.2Caregiver_spouse", "C9.3.Caregiver_children","TONG_LYTHUYET", "C17.Smoking_present", "TONG_KIENTHUCCHUNG", "PL_THUCHANH", "TONGmMRC", "TONGCAT", 'TONGYDINH',"TONGATB" , "TONGSN", "TAI2group"))Define the variables
- Descriptive results
library(compareGroups)t1=compareGroups(TAI2group~C17.Smoking_present+TONG_KIENTHUCCHUNG+PL_THUCHANH++ C9.1Caregiver_myself+C9.2Caregiver_spouse+C9.3.Caregiver_children+TONGmMRC+TONGYDINH+TONGATB+TONGSN+TONG_LYTHUYET, data=data)Warning: Chi-squared approximation may be incorrectTable 1. Distribution for selected variables and associations with outcome
createTable(t1)
--------Summary descriptives table by '01'---------
__________________________________________________________
0 1 p.overall
N=265 N=155
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
C17.Smoking_present: 0.048
Yes 139 (52.5%) 65 (41.9%)
No 126 (47.5%) 90 (58.1%)
TONG_KIENTHUCCHUNG 9.30 (3.43) 10.7 (2.62) <0.001
PL_THUCHANH: <0.001
Chua thuc hanh dung 195 (73.6%) 73 (47.1%)
Thuc hanh dung 70 (26.4%) 82 (52.9%)
C9.1Caregiver_myself: 0.245
yes 259 (97.7%) 148 (95.5%)
no 6 (2.26%) 7 (4.52%)
C9.2Caregiver_spouse: 0.047
yes 195 (73.6%) 99 (63.9%)
no 70 (26.4%) 56 (36.1%)
C9.3.Caregiver_children: 0.085
yes 22 (8.33%) 22 (14.2%)
no 242 (91.7%) 133 (85.8%)
TONGmMRC 2.68 (0.58) 2.45 (0.67) <0.001
C17.Smoking_present: 0.048
Yes 139 (52.5%) 65 (41.9%)
No 126 (47.5%) 90 (58.1%)
TONGYDINH 2.26 (0.45) 4.23 (0.51) <0.001
TONGATB 7.06 (1.54) 14.5 (1.17) <0.001
TONGSN 11.4 (2.49) 29.3 (2.49) <0.001
TONG_LYTHUYET 27.8 (5.73) 62.4 (4.78) <0.001
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ Model Model 1 (mediator ~ indepent+covariates)
Model 2 (Ourcome ~ all)
model.m <- lm(TONG_LYTHUYET ~ C17.Smoking_present+TONG_KIENTHUCCHUNG+PL_THUCHANH++ C9.1Caregiver_myself+C9.2Caregiver_spouse+C9.3.Caregiver_children+TONGmMRC+TONGYDINH+TONGATB+TONGSN, data = data)
model.y <- glm(TAI2group ~ TONG_LYTHUYET + C17.Smoking_present+TONG_KIENTHUCCHUNG+PL_THUCHANH++ C9.1Caregiver_myself+C9.2Caregiver_spouse+C9.3.Caregiver_children+TONGmMRC+TONGYDINH+TONGATB+TONGSN, data = data, family="binomial")
out.1 <- mediate(model.m, model.y, sims = 1000, boot = TRUE, treat = "C9.2Caregiver_spouse",
mediator = "TONG_LYTHUYET")
out.2 <- mediate(model.m, model.y, sims = 1000, treat = "C9.2Caregiver_spouse",
mediator = "TONG_LYTHUYET")
summary(out.1)summary(out.2)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