Results

Loading libraries

source("statis2.R")
load("../CLSA_Data.RData")
load("Subsets.RData")

Selecting variables of interest

(Can be done through the GUI)

Variables = bind_cols(
  CLSA_Data %>%
     select(Match_Age = AGE_NMBR_COM,
            Match_Sex = SEX_ASK_COM,
            Match_BMI_Class = HWT_DISW_COM,
            Demo_Drinking = ALC_FREQ_COM,
            Demo_Phys_Health = GEN_HLTH_COM,
            Demo_Ment_Health = GEN_MNTL_COM,
            Demo_BMI = HWT_DBMI_COM,
            Demo_Weight = WGT_WEIGHT_KG_COM,
            starts_with("NUR")),
CLSA_Data %>%
     select(starts_with("NUT")) %>%
     select(-ends_with("NB_COM"))
)
Variables %<>% 
  mutate_at(vars(NUR_DHNR_MCQ, NUR_DSCR_MCQ, NUT_FBR_COM:NUT_CADR_COM), 
            funs(if_else(. >= 98, as.integer(NA), .))) %>% 
  mutate(Demo_Drinking = if_else(Demo_Drinking == 96, 8, Demo_Drinking)) %>% 
  mutate(Match_BMI_Class = case_when(Match_BMI_Class == 1 | Match_BMI_Class == 2 ~ 1,
                                     Match_BMI_Class == 3 ~ 2,
                                     Match_BMI_Class == 4 ~ 3,
                                     Match_BMI_Class == 5 | Match_BMI_Class == 6 ~ 4,
                                     TRUE ~ as.double(Match_BMI_Class)))

Matching

CLSA_Matched = match_control(Subsets, Variables)

Descriptive Statistics

OA cases

CLSA_Matched %>%
  filter(Origin == "Original") %>% 
  select(-c(Origin, Demo_Drinking, Demo_Ment_Health, Demo_Phys_Health, Match_BMI_Class,    
            Match_Sex),
         Demo_Age = Match_Age) %>% 
  group_by(Subset) %>%
  do(describe(select(., -Subset))) %>%
  ungroup() %>% 
  select(-c(Normal, P_Value, Skew, Kurt, S_Size)) %>% 
  separate(Var, c("VarGroup", "Var"), "_", extra = "merge") %>% 
  arrange(VarGroup) %T>% 
  write.table(sep = "\t", "DS.tsv", row.names = F)

Match cases

CLSA_Matched %>%
  filter(Origin == "Matched") %>% 
  select(-c(Origin, Demo_Drinking, Demo_Ment_Health, Demo_Phys_Health, Match_BMI_Class,    
            Match_Sex),
         Demo_Age = Match_Age) %>% 
  group_by(Subset) %>%
  do(describe(select(., -Subset))) %>%
  ungroup() %>% 
  select(-c(Normal, P_Value, Skew, Kurt, S_Size)) %>% 
  separate(Var, c("VarGroup", "Var"), "_", extra = "merge") %>% 
  arrange(VarGroup) %T>% 
  write.table(sep = "\t", "DS2.tsv", row.names = F)

Wilcoxon test -based Matched Analysis

p-value < 0.05

Prepairing Dataset

AR = CLSA_Matched %>%
  select(-c(Demo_BMI, Demo_Weight)) %>% 
  filter(!is.na(Match_BMI_Class)) %>%
  group_by(Subset, Match_BMI_Class) %>% 
  do(match_analysis(.))

MR = AR %>% 
  mutate(Adj_Diff = if_else(Signif, true = Mean_Diff, false = 0, missing = 0)) %>% 
  group_by(Match_BMI_Class, VarGroup, Variable) %>% 
  summarise(Total_Diff = sum(Adj_Diff)) %>% 
  filter(VarGroup != "Match") %>% 
  ungroup()
print(MR)

Plotting Results

plot_wil = function(mData){
  ggplot(mData, aes(x = Match_BMI_Class, y = Variable,  fill = Total_Diff)) +
    ggtitle(paste(mData$VarGroup, "Variable Differences")) +
    theme(plot.title = element_text(hjust = 0.5)) +
    geom_tile() +
    scale_fill_gradient2(low = "red", mid = "black", high = "blue")
}
MR %>%
  group_by(VarGroup) %>% 
  do(p = plot_wil(.)) %>% 
  .$p

[[1]]

[[2]]

[[3]]

Bonferroni Corrected p-value

Prepairing Dataset

AR = CLSA_Matched %>%
  select(-c(Demo_BMI, Demo_Weight)) %>% 
  filter(!is.na(Match_BMI_Class)) %>%
  group_by(Subset, Match_BMI_Class) %>% 
  do(match_analysis(., bonCorrect = T))

MR = AR %>% 
  mutate(Adj_Diff = if_else(Signif, true = Mean_Diff, false = 0, missing = 0)) %>% 
  group_by(Match_BMI_Class, VarGroup, Variable) %>% 
  summarise(Total_Diff = sum(Adj_Diff)) %>% 
  filter(VarGroup != "Match") %>% 
  ungroup()
print(MR)

Plotting Results

plot_wil = function(mData){
  ggplot(mData, aes(x = Match_BMI_Class, y = Variable,  fill = Total_Diff)) +
    ggtitle(paste(mData$VarGroup, "Variable Differences")) +
    theme(plot.title = element_text(hjust = 0.5)) +
    geom_tile() +
    scale_fill_gradient2(low = "red", mid = "black", high = "blue")
}
MR %>%
  group_by(VarGroup) %>% 
  do(p = plot_wil(.)) %>% 
  .$p

[[1]]

[[2]]

[[3]]