# poster size: print at 63" x 31.5" knitr::opts_chunk$set(collapse = TRUE, echo = FALSE, message = FALSE, warning = FALSE) library(tidyverse) devtools::load_all() load_data(update = TRUE) set_options()
ds <- project_data dsBase <- ds %>% dplyr::filter(fVN == "Baseline") dsComplete <- ds %>% dplyr::group_by(SID) %>% dplyr::filter(n() == 3) %>% dplyr::ungroup()
## Include captions below using `captioner` package figNums <- captioner::captioner(prefix = 'Figure') supFigNums <- captioner::captioner(prefix = 'Supplementary Figure') tabNums <- captioner::captioner(prefix = 'Table') supTabNums <- captioner::captioner(prefix = 'Supplementary Table')
Subject characteristics across visit numbers
subCharTable <- tableone::CreateTableOne( vars = c("Age", "Sex", "Ethnicity", "BMI", "Waist", "eGFR", "ACR", "UrineCreatinine", "UrineMicroalbumin", "UrinaryCalcium", "UDBP", "Creatinine", "VitaminD", "PTH", "ALT", "Systolic", "Diastolic", "MeanArtPressure", "Glucose0", "Glucose120", "dmStatus", "SmokeCigs"), strata = c("UDBPStatus"), data = ds, factorVars = c("Sex", "SmokeCigs") ) %>% print(nonnormal = c("Glucose0", "Glucose120", "ALT", "UDBP", "ACR")) %>% knitr::kable()
# Box plot of uVDBP in different albuminuria categories ds %>% filter(fVN == "Baseline") %>% select(acrStatus, udbpCrRatio) %>% na.omit() %>% box_plot_poster("acrStatus", "log(udbpCr)", "Albuminuria", "log uVDBP:Creatinine") # ANOVA anova <- aov(formula = log(udbpCrRatio)~acr_status, data = ds1) summary(anova) TukeyHSD(anova) rm(anova)
# Scatterplot of ACR and uVDBP ---------------------------------- ds %>% filter(fVN == "Baseline") %>% scatter_plot_poster("log(ACR)", "log(udbpCr)", "log Albumin:Creatinine Ratio", "log UDBP:Creatinine") # Spearman Correlation ------------------------------------------ ds %>% filter(fVN == "Baseline") %>% cor.test(formula = ~ ACR + udbpCr, data = ., method = "spearman")
# Boxplot of uVDBP concentrations across eGFR categories -------------- ds %>% filter(fVN == "Baseline") %>% select(eGFRStatus, udbpCrRatio) %>% na.omit() %>% box_plot_poster("eGFRStatus", "log(udbpCrRatio)", "Estimated GFR (ml/min/1.73m^2)", "log uVDBP:Creatinine") # ANOVA anova <- aov(formula = log(udbpCrRatio)~eGFR_status, data = ds1) summary(anova) TukeyHSD(anova) rm(anova)
# Scatterplot of eGFR and uVDBP ---------------------------------- ds %>% dplyr::filter(fVN == "Baseline") %>% scatter_plot_poster("eGFR", "log(udbpCr)", "Estimated Glomerular Filtration Rate (ml/min/1.73m^2)", "log UDBP:Creatinine") # Spearman Correlation ------------------------------------------ ds %>% filter(!(acrStatus == "Macroalbuminuria")) %>% cor.test(formula = ~ eGFR + udbpCr, data = ., method = "spearman")
ds %>% dplyr::filter(eGFR < 45) %>% dplyr::select(SID, VN, Age, eGFR, dm_status, acr_status) ds %>% scatter_plot("Age", "eGFR", "Age", "eGFR")
gee <- ds %>% dplyr::mutate( udbpBase = ifelse(fVN == "Baseline", UDBP, NA), ageBase = ifelse(fVN == "Baseline", Age, NA), DM = ifelse(DM == 1, "diabetes", "non_dia"), fDM = relevel(as.factor(DM), "non_dia"), Ethnicity = ifelse(Ethnicity == "European", Ethnicity, "Other"), Ethnicity = relevel(as.factor(Ethnicity), "Other") ) %>% dplyr::filter(!(fVN == "Baseline" & acrStatus == "Macroalbuminuria")) %>% dplyr::filter(!(fVN == "Baseline" & eGFRStatus == "Moderate")) %>% dplyr::arrange(SID, fVN) %>% dplyr::group_by(SID) %>% tidyr::fill(udbpBase, ageBase) %>% dplyr::ungroup() %>% dplyr::mutate(UDBP = UDBP/1000) %>% dplyr::arrange(SID, VN) %>% mason::design("gee") %>% mason::add_settings( family = stats::gaussian(), corstr = 'ar1', cluster.id = 'SID' ) %>% mason::add_variables("yvars", c("ACR", "eGFR")) %>% mason::add_variables("xvars", "UDBP") %>% mason::add_variables("covariates", c("VN", "ageBase", "Sex", "Ethnicity", "BMI", "fDM")) %>% mason::construct() %>% mason::scrub() %>% dplyr::select(Yterms, Xterms, term, estimate, conf.low, conf.high, p.value)
gee %>% dplyr::mutate(Xterms = term) %>% dplyr::filter(!term == "(Intercept)") %>% dplyr::mutate(Yterms = factor(Yterms, levels = c("ACR", "eGFR"), ordered = TRUE), Xterms = factor(Xterms, levels = rev(c("<-Xterm", "VN", "ageBase", "SexMale", "EthnicityEuropean", "BMI", "fDMdiabetes")), labels = rev(c("uVDBP (ug/mL)", "Follow-up Duration (Years)", "Baseline Age (Years)", "Sex (male)", "Ethnicity (European)", "BMI (kg/m^2)", "Diabetes")), ordered = TRUE)) %>% arrange(Xterms) %>% gee_plot(xlab = "Unit difference with 95% CI in outcome for every unit increase in uVDBP and covariates")
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