Literature update

.gitignore

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 .Rhistory
 .RData
 .Ruserdata
+
+/.quarto/

About.qmd

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+---
+title: "About"
+format: html
+---
+
+# Contributors
+
+-   Namita Mishra – analytic coding, content draft, developed project plan, collaborated via GitHub.
+-   Autumn Wilcox – analytic coding, content draft, structured project workflow, collaborated via GitHub.
+
+![](images/clipboard-3361740677.png){width="212"}
+
+Dr. Namita Mishra is a physician, a Head and Neck surgeon and a public health researcher with a strong foundation in medicine, epidemiology, and data science. She is a graduate student in Data Science (Health Analytics).
+
+Her work focuses on early detection and prevention of non-communicable diseases (cancer, obesity) and on health disparities at community level. She has researched salivary gland tumors, cardiac implants and community based research on healthy food access. Leveraging skills from Data Science, she integrates statistical modeling and Bayesian methods into her analyses. Her Bioinformatics expertise utilizes geodata visualization tools (3D Maps and GIS) for presentations.Passionate about bridging clinical insight with data-driven approaches, dedicated to advancing sustainable, evidence-based solutions in epidemiology and community health.
+
+Outside work she explores - gardening, cooking, singing, and sewing.
+
+📧 Contact: nmishra\@uwf.edu
+
+![](images/clipboard-1601697641.jpeg){width="208"}
+
+Autumn S. Wilcox is a U.S. Navy veteran and Data Science graduate student at the University of West Florida, specializing in Analytics and Modeling. She has over nine years of experience in Network Operations and Technical Writing, including her current role at Navy Federal Credit Union, where she supports enterprise technology and process documentation initiatives. Autumn also holds certification in Clinical Research Quality Management (CRQM) and has contributed to quality oversight and compliance efforts in clinical research settings.
+
+Her background bridges technology, analytics, and healthcare, with a focus on applying data-driven approaches to improve communication and systems reliability. Outside of work, Autumn enjoys traveling, photography, and finding creative inspiration through music.
+
+📧 Contact: awr12\@students.uwf.edu

Introduction.qmd

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+---
+title: "Introduction"
+format: html
+bibliography: references.bib
+execute:
+  warning: false
+  message: false
+  echo: false
+---
+
+# Literature Review
+
+Diabetes mellitus (DM) is a major public health concern closely associated with factors such as obesity, age, race, and gender. Identifying these associated risk factors is essential for targeted interventions @DAngelo2025. **Logistic Regression** (traditional) that estimates the association between risk factors and outcomes is insufficient in analyzing the complex healthcare data (DNA sequences, imaging, patient-reported outcomes, electronic health records (EHRs), longitudinal health measurements, diagnoses, and treatments. @Zeger2020. Classical maximum likelihood estimation (MLE) yields unstable results in samples that are small, have missing data, or presents quasi- and complete separation.
+
+Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) allow analysis of multivariate longitudinal healthcare data with repeated measures within individuals and individuals nested in a population. By integrating prior knowledge and including exogenous (e.g., age, clinical history) and endogenous (e.g., current treatment) covariates, Bayesian models provide posterior distributions and risk predictions for conditions such as pneumonia, prostate cancer, and mental disorders. Parametric assumptions remain a limitation of these models.
+
+In Bayesian inference @Chatzimichail2023, Bayesian inference has shown that parametric models (with fixed parameters) often underperform compared to nonparametric models, which do not assume a prior distribution. Posterior probabilities from Bayesian approaches improve disease classification and better capture heterogeneity in skewed, bimodal, or multimodal data distributions. Bayesian nonparametric models are flexible and robust, integrating multiple diagnostic tests and priors to enhance accuracy and precision, though reliance on prior information and restricted access to resources can limit applicability. Combining Bayesian methods with other statistical or computational approaches helps address systemic biases, incomplete data, and non-representative datasets.
+
+The Bayesian framework described by @VandeSchoot2021 highlights the role of priors, data modeling, inference, posterior sampling, variational inference, and variable selection.Proper variable selection mitigates multicollinearity, overfitting, and limited sampling, improving predictive performance. Priors can be informative, weakly informative, or diffuse, and can be elicited from expert opinion, generic knowledge, or data-driven methods. Sensitivity analysis evaluates the alignment of priors with likelihoods, while MCMC simulations (e.g., brms, blavaan in R) empirically estimate posterior distributions. Spatial and temporal Bayesian models have applications in large-scale cancer genomics, identifying molecular interactions, mutational signatures, patient stratification, and cancer evolution, though temporal autocorrelation and subjective prior elicitation can be limiting.
+
+Bayesian normal linear regression has been applied in metrology for instrument calibration using conjugate Normal–Inverse-Gamma priors @Klauenberg2015. Hierarchical priors add flexibility by modeling uncertainty across multiple levels, improving robustness and interpretability. Bayesian hierarchical/meta-analytic linear regression incorporates both exchangeable and unexchangeable prior information, addressing multiple testing challenges, small sample sizes, and complex relationships among regression parameters across studies @DeLeeuw2012a
+
+**A sequential clinical reasoning model** @Liu2013 Sequential clinical reasoning models demonstrate screening by adding predictors stepwise: (1) demographics, (2) metabolic components, and (3) conventional risk factors, incorporating priors and mimicking clinical evaluation. This approach captures ecological heterogeneity and improves baseline risk estimation, though interactions between predictors and external cross-validation remain limitations.
+
+**Bayesian multiple imputation with logistic regression** addresses missing data in clinical research @Austin2021 in clinical research by classifying missing values (e.g., patient refusal, loss to follow-up, mechanical errors) as MAR, MNAR, or MCAR. Multiple imputation generates plausible values across datasets and pools results for reliable classification of patient health status and mortality.
+
+## References

Nhanes/Nhanes script.R

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+# ---------------------- Load packages ----------------------
+options(repos = c(CRAN = "https://cloud.r-project.org"))
+library(nhanesA)
+library(tidyverse)
+library(forcats)
+library(survey)
+library(mice)
+library(brms)
+library(posterior)
+library(knitr)
+
+# ---------------------- Import NHANES data ----------------------
+
+
+bmx_h  <- nhanes("BMX_H")
+demo_h <- nhanes("DEMO_H")
+diq_h  <- nhanes("DIQ_H")
+
+# ---------------------- Select variables ----------------------
+exam_sub <- bmx_h  %>% select(SEQN, BMXBMI)
+need_demo <- c("SEQN","RIDAGEYR","RIAGENDR","RIDRETH1","SDMVPSU","SDMVSTRA","WTMEC2YR")
+demo_sub <- demo_h %>% select(all_of(need_demo))
+diq_sub  <- diq_h %>% select(SEQN, DIQ010, dplyr::any_of("DIQ050"))
+
+# ---------------------- Merge datasets ----------------------
+merged_data <- demo_sub %>%
+  left_join(exam_sub, by = "SEQN") %>%
+  left_join(diq_sub,  by = "SEQN")
+
+# ---------------------- Convert/clean variables ----------------------
+to_num <- function(x) {
+  if (is.numeric(x)) return(x)
+  xc <- as.character(x)
+  n <- suppressWarnings(readr::parse_number(xc))
+  if (mean(is.na(n)) > 0.8) {
+    xlow <- tolower(trimws(xc))
+    n <- dplyr::case_when(
+      xlow %in% c("1","yes","yes, told") ~ 1,
+      xlow %in% c("2","no","no, not told") ~ 2,
+      xlow %in% c("3","borderline") ~ 3,
+      xlow %in% c("7","refused") ~ 7,
+      xlow %in% c("9","don't know","dont know","unknown") ~ 9,
+      TRUE ~ NA_real_
+    )
+  }
+  as.numeric(n)
+}
+
+merged_data <- merged_data %>%
+  mutate(
+    DIQ010   = to_num(DIQ010),
+    DIQ050   = to_num(if (!"DIQ050" %in% names(.)) NA_real_ else DIQ050),
+    BMXBMI   = as.numeric(BMXBMI),
+    RIDAGEYR = as.numeric(RIDAGEYR),
+    RIAGENDR = as.numeric(RIAGENDR),
+    RIDRETH1 = as.numeric(RIDRETH1),
+    SDMVPSU  = as.numeric(SDMVPSU),
+    SDMVSTRA = as.numeric(SDMVSTRA),
+    WTMEC2YR = as.numeric(WTMEC2YR)
+  )
+
+# ---------------------- Filter adults and create analysis variables ----------------------
+adult <- merged_data %>%
+  filter(RIDAGEYR >= 20) %>%
+  transmute(
+    SDMVPSU, SDMVSTRA, WTMEC2YR,
+    diabetes_dx = case_when(
+      DIQ010 == 1 ~ 1,
+      DIQ010 == 2 ~ 0,
+      DIQ010 %in% c(3,7,9) ~ NA_real_
+    ),
+    bmi  = BMXBMI,
+    age  = RIDAGEYR,
+    sex  = fct_recode(factor(RIAGENDR), Male="1", Female="2"),
+    race = fct_recode(factor(RIDRETH1),
+                      "Mexican American"="1",
+                      "Other Hispanic"="2",
+                      "NH White"="3",
+                      "NH Black"="4",
+                      "Other/Multi"="5"),
+    DIQ050 = DIQ050
+  ) %>%
+  mutate(
+    age_c = as.numeric(scale(age)),
+    bmi_c = as.numeric(scale(bmi)),
+    bmi_cat = cut(bmi, breaks = c(-Inf,18.5,25,30,35,40,Inf),
+                  labels=c("<18.5","18.5–<25","25–<30","30–<35","35–<40","≥40"), right=FALSE),
+    diabetes_dx = ifelse(sex=="Female" & !is.na(DIQ050) & DIQ050==1, 0, diabetes_dx)
+  ) %>%
+  mutate(race = fct_relevel(race, "NH White"))
+
+# ---------------------- Survey design ----------------------
+nhanes_design_adult <- svydesign(
+  id = ~SDMVPSU,
+  strata = ~SDMVSTRA,
+  weights = ~WTMEC2YR,
+  nest = TRUE,
+  data = adult
+)
+
+# ---------------------- Survey-weighted logistic regression ----------------------
+keep_cc <- with(adult, !is.na(diabetes_dx) & !is.na(age_c) & !is.na(bmi_c) &
+                  !is.na(sex) & !is.na(race))
+des_cc <- subset(nhanes_design_adult, keep_cc)
+form_cc <- diabetes_dx ~ age_c + bmi_c + sex + race
+svy_fit <- svyglm(formula = form_cc, design = des_cc, family = quasibinomial())
+svy_or <- broom::tidy(svy_fit, conf.int=TRUE) %>%
+  mutate(OR=exp(estimate), LCL=exp(conf.low), UCL=exp(conf.high)) %>%
+  filter(term != "(Intercept)") %>%
+  select(term, OR, LCL, UCL, p.value)
+
+# ---------------------- Multiple Imputation ----------------------
+mi_dat <- adult %>% select(diabetes_dx, age, bmi, sex, race, WTMEC2YR, SDMVPSU, SDMVSTRA)
+meth <- make.method(mi_dat)
+pred <- make.predictorMatrix(mi_dat)
+meth["diabetes_dx"] <- ""
+pred["diabetes_dx", ] <- 0
+meth["age"] <- "norm"
+meth["bmi"] <- "pmm"
+meth["sex"] <- "polyreg"
+meth["race"] <- "polyreg"
+imp <- mice(mi_dat, m=5, method=meth, predictorMatrix=pred, seed=123)
+fit_mi <- with(imp, {
+  age_c <- scale(age)
+  bmi_c <- scale(bmi)
+  glm(diabetes_dx ~ age_c + bmi_c + sex + race, family=binomial())
+})
+pool_mi <- pool(fit_mi)
+mi_or <- summary(pool_mi, conf.int=TRUE, exponentiate=TRUE) %>%
+  filter(term != "(Intercept)")
+
+# ---------------------- Bayesian Logistic Regression ----------------------
+adult_imp1 <- complete(imp, 1) %>%
+  mutate(
+    age_c = scale(age),
+    bmi_c = scale(bmi),
+    wt_norm = WTMEC2YR / mean(WTMEC2YR, na.rm=TRUE),
+    race = fct_relevel(race, "NH White"),
+    sex  = fct_relevel(sex, "Male")
+  ) %>%
+  filter(!is.na(diabetes_dx), !is.na(age_c), !is.na(bmi_c),
+         !is.na(sex), !is.na(race)) %>% droplevels()
+
+priors <- c(
+  set_prior("normal(0, 2.5)", class="b"),
+  set_prior("student_t(3, 0, 10)", class="Intercept")
+)
+
+bayes_fit <- brm(
+  formula = diabetes_dx | weights(wt_norm) ~ age_c + bmi_c + sex + race,
+  data = adult_imp1,
+  family = bernoulli(link="logit"),
+  prior = priors,
+  chains = 4, iter = 2000, seed = 123,
+  control = list(adapt_delta=0.95),
+  refresh = 0
+)
+
+bayes_or <- posterior_summary(bayes_fit, pars="^b_") %>%
+  as.data.frame() %>%
+  tibble::rownames_to_column("raw") %>%
+  mutate(
+    term = gsub("^b_", "", raw),
+    term = gsub("race", "race:", term),
+    term = gsub("sex", "sex:", term),
+    OR = exp(Estimate),
+    LCL = exp(Q2.5),
+    UCL = exp(Q97.5)
+  ) %>%
+  filter(term != "Intercept") %>%
+  select(term, OR, LCL, UCL)
+
+# ---------------------- Save results ----------------------
+dir.create("outputs", showWarnings = FALSE)
+saveRDS(svy_fit, "outputs/svy_fit.rds")
+saveRDS(pool_mi, "outputs/pool_mi.rds")
+saveRDS(bayes_fit, "outputs/bayes_fit.rds")
+saveRDS(svy_or, "outputs/survey_OR_table.rds")
+saveRDS(mi_or, "outputs/mi_OR_table.rds")
+saveRDS(bayes_or, "outputs/bayes_OR_table.rds")