## ----include = FALSE-------------------------------------------------------------------------------------------------------------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>", message = FALSE, warning = FALSE, fig.width = 12, fig.height = 6, dpi = 150 ) # Use ragg for better font rendering if available if (requireNamespace("ragg", quietly = TRUE)) { knitr::opts_chunk$set(dev = "ragg_png") } old_opts <- options(width = 180) ## ----eval = FALSE----------------------------------------------------------------------------------------------------------------------------------------------------------------- # fullfit(data, outcome, predictors, model_type, ...) ## ----setup------------------------------------------------------------------------------------------------------------------------------------------------------------------------ library(summata) library(survival) data(clintrial) data(clintrial_labels) ## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- screening_vars <- c("age", "sex", "race", "bmi", "smoking", "diabetes", "stage", "ecog", "treatment") example1 <- uniscreen( data = clintrial, outcome = "readmission_30d", predictors = screening_vars, model_type = "glm", labels = clintrial_labels ) example1 ## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- example2 <- uniscreen( data = clintrial, outcome = "readmission_30d", predictors = screening_vars, model_type = "glm", p_threshold = 0.01, labels = clintrial_labels ) example2 ## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- example4 <- uniscreen( data = clintrial, outcome = "los_days", predictors = c("age", "sex", "stage", "diabetes", "ecog"), model_type = "lm", labels = clintrial_labels ) example4 ## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- example3 <- uniscreen( data = clintrial, outcome = "Surv(os_months, os_status)", predictors = c("age", "sex", "treatment", "stage", "ecog"), model_type = "coxph", labels = clintrial_labels ) example3 ## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- example5 <- uniscreen( data = clintrial, outcome = "readmission_30d", predictors = c("age", "sex", "stage"), model_type = "glm", keep_models = TRUE ) # Access individual models models <- attr(example5, "models") names(models) # Examine a specific model summary(models[["age"]]) ## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- example6 <- fit( data = clintrial, outcome = "readmission_30d", predictors = c("age", "sex", "treatment", "stage", "diabetes"), model_type = "glm", labels = clintrial_labels ) example6 ## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- example8 <- fit( data = clintrial, outcome = "los_days", predictors = c("age", "sex", "stage", "ecog"), model_type = "lm", labels = clintrial_labels ) example8 ## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- example7 <- fit( data = clintrial, outcome = "Surv(os_months, os_status)", predictors = c("age", "sex", "treatment", "stage"), model_type = "coxph", labels = clintrial_labels ) example7 ## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- example9 <- fit( data = clintrial, outcome = "fu_count", predictors = c("age", "stage", "treatment", "surgery"), model_type = "glm", family = "poisson", labels = clintrial_labels ) example9 ## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- example10 <- fit( data = clintrial, outcome = "readmission_30d", predictors = c("sex", "stage", "treatment"), model_type = "glm", reference_rows = FALSE, labels = clintrial_labels ) example10 ## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- example11 <- fit( data = clintrial, outcome = "readmission_30d", predictors = c("age", "sex", "stage"), model_type = "glm", conf_level = 0.90 ) example11 ## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- # Default CIs (profile likelihood for GLM, slower but more accurate) example12a <- fit( data = clintrial, outcome = "readmission_30d", predictors = c("age", "sex", "stage"), model_type = "glm", conf_method = "profile" ) example12a # Wald CIs (faster, suitable for simulation studies or exploratory work) example12b <- fit( data = clintrial, outcome = "readmission_30d", predictors = c("age", "sex", "stage"), model_type = "glm", conf_method = "wald" ) example12b ## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- example13 <- fit( data = clintrial, outcome = "readmission_30d", predictors = c("age", "sex", "stage"), model_type = "glm", exponentiate = FALSE ) example13 ## ----eval = FALSE----------------------------------------------------------------------------------------------------------------------------------------------------------------- # fullfit(data, outcome, predictors, model_type, method, ...) ## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- example14 <- fullfit( data = clintrial, outcome = "readmission_30d", predictors = c("age", "sex", "bmi", "smoking", "diabetes", "stage", "treatment"), model_type = "glm", method = "screen", p_threshold = 0.05, labels = clintrial_labels ) example14 ## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- example15 <- fullfit( data = clintrial, outcome = "any_complication", predictors = c("age", "sex", "treatment", "stage"), model_type = "glm", method = "all", labels = clintrial_labels ) example15 ## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- example16 <- fullfit( data = clintrial, outcome = "icu_admission", predictors = c("age", "sex", "bmi", "smoking", "stage", "treatment"), model_type = "glm", method = "custom", multi_predictors = c("age", "sex", "stage", "treatment"), labels = clintrial_labels ) example16 ## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- # Univariable only example17a <- fullfit( data = clintrial, outcome = "wound_infection", predictors = c("age", "sex", "stage"), model_type = "glm", columns = "uni" ) example17a # Multivariable only example17b <- fullfit( data = clintrial, outcome = "wound_infection", predictors = c("age", "sex", "stage"), model_type = "glm", columns = "multi" ) example17b ## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- example18 <- fullfit( data = clintrial, outcome = "Surv(os_months, os_status)", predictors = c("age", "sex", "treatment", "stage", "ecog"), model_type = "coxph", method = "screen", p_threshold = 0.10, labels = clintrial_labels ) example18 ## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- example19 <- fit( data = clintrial, outcome = "ae_count", predictors = c("age", "sex", "diabetes", "treatment"), model_type = "negbin", labels = clintrial_labels ) example19 ## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- # String shorthand: resolves to Gamma(link = "log") example20 <- fit( data = clintrial, outcome = "los_days", predictors = c("age", "ecog", "stage", "treatment"), model_type = "glm", family = "Gamma", labels = clintrial_labels ) example20 ## ----eval = requireNamespace("lme4", quietly = TRUE)------------------------------------------------------------------------------------------------------------------------------ example21 <- fit( data = clintrial, outcome = "los_days", predictors = c("age", "sex", "treatment", "stage", "(1|site)"), model_type = "lmer", labels = clintrial_labels ) example21 ## ----eval = requireNamespace("lme4", quietly = TRUE)------------------------------------------------------------------------------------------------------------------------------ example22 <- fit( data = clintrial, outcome = "readmission_30d", predictors = c("age", "sex", "diabetes", "treatment", "(1|site)"), model_type = "glmer", family = "binomial", labels = clintrial_labels ) example22 ## ----eval = requireNamespace("lme4", quietly = TRUE)------------------------------------------------------------------------------------------------------------------------------ example23 <- fit( data = clintrial, outcome = "fu_count", predictors = c("age", "stage", "treatment", "(1|site)"), model_type = "glmer", family = "poisson", labels = clintrial_labels ) example23 ## ----eval = requireNamespace("coxme", quietly = TRUE)----------------------------------------------------------------------------------------------------------------------------- example24 <- fit( data = clintrial, outcome = "Surv(os_months, os_status)", predictors = c("age", "sex", "treatment", "stage", "(1|site)"), model_type = "coxme", labels = clintrial_labels ) example24 ## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- example25 <- fit( data = clintrial, outcome = "any_complication", predictors = c("age", "sex", "diabetes", "stage"), model_type = "glm", family = "quasibinomial", labels = clintrial_labels ) example25 ## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- # Matched case-control dataset (100 pairs) set.seed(456) n_pairs <- 100 matched_data <- do.call(rbind, lapply(1:n_pairs, function(i) { base_bmi <- rnorm(1, 27, 4) data.frame( match_id = i, case = c(1, 0), smoking = factor(c(rbinom(1, 1, 0.45), rbinom(1, 1, 0.30)), levels = c(0, 1), labels = c("No", "Yes")), diabetes = factor(c(rbinom(1, 1, 0.30), rbinom(1, 1, 0.20)), levels = c(0, 1), labels = c("No", "Yes")), bmi = c(base_bmi + rnorm(1, 1.5, 2), base_bmi + rnorm(1, -0.5, 2)) ) })) example26 <- fit( data = matched_data, outcome = "case", predictors = c("smoking", "diabetes", "bmi"), model_type = "clogit", strata = "match_id" ) example26 ## ----eval = FALSE----------------------------------------------------------------------------------------------------------------------------------------------------------------- # # Microsoft Word # table2docx( # table = example13, # file = file.path(tempdir(), "Table2_Regression.docx"), # caption = "Table 2. Univariable and Multivariable Analysis" # ) # # # PDF # table2pdf( # table = example13, # file = file.path(tempdir(), "Table2_Regression.pdf"), # caption = "Table 2. Regression Results" # ) ## ----eval = FALSE----------------------------------------------------------------------------------------------------------------------------------------------------------------- # # Check missingness # sapply(clintrial[, c("age", "sex", "stage")], function(x) sum(is.na(x))) # # # Create complete-case dataset explicitly # complete_data <- na.omit(clintrial[, c("readmission_30d", "age", "sex", "stage")]) ## ----eval = FALSE----------------------------------------------------------------------------------------------------------------------------------------------------------------- # # Set specific reference level # clintrial$stage <- relevel(factor(clintrial$stage), ref = "I") ## ----eval = FALSE----------------------------------------------------------------------------------------------------------------------------------------------------------------- # # Access model for diagnostics # result <- fit(data, outcome, predictors, model_type = "glm") # model <- attr(result, "model") # # # Check convergence # model$converged # # # Large coefficients may indicate separation # coef(model) ## ----eval = FALSE----------------------------------------------------------------------------------------------------------------------------------------------------------------- # # Fit Poisson model # result <- fit(data, outcome, predictors, model_type = "glm", family = "poisson") # model <- attr(result, "model") # # # Dispersion estimate (should be ~1 for no overdispersion) # sum(residuals(model, type = "pearson")^2) / model$df.residual ## ----eval = FALSE----------------------------------------------------------------------------------------------------------------------------------------------------------------- # # Increase iterations # result <- fit( # data = clintrial, # outcome = "readmission_30d", # predictors = c("age", "treatment", "(1|site)"), # model_type = "glmer", # family = "binomial", # control = lme4::glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 100000)) # ) ## ----include = FALSE---------------------------------------------------------- options(old_opts)