--- title: MMRMG01 subtitle: Plots for Mixed-Effect Model Repeated Measures Analysis --- `s_mmrm` , `g_mmrm_lsmeans` displays for each visit the adjusted means within group and/or difference in adjusted means between groups.`g_mmrm_diagnostic` displays marginal residual plots for evaluating model fit.```{r setup, echo = FALSE} library(dplyr) library(tern.mmrm) library(nestcolor) adsl <- random.cdisc.data::cadsl adqs <- random.cdisc.data::cadqs adqs_f <- adqs %>% dplyr::filter(PARAMCD == "FKSI-FWB" & !AVISIT %in% c("BASELINE")) %>% droplevels() %>% dplyr::mutate(ARM = factor(ARM, levels = c("B: Placebo", "A: Drug X", "C: Combination"))) %>% dplyr::mutate(AVISITN = rank(AVISITN) %>% as.factor() %>% as.numeric() %>% as.factor()) mmrm_results <- fit_mmrm( vars = list( response = "AVAL", covariates = c("STRATA2"), id = "USUBJID", arm = "ARM", visit = "AVISIT" ), data = adqs_f ) ``` ## Least Squares Means: <br/> Estimates Within Groups ### Considering the treatment variable in the model <!-- skip strict because of https://github.com/r-lib/gtable/pull/94 --> ```{r plot1, dev.args = list(pointsize = 6), test = list(plot_v1 = "plot"), opts.label = "skip_test_strict"} plot <- g_mmrm_lsmeans( mmrm_results, select = "estimates", xlab = "Visit" ) plot ``` ### Considering the treatment variable in the model, with lines <!-- skip strict because of https://github.com/r-lib/gtable/pull/94 --> ```{r plot2, dev.args = list(pointsize = 6), test = list(plot_v2 = "plot"), opts.label = "skip_test_strict"} plot <- g_mmrm_lsmeans( mmrm_results, select = "estimates", xlab = "Visit", show_lines = TRUE ) plot ``` ### Considering the treatment variable in the model, with statistics table <!-- skip strict because of https://github.com/r-lib/gtable/pull/94 --> ```{r plot3, dev.args = list(pointsize = 6), fig.height = 7, test = list(plot_v3 = "plot"), opts.label = "skip_test_strict"} plot <- g_mmrm_lsmeans( mmrm_results, select = "estimates", xlab = "Visit", table_stats = c("n", "estimate", "se", "ci"), table_font_size = 4, table_rel_height = 0.6 ) plot ``` ## Least Squares Means: <br/> Contrasts Between Groups `g_mmrm_lsmeans(mmrm_results)` .<!-- skip strict because of https://github.com/r-lib/gtable/pull/94 --> ```{r plot4, dev.args = list(pointsize = 6), test = list(plot_v4 = "plot"), opts.label = "skip_test_strict"} plot <- g_mmrm_lsmeans( mmrm_results, select = "contrasts", titles = c(contrasts = "Contrasts of FKSI-FWB means"), xlab = "Visit" ) plot ``` ## Model Diagnostics: Marginal <br/> Fitted Values vs. Residuals ```{r plot5, dev.args = list(pointsize = 6), test = list(plot_v5 = "plot")} plot <- g_mmrm_diagnostic(mmrm_results) plot ``` ## Model Diagnostics: Normality <br/> of Marginal Residuals ```{r plot6, dev.args = list(pointsize = 6), test = list(plot_v6 = "plot")} plot <- g_mmrm_diagnostic(mmrm_results, type = "q-q-residual") plot ``` ## Data Setup and <br/> Model Fitting ```{r setup} #| code-fold: show ``` ## `teal` App ```{r teal, opts.label = c("skip_if_testing", "app")} library(teal.modules.clinical) ## Data reproducible code data <- teal_data() data <- within(data, { library(dplyr) ADSL <- random.cdisc.data::cadsl ADQS <- random.cdisc.data::cadqs %>% filter(ABLFL != "Y" & ABLFL2 != "Y") %>% filter(AVISIT %in% c("WEEK 1 DAY 8", "WEEK 2 DAY 15", "WEEK 3 DAY 22")) %>% mutate( AVISIT = as.factor(AVISIT), AVISITN = rank(AVISITN) %>% as.factor() %>% as.numeric() %>% as.factor() # making consecutive numeric factor ) }) datanames <- c("ADSL", "ADQS") datanames(data) <- datanames join_keys(data) <- default_cdisc_join_keys[datanames] arm_ref_comp <- list( ARMCD = list( ref = "ARM A", comp = c("ARM B", "ARM C") ) ) ## Reusable Configuration For Modules ADQS <- data[["ADQS"]] ## Setup App app <- init( data = data, modules = modules( tm_a_mmrm( label = "MMRM", dataname = "ADQS", aval_var = choices_selected(c("AVAL", "CHG"), "AVAL"), id_var = choices_selected(c("USUBJID", "SUBJID"), "USUBJID"), arm_var = choices_selected(c("ARM", "ARMCD"), "ARMCD"), visit_var = choices_selected(c("AVISIT", "AVISITN"), "AVISIT"), arm_ref_comp = arm_ref_comp, paramcd = choices_selected( choices = value_choices(ADQS, "PARAMCD", "PARAM"), selected = "FKSI-FWB" ), cov_var = choices_selected(c("BASE", "AGE", "SEX", "BASE:AVISIT"), NULL), conf_level = choices_selected(c(0.95, 0.9, 0.8), 0.95) ) ) ) shinyApp(app$ui, app$server) ``` 
