--- title: "Example 1: Demographics" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Example 1: Demographics} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r setup, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ``` ## Program The following example shows a complete program. The example illustrates how **procs** functions work together, and interact with other **sassy** functions to create a demographics table with p-value statistics. ```{r eval=FALSE, echo=TRUE} library(sassy) library(procs) # Prepare Log ------------------------------------------------------------- options("logr.autolog" = TRUE, "logr.on" = TRUE, "logr.notes" = FALSE, "procs.print" = FALSE) # Get temp directory tmp <- tempdir() # Open log lf <- log_open(file.path(tmp, "example1.log")) # Prepare formats --------------------------------------------------------- sep("Prepare formats") put("Age categories") agecat <- value(condition(x >= 18 & x <= 29, "18 to 29"), condition(x >=30 & x <= 39, "30 to 39"), condition(x >=40 & x <=49, "40 to 49"), condition(x >= 50, ">= 50"), condition(TRUE, "Out of range")) put("Sex decodes") fmt_sex <- value(condition(is.na(x), "Missing"), condition(x == "M", "Male"), condition(x == "F", "Female"), condition(TRUE, "Other")) put("Race decodes") fmt_race <- value(condition(is.na(x), "Missing"), condition(x == "WHITE", "White"), condition(x == "BLACK", "Black or African American"), condition(TRUE, "Other")) put("Compile format catalog") fc <- fcat(MEAN = "%.1f", STD = "(%.2f)", Q1 = "%.1f", Q3 = "%.1f", MIN = "%d", MAX = "%d", CNT = "%2d", PCT = "(%5.1f%%)", AGECAT = agecat, SEX = fmt_sex, RACE = fmt_race, AOV.F = "%5.3f", AOV.P = "(%5.3f)", CHISQ = "%5.3f", CHISQ.P = "(%5.3f)") # Load and Prepare Data --------------------------------------------------- sep("Prepare Data") put("Create sample ADSL data.") adsl <- read.table(header = TRUE, text = ' SUBJID ARM SEX RACE AGE "001" "ARM A" "F" "WHITE" 19 "002" "ARM B" "F" "WHITE" 21 "003" "ARM C" "F" "WHITE" 23 "004" "ARM D" "F" "BLACK" 28 "005" "ARM A" "M" "WHITE" 37 "006" "ARM B" "M" "WHITE" 34 "007" "ARM C" "M" "WHITE" 36 "008" "ARM D" "M" "WHITE" 30 "009" "ARM A" "F" "WHITE" 39 "010" "ARM B" "F" "WHITE" 31 "011" "ARM C" "F" "BLACK" 33 "012" "ARM D" "F" "WHITE" 38 "013" "ARM A" "M" "BLACK" 37 "014" "ARM B" "M" "WHITE" 34 "015" "ARM C" "M" "WHITE" 36 "016" "ARM A" "M" "WHITE" 40') put("Categorize AGE") adsl$AGECAT <- fapply(adsl$AGE, agecat) put("Log starting dataset") put(adsl) put("Get ARM population counts") proc_freq(adsl, tables = ARM, output = long, options = v(nopercent, nonobs)) -> arm_pop # Age Summary Block ------------------------------------------------------- sep("Create summary statistics for age") put("Call means procedure to get summary statistics for age") proc_means(adsl, var = AGE, stats = v(n, mean, std, median, q1, q3, min, max), by = ARM, options = v(notype, nofreq)) -> age_stats put("Combine stats") datastep(age_stats, format = fc, drop = find.names(age_stats, start = 4), { `Mean (SD)` <- fapply2(MEAN, STD) Median <- MEDIAN `Q1 - Q3` <- fapply2(Q1, Q3, sep = " - ") `Min - Max` <- fapply2(MIN, MAX, sep = " - ") }) -> age_comb put("Transpose ARMs into columns") proc_transpose(age_comb, var = names(age_comb), copy = VAR, id = BY, name = LABEL) -> age_trans put("Calculate aov") age_aov <- aov(AGE ~ ARM, data = adsl) |> summary() put("Get aov into proper data frame") age_aov <- age_aov[[1]][1, c("F value", "Pr(>F)")] names(age_aov) <- c("AOV.F", "AOV.P") age_aov <- as.data.frame(age_aov) |> put() put("Combine aov statistics") datastep(age_aov, keep = PVALUE, format = fc, { PVALUE <- fapply2(AOV.F, AOV.P) }) -> age_aov_comb put("Append aov") datastep(age_trans, merge = age_aov_comb, {}) -> age_block # Sex Block --------------------------------------------------------------- sep("Create frequency counts for SEX") put("Get sex frequency counts") proc_freq(adsl, tables = SEX, by = ARM, options = nonobs) -> sex_freq put("Combine counts and percents.") datastep(sex_freq, format = fc, rename = list(CAT = "LABEL"), drop = v(CNT, PCT), { CNTPCT <- fapply2(CNT, PCT) }) -> sex_comb put("Transpose ARMs into columns") proc_transpose(sex_comb, id = BY, var = CNTPCT, copy = VAR, by = LABEL) -> sex_trans put("Clean up") datastep(sex_trans, drop = NAME, { LABEL <- fapply(LABEL, fc$SEX) LABEL <- factor(LABEL, levels = levels(fc$SEX)) }) -> sex_cnts put("Sort by label") proc_sort(sex_cnts, by = LABEL) -> sex_cnts put("Get sex chisq") proc_freq(adsl, tables = v(SEX * ARM), options = v(chisq, notable)) -> sex_chisq put("Combine chisq statistics") datastep(sex_chisq, format = fc, keep = PVALUE, { PVALUE = fapply2(CHISQ, CHISQ.P) }) -> sex_chisq_comb put("Append chisq") datastep(sex_cnts, merge = sex_chisq_comb, {}) -> sex_block # Race block -------------------------------------------------------------- sep("Create frequency counts for RACE") put("Get race frequency counts") proc_freq(adsl, tables = RACE, by = ARM, options = nonobs) -> race_freq put("Combine counts and percents.") datastep(race_freq, format = fc, rename = list(CAT = "LABEL"), drop = v(CNT, PCT), { CNTPCT <- fapply2(CNT, PCT) }) -> race_comb put("Transpose ARMs into columns") proc_transpose(race_comb, id = BY, var = CNTPCT, copy = VAR, by = LABEL) -> race_trans put("Clean up") datastep(race_trans, drop = NAME, where = expression(del == FALSE), { LABEL <- fapply(LABEL, fc$RACE) LABEL <- factor(LABEL, levels = levels(fc$RACE)) }) -> race_cnts put("Sort by label") proc_sort(race_cnts, by = LABEL) -> race_cnts put("Get race chisq") proc_freq(adsl, tables = RACE * ARM, options = v(chisq, notable)) -> race_chisq put("Combine chisq statistics") datastep(race_chisq, format = fc, keep = c("PVALUE"), { PVALUE = fapply2(CHISQ, CHISQ.P) }) -> race_chisq_comb put("Append chisq") datastep(race_cnts, merge = race_chisq_comb, {}) -> race_block # Age Group Block ---------------------------------------------------------- sep("Create frequency counts for Age Group") put("Get age group frequency counts") proc_freq(adsl, table = AGECAT, by = ARM, options = nonobs) -> ageg_freq put("Combine counts and percents and assign age group factor for sorting") datastep(ageg_freq, format = fc, keep = v(VAR, LABEL, BY, CNTPCT), { CNTPCT <- fapply2(CNT, PCT) LABEL <- factor(CAT, levels = levels(fc$AGECAT)) }) -> ageg_comb put("Sort by age group factor") proc_sort(ageg_comb, by = v(BY, LABEL)) -> ageg_sort put("Tranpose age group block") proc_transpose(ageg_sort, var = CNTPCT, copy = VAR, id = BY, by = LABEL) -> ageg_trans put("Some clean up") datastep(ageg_trans, drop = NAME, {}) -> ageg_cnts put("Get ageg chisq") proc_freq(adsl, tables = AGECAT * ARM, options = v(chisq, notable)) -> ageg_chisq put("Combine chisq statistics") datastep(ageg_chisq, format = fc, keep = c("PVALUE"), { PVALUE = fapply2(CHISQ, CHISQ.P) }) -> ageg_chisq_comb put("Append chisq") datastep(ageg_cnts, merge = ageg_chisq_comb, {}) -> ageg_block put("Combine blocks into final data frame") datastep(age_block, set = list(ageg_block, sex_block, race_block), {}) -> final # Report ------------------------------------------------------------------ sep("Create and print report") var_fmt <- c("AGE" = "Age", "AGECAT" = "Age Group", "SEX" = "Sex", "RACE" = "Race") plbl <- "Tests of Association{supsc('1')}\n Value (P-Value)" # Create Table tbl <- create_table(final, first_row_blank = TRUE) |> column_defaults(from = `ARM A`, to = `ARM D`, align = "center", width = 1.1) |> stub(vars = c("VAR", "LABEL"), "Variable", width = 2.5) |> define(VAR, blank_after = TRUE, dedupe = TRUE, label = "Variable", format = var_fmt,label_row = TRUE) |> define(LABEL, indent = .25, label = "Demographic Category") |> define(`ARM A`, label = "Placebo", n = arm_pop["ARM A"]) |> define(`ARM B`, label = "Drug 50mg", n = arm_pop["ARM B"]) |> define(`ARM C`, label = "Drug 100mg", n = arm_pop["ARM C"]) |> define(`ARM D`, label = "Competitor", n = arm_pop["ARM D"]) |> define(PVALUE, label = plbl, width = 2, dedupe = TRUE, align = "center") |> titles("Table 1.0", "Analysis of Demographic Characteristics", "Safety Population", bold = TRUE) |> footnotes("Program: DM_Table.R", "NOTE: Denominator based on number of non-missing responses.", "{supsc('1')}Pearson's Chi-Square tests will be used for " %p% "Categorical variables and ANOVA tests for continuous variables.") rpt <- create_report(file.path(tmp, "ProcsDemoDM.rtf"), output_type = "RTF", font = "Times") |> page_header("Sponsor: Company", "Study: ABC") |> set_margins(top = 1, bottom = 1) |> add_content(tbl) |> page_footer("Date Produced: {Sys.Date()}", right = "Page [pg] of [tpg]") put("Write out the report") res <- write_report(rpt) # Clean Up ---------------------------------------------------------------- sep("Clean Up") put("Close log") log_close() # Uncomment to view report # file.show(res$modified_path) # Uncomment to view log # file.show(lf) ``` ## Output Here is the output: ## Log And here is the log: ``` ========================================================================= Log Path: C:/Users/dbosa/AppData/Local/Temp/RtmpKQddL7/log/example1.log Program Path: C:/Projects/Archytas/Demo/example1.R Working Directory: C:/Projects/Archytas/Demo User Name: dbosa R Version: 4.2.1 (2022-06-23 ucrt) Machine: SOCRATES x86-64 Operating System: Windows 10 x64 build 19044 Base Packages: stats graphics grDevices utils datasets methods base Other Packages: tidylog_1.0.2 procs_0.0.9007 reporter_1.3.7 libr_1.2.6 fmtr_1.5.9 logr_1.3.3 common_1.0.5 sassy_1.0.8 Log Start Time: 2022-10-15 20:01:02 ========================================================================= ========================================================================= Prepare formats ========================================================================= Age categories # A user-defined format: 5 conditions Name Type Expression Label Order 1 obj U x >= 18 & x <= 29 18 to 29 NA 2 obj U x >= 30 & x <= 39 30 to 39 NA 3 obj U x >= 40 & x <= 49 40 to 49 NA 4 obj U x >= 50 >= 50 NA 5 obj U TRUE Out of range NA Sex decodes # A user-defined format: 4 conditions Name Type Expression Label Order 1 obj U is.na(x) Missing NA 2 obj U x == "M" Male NA 3 obj U x == "F" Female NA 4 obj U TRUE Other NA Race decodes # A user-defined format: 4 conditions Name Type Expression Label Order 1 obj U is.na(x) Missing NA 2 obj U x == "WHITE" White NA 3 obj U x == "BLACK" Black or African American NA 4 obj U TRUE Other NA Compile format catalog # A format catalog: 15 formats - $MEAN: type S, "%.1f" - $STD: type S, "(%.2f)" - $Q1: type S, "%.1f" - $Q3: type S, "%.1f" - $MIN: type S, "%d" - $MAX: type S, "%d" - $CNT: type S, "%2d" - $PCT: type S, "(%5.1f%%)" - $AGECAT: type U, 5 conditions - $SEX: type U, 4 conditions - $RACE: type U, 4 conditions - $AOV.F: type S, "%5.3f" - $AOV.P: type S, "(%5.3f)" - $CHISQ: type S, "%5.3f" - $CHISQ.P: type S, "(%5.3f)" ========================================================================= Prepare Data ========================================================================= Create sample ADSL data. Categorize AGE Log starting dataset SUBJID ARM SEX RACE AGE AGECAT 1 1 ARM A F WHITE 19 18 to 29 2 2 ARM B F WHITE 21 18 to 29 3 3 ARM C F WHITE 23 18 to 29 4 4 ARM D F BLACK 28 18 to 29 5 5 ARM A M WHITE 37 30 to 39 6 6 ARM B M WHITE 34 30 to 39 7 7 ARM C M WHITE 36 30 to 39 8 8 ARM D M WHITE 30 30 to 39 9 9 ARM A F WHITE 39 30 to 39 10 10 ARM B F WHITE 31 30 to 39 11 11 ARM C F BLACK 33 30 to 39 12 12 ARM D F WHITE 38 30 to 39 13 13 ARM A M BLACK 37 30 to 39 14 14 ARM B M WHITE 34 30 to 39 15 15 ARM C M WHITE 36 30 to 39 16 16 ARM A M WHITE 40 40 to 49 Get ARM population counts proc_freq: input data set 16 rows and 6 columns tables: ARM view: TRUE output: 1 datasets VAR STAT ARM A ARM B ARM C ARM D 1 ARM CNT 5 4 4 3 ========================================================================= Create summary statistics for age ========================================================================= Call means procedure to get summary statistics for age proc_means: input data set 16 rows and 6 columns by: ARM var: AGE stats: n mean std median q1 q3 min max view: TRUE output: 1 datasets BY VAR N MEAN STD MEDIAN Q1 Q3 MIN MAX 1 ARM A AGE 5 34.4 8.706320 37.0 37 39 19 40 2 ARM B AGE 4 30.0 6.164414 32.5 26 34 21 34 3 ARM C AGE 4 32.0 6.164414 34.5 28 36 23 36 4 ARM D AGE 3 32.0 5.291503 30.0 28 38 28 38 Combine stats datastep: columns decreased from 10 to 7 BY VAR N Mean (SD) Median Q1 - Q3 Min - Max 1 ARM A AGE 5 34.4 (8.71) 37.0 37.0 - 39.0 19 - 40 2 ARM B AGE 4 30.0 (6.16) 32.5 26.0 - 34.0 21 - 34 3 ARM C AGE 4 32.0 (6.16) 34.5 28.0 - 36.0 23 - 36 4 ARM D AGE 3 32.0 (5.29) 30.0 28.0 - 38.0 28 - 38 Transpose ARMs into columns proc_transpose: input data set 4 rows and 7 columns var: BY VAR N Mean (SD) Median Q1 - Q3 Min - Max id: BY copy: VAR name: LABEL output dataset 5 rows and 6 columns VAR LABEL ARM A ARM B ARM C ARM D 1 AGE N 5 4 4 3 2 AGE Mean (SD) 34.4 (8.71) 30.0 (6.16) 32.0 (6.16) 32.0 (5.29) 3 AGE Median 37.0 32.5 34.5 30.0 4 AGE Q1 - Q3 37.0 - 39.0 26.0 - 34.0 28.0 - 36.0 28.0 - 38.0 5 AGE Min - Max 19 - 40 21 - 34 23 - 36 28 - 38 Calculate aov Get aov into proper data frame AOV.F AOV.P ARM 0.2983651 0.8259486 Combine aov statistics datastep: columns decreased from 2 to 1 PVALUE 1 0.298 (0.826) Append aov datastep: columns increased from 6 to 7 VAR LABEL ARM A ARM B ARM C ARM D PVALUE 1 AGE N 5 4 4 3 0.298 (0.826) 2 AGE Mean (SD) 34.4 (8.71) 30.0 (6.16) 32.0 (6.16) 32.0 (5.29) 3 AGE Median 37.0 32.5 34.5 30.0 4 AGE Q1 - Q3 37.0 - 39.0 26.0 - 34.0 28.0 - 36.0 28.0 - 38.0 5 AGE Min - Max 19 - 40 21 - 34 23 - 36 28 - 38 ========================================================================= Create frequency counts for SEX ========================================================================= Get sex frequency counts proc_freq: input data set 16 rows and 6 columns tables: SEX by: ARM view: TRUE output: 1 datasets BY VAR CAT CNT PCT 1 ARM A SEX F 2 40.00000 2 ARM A SEX M 3 60.00000 3 ARM B SEX F 2 50.00000 4 ARM B SEX M 2 50.00000 5 ARM C SEX F 2 50.00000 6 ARM C SEX M 2 50.00000 7 ARM D SEX F 2 66.66667 8 ARM D SEX M 1 33.33333 Combine counts and percents. datastep: columns decreased from 5 to 4 BY VAR LABEL CNTPCT 1 ARM A SEX F 2 ( 40.0%) 2 ARM A SEX M 3 ( 60.0%) 3 ARM B SEX F 2 ( 50.0%) 4 ARM B SEX M 2 ( 50.0%) 5 ARM C SEX F 2 ( 50.0%) 6 ARM C SEX M 2 ( 50.0%) 7 ARM D SEX F 2 ( 66.7%) 8 ARM D SEX M 1 ( 33.3%) Transpose ARMs into columns proc_transpose: input data set 8 rows and 4 columns by: LABEL var: CNTPCT id: BY copy: VAR name: NAME output dataset 2 rows and 7 columns VAR LABEL NAME ARM A ARM B ARM C ARM D 1 SEX F CNTPCT 2 ( 40.0%) 2 ( 50.0%) 2 ( 50.0%) 2 ( 66.7%) 2 SEX M CNTPCT 3 ( 60.0%) 2 ( 50.0%) 2 ( 50.0%) 1 ( 33.3%) Clean up datastep: columns decreased from 7 to 6 VAR LABEL ARM A ARM B ARM C ARM D 1 SEX Female 2 ( 40.0%) 2 ( 50.0%) 2 ( 50.0%) 2 ( 66.7%) 2 SEX Male 3 ( 60.0%) 2 ( 50.0%) 2 ( 50.0%) 1 ( 33.3%) Sort by label proc_sort: input data set 2 rows and 6 columns by: LABEL keep: VAR LABEL ARM A ARM B ARM C ARM D order: a nodupkey: FALSE output data set 2 rows and 6 columns VAR LABEL ARM A ARM B ARM C ARM D 2 SEX Male 3 ( 60.0%) 2 ( 50.0%) 2 ( 50.0%) 1 ( 33.3%) 1 SEX Female 2 ( 40.0%) 2 ( 50.0%) 2 ( 50.0%) 2 ( 66.7%) Get sex chisq proc_freq: input data set 16 rows and 6 columns tables: SEX * ARM view: TRUE output: 1 datasets CHISQ CHISQ.DF CHISQ.P 1 0.5333333 3 0.9115095 Combine chisq statistics datastep: columns decreased from 3 to 1 PVALUE 1 0.533 (0.912) Append chisq datastep: columns increased from 6 to 7 VAR LABEL ARM A ARM B ARM C ARM D PVALUE 1 SEX Male 3 ( 60.0%) 2 ( 50.0%) 2 ( 50.0%) 1 ( 33.3%) 0.533 (0.912) 2 SEX Female 2 ( 40.0%) 2 ( 50.0%) 2 ( 50.0%) 2 ( 66.7%) ========================================================================= Create frequency counts for RACE ========================================================================= Get race frequency counts proc_freq: input data set 16 rows and 6 columns tables: RACE by: ARM view: TRUE output: 1 datasets BY VAR CAT CNT PCT 1 ARM A RACE BLACK 1 20.00000 2 ARM A RACE WHITE 4 80.00000 3 ARM B RACE BLACK 0 0.00000 4 ARM B RACE WHITE 4 100.00000 5 ARM C RACE BLACK 1 25.00000 6 ARM C RACE WHITE 3 75.00000 7 ARM D RACE BLACK 1 33.33333 8 ARM D RACE WHITE 2 66.66667 Combine counts and percents. datastep: columns decreased from 5 to 4 BY VAR LABEL CNTPCT 1 ARM A RACE BLACK 1 ( 20.0%) 2 ARM A RACE WHITE 4 ( 80.0%) 3 ARM B RACE BLACK 0 ( 0.0%) 4 ARM B RACE WHITE 4 (100.0%) 5 ARM C RACE BLACK 1 ( 25.0%) 6 ARM C RACE WHITE 3 ( 75.0%) 7 ARM D RACE BLACK 1 ( 33.3%) 8 ARM D RACE WHITE 2 ( 66.7%) Transpose ARMs into columns proc_transpose: input data set 8 rows and 4 columns by: LABEL var: CNTPCT id: BY copy: VAR name: NAME output dataset 2 rows and 7 columns VAR LABEL NAME ARM A ARM B ARM C ARM D 1 RACE BLACK CNTPCT 1 ( 20.0%) 0 ( 0.0%) 1 ( 25.0%) 1 ( 33.3%) 2 RACE WHITE CNTPCT 4 ( 80.0%) 4 (100.0%) 3 ( 75.0%) 2 ( 66.7%) Clean up datastep: columns decreased from 7 to 6 VAR LABEL ARM A ARM B ARM C ARM D 1 RACE Black or African American 1 ( 20.0%) 0 ( 0.0%) 1 ( 25.0%) 1 ( 33.3%) 2 RACE White 4 ( 80.0%) 4 (100.0%) 3 ( 75.0%) 2 ( 66.7%) Sort by label proc_sort: input data set 2 rows and 6 columns by: LABEL keep: VAR LABEL ARM A ARM B ARM C ARM D order: a nodupkey: FALSE output data set 2 rows and 6 columns VAR LABEL ARM A ARM B ARM C ARM D 2 RACE White 4 ( 80.0%) 4 (100.0%) 3 ( 75.0%) 2 ( 66.7%) 1 RACE Black or African American 1 ( 20.0%) 0 ( 0.0%) 1 ( 25.0%) 1 ( 33.3%) Get race chisq proc_freq: input data set 16 rows and 6 columns tables: RACE * ARM view: TRUE output: 1 datasets CHISQ CHISQ.DF CHISQ.P 1 1.449573 3 0.6939569 Combine chisq statistics datastep: columns decreased from 3 to 1 PVALUE 1 1.450 (0.694) Append chisq datastep: columns increased from 6 to 7 VAR LABEL ARM A ARM B ARM C ARM D 1 RACE White 4 ( 80.0%) 4 (100.0%) 3 ( 75.0%) 2 ( 66.7%) 2 RACE Black or African American 1 ( 20.0%) 0 ( 0.0%) 1 ( 25.0%) 1 ( 33.3%) PVALUE 1 1.450 (0.694) 2 ========================================================================= Create frequency counts for Age Group ========================================================================= Get age group frequency counts proc_freq: input data set 16 rows and 6 columns tables: AGECAT by: ARM view: TRUE output: 1 datasets BY VAR CAT CNT PCT 1 ARM A AGECAT 18 to 29 1 20.00000 2 ARM A AGECAT 30 to 39 3 60.00000 3 ARM A AGECAT 40 to 49 1 20.00000 4 ARM B AGECAT 18 to 29 1 25.00000 5 ARM B AGECAT 30 to 39 3 75.00000 6 ARM B AGECAT 40 to 49 0 0.00000 7 ARM C AGECAT 18 to 29 1 25.00000 8 ARM C AGECAT 30 to 39 3 75.00000 9 ARM C AGECAT 40 to 49 0 0.00000 10 ARM D AGECAT 18 to 29 1 33.33333 11 ARM D AGECAT 30 to 39 2 66.66667 12 ARM D AGECAT 40 to 49 0 0.00000 Combine counts and percents and assign age group factor for sorting datastep: columns decreased from 5 to 4 VAR LABEL BY CNTPCT 1 AGECAT 18 to 29 ARM A 1 ( 20.0%) 2 AGECAT 30 to 39 ARM A 3 ( 60.0%) 3 AGECAT 40 to 49 ARM A 1 ( 20.0%) 4 AGECAT 18 to 29 ARM B 1 ( 25.0%) 5 AGECAT 30 to 39 ARM B 3 ( 75.0%) 6 AGECAT 40 to 49 ARM B 0 ( 0.0%) 7 AGECAT 18 to 29 ARM C 1 ( 25.0%) 8 AGECAT 30 to 39 ARM C 3 ( 75.0%) 9 AGECAT 40 to 49 ARM C 0 ( 0.0%) 10 AGECAT 18 to 29 ARM D 1 ( 33.3%) 11 AGECAT 30 to 39 ARM D 2 ( 66.7%) 12 AGECAT 40 to 49 ARM D 0 ( 0.0%) Sort by age group factor proc_sort: input data set 12 rows and 4 columns by: BY LABEL keep: VAR LABEL BY CNTPCT order: a a nodupkey: FALSE output data set 12 rows and 4 columns VAR LABEL BY CNTPCT 1 AGECAT 18 to 29 ARM A 1 ( 20.0%) 2 AGECAT 30 to 39 ARM A 3 ( 60.0%) 3 AGECAT 40 to 49 ARM A 1 ( 20.0%) 4 AGECAT 18 to 29 ARM B 1 ( 25.0%) 5 AGECAT 30 to 39 ARM B 3 ( 75.0%) 6 AGECAT 40 to 49 ARM B 0 ( 0.0%) 7 AGECAT 18 to 29 ARM C 1 ( 25.0%) 8 AGECAT 30 to 39 ARM C 3 ( 75.0%) 9 AGECAT 40 to 49 ARM C 0 ( 0.0%) 10 AGECAT 18 to 29 ARM D 1 ( 33.3%) 11 AGECAT 30 to 39 ARM D 2 ( 66.7%) 12 AGECAT 40 to 49 ARM D 0 ( 0.0%) Tranpose age group block proc_transpose: input data set 12 rows and 4 columns by: LABEL var: CNTPCT id: BY copy: VAR name: NAME output dataset 3 rows and 7 columns VAR LABEL NAME ARM A ARM B ARM C ARM D 1 AGECAT 18 to 29 CNTPCT 1 ( 20.0%) 1 ( 25.0%) 1 ( 25.0%) 1 ( 33.3%) 2 AGECAT 30 to 39 CNTPCT 3 ( 60.0%) 3 ( 75.0%) 3 ( 75.0%) 2 ( 66.7%) 3 AGECAT 40 to 49 CNTPCT 1 ( 20.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) Some clean up datastep: columns decreased from 7 to 6 VAR LABEL ARM A ARM B ARM C ARM D 1 AGECAT 18 to 29 1 ( 20.0%) 1 ( 25.0%) 1 ( 25.0%) 1 ( 33.3%) 2 AGECAT 30 to 39 3 ( 60.0%) 3 ( 75.0%) 3 ( 75.0%) 2 ( 66.7%) 3 AGECAT 40 to 49 1 ( 20.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) Get ageg chisq proc_freq: input data set 16 rows and 6 columns tables: AGECAT * ARM view: TRUE output: 1 datasets CHISQ CHISQ.DF CHISQ.P 1 2.436364 6 0.8755205 Combine chisq statistics datastep: columns decreased from 3 to 1 PVALUE 1 2.436 (0.876) Append chisq datastep: columns increased from 6 to 7 VAR LABEL ARM A ARM B ARM C ARM D PVALUE 1 AGECAT 18 to 29 1 ( 20.0%) 1 ( 25.0%) 1 ( 25.0%) 1 ( 33.3%) 2.436 (0.876) 2 AGECAT 30 to 39 3 ( 60.0%) 3 ( 75.0%) 3 ( 75.0%) 2 ( 66.7%) 3 AGECAT 40 to 49 1 ( 20.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) Combine blocks into final data frame datastep: columns started with 7 and ended with 7 VAR LABEL ARM A ARM B ARM C ARM D 1 AGE N 5 4 4 3 2 AGE Mean (SD) 34.4 (8.71) 30.0 (6.16) 32.0 (6.16) 32.0 (5.29) 3 AGE Median 37.0 32.5 34.5 30.0 4 AGE Q1 - Q3 37.0 - 39.0 26.0 - 34.0 28.0 - 36.0 28.0 - 38.0 5 AGE Min - Max 19 - 40 21 - 34 23 - 36 28 - 38 6 AGECAT 18 to 29 1 ( 20.0%) 1 ( 25.0%) 1 ( 25.0%) 1 ( 33.3%) 7 AGECAT 30 to 39 3 ( 60.0%) 3 ( 75.0%) 3 ( 75.0%) 2 ( 66.7%) 8 AGECAT 40 to 49 1 ( 20.0%) 0 ( 0.0%) 0 ( 0.0%) 0 ( 0.0%) 9 SEX Male 3 ( 60.0%) 2 ( 50.0%) 2 ( 50.0%) 1 ( 33.3%) 10 SEX Female 2 ( 40.0%) 2 ( 50.0%) 2 ( 50.0%) 2 ( 66.7%) 11 RACE White 4 ( 80.0%) 4 (100.0%) 3 ( 75.0%) 2 ( 66.7%) 12 RACE Black or African American 1 ( 20.0%) 0 ( 0.0%) 1 ( 25.0%) 1 ( 33.3%) PVALUE 1 0.298 (0.826) 2 3 4 5 6 2.436 (0.876) 7 8 9 0.533 (0.912) 10 11 1.450 (0.694) 12 ========================================================================= Create and print report ========================================================================= Write out the report # A report specification: 1 pages - file_path: 'C:\Users\dbosa\AppData\Local\Temp\RtmpKQddL7/ProcsDemoDM.rtf' - output_type: RTF - units: inches - orientation: landscape - margins: top 1 bottom 1 left 1 right 1 - line size/count: 9/36 - page_header: left=Sponsor: Company right=Study: ABC - page_footer: left=Date Produced: 2022-10-15 center= right=Page [pg] of [tpg] - content: # A table specification: - data: data.frame 'final' 12 rows 7 cols - show_cols: all - use_attributes: all - title 1: 'Table 1.0' - title 2: 'Analysis of Demographic Characteristics' - title 3: 'Safety Population' - footnote 1: 'Program: DM_Table.R' - footnote 2: 'NOTE: Denominator based on number of non-missing responses.' - footnote 3: '¹Pearson's Chi-Square tests will be used for Categorical variables and ANOVA tests for continuous variables.' - stub: VAR LABEL 'Variable' width=2.5 align='left' - define: VAR 'Variable' dedupe='TRUE' - define: LABEL 'Demographic Category' - define: ARM A 'Placebo' - define: ARM B 'Drug 50mg' - define: ARM C 'Drug 100mg' - define: ARM D 'Competitor' - define: PVALUE 'Tests of Association¹ Value (P-Value)' width=2 align='center' dedupe='TRUE' ========================================================================= Clean Up ========================================================================= Unload libname lib_sync: synchronized data in library 'sdtm' lib_unload: library 'sdtm' unloaded Close log ========================================================================= Log End Time: 2022-10-15 20:01:03 Log Elapsed Time: 0 00:00:00 ========================================================================= ```