Program

Below is another complete example showing how the reporter package interacts with tidyverse and sassy functions to create a survival analysis.

library(tidyverse)
library(sassy)
library(broom)
library(survival)
library(survminer)


options("logr.autolog" = TRUE,
        "logr.notes" = FALSE)

# Get temp location for log and report output
tmp <- tempdir()

# Open Log
lf <- log_open(file.path(tmp, "example5.log"))


# Load and Filter Data  --------------------------------------------------

sep("Load and Filter Data")

# Get path to sample data
pkg <- system.file("extdata", package = "reporter")

# Get adam data
libname(adam, pkg, "sas7bdat")

# Load data into memory
lib_load(adam)

# Filter data
adsl <- adam.adsl %>%    
  select(USUBJID, SEX, AGEGR1, AGE, ARM) %>% 
  filter(ARM != "SCREEN FAILURE") %>% put()

adpsga <- adam.adpsga %>%
  filter(PARAMCD =="PSGA" & TRTA != "" & !is.na(AVISITN)) %>%
  select(USUBJID, TRTA, AVISIT, AVISITN, AVAL, CRIT1FL) %>% put()

# Get population counts
arm_pop <- adsl %>% count(ARM) %>% deframe() %>% put()


# Prepare Data ------------------------------------------------------------

sep("Prepare data for analysis")

put("Determine minimum visit at which success was achieved")
adpsga_minvsuccess <-
  adpsga %>% 
  filter(CRIT1FL == 'Y') %>% 
  group_by(USUBJID) %>% 
  summarize(minvisit = min(AVISITN)) 

put("Get subjects which did not achieve success")
adpsga_nosuccess <-
  anti_join(adpsga, adpsga_minvsuccess, by = ('USUBJID')) %>% 
  group_by(USUBJID) %>% 
  summarize(maxvisit = max(AVISITN)) 

put("Combine subjects cured with subjects not cured")
adslpsga_final <-
  inner_join(adsl, adpsga, by = c('USUBJID')) %>% 
  left_join(adpsga_minvsuccess, by = c('USUBJID')) %>% 
  left_join(adpsga_nosuccess, by = c('USUBJID')) %>%
  filter((AVISITN == minvisit & !is.na(minvisit)) | 
           (AVISITN == maxvisit & !is.na(maxvisit))) %>% 
  mutate(cured    = case_when(CRIT1FL == "Y" ~ TRUE,
                              TRUE ~ as.logical(FALSE))) %>% 
  select(-minvisit, -maxvisit)


# Counts  ---------------------------------------------------------------

sep("Perform Counts and Statistical Tests")

put("Count patients with PSGA <= 1")
success_counts <- adslpsga_final %>% 
  filter(cured == TRUE) %>% 
  count(TRTA) %>% 
  pivot_wider(names_from = TRTA, 
              values_from = n) %>%
  transmute(block = "counts",
            label = "Number of patients with PSGA <= 1",
            "ARM A" = as.character(`ARM A`),
            "ARM B" = as.character(`ARM B`),
            "ARM C" = as.character(`ARM C`),
            "ARM D" = as.character(`ARM D`)) %>% 
            put()

put("Count patients with PSGA > 1")
failed_counts <- adslpsga_final %>% 
  filter(cured == FALSE) %>% 
  count(TRTA) %>% 
  pivot_wider(names_from = TRTA, 
              values_from = n) %>%
  transmute(block = "counts",
            label = "Number of Censored Subjects (PSGA > 1)",
            "ARM A" = as.character(`ARM A`),
            "ARM B" = as.character(`ARM B`),
            "ARM C" = as.character(`ARM C`),
            "ARM D" = as.character(`ARM D`)) %>% 
  put()

count_block <- bind_rows(success_counts, failed_counts)


# Kaplan-Meier estimates ----------------------------------------------------


sep("Perform Kaplan-Meier Tests")

put("Create survival vector")
surv_vct <- Surv(time = adslpsga_final$AVISITN, event = adslpsga_final$cured) %>% 
  put()

put("Fit model on survival vector")
stats_survfit_trta <- survival::survfit(surv_vct ~ TRTA, data = adslpsga_final, ) %>% 
  put()

put("Get model quantiles")
stats_survfit_quantiles <- quantile(stats_survfit_trta)

put("Get lower confidence intervals")
ci_lower <- 
  as.data.frame(stats_survfit_quantiles$lower) %>% 
  rownames_to_column  %>% 
  mutate(block = "surv",
         TRTA = substring(rowname,6)) %>% 
  pivot_longer(cols = c(`25`, `50`, `75`),
               names_to = "Q",
               values_to = "lower") %>% 
  put()

put("Get upper confidence intervals")
ci_upper <- 
  as.data.frame(stats_survfit_quantiles$upper) %>% 
  rownames_to_column %>% 
  mutate(block = "surv",
         TRTA = substring(rowname,6)) %>% 
  pivot_longer(cols = c(`25`, `50`, `75`),
               names_to = "Q",
               values_to = "upper") %>% 
  put()

put("Get confidence intervals")
ci <-
  inner_join(ci_lower, ci_upper)  %>% 
  mutate(ci = paste0("(", ifelse(is.na(lower), "-", lower)
                     , ", ", ifelse(is.na(upper), "-", upper), ")")) %>% 
  pivot_wider(id_cols = c("block", "Q"),
              names_from = TRTA,
              values_from = ci) %>% 
  mutate(order=2,
         label1   = case_when(Q == 25 ~ "25th  percentile (weeks)",
                              Q == 50 ~ "Median (weeks)",
                              Q == 75 ~ "75th  percentile (weeks)"),
         label2   = "95% Confidence Interval**") %>% 
  select(block, Q, order, label1, label2, 
         `ARM A`, `ARM B`, `ARM C`, `ARM D`) %>% 
  put()


put("Get quantiles")
quants <-
  as.data.frame(stats_survfit_quantiles$quantile) %>% 
  rownames_to_column %>% 
  mutate(block = "surv",
         TRTA = substring(rowname,6)) %>% 
  pivot_longer(cols = c(`25`, `50`, `75`),
               names_to = "Q",
               values_to = "value") %>% 
  pivot_wider(id_cols = c("block", "Q"),
              names_from = TRTA,
              values_from = value) %>% 
  mutate(order=1,
         label1   = case_when(Q == 25 ~ "25th  percentile (weeks)",
                              Q == 50 ~ "Median (weeks)",
                              Q == 75 ~ "75th  percentile (weeks)"),
         label2   = "",
         `ARM A`  = as.character(`ARM A`),
         `ARM B`  = as.character(`ARM B`),
         `ARM C`  = as.character(`ARM C`),
         `ARM D`  = as.character(`ARM D`)) %>% 
  select(block, Q, order, label1, label2, 
         `ARM A`, `ARM B`, `ARM C`, `ARM D`) %>% 
  put()

put("Final arrangement")
kaplan_block <-
  bind_rows(quants, ci) %>% 
  arrange(block, Q, order) %>% 
  transmute(block, 
            label1, 
            label2 = ifelse(label2 == "", NA, label2),
            `ARM A`, `ARM B`, `ARM C`, `ARM D`) %>% 
  put()


# Cox Proportional Hazards  -----------------------------------------------

sep("Perform Cox Proportional Hazards Test")

put("Run Cox tests")
stats_surv_cph   <- survival::coxph(surv_vct ~ TRTA, data = adslpsga_final) %>%
  put()

put("Create summary statistics on Cox results")
cph_summary <-
  summary(stats_surv_cph) %>% 
  put()

put("Extract coefficients")
cph_coef <- 
  as.data.frame(cph_summary$coefficients) %>% 
  rownames_to_column  %>% 
  mutate(block = "surv",
         TRTA = substring(rowname,5)) %>% 
  put()

put("Extract confidence intervals")
cph_ci <- 
  cph_summary$conf.int %>% 
  as.data.frame(cph_summary$conf) %>% 
  rownames_to_column  %>% 
  put()
  
put("Create cox statistics block")
cox_block <-
  bind_cols(cph_coef, cph_ci) %>% 
  rename(hazard = `exp(coef)...3`, pval = `Pr(>|z|)`, 
         lower = `lower .95`, upper = `upper .95`) %>% 
  select(TRTA, hazard, pval, lower, upper) %>% 
  mutate(block = "cox", 
         ci = paste0("(", ifelse(is.na(lower), "-", sprintf("%.2f", lower))
                        , ", ", ifelse(is.na(upper), "-", sprintf("%.2f", upper)), ")"),
         hazard = sprintf("%.2f", hazard),
         pval   = sprintf("%.3f", pval)) %>% 
  pivot_longer(cols = c("hazard", "pval", "ci"),
               names_to = "stat",
               values_to = "value") %>% 
  pivot_wider(id_cols = c("block", "stat"),
              names_from = TRTA,
              values_from = value) %>% 
  mutate(label1 = case_when(stat == "hazard"  
                              ~ "Hazard Ratio (Each Treatment Group - ARM A)***",
                            stat == "pval" ~ "P-value",
                            TRUE ~ "95% CI of Hazard Ratio"),
         label2 = as.character(NA),
         `ARM A` = as.character(NA)) %>% 
  select(block, label1, label2, `ARM A`, `ARM B`, `ARM C`, `ARM D`) %>% 
  put()
  


put("Combine statistics blocks")
stat_block <- bind_rows(kaplan_block, cox_block) %>% put()

# Create Survival Plot -------------------------------------------------------

sep("Create survival plot")

put("Create data frame with zero values for each visit")
arms <- unique(adslpsga_final$ARM)
visits <- unique(adslpsga_final$AVISITN)
all_visits <- rep(arms, length(visits))
all_visits <- all_visits[order(all_visits)] 

put("Create visit template")
df <- data.frame(ARM = all_visits, 
                 AVISIT = paste("Week", visits), 
                 AVISITN = visits,
                 cured = FALSE) %>% put()

put("Calculate cummulative sum and percent")
adslpsga_plot <- adslpsga_final %>% 
  select(ARM, AVISIT, AVISITN, cured) %>% 
  bind_rows(df) %>% 
  group_by(ARM, AVISIT, AVISITN) %>% 
  summarize(sumc = sum(cured)) %>% 
  arrange(ARM, AVISITN) %>% 
  group_by(ARM) %>% 
  mutate(AVISIT = ifelse(AVISIT == "Week 0", "Day 1 Baseline", AVISIT),
         csumc = cumsum(sumc)) %>% 
  distinct() %>% 
  mutate(pct = case_when(ARM == "ARM A" ~ csumc / arm_pop["ARM A"], 
                         ARM == "ARM B" ~ csumc / arm_pop["ARM B"],
                         ARM == "ARM C" ~ csumc / arm_pop["ARM C"], 
                         ARM == "ARM D" ~ csumc / arm_pop["ARM D"])) %>% 
  put()

# Add factor to ensure sort order is correct
adslpsga_plot$AVISIT <- factor(adslpsga_plot$AVISIT, 
                               levels = c("Day 1 Baseline", 
                                          "Week 2", 
                                          "Week 4", 
                                          "Week 6", 
                                          "Week 8", 
                                          "Week 12",
                                          "Week 16"))


put("Generate plot")
surv_gg <- adslpsga_plot %>% 
  ggplot(mapping = aes(y = pct, x = AVISIT , group = ARM)) + 
  geom_point(aes(shape = ARM, color = ARM)) + 
  geom_step(aes(linetype = ARM, color = ARM)) +
  scale_x_discrete(name = "Study Week") +
  scale_y_continuous(name = "Proportion of Subjects with Initial Success")





# Print Report ------------------------------------------------------------

sep("Create and print report")


# Create Table 1 with header
tbl1 <- create_table(count_block, width = 9) %>% 
  column_defaults(from = `ARM A`, to = `ARM D`, align = "center", width = 1.1) %>% 
  define(block, visible = FALSE) %>% 
  define(label, label = "", width = 4.25) %>% 
  define(`ARM A`,  n = arm_pop["ARM A"]) %>% 
  define(`ARM B`,  n = arm_pop["ARM B"]) %>% 
  define(`ARM C`,  n = arm_pop["ARM C"]) %>% 
  define(`ARM D`,  n = arm_pop["ARM D"]) %>% 
  titles("Table 5.0", bold = TRUE, blank_row = "above") %>% 
  titles("Analysis of Time to Initial PSGA Success* in Weeks", 
         "Safety Population")
  
  label_lookup <-  c(surv = "Kaplan-Meier estimates",
                     cox = "Results of Proportional Hazards Regression Analysis")
  
# Create table 2 for statistics with stub and without header
tbl2 <- create_table(stat_block, width = 9, headerless = TRUE) %>% 
  column_defaults(from = `ARM A`, to = `ARM D`, align = "center", width = 1.1) %>% 
  stub(c(block, label1, label2), width = 4.25) %>% 
  define(block, label_row = TRUE, format = label_lookup, blank_after = TRUE) %>% 
  define(label1, indent = .25) %>% 
  define(label2, indent = .5) %>% 
  define(`ARM A`,  n = arm_pop["ARM A"]) %>% 
  define(`ARM B`,  n = arm_pop["ARM B"]) %>% 
  define(`ARM C`,  n = arm_pop["ARM C"]) %>% 
  define(`ARM D`,  n = arm_pop["ARM D"]) 


# Create plot
plt <- create_plot(surv_gg, 3.5, 9) %>% 
  titles("Figure 5.0", bold = TRUE, blank_row = "above") %>% 
  titles("Kaplan-Meier Plot for Time to Initial PSGA Success (PSGA <= 1)",
         "Safety Population", blank_row = "none")

put("Create Report")
# Add table 1, table 2, and plot content to the same report.
# Plot will be on a separate page with it's own title.
rpt <- create_report(file.path(tmp, "output/example5.rtf"), output_type = "RTF", 
                     font = "Arial", missing = "-") %>% 
  set_margins(top = 1, bottom = 1) %>% 
  page_header("Sponsor: Client", "Study: ABC/BBC") %>% 
  add_content(tbl1, page_break = FALSE) %>% 
  add_content(tbl2) %>% 
  add_content(plt) %>% 
  footnotes("Program: Surv_Table.R",
            "* Success: PSGA <= 1: PSGA > 1",
            "** Based on R survival package survfit() function",
            paste("*** Based on proportional hazards model with treatment",
                  "indicator variables as explanatory variables"),
            paste("Note: The end-point is cure of the disease (PSGA <= 1)."),
            "  The probability of remaining diseased (PSGA > 1) defines the survival function",
            paste("Note: A subject who is not cured by the end of 12 weeks or is lost",
            "to follow provides a censored observation for the analysis."),
            "\"-\" = Not Applicable") %>% 
  page_footer(paste0("Date Produced: ", fapply(Sys.time(), "%d%b%y %H:%M")), 
              right = "Page [pg] of [tpg]")

put("Write out the report")
res <- write_report(rpt) 


# Clean Up ----------------------------------------------------------------

sep("Clean Up")

lib_unload(adam)

# Close log
log_close()

# View log
writeLines(readLines(lf, encoding = "UTF-8"))

# View report
# file.show(res$file_path)

Log

The above program produces the following log:

=========================================================================
Log Path: C:/Users/dbosa/AppData/Local/Temp/RtmpgrpGQ1/log/example5.log
Program Path: C:\packages\Testing\example5.R
Working Directory: C:/packages/Testing
User Name: dbosa
R Version: 4.1.2 (2021-11-01)
Machine: SOCRATES x86-64
Operating System: Windows 10 x64 build 19041
Base Packages: stats graphics grDevices utils datasets methods base
Other Packages: tidylog_1.0.2 survminer_0.4.9 ggpubr_0.4.0 survival_3.2-13 broom_0.7.10
                reporter_1.2.6 libr_1.2.1 fmtr_1.5.4 logr_1.2.7 sassy_1.0.5
                forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4 readr_2.0.2
                tidyr_1.1.4 tibble_3.1.5 ggplot2_3.3.5 tidyverse_1.3.1 
Log Start Time: 2021-11-21 15:30:37
=========================================================================

=========================================================================
Load and Filter Data
=========================================================================

# library 'adam': 2 items
- attributes: sas7bdat not loaded
- path: C:/Users/dbosa/Documents/R/win-library/4.1/reporter/extdata
- items:
    Name Extension Rows Cols     Size
1 adpsga  sas7bdat 1206   42 424.8 Kb
2   adsl  sas7bdat  152   56  98.7 Kb
         LastModified
1 2021-10-09 13:57:48
2 2021-10-09 13:57:48

lib_load: library 'adam' loaded

select: dropped 51 variables (STUDYID, SUBJID, SITEID, AGEU, RACE, <U+0085>)

filter: removed 2 rows (1%), 150 rows remaining

# A tibble: 150 x 5
   USUBJID    SEX   AGEGR1        AGE ARM  
   <chr>      <chr> <chr>       <dbl> <chr>
 1 ABC-01-049 M     30-39 years    39 ARM D
 2 ABC-01-050 M     40-49 years    47 ARM B
 3 ABC-01-051 M     30-39 years    34 ARM A
 4 ABC-01-052 F     40-49 years    45 ARM C
 5 ABC-01-053 F     18-29 years    26 ARM B
 6 ABC-01-054 M     40-49 years    44 ARM D
 7 ABC-01-055 F     40-49 years    47 ARM C
 8 ABC-01-056 M     30-39 years    31 ARM A
 9 ABC-01-113 M     >65 years      74 ARM D
10 ABC-01-114 F     >65 years      72 ARM B
# ... with 140 more rows

filter: removed 256 rows (21%), 950 rows remaining

select: dropped 36 variables (STUDYID, SUBJID, SITEID, QSSEQ, TRTP, <U+0085>)

# A tibble: 950 x 6
   USUBJID    TRTA  AVISIT     AVISITN  AVAL
   <chr>      <chr> <chr>        <dbl> <dbl>
 1 ABC-01-049 ARM D Day 1 Bas~       0     3
 2 ABC-01-049 ARM D Week 2           2     2
 3 ABC-01-049 ARM D Week 4           4     3
 4 ABC-01-049 ARM D Week 6           6     3
 5 ABC-01-049 ARM D Week 8           8     2
 6 ABC-01-049 ARM D Week 8           8     2
 7 ABC-01-049 ARM D Week 12         12     2
 8 ABC-01-049 ARM D Week 16         16     3
 9 ABC-01-050 ARM B Day 1 Bas~       0     3
10 ABC-01-050 ARM B Week 2           2     3
# ... with 940 more rows, and 1 more
#   variable: CRIT1FL <chr>

count: now 4 rows and 2 columns, ungrouped

ARM A ARM B ARM C ARM D 
   36    38    38    38 

=========================================================================
Prepare data for analysis
=========================================================================

Determine minimum visit at which success was achieved

filter: removed 833 rows (88%), 117 rows remaining

group_by: one grouping variable (USUBJID)

summarize: now 43 rows and 2 columns, ungrouped

Get subjects which did not achieve success

anti_join: added no columns

           > rows only in x   659

           > rows only in y  (  0)

           > matched rows    (291)

           >                 =====

           > rows total       659

group_by: one grouping variable (USUBJID)

summarize: now 102 rows and 2 columns, ungrouped

Combine subjects cured with subjects not cured

inner_join: added 5 columns (TRTA, AVISIT, AVISITN, AVAL, CRIT1FL)

            > rows only in x  (  5)

            > rows only in y  (  0)

            > matched rows     950    (includes duplicates)

            >                 =====

            > rows total       950

left_join: added one column (minvisit)

           > rows only in x   659

           > rows only in y  (  0)

           > matched rows     291

           >                 =====

           > rows total       950

left_join: added one column (maxvisit)

           > rows only in x   291

           > rows only in y  (  0)

           > matched rows     659

           >                 =====

           > rows total       950

filter: removed 803 rows (85%), 147 rows remaining

mutate: new variable 'cured' (logical) with 2 unique values and 0% NA

select: dropped 2 variables (minvisit, maxvisit)

=========================================================================
Perform Counts and Statistical Tests
=========================================================================

Count patients with PSGA <= 1

filter: removed 104 rows (71%), 43 rows remaining

count: now 4 rows and 2 columns, ungrouped

pivot_wider: reorganized (TRTA, n) into (ARM A, ARM B, ARM C, ARM D) [was 4x2, now 1x4]

transmute: new variable 'block' (character) with one unique value and 0% NA

           new variable 'label' (character) with one unique value and 0% NA

           converted 'ARM A' from integer to character (0 new NA)

           converted 'ARM B' from integer to character (0 new NA)

           converted 'ARM C' from integer to character (0 new NA)

           converted 'ARM D' from integer to character (0 new NA)

# A tibble: 1 x 6
  block  label       `ARM A` `ARM B` `ARM C`
  <chr>  <chr>       <chr>   <chr>   <chr>  
1 counts Number of ~ 5       11      16     
# ... with 1 more variable: ARM D <chr>

Count patients with PSGA > 1

filter: removed 43 rows (29%), 104 rows remaining

count: now 4 rows and 2 columns, ungrouped

pivot_wider: reorganized (TRTA, n) into (ARM A, ARM B, ARM C, ARM D) [was 4x2, now 1x4]

transmute: new variable 'block' (character) with one unique value and 0% NA

           new variable 'label' (character) with one unique value and 0% NA

           converted 'ARM A' from integer to character (0 new NA)

           converted 'ARM B' from integer to character (0 new NA)

           converted 'ARM C' from integer to character (0 new NA)

           converted 'ARM D' from integer to character (0 new NA)

# A tibble: 1 x 6
  block  label       `ARM A` `ARM B` `ARM C`
  <chr>  <chr>       <chr>   <chr>   <chr>  
1 counts Number of ~ 31      26      21     
# ... with 1 more variable: ARM D <chr>

=========================================================================
Perform Kaplan-Meier Tests
=========================================================================

Create survival vector

  [1] 16+ 12+ 12+  4  12+ 12+ 12+  6  16+
 [10]  4  16+ 16+ 16+ 16+ 16+  2  16+ 16+
 [19] 16+ 16+ 16+  8  16  16+ 16+ 16+ 16+
 [28] 16+ 16+ 16+ 16+ 16+ 16+  2  16+ 16+
 [37]  8  16+ 16+ 16+ 16+ 16+ 16+ 16+ 16+
 [46] 16+ 12  16+ 16+ 16+ 12+  4  16+ 16+
 [55] 12+  2  12   4  16+  6   6   6  12 
 [64] 16+ 16+ 16  12+ 12+  8+ 12+ 12+  6+
 [73] 12  12  16+ 16+  0+  6  16+ 16+ 16+
 [82] 16  16+ 12+ 12+  4  16   6   4   6 
 [91] 16+ 16+  6   6+ 16+  8  16+ 16+ 16+
[100] 16+ 16+ 16+  6   4+ 16+ 12+  6  16+
[109]  8   2  12+  8  16+ 16+ 16   6  16+
[118] 12+  2+ 16+  4  16+ 16+ 12+ 16+ 16+
[127] 16+ 12  16  16+ 16+ 12+  6  16+ 16+
[136]  2   4  16+ 16+ 16+ 16+ 12+ 12+ 16+
[145] 16+ 16+  6 

Fit model on survival vector

Call: survfit(formula = surv_vct ~ TRTA, data = adslpsga_final)

            n events median 0.95LCL 0.95UCL
TRTA=ARM A 36      5     NA      NA      NA
TRTA=ARM B 37     11     NA      NA      NA
TRTA=ARM C 37     16     NA      16      NA
TRTA=ARM D 37     11     NA      NA      NA

Get model quantiles

Get lower confidence intervals

mutate: new variable 'block' (character) with one unique value and 0% NA

        new variable 'TRTA' (character) with 4 unique values and 0% NA

pivot_longer: reorganized (25, 50, 75) into (Q, lower) [was 4x6, now 12x5]

# A tibble: 12 x 5
   rowname    block TRTA  Q     lower
   <chr>      <chr> <chr> <chr> <dbl>
 1 TRTA=ARM A surv  ARM A 25       NA
 2 TRTA=ARM A surv  ARM A 50       NA
 3 TRTA=ARM A surv  ARM A 75       NA
 4 TRTA=ARM B surv  ARM B 25        6
 5 TRTA=ARM B surv  ARM B 50       NA
 6 TRTA=ARM B surv  ARM B 75       NA
 7 TRTA=ARM C surv  ARM C 25        6
 8 TRTA=ARM C surv  ARM C 50       16
 9 TRTA=ARM C surv  ARM C 75       NA
10 TRTA=ARM D surv  ARM D 25        6
11 TRTA=ARM D surv  ARM D 50       NA
12 TRTA=ARM D surv  ARM D 75       NA

Get upper confidence intervals

mutate: new variable 'block' (character) with one unique value and 0% NA

        new variable 'TRTA' (character) with 4 unique values and 0% NA

pivot_longer: reorganized (25, 50, 75) into (Q, upper) [was 4x6, now 12x5]

# A tibble: 12 x 5
   rowname    block TRTA  Q     upper
   <chr>      <chr> <chr> <chr> <dbl>
 1 TRTA=ARM A surv  ARM A 25       NA
 2 TRTA=ARM A surv  ARM A 50       NA
 3 TRTA=ARM A surv  ARM A 75       NA
 4 TRTA=ARM B surv  ARM B 25       NA
 5 TRTA=ARM B surv  ARM B 50       NA
 6 TRTA=ARM B surv  ARM B 75       NA
 7 TRTA=ARM C surv  ARM C 25       16
 8 TRTA=ARM C surv  ARM C 50       NA
 9 TRTA=ARM C surv  ARM C 75       NA
10 TRTA=ARM D surv  ARM D 25       NA
11 TRTA=ARM D surv  ARM D 50       NA
12 TRTA=ARM D surv  ARM D 75       NA

Get confidence intervals

inner_join: added one column (upper)

            > rows only in x  ( 0)

            > rows only in y  ( 0)

            > matched rows     12

            >                 ====

            > rows total       12

mutate: new variable 'ci' (character) with 4 unique values and 0% NA

pivot_wider: reorganized (rowname, TRTA, lower, upper, ci) into (ARM A, ARM B, ARM C, ARM D) [was 12x7, now 3x6]

mutate: new variable 'order' (double) with one unique value and 0% NA

        new variable 'label1' (character) with 3 unique values and 0% NA

        new variable 'label2' (character) with one unique value and 0% NA

select: columns reordered (block, Q, order, label1, label2, <U+0085>)

# A tibble: 3 x 9
  block Q     order label1   label2  `ARM A`
  <chr> <chr> <dbl> <chr>    <chr>   <chr>  
1 surv  25        2 25th  p~ 95% Co~ (-, -) 
2 surv  50        2 Median ~ 95% Co~ (-, -) 
3 surv  75        2 75th  p~ 95% Co~ (-, -) 
# ... with 3 more variables: ARM B <chr>,
#   ARM C <chr>, ARM D <chr>

Get quantiles

mutate: new variable 'block' (character) with one unique value and 0% NA

        new variable 'TRTA' (character) with 4 unique values and 0% NA

pivot_longer: reorganized (25, 50, 75) into (Q, value) [was 4x6, now 12x5]

pivot_wider: reorganized (rowname, TRTA, value) into (ARM A, ARM B, ARM C, ARM D) [was 12x5, now 3x6]

mutate: converted 'ARM A' from double to character (0 new NA)

        converted 'ARM B' from double to character (0 new NA)

        converted 'ARM C' from double to character (0 new NA)

        converted 'ARM D' from double to character (0 new NA)

        new variable 'order' (double) with one unique value and 0% NA

        new variable 'label1' (character) with 3 unique values and 0% NA

        new variable 'label2' (character) with one unique value and 0% NA

select: columns reordered (block, Q, order, label1, label2, <U+0085>)

# A tibble: 3 x 9
  block Q     order label1    label2 `ARM A`
  <chr> <chr> <dbl> <chr>     <chr>  <chr>  
1 surv  25        1 25th  pe~ ""     <NA>   
2 surv  50        1 Median (~ ""     <NA>   
3 surv  75        1 75th  pe~ ""     <NA>   
# ... with 3 more variables: ARM B <chr>,
#   ARM C <chr>, ARM D <chr>

Final arrangement

transmute: dropped 2 variables (Q, order)

           changed 3 values (50%) of 'label2' (3 new NA)

# A tibble: 6 x 7
  block label1     label2    `ARM A` `ARM B`
  <chr> <chr>      <chr>     <chr>   <chr>  
1 surv  25th  per~ <NA>      <NA>    12     
2 surv  25th  per~ 95% Conf~ (-, -)  (6, -) 
3 surv  Median (w~ <NA>      <NA>    <NA>   
4 surv  Median (w~ 95% Conf~ (-, -)  (-, -) 
5 surv  75th  per~ <NA>      <NA>    <NA>   
6 surv  75th  per~ 95% Conf~ (-, -)  (-, -) 
# ... with 2 more variables: ARM C <chr>,
#   ARM D <chr>

=========================================================================
Perform Cox Proportional Hazards Test
=========================================================================

Run Cox tests

Call:
survival::coxph(formula = surv_vct ~ TRTA, data = adslpsga_final)

            coef exp(coef) se(coef)     z
TRTAARM B 0.9185    2.5055   0.5395 1.703
TRTAARM C 1.3111    3.7102   0.5127 2.557
TRTAARM D 0.8648    2.3744   0.5394 1.603
               p
TRTAARM B 0.0886
TRTAARM C 0.0105
TRTAARM D 0.1089

Likelihood ratio test=7.87  on 3 df, p=0.04877
n= 147, number of events= 43 

Create summary statistics on Cox results

Call:
survival::coxph(formula = surv_vct ~ TRTA, data = adslpsga_final)

  n= 147, number of events= 43 

            coef exp(coef) se(coef)     z
TRTAARM B 0.9185    2.5055   0.5395 1.703
TRTAARM C 1.3111    3.7102   0.5127 2.557
TRTAARM D 0.8648    2.3744   0.5394 1.603
          Pr(>|z|)  
TRTAARM B   0.0886 .
TRTAARM C   0.0105 *
TRTAARM D   0.1089  
---
Signif. codes:    0 <U+0091>***<U+0092> 0.001 <U+0091>**<U+0092> 0.01 <U+0091>*<U+0092> 0.05 <U+0091>.<U+0092>
  0.1 <U+0091> <U+0092> 1

          exp(coef) exp(-coef) lower .95
TRTAARM B     2.505     0.3991    0.8704
TRTAARM C     3.710     0.2695    1.3584
TRTAARM D     2.374     0.4212    0.8249
          upper .95
TRTAARM B     7.212
TRTAARM C    10.134
TRTAARM D     6.834

Concordance= 0.61  (se = 0.042 )
Likelihood ratio test= 7.87  on 3 df,   p=0.05
Wald test            = 6.68  on 3 df,   p=0.08
Score (logrank) test = 7.32  on 3 df,   p=0.06


Extract coefficients

mutate: new variable 'block' (character) with one unique value and 0% NA

        new variable 'TRTA' (character) with 3 unique values and 0% NA

    rowname      coef exp(coef)  se(coef)
1 TRTAARM B 0.9184774  2.505473 0.5394543
2 TRTAARM C 1.3110748  3.710159 0.5126631
3 TRTAARM D 0.8647564  2.374428 0.5394037
         z   Pr(>|z|) block  TRTA
1 1.702605 0.08864206  surv ARM B
2 2.557381 0.01054637  surv ARM C
3 1.603171 0.10889687  surv ARM D

Extract confidence intervals

  rowname exp(coef) exp(-coef) lower .95
1       1  2.505473  0.3991263 0.8703729
2       2  3.710159  0.2695302 1.3583539
3       3  2.374428  0.4211541 0.8249311
  upper .95
1  7.212304
2 10.133796
3  6.834396

Create cox statistics block

rename: renamed 4 variables (hazard, pval, lower, upper)

select: dropped 8 variables (rowname...1, coef, se(coef), z, block, <U+0085>)

mutate: converted 'hazard' from double to character (0 new NA)

        converted 'pval' from double to character (0 new NA)

        new variable 'block' (character) with one unique value and 0% NA

        new variable 'ci' (character) with 3 unique values and 0% NA

pivot_longer: reorganized (hazard, pval, ci) into (stat, value) [was 3x7, now 9x6]

pivot_wider: reorganized (TRTA, lower, upper, value) into (ARM B, ARM C, ARM D) [was 9x6, now 3x5]

mutate: new variable 'label1' (character) with 3 unique values and 0% NA

        new variable 'label2' (character) with one unique value and 100% NA

        new variable 'ARM A' (character) with one unique value and 100% NA

select: dropped one variable (stat)

# A tibble: 3 x 7
  block label1        label2 `ARM A` `ARM B`
  <chr> <chr>         <chr>  <chr>   <chr>  
1 cox   Hazard Ratio~ <NA>   <NA>    2.51   
2 cox   P-value       <NA>   <NA>    0.089  
3 cox   95% CI of Ha~ <NA>   <NA>    (0.87,~
# ... with 2 more variables: ARM C <chr>,
#   ARM D <chr>

Combine statistics blocks

# A tibble: 9 x 7
  block label1      label2   `ARM A` `ARM B`
  <chr> <chr>       <chr>    <chr>   <chr>  
1 surv  25th  perc~ <NA>     <NA>    12     
2 surv  25th  perc~ 95% Con~ (-, -)  (6, -) 
3 surv  Median (we~ <NA>     <NA>    <NA>   
4 surv  Median (we~ 95% Con~ (-, -)  (-, -) 
5 surv  75th  perc~ <NA>     <NA>    <NA>   
6 surv  75th  perc~ 95% Con~ (-, -)  (-, -) 
7 cox   Hazard Rat~ <NA>     <NA>    2.51   
8 cox   P-value     <NA>     <NA>    0.089  
9 cox   95% CI of ~ <NA>     <NA>    (0.87,~
# ... with 2 more variables: ARM C <chr>,
#   ARM D <chr>

=========================================================================
Create survival plot
=========================================================================

Create data frame with zero values for each visit

Create visit template

     ARM  AVISIT AVISITN cured
1  ARM A Week 16      16 FALSE
2  ARM A Week 12      12 FALSE
3  ARM A  Week 4       4 FALSE
4  ARM A  Week 6       6 FALSE
5  ARM A  Week 2       2 FALSE
6  ARM A  Week 8       8 FALSE
7  ARM A  Week 0       0 FALSE
8  ARM B Week 16      16 FALSE
9  ARM B Week 12      12 FALSE
10 ARM B  Week 4       4 FALSE
11 ARM B  Week 6       6 FALSE
12 ARM B  Week 2       2 FALSE
13 ARM B  Week 8       8 FALSE
14 ARM B  Week 0       0 FALSE
15 ARM C Week 16      16 FALSE
16 ARM C Week 12      12 FALSE
17 ARM C  Week 4       4 FALSE
18 ARM C  Week 6       6 FALSE
19 ARM C  Week 2       2 FALSE
20 ARM C  Week 8       8 FALSE
21 ARM C  Week 0       0 FALSE
22 ARM D Week 16      16 FALSE
23 ARM D Week 12      12 FALSE
24 ARM D  Week 4       4 FALSE
25 ARM D  Week 6       6 FALSE
26 ARM D  Week 2       2 FALSE
27 ARM D  Week 8       8 FALSE
28 ARM D  Week 0       0 FALSE

Calculate cummulative sum and percent

select: dropped 7 variables (USUBJID, SEX, AGEGR1, AGE, TRTA, <U+0085>)

group_by: 3 grouping variables (ARM, AVISIT, AVISITN)

summarize: now 29 rows and 4 columns, 2 group variables remaining (ARM, AVISIT)

group_by: one grouping variable (ARM)

mutate (grouped): changed 4 values (14%) of 'AVISIT' (0 new NA)

                  new variable 'csumc' (integer) with 13 unique values and 0% NA

distinct (grouped): removed one row (3%), 28 rows remaining

mutate (grouped): new variable 'pct' (double) with 16 unique values and 0% NA

# A tibble: 28 x 6
# Groups:   ARM [4]
   ARM   AVISIT   AVISITN  sumc csumc    pct
   <chr> <chr>      <dbl> <int> <int>  <dbl>
 1 ARM A Day 1 B~       0     0     0 0     
 2 ARM A Week 2         2     0     0 0     
 3 ARM A Week 4         4     1     1 0.0278
 4 ARM A Week 6         6     3     4 0.111 
 5 ARM A Week 8         8     1     5 0.139 
 6 ARM A Week 12       12     0     5 0.139 
 7 ARM A Week 16       16     0     5 0.139 
 8 ARM B Day 1 B~       0     0     0 0     
 9 ARM B Week 2         2     1     1 0.0263
10 ARM B Week 4         4     4     5 0.132 
# ... with 18 more rows

Generate plot

=========================================================================
Create and print report
=========================================================================

Create Report

Write out the report

# A report specification: 2 pages
- file_path: 'output/example5.rtf'
- output_type: RTF
- units: inches
- orientation: landscape
- margins: top 1 bottom 1 left 1 right 1
- line size/count: 9/40
- page_header: left=Sponsor: Client right=Study: ABC/BBC
- footnote 1: 'Program: Surv_Table.R'
- footnote 2: '* Success: PSGA <= 1: PSGA > 1'
- footnote 3: '** Based on R survival package survfit() function'
- footnote 4: '*** Based on proportional hazards model with treatment indicator variables as explanatory variables'
- footnote 5: 'Note: The end-point is cure of the disease (PSGA <= 1).'
- footnote 6: '  The probability of remaining diseased (PSGA > 1) defines the survival function'
- footnote 7: 'Note: A subject who is not cured by the end of 12 weeks or is lost to follow provides a censored observation for the analysis.'
- footnote 8: '"-" = Not Applicable'
- page_footer: left=Date Produced: 21Nov21 15:30 center= right=Page [pg] of [tpg]
- content: 
# A table specification:
- data: tibble 'count_block' 2 rows 6 cols
- show_cols: all
- use_attributes: all
- width: 9
- title 1: 'Table 5.0'
- title 2: 'Analysis of Time to Initial PSGA Success* in Weeks'
- title 3: 'Safety Population'
- define: block visible='FALSE' 
- define: label '' width=4.25 
- define: ARM A 
- define: ARM B 
- define: ARM C 
- define: ARM D 
# A table specification:
- data: tibble 'stat_block' 9 rows 7 cols
- show_cols: all
- use_attributes: all
- width: 9
- headerless: TRUE
- stub: block label1 label2 width=4.25 align='left' 
- define: block dedupe='TRUE' 
- define: label1 
- define: label2 
- define: ARM A 
- define: ARM B 
- define: ARM C 
- define: ARM D 
# A plot specification: 
- data: 28 rows, 7 cols
- layers: 2
- height: 3.5
- width: 9
- title 1: 'Figure 5.0'
- title 2: 'Kaplan-Meier Plot for Time to Initial PSGA Success (PSGA <= 1)'
- title 3: 'Safety Population'

=========================================================================
Clean Up
=========================================================================

lib_sync: synchronized data in library 'adam'

lib_unload: library 'adam' unloaded

=========================================================================
Log End Time: 2021-11-21 15:30:39
Log Elapsed Time: 0 00:00:02
=========================================================================

Output

Here is the report produced by the sample program above: