9 Weekly low-dose regimen of decitabine and venetoclax
10 Load required packages
library(knitr) library(kableExtra) library(dplyr)
11 Set knitr options
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
12 Create data frame for Table 1
table1_data <- data.frame( Characteristics = c( “Median age (range), y”, “Age >75 y, n (%)”, “Sex, n (%) - Male”, “Sex, n (%) - Female”, “ECOG PS, n (%) - 0”, “ECOG PS, n (%) - 1”, “ECOG PS, n (%) - 2”, “AML type, n (%) - De novo”, “AML type, n (%) - Secondary or t-AML”, “Bone marrow blast count, n (%) - <30%”, “Bone marrow blast count, n (%) - ≥30%”, “ELN, n (%) - Adverse risk”, “ELN, n (%) - Intermediate risk”, “ELN, n (%) - Favorable risk”, “R-IPSS, n (%) - High”, “R-IPSS, n (%) - Very high”, “Somatic mutations, n (%) - IDH1 or IDH2”, “Somatic mutations, n (%) - NPM1”, “Somatic mutations, n (%) - FLT3 ITD”, “Somatic mutations, n (%) - N/KRAS”, “Somatic mutations, n (%) - TP53”, “Baseline transfusion dependence, n (%) - Red cells and/or platelets”, “Race, n (%) - White”, “Race, n (%) - Black”, “Race, n (%) - Hispanic”, “Race, n (%) - Asian” ), AML = c( “75 (62-83)”, “9 (43)”, “10 (48)”, “11 (52)”, “7 (33)”, “10 (48)”, “4 (19)”, “15 (71)”, “6 (29)”, “13 (62)”, “8 (38)”, “14 (66)”, “6 (29)”, “1 (5)”, NA, NA, “6 (29)”, “1 (5)”, “2 (10)”, “4 (19)”, “5 (24)”, “11 (53)”, “12 (57)”, “4 (19)”, “4 (19)”, “1 (5)” ), HR_MDS = c( “61.5 (52-87)”, “1 (17)”, “2 (33)”, “4 (67)”, “4 (67)”, “2 (33)”, “0 (0)”, NA, NA, NA, NA, NA, NA, NA, “1 (17)”, “5 (83)”, NA, NA, NA, NA, “6 (100)”, “5 (83)”, “4 (66)”, “1 (17)”, “1 (17)”, “0 (0)” ), Other = c( “67.5 (62-70)”, “0 (0)”, “2 (50)”, “2 (50)”, “2 (50)”, “2 (50)”, “0 (0)”, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, “2 (50)”, “0 (0)”, “1 (25)”, “2 (50)”, “1 (25)” ), Total = c( “73 (52-87)”, “10 (32)”, “14 (45)”, “17 (55)”, “13 (42)”, “14 (45)”, “4 (13)”, NA, NA, NA, NA, NA, NA, NA, NA, NA, “6 (29)”, “1 (5)”, “2 (10)”, “4 (19)”, “11 (36)”, “18 (58)”, “16 (52)”, “6 (19)”, “7 (23)”, “2 (6)” ) )
13 Generate table with kableExtra
kable(table1_data, format = “html”, caption = “Baseline Demographic and Clinical Characteristics of the Patients”) %>% kable_styling(bootstrap_options = c(“striped”, “hover”, “condensed”), full_width = FALSE) %>% column_spec(1, bold = TRUE) %>% collapse_rows(columns = 1, valign = “top”)
14 Create data frame for Table 2
table2_data <- data.frame( Characteristics = c( “Overall survival, mo”, “ORR (CR + MLFS or CR + mCR), n (%)”, “CR/CRi, n (%)”, “Best response - CR, n (%)”, “Best response - mCR, n (%)”, “Best response - CRi, n (%)”, “Best response - MLFS, n (%)”, “No response, n (%)”, “MRD- (hematologic flow), n (%)”, “Median duration of response, mo (range)”, “Mortality rate at 8 wk, n (%)” ), AML_ITT = c( “16.1”, “12 (57)”, “11 (52)”, “10 (48)”, NA, “1 (5)”, “1 (5)”, “9 (43)”, “9 (43)”, NA, “0 (0)” ), AML_ITT_PP = c( NA, “12 (63)”, “11 (58)”, “10 (53)”, NA, “1 (5)”, “1 (5)”, “7 (37)”, “9 (47)”, NA, “0 (0)” ), HR_MDS = c( “9.6”, “4 (67)”, NA, “3 (50)”, “1 (17)”, “2 (33)”, NA, NA, “4 (67)”, “5.2 (4.8 to NR)”, “0 (0)” ) )
15 Generate table with kableExtra
kable(table2_data, format = “html”, caption = “Response Rate and Early Mortality for Patients with MDS and AML”) %>% kable_styling(bootstrap_options = c(“striped”, “hover”, “condensed”), full_width = FALSE) %>% column_spec(1, bold = TRUE) %>% collapse_rows(columns = 1, valign = “top”)