Type:	Package
Title:	Acute Lymphoblastic Leukemia Maintenance Therapy Analysis
Version:	0.1.0
Maintainer:	Tushar Mungle <tushar.mungle@gmail.com>
Description:	Evaluates acute lymphoblastic leukemia maintenance therapy practice at patient and cohort level.
License:	GPL (≥ 3)
Encoding:	UTF-8
RoxygenNote:	7.2.3
Imports:	dplyr (≥ 1.0.10), ggplot2 (≥ 3.3.6), htmlTable (≥ 2.2.1), plyr (≥ 1.8.6), readxl (≥ 1.3.1), reshape2 (≥ 1.4.4), rio (≥ 0.5.29), scales (≥ 1.2.1), stringr (≥ 1.4.1), survival (≥ 3.2.11), survminer (≥ 0.4.9), tibble (≥ 3.1.8), tools (≥ 3.6.1), utils (≥ 3.6.1)
Suggests:	bibtex (≥ 0.5.0), bookdown (≥ 0.30), knitr (≥ 1.39), rmarkdown (≥ 2.18)
Collate:	'UPN_914.R' 'UPN_915.R' 'UPN_916.R' 'rounding_off.R' 'assess_anemia.R' 'assess_increased_doses.R' 'assess_neutropenia.R' 'assess_reduced_doses.R' 'assess_stop_doses.R' 'assess_thrombocytopenia.R' 'compare_cohorts.R' 'convert_external_format.R' 'convert_tmc_format.R' 'plot_progression.R' 'summarize_cohortMT.R' 'summarize_cycle_progression.R' 'time_to_first_dose_increase.R'
VignetteBuilder:	knitr
URL:	https://github.com/tmungle/allMT, https://tmungle.github.io/allMT/
BugReports:	https://github.com/tmungle/allMT/issues
Depends:	R (≥ 2.10)
LazyData:	true
NeedsCompilation:	no
Packaged:	2023-04-20 10:57:10 UTC; Shubham
Author:	Tushar Mungle [aut, cre], Ananya Mahadevan [aut], Shekhar Krishnan [aut]
Repository:	CRAN
Date/Publication:	2023-04-20 17:32:33 UTC

Sample data for a patient with unique patient number (UPN) 914

Description

Data for the patient consists of a dataframe that is used for viewing data

Usage

UPN_914

Format

A dataframe with 10 columns

1. Cycle: 96 weeks of maintenance therapy (MT) is divided into 8 cycles. The column represents the cycles number for the patient with new assessment

2. Date: Patient's new assessment date when doses were prescribed

3. Weeks: MT consist of 96 weeks. Column represent week number for new assessment

4. ANC: Absolute neutrophil count (ANC) values for new assessment

5. PLT: Platelet count (PLT) values for new assessment

6. Hb: Hemoglobin (Hb) values for new assessment

7. MP: 6-mercaptopurine (MP or 6MP) doses for new assessment

8. MTX: Methotraxate (MTX) doses for new assessment

9. MP_adj: MP dose intensity (%). See note for dose intensity calculation

10. MTX_adj: MTX dose intensity (%). See note for dose intensity calculation

Note

1. For running examples you need to read the patient data as csv. The data (rda) is for viewing only

2. Dose intensity (%) = (a*100)/b; where a = MP dose prescribed on new assessment and b = Protocol based MP dose adjusted to patient's body surface area

Sample data for a patient with unique patient number (UPN) 915

Description

Data for the patient consists of a dataframe that is used for viewing data

Usage

UPN_915

Format

A dataframe with 10 columns

1. Cycle: 96 weeks of maintenance therapy (MT) is divided into 8 cycles. The column represents the cycles number for the patient with new assessment

2. Date: Patient's new assessment date when doses were prescribed

3. Weeks: MT consist of 96 weeks. Column represent week number for new assessment

4. ANC: Absolute neutrophil count (ANC) values for new assessment

5. PLT: Platelet count (PLT) values for new assessment

6. Hb: Hemoglobin (Hb) values for new assessment

7. MP: 6-mercaptopurine (MP or 6MP) doses for new assessment

8. MTX: Methotraxate (MTX) doses for new assessment

9. MP_adj: MP dose intensity (%). See note for dose intensity calculation

10. MTX_adj: MTX dose intensity (%). See note for dose intensity calculation

Note

1. For running examples you need to read the patient data as csv. The data (rda) is for viewing only

2. Dose intensity (%) = (a*100)/b; where a = MP dose prescribed on new assessment and b = Protocol based MP dose adjusted to patient's body surface area

Sample data for a patient with unique patient number (UPN) 916

Description

Data for the patient consists of a dataframe that is used for viewing data

Usage

UPN_916

Format

A dataframe with 10 columns

1. Cycle: 96 weeks of maintenance therapy (MT) is divided into 8 cycles. The column represents the cycles number for the patient with new assessment

2. Date: Patient's new assessment date when doses were prescribed

3. Weeks: MT consist of 96 weeks. Column represent week number for new assessment

4. ANC: Absolute neutrophil count (ANC) values for new assessment

5. PLT: Platelet count (PLT) values for new assessment

6. Hb: Hemoglobin (Hb) values for new assessment

7. MP: 6-mercaptopurine (MP or 6MP) doses for new assessment

8. MTX: Methotraxate (MTX) doses for new assessment

9. MP_adj: MP dose intensity (%). See note for dose intensity calculation

10. MTX_adj: MTX dose intensity (%). See note for dose intensity calculation

Note

1. For running examples you need to read the patient data as csv. The data (rda) is for viewing only

2. Dose intensity (%) = (a*100)/b; where a = MP dose prescribed on new assessment and b = Protocol based MP dose adjusted to patient's body surface area

Assess hematological toxicities: Anemia

Description

Evaluate number of anemia episodes and their duration for a given patient or cohort

Usage

assess_anemia(input_files_path, hb_range, duration_hb = NA)

Arguments

Value

Returns a list with (1) the anemia information for each patient as listed below, (2) analysis summary as dataframe (3) analysis summary as HTML table.

1. Pat ID

2. Number of particular toxicity episodes

3. Duration of particular toxicity (in weeks)

4. Number of long duration toxicity episodes

5. Duration of long duration toxicity (in weeks)

Note

1. If the function is used for cohort analysis then values are represented as median and interquartile range (IQR) (25%-75%). The median and IQR is rounded off to upper integer value if decimal value is greater or equal to 0.5, else to lower integer value. Example 1.4->1 and 3.75->4

2. Long duration toxicity is only analyzed if "duration_hb" is included in provided arguments

3. User may save the result as a list, if required, to analyze each patient seperatly please use 1st element of list

See Also

assess_neutropenia(), assess_thrombocytopenia()

Examples

pat_data = system.file("extdata/processed_data/", "UPN_915.csv", package = "allMT")
assess_anemia(input_files_path = pat_data,
                      hb_range = c(7, 8), duration_hb = 3)



cohort_path = paste0(system.file("extdata/processed_data/", package = "allMT"), "/")
assess_anemia(input_files_path = cohort_path,
                      hb_range = c(7, 8), duration_hb = 3)

result <- assess_anemia(input_files_path = pat_data,
                      hb_range = c(7, 8), duration_hb = 3)
print(result[[1]])
print(result[[2]])
print(result[[3]])

Analyze physicians' compliance to dosing guidelines: INCREASE DOSE

Description

Evaluate number of times blood counts did not support physicians' INCREASE DOSE decision

Usage

assess_increased_doses(
  input_files_path,
  anc_threshold = NA,
  plt_threshold = NA,
  hb_threshold = NA,
  escalation_factor,
  tolerated_dose_duration
)

Arguments

Value

Returns a list with (1) the 'INCREASE DOSE' analysis for each patient as listed below, (2) analysis summary as dataframe (3) analysis summary as HTML table in viewer.

1. Pat ID

2. Number of decisions where the physician increased dose (a)

3. Number of times blood counts did not support dose suspension (b)

4. Discordance (%) = (1 - (b/a)*100)

Note

1. Atleast one of the threshold parameters (anc_threshold, plt_threshold, hb_threshold) must be provided to carry out analysis. Missing threshold parameter will not be considered.

2. If the function is used for cohort analysis then a and b will be represented as median and interquartile range (IQR) (25%-75%). The median and IQR is rounded off to upper integer value if decimal value is greater or equal to 0.5, else to lower integer value Example 1.4->1 and 3.75->4

3. User may save the result as a list, if required, to analyze each patient separately - use 1st element of list. Please refer to examples from assess_anemia

See Also

time_to_first_dose_increase(), assess_reduced_doses(), assess_stop_doses()

Examples

cohort_path = paste0(system.file("extdata/processed_data/", package = "allMT"), "/")
assess_increased_doses(input_files_path = cohort_path, anc_threshold = 0.75,
                      plt_threshold = 75, hb_threshold = 8,
                      escalation_factor = 25, tolerated_dose_duration = 8)


pat_data = system.file("extdata/processed_data/", "UPN_914.csv", package = "allMT")
assess_increased_doses (input_files_path = pat_data, anc_threshold = 0.75,
                      plt_threshold = 75, hb_threshold = 8,
                      escalation_factor = 15, tolerated_dose_duration = 6)

assess_increased_doses(input_files_path = cohort_path,
                      anc_threshold = 0.7, plt_threshold = 60)

Assess hematological toxicities: Neutropenia

Description

Evaluate number of neutropenia episodes and their duration for a given patient or cohort

Usage

assess_neutropenia(input_files_path, anc_range, duration_anc = NA)

Arguments

Value

Returns a list with (1) the neutropenia information for each patient as listed below, (2) analysis summary as dataframe (3) analysis summary as HTML table.

1. Pat ID

2. Number of particular toxicity episodes

3. Duration of particular toxicity (in weeks)

4. Number of long duration toxicity episodes

5. Duration of long duration toxicity (in weeks)

Note

1. If the function is used for cohort analysis then values are represented as median and interquartile range (IQR) (25%-75%). The median and IQR is rounded off to upper integer value if decimal value is greater or equal to 0.5, else to lower integer value. Example 1.4->1 and 3.75->4

2. Long duration toxicity is only analyzed if "duration_anc" is included in provided arguments

3. User may save the result as a list, if required, to analyze each patient neutropenia analysis by analyzing 1st element of list

See Also

assess_anemia(), assess_thrombocytopenia()

Examples

pat_data = system.file("extdata/processed_data/", "UPN_915.csv", package = "allMT")
assess_neutropenia(input_files_path = pat_data,
                      anc_range = c(0.5, 0.75), duration_anc = 3)



cohort_path = paste0(system.file("extdata/processed_data/", package = "allMT"), "/")
assess_neutropenia(input_files_path = cohort_path,
                      anc_range = c(0.5, 0.75), duration_anc = 3)


result <- assess_neutropenia(input_files_path = pat_data,
                      anc_range = c(0.5, 0.75), duration_anc = 3)
print(result[[1]])
print(result[[2]])
print(result[[3]])

Analyze physicians' compliance to dosing guidelines: REDUCE DOSE

Description

Evaluate number of times blood counts did not support physicians' REDUCE DOSE decision

Usage

assess_reduced_doses(
  input_files_path,
  anc_range = NA,
  plt_range = NA,
  hb_range = NA,
  reduction_factor
)

Arguments

Value

Returns a list with (1) the 'REDUCE DOSE' analysis for each patient as listed below, (2) analysis summary as dataframe (3) analysis summary as HTML table in viewer.

1. Pat ID

2. Number of decisions where the physician reduced dose (a)

3. Number of times blood counts did not support dose reduction (b)

4. Discordance (%) = ((b/a)*100)

#' @note

1. Atleast one of the threshold parameters (anc_threshold, plt_threshold, hb_threshold) must be provided to carry out analysis. Missing threshold parameter will not be considered.

2. If the function is used for cohort analysis then a and b will be represented as median and interquartile range (IQR) (25%-75%). The median and IQR is rounded off to upper integer value if decimal value is greater or equal to 0.5, else to lower integer value Example 1.4->1 and 3.75->4

3. User may save the result as a list, if required, to analyze each patient separately - use 1st element of list. Please refer to examples from assess_anemia

See Also

assess_stop_doses(), assess_increased_doses()

Examples

pat_data <- system.file("extdata/processed_data/", "UPN_915.csv", package = "allMT")
assess_reduced_doses(input_files_path = pat_data,
                     anc_range = c(0.5,0.75), plt_range = c(50,75),
                     hb_range = c(7,8), reduction_factor = 50)


cohort_path = paste0(system.file("extdata/processed_data/", package = "allMT"), "/")
assess_reduced_doses(input_files_path = cohort_path,
                     anc_range = c(0.5,0.75), plt_range = c(50,75),
                     hb_range = c(7,8), reduction_factor = 50)

Analyze physicians' compliance to dosing guidelines: STOP DOSE

Description

Evaluate number of times blood counts did not support physicians' STOP DOSE decision

Usage

assess_stop_doses(
  input_files_path,
  anc_threshold = NA,
  plt_threshold = NA,
  hb_threshold = NA
)

Arguments

Value

Returns a list with (1) the 'STOP DOSE' analysis for each patient as listed below, (2) analysis summary as dataframe (3) analysis summary as HTML table in viewer.

1. Pat ID

2. Number of decisions where the physician stopped dose (a)

3. Number of times blood counts did not support dose suspension (b)

4. Discordance (%) = ((b/a)*100)

Note

1. Atleast one of the threshold parameters (anc_threshold, plt_threshold, hb_threshold) must be provided to carry out analysis. Missing threshold parameter will not be considered.

2. If the function is used for cohort analysis then a and b will be represented as median and interquartile range (IQR) (25%-75%). The median and IQR is rounded off to upper integer value if decimal value is greater or equal to 0.5, else to lower integer value Example 1.4->1 and 3.75->4

3. User may save the result as a list, if required, to analyze each patient separately - use 1st element of list. Please refer to examples from assess_anemia

See Also

assess_reduced_doses(), assess_increased_doses()

Examples

pat_data <- system.file("extdata/processed_data/", "UPN_915.csv", package = "allMT")
assess_stop_doses(input_files_path = pat_data,
                  anc_threshold = 0.5, plt_threshold = 50, hb_threshold = 7)


assess_stop_doses(input_files_path = pat_data,
                  anc_threshold = 0.5)

cohort_path = paste0(system.file("extdata/processed_data/", package = "allMT"), "/")
assess_stop_doses(input_files_path = cohort_path,
                  anc_threshold = 0.5,plt_threshold = 50, hb_threshold = 7)

Assess hematological toxicities : Thrombocytopenia

Description

Evaluate number of thrombocytopenia episodes and their duration for a given patient or cohort

Usage

assess_thrombocytopenia(input_files_path, plt_range, duration_plt = NA)

Arguments

Value

Returns a list with (1) the thrombocytopenia information for each patient as listed below, (2) analysis summary as dataframe (3) analysis summary as HTML table.

1. Pat ID

2. Number of particular toxicity episodes

3. Duration of particular toxicity (in weeks)

4. Number of long duration toxicity episodes

5. Duration of long duration toxicity (in weeks)

Note

1. If the function is used for cohort analysis then values are represented as median and interquartile range (IQR) (25%-75%). The median and IQR is rounded off to upper integer value if decimal value is greater or equal to 0.5, else to lower integer value. Example 1.4->1 and 3.75->4

2. Long duration toxicity is only analyzed if "duration_plt" is included in provided arguments

3. User may save the result as a list, if required, to analyze each patient thrombocytopenia analysis by analyzing 1st element of list

See Also

assess_neutropenia(), assess_anemia()

Examples

pat_data = system.file("extdata/processed_data/", "UPN_914.csv", package = "allMT")
assess_thrombocytopenia(input_files_path = pat_data,
                      plt_range = c(50, 75), duration_plt = 3)


cohort_path = paste0(system.file("extdata/processed_data/", package = "allMT"), "/")
assess_thrombocytopenia(input_files_path = cohort_path,
                      plt_range = c(50, 75), duration_plt = 3)


result <- assess_thrombocytopenia(input_files_path = pat_data,
                      plt_range = c(0.5, 0.75), duration_plt = 3)
print(result[[1]])
print(result[[2]])
print(result[[3]])

Plot summarized maintenance therapy (MT) data to compare two or more cohorts

Description

Create an integrated summary graph facetted (by cohort). Graph illustrates weighted mean absolute neutrophil count (ANC) and dose information for each patient.

Usage

compare_cohorts(
  input_files_path,
  unit,
  anc_range,
  dose_intensity_threshold,
  method,
  intervention_date,
  group_data_path
)

Arguments

Value

Comparative summary graph

Note

• If more than one chort need to be compared then only "M2" method is applicable

See Also

summarize_cohortMT()

Examples

cohort_path = paste0(system.file("extdata/processed_data/", package = "allMT"), "/")
compare_cohorts(input_files_path = cohort_path,
        method = "M1", intervention_date = "2020-12-01",
        unit = "billion", anc_range = c(0.75, 1.5), dose_intensity_threshold = 100)


group_path = system.file("extdata/grouped_data/group_data.xlsx", package = "allMT")
compare_cohorts(input_files_path = cohort_path,
       method = "M2", group_data_path = group_path,
       unit = "billion", anc_range = c(0.75, 1.5), dose_intensity_threshold = 80)

Get standard data structure

Description

Convert a maintenance therapy excel sheet created by user into a standard format for analysis

Usage

convert_external_format(
  inputpath_to_excelfolder,
  exportpath_to_csvfolder,
  pat_data_file_path,
  daily_mp_dose = 60,
  weekly_mtx_dose = 20
)

Arguments

Value

Folder with converted csv files

Note

If MT excel files are missing in the input folder for any patient IDs provided in the pat_data_file ID column, the function will show a message of missing IDs but will continue to convert the available files.

See Also

convert_tmc_format()

Examples

# As per ICiCLe-ALL-14 protocol (Reference PMID - 35101099):
path_to_excel = paste0(system.file("extdata/external_data/", package = "allMT"), "/")
save_path = paste0(tempdir(),"/")
path_to_bsa = system.file("extdata/external_data/", "BSA.xlsx", package = "allMT")
convert_external_format(inputpath_to_excelfolder = path_to_excel,
                   exportpath_to_csvfolder = save_path,
                   pat_data_file_path = path_to_bsa,
                   daily_mp_dose = 60,
                   weekly_mtx_dose = 20)

# As per ICiCLe-ALL-14 protocol (Reference PMID - 35101099):
convert_external_format(inputpath_to_excelfolder = "../csv_trial/",
                   exportpath_to_csvfolder = save_path,
                   pat_data_file_path = "BSAFile.xlsx")

# As per BFM protocol (Reference PMID - 15902295):
convert_external_format(inputpath_to_excelfolder = "../csv_trial/",
                   exportpath_to_csvfolder = save_path,
                   pat_data_file_path = "BSAFile.xlsx",
                   daily_mp_dose = 50,
                   weekly_mtx_dose = 20)

# As per St Jude protocol (Reference PMID - 15902295):
convert_external_format(inputpath_to_excelfolder = "../csv_trial/",
                   exportpath_to_csvfolder = save_path,
                   pat_data_file_path = "BSAFile.xlsx",
                   daily_mp_dose = 75,
                   weekly_mtx_dose = 40)

Get standard data structure

Description

Convert a Tata Medical Center Kolkata India (TMC) based excel workbook into a standard format for analysis

Convert a maintenance therapy excel workbook (with individual sheets per cycle) into a single csv file with longitudinal data of blood count parameters, absolute doses and dose intensities of administered drugs.

Usage

convert_tmc_format(
  inputpath_to_excelfolder,
  exportpath_to_csvfolder,
  daily_mp_dose = 60,
  weekly_mtx_dose = 20
)

Arguments

Value

Folder with converted csv files

See Also

convert_external_format()

Examples

# As per ICiCLe-ALL-14 protocol (Reference PMID - 35101099):
path_to_excel = paste0(system.file("extdata/tmc_data/", package = "allMT"), "/")
save_path = paste0(tempdir(),"/")
convert_tmc_format(inputpath_to_excelfolder = path_to_excel,
                   exportpath_to_csvfolder = save_path,
                   daily_mp_dose = 60,
                   weekly_mtx_dose = 20)


# As per ICiCLe-ALL-14 protocol (Reference PMID - 35101099):
convert_tmc_format(inputpath_to_excelfolder = path_to_excel,
                   exportpath_to_csvfolder = save_path)

# As per BFM protocol (Reference PMID - 15902295):
convert_tmc_format(inputpath_to_excelfolder = path_to_excel,
                   exportpath_to_csvfolder = save_path,
                   daily_mp_dose = 50,
                   weekly_mtx_dose = 20)

# As per St Jude protocol (Reference PMID - 15902295):
convert_tmc_format(inputpath_to_excelfolder = path_to_excel,
                   exportpath_to_csvfolder = save_path,
                   daily_mp_dose = 75,
                   weekly_mtx_dose = 40)

Graphical representation of maintenance therapy data for single patient

Description

Creates a line graph (trends) for absolute neutrophil count (ANC), 6-Mercaptopurine (6MP) and Methotrexate (MTX) doses across all visits for the given patient

Usage

plot_progression(input_file_path, anc_range, unit)

Arguments

Value

plot image

Note

• If there is only one threshold for anc_range parameter, please specify the respective value and keep the other threshold as NA. eg : c(2000, NA)

• Horizontal dotted lines on the graph indicate anc_range thresholds and starting doses for 6MP and MTX as a reference (colour coded to corresponding parameter)

See Also

summarize_cycle_progression()

Examples

pat_data = system.file("extdata/processed_data/", "UPN_915.csv", package = "allMT")
plot_progression(input_file_path = pat_data, anc_range = c(0.75,1.5), unit = "billion")


# As per BFM protocol (Reference PMID - 15902295):
plot_progression(input_file_path = "../UPN1.csv", anc_range = c(2, NA), unit = "billion")

# As per St Jude protocol (Reference PMID - 15902295):
plot_progression(input_file_path = "../../UPN1.csv", anc_range = c(0.3, 1), unit = "billion")

Plot summarized maintenance therapy (MT) data for a cohort

Description

Create an integrated summary graph illustrating weighted mean absolute neutrophil count (ANC) and dose information for each patient in the cohort.

Usage

summarize_cohortMT(input_files_path, anc_range, unit, dose_intensity_threshold)

Arguments

Value

Summary graph

Note

• If there is only one threshold for anc_range or dose_intensity_range parameters, please specify the respective value and keep the other threshold as NA. eg : c(2000, NA), c(100, NA)

• Horizontal dotted lines on the graph indicate anc_range thresholds.Red dot represents summarized overall MT data.

See Also

compare_cohorts()

Examples

cohort_path = paste0(system.file("extdata/processed_data/", package = "allMT"), "/")
summarize_cohortMT (input_files_path = cohort_path, unit = "billion",
                    anc_range = c(0.75, 1.5), dose_intensity_threshold = c(80, 100))


summarize_cohortMT(input_files_path = cohort_path,
                 unit = "billion", anc_range = c(0.8, 2), dose_intensity_threshold = 150)

# As per BFM protocol (Reference PMID - 15902295):
summarize_cohortMT (input_files_path = cohort_path,
                 unit = "billion", anc_range = c(2, NA), dose_intensity_threshold = 150)

# As per St Jude protocol (Reference PMID - 15902295):
summarize_cohortMT (input_files_path = cohort_path,
                 unit = "billion", anc_range = c(0.8, 2), dose_intensity_threshold = 100)

Plot a cycle-specific summary graph of maintenance therapy (MT) data for single patient

Description

Create summary graph with maintenance therapy data. Weighted mean absolute neutrophil count (ANC) and dose information is calculated and plotted for each cycle.

Usage

summarize_cycle_progression(input_file_path, anc_range, unit)

Arguments

Value

Plot image

Note

• If there is only one threshold for anc_range parameter, please specify the respective value and keep the other threshold as NA. eg : c(2000, NA)

• Horizontal dotted lines on the graph indicate anc_range thresholds.Red dot represents summarized overall MT data.

See Also

plot_progression()

Examples

pat_data = system.file("extdata/processed_data/", "UPN_916.csv", package = "allMT")
summarize_cycle_progression(input_file_path = pat_data, anc_range = c(0.75, 1.5),
                 unit = "billion")

summarize_cycle_progression(input_file_path = pat_data,
                 anc_range = c(0.8, 2), unit = "billion")

# As per BFM protocol (Reference PMID - 15902295):
summarize_cycle_progression(input_file_path = pat_data,
                 anc_range = c(2, NA),
                 unit = "billion")

# As per St Jude protocol (Reference PMID - 15902295):
summarize_cycle_progression(input_file_path = pat_data,
                 anc_range = c(0.8, 2),
                 unit = "billion")

Plot the time taken for FIRST 6-Mercaptopurine (6MP) dose increase in cohort

Description

Evaluate median time (in weeks) to first 6MP dose increase for the cohort using Kaplan-Meier cumulative incidence estimator using ggsurvplot package

Usage

time_to_first_dose_increase(input_files_path, escalation_factor)

Arguments

Value

Median time (in weeks) to first 6MP dose increase plot

See Also

assess_increased_doses()

Examples

cohort_path = paste0(system.file("extdata/processed_data/", package = "allMT"), "/")
time_to_first_dose_increase(input_files_path = cohort_path,
                escalation_factor = 10)