Package 'BiostatsUHNplus' reference manual

Title:	Nested Data Summary, Adverse Events and REDCap
Description:	Tools and code snippets for summarizing nested data, adverse events and REDCap study information.
Authors:	Lisa Avery [aut] , Zeynep Baskurt [aut], Osvaldo Espin-Garcia [aut], Katrina Hueniken [aut], Katherine Lajkosz [aut] , Xuan Li [aut], Tyler Pittman [cre, aut] , Anna Santiago [aut] , Jessica Weiss [aut], Wei Xu [aut]
Maintainer:	Tyler Pittman <[email protected]>
License:	MIT + file LICENSE
Version:	0.0.10
Built:	2024-08-23 04:02:24 UTC
Source:	https://github.com/cran/BiostatsUHNplus

Simulated adverse events for patients receiving two study agents.

Description

Simulated adverse events for patients receiving two study agents.

Usage

ae
ae

Format

A data frame with 394 rows and 9 variables:

Subject: Patient ID
ae_detail: Adverse event detail, also known as lowest level term
ae_category: Adverse event category, also known as system organ class
CTCAE5_LLT_NM: Common Terminology Criteria for Adverse Events (CTCAE) version 5
AE_VERBATIM_TRM_TXT: Adverse event verbatim text entered by clinical registered nurse, for "Other, specify"
AE_SEV_GD: Adverse event severity grade, scale from 1 to 5
AE_ONSET_DT_INT: Adverse event onset date
CTC_AE_ATTR_SCALE: Attribution scale of adverse event to first study agent
CTC_AE_ATTR_SCALE_1: Attribution scale of adverse event to second study agent

Outputs related adverse event timeline plots including just system organ class (AE category), or system organ class and lowest level term (AE detail). This function can fit up to 5 different attributions. Modify width, height and scale parameters in ggsave() to customize fit for large plot.

Description

Outputs related adverse event timeline plots including just system organ class (AE category), or system organ class and lowest level term (AE detail). This function can fit up to 5 different attributions. Modify width, height and scale parameters in ggsave() to customize fit for large plot.

Usage

ae_timeline_plot(
  subjID,
  subjID_ineligText = NULL,
  baseline_datasets,
  ae_dataset,
  ae_attribVars,
  ae_attribVarsName = NULL,
  ae_attribVarText = NULL,
  startDtVars,
  ae_detailVar,
  ae_categoryVar,
  ae_severityVar,
  ae_onsetDtVar,
  time_unit = c("day", "week", "month", "year"),
  include_ae_detail = TRUE,
  legendPerSpace = NULL,
  fonts = NULL,
  fontColours = NULL,
  panelColours = NULL,
  attribColours = NULL,
  attribSymbols = NULL,
  columnWidths = NULL
)
ae_timeline_plot(
  subjID,
  subjID_ineligText = NULL,
  baseline_datasets,
  ae_dataset,
  ae_attribVars,
  ae_attribVarsName = NULL,
  ae_attribVarText = NULL,
  startDtVars,
  ae_detailVar,
  ae_categoryVar,
  ae_severityVar,
  ae_onsetDtVar,
  time_unit = c("day", "week", "month", "year"),
  include_ae_detail = TRUE,
  legendPerSpace = NULL,
  fonts = NULL,
  fontColours = NULL,
  panelColours = NULL,
  attribColours = NULL,
  attribSymbols = NULL,
  columnWidths = NULL
)

Arguments

`subjID`	key identifier field for participant ID in data sets
`subjID_ineligText`	character text that denotes participant IDs to exclude, for example, c("New Subject") (if provided)
`baseline_datasets`	list of data frames that contain baseline participant characteristics, for example, list(enrollment_DF,demography_DF,ineligibility_DF)
`ae_dataset`	data frame that contains subject AEs
`ae_attribVars`	field(s) that denotes attribution to intervention under study, for example, c("CTC_AE_ATTR_SCALE","CTC_AE_ATTR_SCALE_1") (if provided)
`ae_attribVarsName`	character text that denotes name of interventions under study, for example, c("Drug 1", "Drug 2") (if provided)
`ae_attribVarText`	character text that denotes related attribution, for example c("Definite", "Probable", "Possible") (if provided)
`startDtVars`	field(s) that denotes participant start date (i.e. 10MAY2021). For example, it could be enrollment date or screening date. If more than one field given (unique names are required), each field is assumed to be specific start date for attribution in corresponding field order
`ae_detailVar`	field that denotes participant AE detail (lowest level term)
`ae_categoryVar`	field that denotes participant AE category (system organ class)
`ae_severityVar`	field that denotes participant AE severity grade (numeric)
`ae_onsetDtVar`	field that denotes participant AE onset date
`time_unit`	character text that denotes time unit for desired timeline, for example, could be one of c("day","week","month","year") (if provided)
`include_ae_detail`	boolean that denotes if AE detail should be included in timeline plot. Default is True
`legendPerSpace`	parameter at denotes proportion of vertical image space dedicated to legend at bottom. Default is 0.05 for AE detail and 0.1 for AE Category
`fonts`	character text that denotes font for AE category, AE detail, axis, legend and plot labels (if provided)
`fontColours`	character text that denotes system font colours for AE category and AE detail (if provided)
`panelColours`	character text that denotes panel background colours for AE category, AE detail and plot area (if provided)
`attribColours`	character text that denotes colours for attributions, supports up to 10 distinct colours (if provided)
`attribSymbols`	text that denotes median plot symbols for attributions, supports up to 10 distinct symbols (if provided)
`columnWidths`	text that denotes character columns widths for AE category and AE detail columns (if provided)

Value

ggplot object of AE timeline plot

Examples

data("drug1_admin", "drug2_admin", "ae");
p <- ae_timeline_plot(subjID="Subject",subjID_ineligText=c("01","11"),
                      baseline_datasets=list(drug1_admin, drug2_admin),
                      ae_dataset=ae,
                      ae_attribVars=c("CTC_AE_ATTR_SCALE","CTC_AE_ATTR_SCALE_1"),
                      ae_attribVarsName=c("Drug 1","Drug 2"),
                      ae_attribVarText=c("Definite", "Probable", "Possible"),
                      startDtVars=c("TX1_DATE_INT","TX2_DATE_INT"),
                      ae_detailVar="ae_detail",
                      ae_categoryVar="ae_category",ae_severityVar="AE_SEV_GD",
                      ae_onsetDtVar="AE_ONSET_DT_INT",time_unit="month",
                      include_ae_detail=FALSE,
                      fonts=c("Calibri","Albany AMT","Gadugi","French Script MT","Forte"),
                      fontColours=c("#FFE135"),
                      panelColours=c("#E52B50",NA,"#FFE4C4"),
                      attribColours=c("#9AB973","#01796F","#FFA343","#CC7722"),   
                      attribSymbols=c(7,8,5,6),
                      columnWidths=c(23))
data("drug1_admin", "drug2_admin", "ae");
p <- ae_timeline_plot(subjID="Subject",subjID_ineligText=c("01","11"),
                      baseline_datasets=list(drug1_admin, drug2_admin),
                      ae_dataset=ae,
                      ae_attribVars=c("CTC_AE_ATTR_SCALE","CTC_AE_ATTR_SCALE_1"),
                      ae_attribVarsName=c("Drug 1","Drug 2"),
                      ae_attribVarText=c("Definite", "Probable", "Possible"),
                      startDtVars=c("TX1_DATE_INT","TX2_DATE_INT"),
                      ae_detailVar="ae_detail",
                      ae_categoryVar="ae_category",ae_severityVar="AE_SEV_GD",
                      ae_onsetDtVar="AE_ONSET_DT_INT",time_unit="month",
                      include_ae_detail=FALSE,
                      fonts=c("Calibri","Albany AMT","Gadugi","French Script MT","Forte"),
                      fontColours=c("#FFE135"),
                      panelColours=c("#E52B50",NA,"#FFE4C4"),
                      attribColours=c("#9AB973","#01796F","#FFA343","#CC7722"),   
                      attribSymbols=c(7,8,5,6),
                      columnWidths=c(23))

Modification of the as.numeric function that prints entries that fail to parse as a message

Description

Modification of the as.numeric function that prints entries that fail to parse as a message

Usage

as_numeric_parse(x)
as_numeric_parse(x)

Arguments

`x`	string or vector to coerce to numeric

Value

No return value, called for side effects

Examples

z <- as_numeric_parse(c(1:5, "String1",6:10,"String2"))
z
z <- as_numeric_parse(c(1:5, "String1",6:10,"String2"))
z

Caterpillar plot. Useful for plotting random effects from hierarchical models, such as MCMCglmm::MCMCglmm() object, that have binary outcome.

Description

Caterpillar plot. Useful for plotting random effects from hierarchical models, such as MCMCglmm::MCMCglmm() object, that have binary outcome.

Usage

caterpillar_plot(
  subjID,
  subjLabel = NULL,
  remove.text.subjID = FALSE,
  mcmcglmm_object,
  orig_dataset,
  binaryOutcomeVar,
  prob = NULL,
  title = NULL,
  no.title = FALSE,
  subtitle = NULL,
  ncol = NULL,
  fonts = NULL,
  columnTextWidth = NULL,
  break.label.summary = FALSE
)
caterpillar_plot(
  subjID,
  subjLabel = NULL,
  remove.text.subjID = FALSE,
  mcmcglmm_object,
  orig_dataset,
  binaryOutcomeVar,
  prob = NULL,
  title = NULL,
  no.title = FALSE,
  subtitle = NULL,
  ncol = NULL,
  fonts = NULL,
  columnTextWidth = NULL,
  break.label.summary = FALSE
)

Arguments

`subjID`	key identifier field for participant ID in data sets
`subjLabel`	text label field in dataset to replace key identifier field for participant ID with in plot (if provided)
`remove.text.subjID`	boolean indicating if non-numeric text should be removed from subjID in plot label. Note that this can only be used if there are non-duplicate participant IDs when non-numeric text is removed. Default is FALSE (if provided)
`mcmcglmm_object`	MCMCglmm model output
`orig_dataset`	data frame supplied to MCMCglmm function
`binaryOutcomeVar`	name of binary variable (0,1) that denotes outcome in MCMCglmm model
`prob`	probability for highest posterior density interval, similar to a confidence interval. Default is 0.95 (if provided)
`title`	title of the plot. Overrides default title (if provided)
`no.title`	boolean that denotes if title should be outputted in plot. Default is TRUE (if provided)
`subtitle`	subtitle of the plot. Overrides default subtitle (if provided)
`ncol`	number of columns in plot. Default is 2 (if provided)
`fonts`	character text that denotes font for title, subtitle, category labels, x-axis plot labels (if provided)
`columnTextWidth`	numeric that denotes character width for label text before breaking to start new line. Default is 20 characters (if provided)
`break.label.summary`	boolean to indicate if new line should start in label before (n, event) summary. Default is FALSE

Value

ggplot object of caterpillar plot

Examples

data("ae");

ae$G3Plus <- 0;
ae$G3Plus[ae$AE_SEV_GD %in% c("3", "4", "5")] <- 1;
ae$Drug_1_Attribution <- 0;
ae$Drug_1_Attribution[ae$CTC_AE_ATTR_SCALE %in% c("Definite", "Probable", "Possible")] <- 1;
ae$Drug_2_Attribution <- 0;
ae$Drug_2_Attribution[ae$CTC_AE_ATTR_SCALE_1 %in% c("Definite", "Probable", "Possible")] <- 1;

prior2RE <- list(R = list(V = diag(1), fix = 1),
  G=list(G1=list(V=1, nu=0.02), G2=list(V=1, nu=0.02)));
  
model1 <- MCMCglmm::MCMCglmm(G3Plus ~ Drug_1_Attribution + Drug_2_Attribution, 
  random=~Subject + ae_category, family="categorical", data=ae, saveX=TRUE, 
  verbose=FALSE, burnin=2000, nitt=10000, thin=10, pr=TRUE, prior=prior2RE);
  
p <- caterpillar_plot(subjID = "Subject",
  mcmcglmm_object = model1,
  prob = 0.99,
  orig_dataset = ae,
  binaryOutcomeVar = "G3Plus")
  
p <- caterpillar_plot(subjID = "ae_category",
  mcmcglmm_object = model1,
  prob = 0.95,
  orig_dataset = ae,
  remove.text.subjID = FALSE,
  ncol = 4,
  binaryOutcomeVar = "G3Plus",
  subtitle = "System organ class (n, event)",
  title = "Odds Ratio for G3+ Severity with 95% Highest Posterior Density Interval",
  fonts = c("Arial", "Arial", "Arial", "Arial"),
  break.label.summary = TRUE)
data("ae");

ae$G3Plus <- 0;
ae$G3Plus[ae$AE_SEV_GD %in% c("3", "4", "5")] <- 1;
ae$Drug_1_Attribution <- 0;
ae$Drug_1_Attribution[ae$CTC_AE_ATTR_SCALE %in% c("Definite", "Probable", "Possible")] <- 1;
ae$Drug_2_Attribution <- 0;
ae$Drug_2_Attribution[ae$CTC_AE_ATTR_SCALE_1 %in% c("Definite", "Probable", "Possible")] <- 1;

prior2RE <- list(R = list(V = diag(1), fix = 1),
  G=list(G1=list(V=1, nu=0.02), G2=list(V=1, nu=0.02)));
  
model1 <- MCMCglmm::MCMCglmm(G3Plus ~ Drug_1_Attribution + Drug_2_Attribution, 
  random=~Subject + ae_category, family="categorical", data=ae, saveX=TRUE, 
  verbose=FALSE, burnin=2000, nitt=10000, thin=10, pr=TRUE, prior=prior2RE);
  
p <- caterpillar_plot(subjID = "Subject",
  mcmcglmm_object = model1,
  prob = 0.99,
  orig_dataset = ae,
  binaryOutcomeVar = "G3Plus")
  
p <- caterpillar_plot(subjID = "ae_category",
  mcmcglmm_object = model1,
  prob = 0.95,
  orig_dataset = ae,
  remove.text.subjID = FALSE,
  ncol = 4,
  binaryOutcomeVar = "G3Plus",
  subtitle = "System organ class (n, event)",
  title = "Odds Ratio for G3+ Severity with 95% Highest Posterior Density Interval",
  fonts = c("Arial", "Arial", "Arial", "Arial"),
  break.label.summary = TRUE)

Nested version of reportRmd covsum()

Description

Nested version of reportRmd covsum()

Usage

covsum_nested(
  data,
  covs,
  maincov = NULL,
  id = NULL,
  digits = 1,
  numobs = NULL,
  markup = TRUE,
  sanitize = TRUE,
  nicenames = TRUE,
  IQR = FALSE,
  all.stats = FALSE,
  pvalue = TRUE,
  effSize = TRUE,
  show.tests = TRUE,
  nCores = NULL,
  nested.test = NULL,
  nsim = NULL,
  excludeLevels = NULL,
  dropLevels = TRUE,
  full = TRUE,
  digits.cat = 0,
  testcont = c("rank-sum test", "ANOVA"),
  testcat = c("Chi-squared", "Fisher"),
  include_missing = FALSE,
  percentage = c("column", "row")
)
covsum_nested(
  data,
  covs,
  maincov = NULL,
  id = NULL,
  digits = 1,
  numobs = NULL,
  markup = TRUE,
  sanitize = TRUE,
  nicenames = TRUE,
  IQR = FALSE,
  all.stats = FALSE,
  pvalue = TRUE,
  effSize = TRUE,
  show.tests = TRUE,
  nCores = NULL,
  nested.test = NULL,
  nsim = NULL,
  excludeLevels = NULL,
  dropLevels = TRUE,
  full = TRUE,
  digits.cat = 0,
  testcont = c("rank-sum test", "ANOVA"),
  testcat = c("Chi-squared", "Fisher"),
  include_missing = FALSE,
  percentage = c("column", "row")
)

Arguments

`data`	dataframe containing data
`covs`	character vector with the names of columns to include in table
`maincov`	covariate to stratify table by
`id`	covariates to nest summary by
`digits`	number of digits for summarizing mean data, does not affect p-values
`numobs`	named list overriding the number of people you expect to have the covariate
`markup`	boolean indicating if you want latex markup
`sanitize`	boolean indicating if you want to sanitize all strings to not break LaTeX
`nicenames`	boolean indicating if you want to replace . and _ in strings with a space
`IQR`	boolean indicating if you want to display the inter quantile range (Q1,Q3) as opposed to (min,max) in the summary for continuous variables
`all.stats`	boolean indicating if all summary statistics (Q1,Q3 + min,max on a separate line) should be displayed. Overrides IQR.
`pvalue`	boolean indicating if you want p-values included in the table
`effSize`	boolean indicating if you want effect sizes included in the table. Can only be obtained if pvalue is also requested.
`show.tests`	boolean indicating if the type of statistical used should be shown in a column beside the p-values. Ignored if pvalue=FALSE.
`nCores`	if > 1, specifies number of cores to use for parallel processing for calculating the nested p-value (default: 1).
`nested.test`	specifies test used for calculating nested p-value from afex::mixed function. Either parametric bootstrap method or likelihood ratio test method (default: "LRT"). Parametric bootstrap takes longer.
`nsim`	specifies number of simulations to use for calculating nested p-value with parametric bootstrap method used for nested.test (default: 1000).
`excludeLevels`	a named list of covariate levels to exclude from statistical tests in the form list(varname =c('level1','level2')). These levels will be excluded from association tests, but not the table. This can be useful for levels where there is a logical skip (i.e. not missing, but not presented). Ignored if pvalue=FALSE.
`dropLevels`	logical, indicating if empty factor levels be dropped from the output, default is TRUE.
`full`	boolean indicating if you want the full sample included in the table, ignored if maincov is NULL
`digits.cat`	number of digits for the proportions when summarizing categorical data (default: 0)
`testcont`	test of choice for continuous variables,one of rank-sum (default) or ANOVA
`testcat`	test of choice for categorical variables,one of Chi-squared (default) or Fisher
`include_missing`	Option to include NA values of maincov. NAs will not be included in statistical tests
`percentage`	choice of how percentages are presented, one of column (default) or row

Simulated demography for patients.

Description

Simulated demography for patients.

Usage

demography
demography

Format

A data frame with 12 rows and 2 variables:

Subject: Patient ID
GENDER_CODE: Patient gender

Simulated study agent 1 for patients.

Description

Simulated study agent 1 for patients.

Usage

drug1_admin
drug1_admin

Format

A data frame with 12 rows and 2 variables:

Subject: Patient ID
TX1_DATE_INT: Study agent 1 start date of patient on study

Simulated study agent 2 for patients.

Description

Simulated study agent 2 for patients.

Usage

drug2_admin
drug2_admin

Format

A data frame with 12 rows and 2 variables:

Subject: Patient ID
TX2_DATE_INT: Study agent 2 start date of patient on study

Outputs the three DSMB-CCRU AE summary tables in Excel format per UHN template

Description

Outputs the three DSMB-CCRU AE summary tables in Excel format per UHN template

Usage

dsmb_ccru(
  protocol,
  setwd,
  title,
  comp = NULL,
  pi,
  presDate,
  cutDate,
  boundDate = NULL,
  subjID,
  subjID_ineligText = NULL,
  baseline_datasets,
  ae_dataset,
  ineligVar = NULL,
  ineligVarText = NULL,
  genderVar,
  enrolDtVar,
  ae_detailVar,
  ae_categoryVar,
  ae_severityVar,
  ae_onsetDtVar,
  ae_detailOtherText = NULL,
  ae_detailOtherVar = NULL,
  ae_verbatimVar = NULL,
  numSubj = NULL,
  fileNameUnderscore = TRUE
)
dsmb_ccru(
  protocol,
  setwd,
  title,
  comp = NULL,
  pi,
  presDate,
  cutDate,
  boundDate = NULL,
  subjID,
  subjID_ineligText = NULL,
  baseline_datasets,
  ae_dataset,
  ineligVar = NULL,
  ineligVarText = NULL,
  genderVar,
  enrolDtVar,
  ae_detailVar,
  ae_categoryVar,
  ae_severityVar,
  ae_onsetDtVar,
  ae_detailOtherText = NULL,
  ae_detailOtherVar = NULL,
  ae_verbatimVar = NULL,
  numSubj = NULL,
  fileNameUnderscore = TRUE
)

Arguments

`protocol`	study protocol name (uppercase, no spaces permitted)
`setwd`	directory to write Excel summary files to
`title`	full character vector with name of study
`comp`	baseline comparison group, for example, cohort (if provided)
`pi`	character vector name of study principal investigator
`presDate`	presentation date (i.e. 17NOV2023) for DSMB
`cutDate`	recent cutoff date for AEs (i.e. 31AUG2023)
`boundDate`	lower bound cutoff date for AEs (if provided)
`subjID`	key identifier field for participant ID in data sets
`subjID_ineligText`	character text that denotes participant IDs to exclude, for example, c("New Subject") (if provided)
`baseline_datasets`	list of data frames that contain baseline participant characteristics, for example, list(enrollment_DF,demography_DF,ineligibility_DF)
`ae_dataset`	data frame that contains subject AEs
`ineligVar`	field that denotes participant ineligibility (if provided)
`ineligVarText`	character text that denotes participant ineligibility, for example, c("Yes", "Y") (if provided)
`genderVar`	field that denotes participant gender
`enrolDtVar`	field that denotes participant enrollment date (i.e. 10MAY2021)
`ae_detailVar`	field that denotes participant AE detail (lowest level term)
`ae_categoryVar`	field that denotes participant AE category (system organ class)
`ae_severityVar`	field that denotes participant AE severity grade (numeric)
`ae_onsetDtVar`	field that denotes participant AE onset date
`ae_detailOtherText`	character text that denotes referencing verbatim AE field, for example, c("Other, specify", "OTHER") (if provided)
`ae_detailOtherVar`	field that denotes participant AE detail other (if provided)
`ae_verbatimVar`	field that denotes participant AE detail verbatim (if provided)
`numSubj`	vector to override value for number of participants in summary (if provided)
`fileNameUnderscore`	boolean that denotes if spaces should be underscore in filename

Value

three Excel files containing DSMB-CCRU AE summary tables

Examples

data("enrollment", "demography", "ineligibility", "ae");
dsmb_ccru(protocol="EXAMPLE_STUDY",setwd="./man/tables/",
  title="Phase X Study to Evaluate Treatments A-D",
  comp="COHORT",pi="Dr. Principal Investigator",
  presDate="30OCT2020",cutDate="31AUG2020",
  boundDate=NULL,subjID="Subject",subjID_ineligText=c("New Subject","Test"),
  baseline_datasets=list(enrollment,demography,ineligibility),
  ae_dataset=ae,ineligVar="INELIGIBILITY_STATUS",ineligVarText=c("Yes","Y"),
  genderVar="GENDER_CODE",enrolDtVar="ENROL_DATE_INT",ae_detailVar="ae_detail",
  ae_categoryVar="ae_category",ae_severityVar="AE_SEV_GD",
  ae_onsetDtVar="AE_ONSET_DT_INT",ae_detailOtherText="Other, specify",
  ae_detailOtherVar="CTCAE5_LLT_NM",ae_verbatimVar="AE_VERBATIM_TRM_TXT",
  numSubj=c(2,4,5,6))
data("enrollment", "demography", "ineligibility", "ae");
dsmb_ccru(protocol="EXAMPLE_STUDY",setwd="./man/tables/",
  title="Phase X Study to Evaluate Treatments A-D",
  comp="COHORT",pi="Dr. Principal Investigator",
  presDate="30OCT2020",cutDate="31AUG2020",
  boundDate=NULL,subjID="Subject",subjID_ineligText=c("New Subject","Test"),
  baseline_datasets=list(enrollment,demography,ineligibility),
  ae_dataset=ae,ineligVar="INELIGIBILITY_STATUS",ineligVarText=c("Yes","Y"),
  genderVar="GENDER_CODE",enrolDtVar="ENROL_DATE_INT",ae_detailVar="ae_detail",
  ae_categoryVar="ae_category",ae_severityVar="AE_SEV_GD",
  ae_onsetDtVar="AE_ONSET_DT_INT",ae_detailOtherText="Other, specify",
  ae_detailOtherVar="CTCAE5_LLT_NM",ae_verbatimVar="AE_VERBATIM_TRM_TXT",
  numSubj=c(2,4,5,6))

Enrollment data Simulated enrollment for patients.

Description

Enrollment data

Simulated enrollment for patients.

Usage

enrollment
enrollment

Format

A data frame with 12 rows and 3 variables:

Subject: Patient ID
COHORT: Study cohort for patient
ENROL_DATE_INT: Enrollment date of patient to study

Simulated ineligibility for patients.

Description

Simulated ineligibility for patients.

Usage

ineligibility
ineligibility

Format

A data frame with 11 rows and 2 variables:

Subject: Patient ID
INELIGIBILITY_STATUS: Recorded ineligibility status of patient to study

Nice table of model output from MCMCglmm::MCMCglmm()

Description

Nice table of model output from MCMCglmm::MCMCglmm()

Usage

nice_mcmcglmm(mcmcglmm_object, dataset)
nice_mcmcglmm(mcmcglmm_object, dataset)

Arguments

`mcmcglmm_object`	returned output from MCMCglmm()
`dataset`	dataframe containing data

Value

grouped_df

Examples

## Not run: 
data(ae)

ae$AE_SEV_GD <- as.numeric(ae$AE_SEV_GD);
ae$Drug_1_Attribution <- "No";
ae$Drug_1_Attribution[ae$CTC_AE_ATTR_SCALE %in% c("Definite", "Probable", "Possible")] <- "Yes";
ae$Drug_1_Attribution <- as.factor(ae$Drug_1_Attribution);
ae$Drug_2_Attribution <- "No";
ae$Drug_2_Attribution[ae$CTC_AE_ATTR_SCALE_1 %in% c("Definite", "Probable", "Possible")] <- "Yes";
ae$Drug_2_Attribution <- as.factor(ae$Drug_2_Attribution);

prior2RE <- list(R = list(V = diag(1), fix = 1), G=list(G1=list(V=1, nu=0.02), 
          G2=list(V=1, nu=0.02)));

model1 <- MCMCglmm::MCMCglmm(Drug_1_Attribution ~ AE_SEV_GD + Drug_2_Attribution, 
          random=~ae_detail + Subject, family="categorical", data=ae, saveX=TRUE, 
          verbose=FALSE, burnin=2000, nitt=10000, thin=10, pr=TRUE, prior=prior2RE);

mcmcglmm_mva <- nice_mcmcglmm(model1, ae);

## End(Not run)
## Not run: 
data(ae)

ae$AE_SEV_GD <- as.numeric(ae$AE_SEV_GD);
ae$Drug_1_Attribution <- "No";
ae$Drug_1_Attribution[ae$CTC_AE_ATTR_SCALE %in% c("Definite", "Probable", "Possible")] <- "Yes";
ae$Drug_1_Attribution <- as.factor(ae$Drug_1_Attribution);
ae$Drug_2_Attribution <- "No";
ae$Drug_2_Attribution[ae$CTC_AE_ATTR_SCALE_1 %in% c("Definite", "Probable", "Possible")] <- "Yes";
ae$Drug_2_Attribution <- as.factor(ae$Drug_2_Attribution);

prior2RE <- list(R = list(V = diag(1), fix = 1), G=list(G1=list(V=1, nu=0.02), 
          G2=list(V=1, nu=0.02)));

model1 <- MCMCglmm::MCMCglmm(Drug_1_Attribution ~ AE_SEV_GD + Drug_2_Attribution, 
          random=~ae_detail + Subject, family="categorical", data=ae, saveX=TRUE, 
          verbose=FALSE, burnin=2000, nitt=10000, thin=10, pr=TRUE, prior=prior2RE);

mcmcglmm_mva <- nice_mcmcglmm(model1, ae);

## End(Not run)

Nice table of intraclass correlation coefficients from MCMCglmm::MCMCglmm() model output

Description

Nice table of intraclass correlation coefficients from MCMCglmm::MCMCglmm() model output

Usage

nice_mcmcglmm_icc(mcmcglmm_object, prob = NULL, decimals = NULL)
nice_mcmcglmm_icc(mcmcglmm_object, prob = NULL, decimals = NULL)

Arguments

`mcmcglmm_object`	returned output from MCMCglmm()
`prob`	probability for highest posterior density interval, similar to a confidence interval. Default is 0.95 (if provided)
`decimals`	number of decimal places to use in estimates

Value

grouped_df

Examples

## Not run: 
data(ae)
ae$AE_SEV_GD <- as.numeric(ae$AE_SEV_GD);
ae$Drug_1_Attribution <- 0;
ae$Drug_1_Attribution[ae$CTC_AE_ATTR_SCALE %in% c("Definite", "Probable", "Possible")] <- 1;
ae$Drug_2_Attribution <- 0;
ae$Drug_2_Attribution[ae$CTC_AE_ATTR_SCALE_1 %in% c("Definite", "Probable", "Possible")] <- 1;
prior2RE <- list(R = list(V = diag(1), fix = 1), G=list(G1=list(V=1, nu=0.02), 
   G2=list(V=1, nu=0.02)));
model1 <- MCMCglmm::MCMCglmm(Drug_1_Attribution ~ AE_SEV_GD + Drug_2_Attribution, 
   random=~ae_detail + Subject, family="categorical", data=ae, saveX=TRUE, 
   verbose=FALSE, burnin=2000, nitt=10000, thin=10, pr=TRUE, prior=prior2RE);
mcmcglmm_icc <- nice_mcmcglmm_icc(model1);

## End(Not run)
## Not run: 
data(ae)
ae$AE_SEV_GD <- as.numeric(ae$AE_SEV_GD);
ae$Drug_1_Attribution <- 0;
ae$Drug_1_Attribution[ae$CTC_AE_ATTR_SCALE %in% c("Definite", "Probable", "Possible")] <- 1;
ae$Drug_2_Attribution <- 0;
ae$Drug_2_Attribution[ae$CTC_AE_ATTR_SCALE_1 %in% c("Definite", "Probable", "Possible")] <- 1;
prior2RE <- list(R = list(V = diag(1), fix = 1), G=list(G1=list(V=1, nu=0.02), 
   G2=list(V=1, nu=0.02)));
model1 <- MCMCglmm::MCMCglmm(Drug_1_Attribution ~ AE_SEV_GD + Drug_2_Attribution, 
   random=~ae_detail + Subject, family="categorical", data=ae, saveX=TRUE, 
   verbose=FALSE, burnin=2000, nitt=10000, thin=10, pr=TRUE, prior=prior2RE);
mcmcglmm_icc <- nice_mcmcglmm_icc(model1);

## End(Not run)

Combines exported REDCap raw and label .csv files together with data dictionary. Tranforms the exported data into Excel sheets by survey instrument with one row per participant

Description

Combines exported REDCap raw and label .csv files together with data dictionary. Tranforms the exported data into Excel sheets by survey instrument with one row per participant

Usage

redcap_data_out(
  protocol,
  pullDate = NULL,
  subjID,
  subjID_ineligText = NULL,
  subjID_eligPattern = NULL,
  varFilter = NULL,
  varFilter_eligPattern = NULL,
  setWD_files,
  setWD_dataDict = NULL,
  outDir
)
redcap_data_out(
  protocol,
  pullDate = NULL,
  subjID,
  subjID_ineligText = NULL,
  subjID_eligPattern = NULL,
  varFilter = NULL,
  varFilter_eligPattern = NULL,
  setWD_files,
  setWD_dataDict = NULL,
  outDir
)

Arguments

`protocol`	study protocol name (i.e. Example_Study)
`pullDate`	date of data pull, for example, 2024_01_02 (if provided)
`subjID`	key identifier field(s) for participant ID in data sets
`subjID_ineligText`	character text that denotes participant IDs to exclude using first key identifier field. For example, c("New Subject") (if provided)
`subjID_eligPattern`	character text that denotes pattern for participant IDs to include using first key identifier field. For example, c("^Site_A") (if provided)
`varFilter`	field to use for filtering data (if provided)
`varFilter_eligPattern`	character text that denotes pattern for filter variable to include, for example, c("^Arm_A") (if provided)
`setWD_files`	directory where the both raw and label REDCap export .csv files are stored, following the convention for file names of 1_DATA.csv, 1_DATA_LABELs.csv, 2_DATA.csv, 2_DATA_LABELs.csv, etc
`setWD_dataDict`	directory where the REDCap .csv data dictionary is stored. Make sure that file is saved as basic .csv file in Excel, and not UTF-8. Must contain "DataDictionary" in file name (if provided)
`outDir`	output directory where the Excel files are saved

Value

two Excel files, one containing variable names and labels and the other containing REDCap survey instrument data by sheet

Examples

## Not run: 
redcap_data_out(protocol="Example_Study",pullDate="2024_01_03",

## End(Not run)

## Not run: 
redcap_data_out(protocol="Example_Study",pullDate="2024_01_03",

## End(Not run)

Outputs a nested version of reportRmd::rm_covsum()

Description

Outputs a nested version of reportRmd::rm_covsum()

Usage

rm_covsum_nested(
  data,
  covs,
  maincov = NULL,
  id = NULL,
  caption = NULL,
  tableOnly = FALSE,
  covTitle = "",
  digits = 1,
  digits.cat = 0,
  nicenames = TRUE,
  IQR = FALSE,
  all.stats = FALSE,
  pvalue = TRUE,
  effSize = TRUE,
  p.adjust = "none",
  unformattedp = FALSE,
  show.tests = TRUE,
  just.nested.pvalue = FALSE,
  nCores = NULL,
  nested.test = NULL,
  nsim = NULL,
  testcont = c("rank-sum test", "ANOVA"),
  testcat = c("Chi-squared", "Fisher"),
  full = TRUE,
  include_missing = FALSE,
  percentage = c("column", "row"),
  dropLevels = TRUE,
  excludeLevels = NULL,
  numobs = NULL,
  markup = TRUE,
  sanitize = TRUE,
  chunk_label
)
rm_covsum_nested(
  data,
  covs,
  maincov = NULL,
  id = NULL,
  caption = NULL,
  tableOnly = FALSE,
  covTitle = "",
  digits = 1,
  digits.cat = 0,
  nicenames = TRUE,
  IQR = FALSE,
  all.stats = FALSE,
  pvalue = TRUE,
  effSize = TRUE,
  p.adjust = "none",
  unformattedp = FALSE,
  show.tests = TRUE,
  just.nested.pvalue = FALSE,
  nCores = NULL,
  nested.test = NULL,
  nsim = NULL,
  testcont = c("rank-sum test", "ANOVA"),
  testcat = c("Chi-squared", "Fisher"),
  full = TRUE,
  include_missing = FALSE,
  percentage = c("column", "row"),
  dropLevels = TRUE,
  excludeLevels = NULL,
  numobs = NULL,
  markup = TRUE,
  sanitize = TRUE,
  chunk_label
)

Arguments

`data`	dataframe containing data
`covs`	character vector with the names of columns to include in table
`maincov`	covariate to stratify table by
`id`	covariates to nest summary by
`caption`	character containing table caption (default is no caption)
`tableOnly`	Logical, if TRUE then a dataframe is returned, otherwise a formatted printed object is returned (default).
`covTitle`	character with the names of the covariate (predictor) column. The default is to leave this empty for output or, for table only output to use the column name 'Covariate'.
`digits`	number of digits for summarizing mean data
`digits.cat`	number of digits for the proportions when summarizing categorical data (default: 0)
`nicenames`	boolean indicating if you want to replace . and _ in strings with a space
`IQR`	boolean indicating if you want to display the inter quantile range (Q1,Q3) as opposed to (min,max) in the summary for continuous variables
`all.stats`	boolean indicating if all summary statistics (Q1,Q3 + min,max on a separate line) should be displayed. Overrides IQR.
`pvalue`	boolean indicating if you want p-values included in the table
`effSize`	boolean indicating if you want effect sizes included in the table. Can only be obtained if pvalue is also requested.
`p.adjust`	p-adjustments to be performed
`unformattedp`	boolean indicating if you would like the p-value to be returned unformatted (ie not rounded or prefixed with '<'). Best used with tableOnly = T and outTable function.
`show.tests`	boolean indicating if the type of statistical used should be shown in a column beside the p-values. Ignored if pvalue=FALSE.
`just.nested.pvalue`	boolean indicating if the just the nested p-value should be shown in a column, and not unnested p-value, unnested statistical tests and effect size. Overrides effSize and show.tests arguments.
`nCores`	number of cores to use for parallel processing if calculating the nested p-value (if provided).
`nested.test`	specifies test used for calculating nested p-value from afex::mixed function. Either parametric bootstrap method or likelihood ratio test method (default: "LRT"). Parametric bootstrap takes longer.
`nsim`	specifies number of simulations to use for calculating nested p-value with parametric bootstrap method used for nested.test (default: 1000).
`testcont`	test of choice for continuous variables,one of rank-sum (default) or ANOVA
`testcat`	test of choice for categorical variables,one of Chi-squared (default) or Fisher
`full`	boolean indicating if you want the full sample included in the table, ignored if maincov is NULL
`include_missing`	Option to include NA values of maincov. NAs will not be included in statistical tests
`percentage`	choice of how percentages are presented, one of column (default) or row
`dropLevels`	logical, indicating if empty factor levels be dropped from the output, default is TRUE.
`excludeLevels`	a named list of covariate levels to exclude from statistical tests in the form list(varname =c('level1','level2')). These levels will be excluded from association tests, but not the table. This can be useful for levels where there is a logical skip (ie not missing, but not presented). Ignored if pvalue=FALSE.
`numobs`	named list overriding the number of people you expect to have the covariate
`markup`	boolean indicating if you want latex markup
`sanitize`	boolean indicating if you want to sanitize all strings to not break LaTeX
`chunk_label`	only used if output is to Word to allow cross-referencing

Value

A character vector of the table source code, unless tableOnly=TRUE in which case a data frame is returned

Examples

## Not run: 
# Example 1
data(ae)
rm_covsum_nested(data = ae, id = c("ae_detail", "Subject"), covs = c("AE_SEV_GD", 
"AE_ONSET_DT_INT"), maincov = "CTC_AE_ATTR_SCALE")

# Example 2: set variable labels and other options, save output with markup
data("ae")
lbls <- data.frame(c1=c('AE_SEV_GD','AE_ONSET_DT_INT'),
    c2=c('Adverse event severity grade','Adverse event onset date'))
ae$AE_SEV_GD <- as.numeric(ae$AE_SEV_GD)
ae <- reportRmd::set_labels(ae, lbls)
output_tab <- rm_covsum_nested(data = ae, id = c("ae_detail", "Subject"), 
    covs = c("AE_SEV_GD", "AE_ONSET_DT_INT"), maincov = "CTC_AE_ATTR_SCALE", 
    testcat = "Fisher", percentage = c("col"), show.tests = FALSE, pvalue = TRUE, 
    effSize = FALSE, full = TRUE, IQR = FALSE, nicenames = TRUE, sanitize = TRUE, 
    markup = TRUE, include_missing = TRUE, just.nested.pvalue = TRUE, 
    tableOnly = TRUE)
cat(reportRmd::outTable(tab=output_tab))
cat(reportRmd::outTable(output_tab, format="html"), file = paste("./man/tables/", 
    "output_tab.html", sep=""))
cat(reportRmd::outTable(output_tab, format="latex"), file = paste("./man/tables/", 
    "output_tab.tex", sep=""))

## End(Not run)
## Not run: 
# Example 1
data(ae)
rm_covsum_nested(data = ae, id = c("ae_detail", "Subject"), covs = c("AE_SEV_GD", 
"AE_ONSET_DT_INT"), maincov = "CTC_AE_ATTR_SCALE")

# Example 2: set variable labels and other options, save output with markup
data("ae")
lbls <- data.frame(c1=c('AE_SEV_GD','AE_ONSET_DT_INT'),
    c2=c('Adverse event severity grade','Adverse event onset date'))
ae$AE_SEV_GD <- as.numeric(ae$AE_SEV_GD)
ae <- reportRmd::set_labels(ae, lbls)
output_tab <- rm_covsum_nested(data = ae, id = c("ae_detail", "Subject"), 
    covs = c("AE_SEV_GD", "AE_ONSET_DT_INT"), maincov = "CTC_AE_ATTR_SCALE", 
    testcat = "Fisher", percentage = c("col"), show.tests = FALSE, pvalue = TRUE, 
    effSize = FALSE, full = TRUE, IQR = FALSE, nicenames = TRUE, sanitize = TRUE, 
    markup = TRUE, include_missing = TRUE, just.nested.pvalue = TRUE, 
    tableOnly = TRUE)
cat(reportRmd::outTable(tab=output_tab))
cat(reportRmd::outTable(output_tab, format="html"), file = paste("./man/tables/", 
    "output_tab.html", sep=""))
cat(reportRmd::outTable(output_tab, format="latex"), file = paste("./man/tables/", 
    "output_tab.tex", sep=""))

## End(Not run)

Package 'BiostatsUHNplus'

Help Index

Simulated adverse events for patients receiving two study agents.

Description

Usage

Format

Outputs related adverse event timeline plots including just system organ class (AE category), or system organ class and lowest level term (AE detail). This function can fit up to 5 different attributions. Modify width, height and scale parameters in ggsave() to customize fit for large plot.

Description

Usage

Arguments

Value

Examples

Modification of the as.numeric function that prints entries that fail to parse as a message

Description

Usage

Arguments

Value

Examples

Caterpillar plot. Useful for plotting random effects from hierarchical models, such as MCMCglmm::MCMCglmm() object, that have binary outcome.

Description

Usage

Arguments

Value

Examples

Nested version of reportRmd covsum()

Description

Usage

Arguments

See Also

Simulated demography for patients.

Description

Usage

Format

Simulated study agent 1 for patients.

Description

Usage

Format

Simulated study agent 2 for patients.

Description

Usage

Format

Outputs the three DSMB-CCRU AE summary tables in Excel format per UHN template

Description

Usage

Arguments

Value

Examples

Enrollment data Simulated enrollment for patients.

Description

Usage

Format

Simulated ineligibility for patients.

Description

Usage

Format

Nice table of model output from MCMCglmm::MCMCglmm()

Description

Usage

Arguments

Value

Examples

Nice table of intraclass correlation coefficients from MCMCglmm::MCMCglmm() model output

Description

Usage

Arguments

Value

Examples

Combines exported REDCap raw and label .csv files together with data dictionary. Tranforms the exported data into Excel sheets by survey instrument with one row per participant

Description

Usage

Arguments

Value

Examples

Outputs a nested version of reportRmd::rm_covsum()

Description

Usage

Arguments

Value

See Also

Examples