singlearm: Single Arm Survival

In sbalci/jsurvival: Survival Module of ClinicoPath for jamovi

Single Arm Survival

Description

Performs survival analysis for a single cohort of patients without group comparisons. Use this when you want to analyze overall survival characteristics of your entire study population - for example, to determine median survival time or 1/3/5-year survival rates for all patients collectively. This differs from regular survival analysis which compares survival between groups.

Usage

singlearm(
  data,
  elapsedtime,
  tint = FALSE,
  dxdate,
  fudate,
  outcome,
  outcomeLevel,
  dod,
  dooc,
  awd,
  awod,
  analysistype = "overall",
  cutp = "12, 36, 60",
  timetypedata = "ymd",
  timetypeoutput = "months",
  uselandmark = FALSE,
  landmark = 3,
  sc = FALSE,
  kmunicate = FALSE,
  ce = FALSE,
  ch = FALSE,
  endplot = 60,
  ybegin_plot = 0,
  yend_plot = 1,
  byplot = 12,
  multievent = FALSE,
  ci95 = FALSE,
  risktable = FALSE,
  censored = FALSE,
  medianline = "none"
)

Arguments

data	The data as a data frame.
elapsedtime	The time-to-event or follow-up duration for each patient. Should be numeric and continuous, measured in consistent units (e.g., months or years). Can be calculated automatically from dates if using the date options below.
tint	Enable this option if you want to calculate survival time from dates in your data. This is useful when you have separate columns for diagnosis date and follow-up date and want to calculate the time elapsed between them.
dxdate	The date of diagnosis or study entry for each patient. Should be in a consistent date format (e.g., YYYY-MM-DD).
fudate	The date of last follow-up or event for each patient. Should be in a consistent date format (e.g., YYYY-MM-DD).
outcome	The outcome or event of interest for each patient. Should be a factor or numeric variable indicating whether the patient experienced the event (e.g., death) or censoring (e.g., end of follow-up).
outcomeLevel	Select the level of the outcome variable that represents the event of interest. For example, if the outcome variable is "death_status" with levels "Alive" and "Dead", select "Dead" as the event level.
dod	Select the level of the outcome variable that represents death due to disease. This is useful for competing risk analysis when there are multiple event types.
dooc	Select the level of the outcome variable that represents death due to other causes. This is useful for competing risk analysis when there are multiple event types.
awd	Select the level of the outcome variable that represents being alive with disease. This is useful for competing risk analysis when there are multiple event types.
awod	Select the level of the outcome variable that represents being alive without disease. This is useful for competing risk analysis when there are multiple event types.
analysistype	Select the type of survival analysis to perform. "Overall" analyzes the survival of all patients regardless of event type. "Cause Specific" analyzes the survival for a specific event type (e.g., death due to disease). "Competing Risk" analyzes the survival for multiple event types simultaneously.
cutp	Specify the time points at which to calculate survival probabilities. Enter a comma-separated list of time points in consistent units (e.g., months or years). For example, "12, 36, 60" calculates survival probabilities at 1, 3, and 5 years.
timetypedata	select the time type in data (e.g., YYYY-MM-DD)
timetypeoutput	select the time type in output (default is months)
uselandmark	Enables landmark analysis, which addresses immortal time bias by analyzing survival only for patients who survive to a specified timepoint (the landmark). Use this when you want to eliminate the effect of early deaths or when comparing treatments that can only be given to patients who survive long enough to receive them.
landmark	Enables landmark analysis, which addresses immortal time bias by analyzing survival only for patients who survive to a specified timepoint (the landmark). Use this when you want to eliminate the effect of early deaths or when comparing treatments that can only be given to patients who survive long enough to receive them.
sc	Enable this option to generate a Kaplan-Meier survival plot with confidence intervals. This plot shows the estimated survival probability over time and is useful for visualizing survival trends in your data.
kmunicate	Enable this option to generate a publication-ready survival plot in the style of KMunicate. This plot shows the estimated survival probability over time with confidence intervals and is suitable for publication or presentation.
ce	Enable this option to calculate and plot the cumulative number of events over time. This plot shows the total number of events (e.g., deaths) that have occurred at each time point and is useful for visualizing event rates in your data.
ch	Enable this option to calculate and plot the cumulative hazard function over time. This plot shows the cumulative risk of experiencing the event (e.g., death) at each time point and is useful for visualizing the risk of the event over time.
endplot	The maximum time point to include in the survival plots. This is the end time for the survival curves and cumulative event/hazard plots. Enter a positive integer representing the time in consistent units (e.g., months or years).
ybegin_plot	The minimum value for the y-axis in the survival plots. Enter a number between 0 and 1 to set the lower limit of the y-axis.
yend_plot	The maximum value for the y-axis in the survival plots. Enter a number between 0 and 1 to set the upper limit of the y-axis.
byplot	The interval for plotting survival probabilities. Enter a positive integer representing the time interval in consistent units (e.g., months or years).
multievent	Enable this option to perform survival analysis for datasets with multiple event levels. This is useful for competing risk analysis when there are multiple event types (e.g., death due to disease, death due to other causes).
ci95	Enable this option to display 95\ survival plots. These intervals show the range of uncertainty around the estimated survival probabilities and are useful for assessing the precision of the estimates.
risktable	Enable this option to display a table of risk estimates at each time point. This table shows the estimated survival probability, cumulative event rate, and cumulative hazard at each time point and is useful for summarizing the survival characteristics of your data.
censored	Enable this option to display censored observations on the survival plots. Censored observations are patients who have not experienced the event of interest by the end of follow-up and are indicated by vertical ticks on the survival curves.
medianline	If true, displays a line indicating the median survival time on the survival plot.

Value

A results object containing:

results$todo					a html
results$medianSummary					a preformatted
results$medianTable					a table
results$survTableSummary					a preformatted
results$survTable					a table
results$plot					an image
results$plot2					an image
results$plot3					an image
results$plot6					an image
results$calculatedtime					an output
results$outcomeredefined					an output

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$medianTable$asDF

as.data.frame(results$medianTable)

Examples

# Example analyzing overall survival for a patient cohort:
# singlearm(
#   data = patient_data,
#   time = "months_survival",
#   outcome = "death_status",
#   outcome_level = "Dead"
# )

sbalci/jsurvival documentation built on Feb. 26, 2025, 1:20 a.m.