Multivariable Survival Analysis — multisurvival • jsurvival

Function for Multivariable Survival Analysis using Cox-regression.

multisurvival(
  data,
  elapsedtime,
  tint = FALSE,
  dxdate,
  fudate,
  outcome,
  outcomeLevel,
  dod,
  dooc,
  awd,
  awod,
  explanatory,
  contexpl,
  multievent = FALSE,
  analysistype = "overall",
  timetypedata = "ymd",
  timetypeoutput = "months",
  uselandmark = FALSE,
  landmark = 3,
  hr = FALSE,
  sty = "t1",
  ph_cox = FALSE,
  calculateRiskScore = FALSE,
  plotRiskGroups = FALSE,
  km = FALSE,
  endplot = 60,
  byplot = 12,
  ci95 = FALSE,
  risktable = FALSE,
  censored = FALSE,
  pplot = TRUE,
  ac = FALSE,
  adjexplanatory,
  ac_method = "average",
  ac_summary = FALSE,
  ac_timepoints = "12, 36, 60",
  ac_compare = FALSE,
  reduced_explanatory,
  compare_models = FALSE,
  use_modelSelection = FALSE,
  modelSelection = "enter",
  selectionCriteria = "aic",
  pEntry = 0.05,
  pRemoval = 0.1,
  use_stratify = FALSE,
  stratvar
)

Arguments

data: The dataset to be analyzed, provided as a data frame. Must contain the variables specified in the options below.
elapsedtime: The numeric variable representing follow-up time until the event or last observation. If tint = false, this should be a pre-calculated numeric time variable. If tint = true, dxdate and fudate will be used to calculate this time.
tint: If true, survival time will be calculated from dxdate and fudate. If false, elapsedtime should be provided as a pre-calculated numeric variable.
dxdate: Date of diagnosis. Required if tint = true. Must match the format specified in timetypedata.
fudate: Follow-up date or date of last observation. Required if tint = true. Must match the format specified in timetypedata.
outcome: The outcome variable. Typically indicates event status (e.g., death, recurrence). For survival analysis, this may be a factor or numeric event indicator.
outcomeLevel: The level of outcome considered as the event. For example, if outcome is a factor, specify which level indicates the event occurrence.
dod: The level of outcome corresponding to death due to disease, if applicable.
dooc: The level of outcome corresponding to death due to other causes, if applicable.
awd: The level of outcome corresponding to alive with disease, if applicable.
awod: The level of outcome corresponding to alive without disease, if applicable.
explanatory: Categorical explanatory (predictor) variables included in the Cox model.
contexpl: Continuous explanatory (predictor) variables included in the Cox model.
multievent: If true, multiple event levels will be considered for competing risks analysis. Requires specifying dod, dooc, etc.
analysistype: Type of survival analysis: - overall: All-cause survival - cause: Cause-specific survival - compete: Competing risks analysis
timetypedata: Specifies the format of the date variables in the input data. This is critical if tint = true, as dxdate and fudate will be parsed according to this format to calculate survival time. For example, if your data files record dates as "YYYY-MM-DD", select ymd.
timetypeoutput: The units in which survival time is reported in the output. Choose from days, weeks, months, or years.
uselandmark: If true, applies a landmark analysis starting at a specified time point.
landmark: The time point (in the units defined by timetypeoutput) at which to start landmark analyses. Only used if uselandmark = true.
hr: If true, generates a plot of hazard ratios for each explanatory variable in the Cox model.
sty: The style of the hazard ratio (forest) plot. "finalfit" or "survminer forestplot".
ph_cox: If true, tests the proportional hazards assumption for the Cox model. Use if you suspect violations of the PH assumption.
calculateRiskScore: If true, calculates a risk score from the Cox model coefficients for each individual.
plotRiskGroups: If true, stratifies individuals into risk groups based on their calculated risk scores and plots their survival curves.
km: If true, produces a Kaplan-Meier survival plot. Useful for visualization of survival functions without covariate adjustment.
endplot: The maximum follow-up time (in units defined by timetypeoutput) to display on survival plots.
byplot: The interval (in units defined by timetypeoutput) at which time points or labels are shown on plots.
ci95: If true, displays 95\ estimates on plots.
risktable: If true, displays the number of subjects at risk at each time point below the survival plot.
censored: If true, marks censored observations (e.g., using tick marks) on the survival plot.
pplot: If true, displays the p-value from the survival comparison test on the survival plot.
ac: .
adjexplanatory: .
ac_method: Method for computing adjusted survival curves
ac_summary: Calculate and display summary statistics for adjusted curves
ac_timepoints: Timepoints for calculating summary statistics (comma-separated)
ac_compare: Perform statistical comparison between adjusted curves
reduced_explanatory: Variables to include in a reduced model for comparison. This can be used to test whether excluding some variables affects model fit.
compare_models: If true, compares the full model against the reduced model to assess the impact of removing certain explanatory variables.
use_modelSelection: If true, applies a variable selection procedure to find the best-fitting model based on criteria like AIC or likelihood ratio tests.
modelSelection: The method used to select variables: - enter: Includes all variables (no selection) - forward: Adds variables one at a time if they improve the model - backward: Removes variables that do not significantly contribute - both: Combination of forward and backward steps
selectionCriteria: The criterion used for adding or removing variables in model selection: - aic: Balances model fit and complexity - lrt: Uses likelihood ratio tests to decide inclusion/removal
pEntry: Significance level at which a variable enters the model during forward or stepwise selection.
pRemoval: Significance level at which a variable is removed from the model during backward or stepwise selection.
use_stratify: If true, uses stratification to handle variables that violate the proportional hazards assumption. Stratification creates separate baseline hazard functions for different groups.
stratvar: Variables used for stratification. When proportional hazards are not met, stratification can adjust the model to better fit the data by allowing different baseline hazards.

Value

A results object containing:

`results$todo`					a html
`results$text`					a html
`results$text2`					a html
`results$plot`					an image
`results$plot3`					an image
`results$cox_ph`					a preformatted
`results$plot8`					an image
`results$plotKM`					an image
`results$riskScoreTable`					a table
`results$riskScoreMetrics`					a html
`results$riskGroupPlot`					an image
`results$calculatedtime`					an output
`results$outcomeredefined`					an output
`results$addRiskScore`					an output
`results$addRiskGroup`					an output
`results$plot_adj`					an image
`results$adjustedSummaryTable`					a table
`results$adjustedComparison`					a preformatted
`results$adjustedSurvTable`					a table
`results$adjustedSurvTableSummary`					a html
`results$adjustedPairwiseTable`					a table
`results$adjustedPairwiseSummary`					a html
`results$adjustedMedianTable`					a table
`results$adjustedMedianSummary`					a html
`results$adjustedCoxTable`					a table
`results$adjustedCoxText`					a html
`results$adjustedCoxSummary`					a html
`results$adjustedCoxPH`					a html
`results$adjustedCoxPHPlot`					an image
`results$model_comparison`					a html
`results$reduced_model_metrics`					a html
`results$text_model_selection`					a html
`results$text2_model_selection`					a html
`results$selectionSteps`					a table

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

results$riskScoreTable$asDF

as.data.frame(results$riskScoreTable)

Examples

# \donttest{
# example will be added
# }