use_parametric_survival: ###########################################################################...

In packDAMipd: Decision Analysis Modelling Package with Parameters Estimation Ability from Individual Patient Level Data

Description

########################################################################### Get the parameter values using the survival analysis parametric survival

Usage

use_parametric_survival(
  param_to_be_estimated,
  dataset,
  indep_var,
  info_distribution,
  covariates,
  timevar_survival,
  cluster_var = NA
)

Arguments

param_to_be_estimated	parameter of interest
dataset	data set to be provided
indep_var	the independent variable (column name in data file)
info_distribution	distribution name eg. for logistic regression -binomial
covariates	list of covariates
timevar_survival	time variable for survival analysis, default is NA
cluster_var	cluster variable for survival analysis

Details

This function is the last in the layer of function for parametric survival analysis. This then returns the parameters of interest, plots the results etc if the distribution is weibull it uses the package SurvRegCensCov for easy interpretation of results Returns the fit result, summary of regression, variance-covariance matrix of coeff, cholesky decomposition, the parameters that define the assumed distribution and the plot of model prediction Using survfit from survival package to plot the survival curve R's weibull distribution is defined as std weibull in terms of a and b as (a/b) (x/b)^ (a-1) exp((-x/b)^a) where a is the shape and b is the scale In HE the weibull distribution is parameterised as bit different it is like gamma.lambda. t^(gamma-1) .exp(-lambda*t^gamma) where gamma is the shape and lambda is the scale. The relationship is as below. HE_shape = rweibull_shape HE_scale = rweibull_scale ^(-rweibull_shape) The survreg shape and scale are again bit different and they are rweibull's shape and scale as below. rweibull_shape = 1/fit$scale rweibull_scale = exp(fit intercept)= exp(fit$coefficients) remember to use 1st of coefficients This has been utilised in SurvRegCensCov::ConvertWeibull predict() for survreg object with type =quantile will provide the failure times as survival function is 1-CDF of failure time.

Value

the results of the regression analysis

Examples

data_for_survival <- survival::lung
surv_estimated <- use_parametric_survival("status",
data_for_survival, "sex", info_distribution = "weibull",
covariates = c("ph.ecog"), "time")

packDAMipd documentation built on March 3, 2021, 5:07 p.m.