Pooling and Selection of Cox Regression Models"

In psfmi: Prediction Model Pooling, Selection and Performance Evaluation Across Multiply Imputed Datasets

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Introduction

With the psfmi package you can pool Cox regression models by using
the following pooling methods: RR (Rubin's Rules), D1, D2, and MPR (Median R Rule). You can also use forward or backward selection from the pooled model. This vignette show you examples of how to apply these procedures.

Examples

• [Cox regression]
  • [Pooling without BW and method D1]
  • [Pooling with FW and method MPR]
  • [Pooling with FW including interaction terms and method D1]
  • [Pooling with BW including spline coefficients and method D1]
  • [Pooling with FW including spline coefficients and method MPR]
  • [Pooling with BW for a stratified Cox model]

Pooling without BW and method D1

If you set p.crit at 1 than no selection of variables takes place. Either using direction = "FW" or direction = "BW" will produce the same result.

  library(psfmi)
  pool_coxr <- psfmi_coxr(data=lbpmicox, nimp=5, impvar="Impnr", 
                formula = Surv(Time, Status) ~ Duration + Radiation + Onset + 
                Function + Age + Previous + Tampascale + JobControl + 
                JobDemand + Social + factor(Expect_cat), p.crit=1,
                method="D1", direction = "BW")

  pool_coxr$RR_model
  pool_coxr$multiparm

Back to [Examples]

Pooling with FW and method MPR

  library(psfmi)
  pool_coxr <- psfmi_coxr(data=lbpmicox, nimp=5, impvar="Impnr", 
                formula = Surv(Time, Status) ~ Duration + Radiation + Onset + 
                Function + Age + Previous + Tampascale + JobControl + 
                JobDemand + Social + factor(Expect_cat), p.crit=0.05,
                method="D1", direction = "FW")

  pool_coxr$RR_model_final
  pool_coxr$multiparm_final
  pool_coxr$predictors_in

Back to [Examples]

Pooling with FW including interaction terms and method D1

Pooling Cox regression models over 5 imputed datasets with backward selection using a p-value of 0.05 and as method D1 including interaction terms with a categorical predictor and forcing the predictor Tampascale in the models during backward selection.

  library(psfmi)

  pool_coxr <- psfmi_coxr(data=lbpmicox, nimp=5, impvar="Impnr", 
                formula = Surv(Time, Status) ~ Duration + Radiation + Onset + 
                Function + Age + Previous + Tampascale + factor(Expect_cat) +
                factor(Satisfaction) + Tampascale:Radiation + 
                factor(Expect_cat):Tampascale, keep.predictors = "Tampascale",
                p.crit=0.05, method="D1", direction = "FW")

  pool_coxr$RR_model_final
  pool_coxr$multiparm_final
  pool_coxr$predictors_in

Back to [Examples]

Pooling with BW including spline coefficients and method D1

Pooling Cox regression models over 5 imputed datasets with backward selection using a p-value of 0.05 and as method D1 including a restricted cubic spline predictor and forcing Tampascale in the models during backward selection.

  library(psfmi)

  pool_coxr <- psfmi_coxr(data=lbpmicox, nimp=5, impvar="Impnr", 
                formula = Surv(Time, Status) ~ Duration + Radiation + Onset + 
                Function + Previous + rcs(Tampascale, 3) + 
                factor(Satisfaction) + rcs(Tampascale, 3):Radiation,  
                keep.predictors = "Tampascale",
                p.crit=0.05, method="D1", direction = "BW")

  pool_coxr$RR_model_final
  pool_coxr$multiparm_final
  pool_coxr$predictors_in

Back to [Examples]

Pooling with FW including spline coefficients and method MPR

Pooling Cox regression models over 5 imputed datasets with forward selection using a p-value of 0.05 and as method MPR including a restricted cubic spline predictor and forcing Tampascale in the models during forward selection.

  library(psfmi)
  pool_coxr <- psfmi_coxr(data=lbpmicox, nimp=5, impvar="Impnr", 
                formula = Surv(Time, Status) ~ Duration + Radiation + Onset + 
                Function + Previous + rcs(Tampascale, 3) + 
                factor(Satisfaction) + rcs(Tampascale, 3):Radiation,  
                keep.predictors = "Tampascale",
                p.crit=0.05, method="MPR", direction = "FW")

  pool_coxr$RR_model_final
  pool_coxr$multiparm_final
  pool_coxr$predictors_in

Back to [Examples]

Pooling with BW for a stratified Cox model

Pooling Cox regression models over 5 imputed datasets with backward selection using a p-value of 0.05 and as method MPR for a stratified Cox model.

  library(psfmi)
  pool_coxr <- psfmi_coxr(data=lbpmicox, nimp=5, impvar="Impnr", 
                formula = Surv(Time, Status) ~ Duration + Onset + 
                Function + Previous + rcs(Tampascale, 3) + 
                factor(Satisfaction) + strata(Radiation), 
                p.crit=0.05, method="MPR", direction = "BW")

  pool_coxr$RR_model_final
  pool_coxr$multiparm_final
  pool_coxr$formula_step

Back to [Examples]

psfmi documentation built on July 9, 2023, 7:02 p.m.