knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
With the psfmi
package you can pool Cox regression models by using
the following pooling methods: RR (Rubin's Rules), D1, D2, and MPR
(Median R Rule). You can also use forward or backward selection from
the pooled model. This vignette show you examples of how to apply these
procedures.
If you set p.crit at 1 than no selection of variables takes place. Either using direction = "FW" or direction = "BW" will produce the same result.
library(psfmi) pool_coxr <- psfmi_coxr(data=lbpmicox, nimp=5, impvar="Impnr", formula = Surv(Time, Status) ~ Duration + Radiation + Onset + Function + Age + Previous + Tampascale + JobControl + JobDemand + Social + factor(Expect_cat), p.crit=1, method="D1", direction = "BW") pool_coxr$RR_model pool_coxr$multiparm
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library(psfmi) pool_coxr <- psfmi_coxr(data=lbpmicox, nimp=5, impvar="Impnr", formula = Surv(Time, Status) ~ Duration + Radiation + Onset + Function + Age + Previous + Tampascale + JobControl + JobDemand + Social + factor(Expect_cat), p.crit=0.05, method="D1", direction = "FW") pool_coxr$RR_model_final pool_coxr$multiparm_final pool_coxr$predictors_in
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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$predictors_in
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Pooling Cox regression models over 5 imputed datasets with backward selection using a p-value of 0.05 and as method D1 including a restricted cubic spline predictor and forcing Tampascale in the models during backward selection.
library(psfmi) pool_coxr <- psfmi_coxr(data=lbpmicox, nimp=5, impvar="Impnr", formula = Surv(Time, Status) ~ Duration + Radiation + Onset + Function + Previous + rcs(Tampascale, 3) + factor(Satisfaction) + rcs(Tampascale, 3):Radiation, keep.predictors = "Tampascale", p.crit=0.05, method="D1", direction = "BW") pool_coxr$RR_model_final pool_coxr$multiparm_final pool_coxr$predictors_in
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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$predictors_in
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Pooling Cox regression models over 5 imputed datasets with backward selection using a p-value of 0.05 and as method MPR for a stratified Cox model.
library(psfmi) pool_coxr <- psfmi_coxr(data=lbpmicox, nimp=5, impvar="Impnr", formula = Surv(Time, Status) ~ Duration + Onset + Function + Previous + rcs(Tampascale, 3) + factor(Satisfaction) + strata(Radiation), p.crit=0.05, method="MPR", direction = "BW") pool_coxr$RR_model_final pool_coxr$multiparm_final pool_coxr$formula_step
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