问如何将mimira对象(Cox回归模型，从多重估算和倾向分数匹配(MatchThem pkg))转换为森林地块

EN

Error in ggforest(tbl_regression_object, data = mimira_object) : 
  inherits(model, "coxph") is not TRUE 

#Data
library(fabricatr)
library(simsurv)

# Simulate patient data in a clinical trial
participant_data <- fabricate(
  N = 2000,
  age = runif(N, min = 18, max = 85),
  is_female = draw_binary(prob = 0.5, N = N),
  is_smoker = draw_binary(prob = 0.2 + 0.2 * (age > 50), N = N),
  disease_stage = round(runif(N, min = 1 + 0.5 * (age > 65), max = 4)),
  treatment = draw_binary(prob = 0.5, N = N),
  kps = runif(N, min = 40, max = 100)
)

# Simulate data in the survival context
survival_data <- simsurv(
  lambdas = 0.1, gammas = 1.8,
  x = participant_data, 
  betas = c(is_female = -0.2, is_smoker = 1.2,
            treatment = -0.4, kps = -0.005,
            disease_stage = 0.2),
  maxt = 5)

# Merging df
library(dplyr)
mydata_complete <- bind_cols(survival_data, participant_data)

# generating missing value
library(missMethods)
mydata_uncomp <- delete_MCAR(mydata_complete, 0.3)
mydata <- mydata_uncomp

#1 imputation with "mice"
library(mice)
mydata$nelsonaalen <- nelsonaalen(mydata, eventtime, status)
mydata_mice_imp_m3 <- mice(mydata, maxit = 2, m = 3, seed = 20200801) # m=3 is for testing


#2 matching (PSM 1:3) with "MatchThem"
library(MatchThem)
mydata_imp_m3_psm <- matchthem(treatment ~ age + is_female + disease_stage, data = mydata_mice_imp_m3, approach = "within" ,ratio= 1, method = "optimal")

#3 Pooling Coxph models in multiple imputed datasets and PSM with "survey"
library(survey)
mimira_object <- with(data = mydata_imp_m3_psm, expr = svycoxph(Surv(eventtime, status) ~ age+ is_smoker + disease_stage))
pool_and_tidy_mice(mimira_object, exponentiate = TRUE, conf.int=TRUE) -> pooled_imp_m3_cph

    # estimates with pool_and_tidy_mice() works with mimira_object but cannot bring me de degree of freedoms. Warning message :
In get.dfcom(object, dfcom) : Infinite sample size assumed.
> pooled_imp_m3_cph
           term  estimate   std.error  statistic p.value conf.low conf.high            b  df dfcom fmi    lambda m      riv         ubar
1           age 0.9995807 0.001961343 -0.2138208     NaN      NaN       NaN 1.489769e-06 NaN   Inf NaN 0.5163574 3 1.067643 1.860509e-06
2     is_smoker 2.8626952 0.093476026 11.2516931     NaN      NaN       NaN 4.182884e-03 NaN   Inf NaN 0.6382842 3 1.764601 3.160589e-03
3 disease_stage 1.2386947 0.044092483  4.8547535     NaN      NaN       NaN 8.995628e-04 NaN   Inf NaN 0.6169374 3 1.610540 7.447299e-04

#4 Table summary of the pooled results
library(gtsummary)
tbl_regression_object <- tbl_regression(mimira_object, exp=TRUE, conf.int = TRUE) # 95% CI and p-value are missing due to an issue with an other issue in the pooling of the mimira_object. The Matchthem:::get.2dfcom function gives a dfcom = 999999 (another issue to be solved in my concern)


#5 What it should looks like as graphical summary
library(survival)
mydata.cox <- coxph(Surv(eventtime, status) ~ age+ is_smoker + disease_stage, mydata_uncomp) # (df mydata_uncomp is without imputation and PSM)

    #with gtsummary
forestGT <- 
  mydata.cox %>%
  tbl_regression(exponentiate = TRUE,
                 add_estimate_to_reference_rows = TRUE) %>% 
  plot()
(forestGT) # See picture GT_plot1. Almost perfect. Would have been great to know how to add N, 95% CI, HR, p-value and parameters of the model (AIC, events, concordance, etc.)

    #with survminer
HRforest <- 
  survminer::ggforest(mydata.cox, data = mydata_uncomp)
(HRforest) # See picture Ggforest. Everything I need to know about my cox regression is all in there. For me it is just a great regression cox forest plot.

#6 Actually what happens when I do the same thing with imputed and matched df

    #with gtsummary
forestGT_imp_psm <- 
  mimira_object %>%
  tbl_regression(exponentiate = TRUE,
                 add_estimate_to_reference_rows = TRUE) %>% 
  plot() # WARNING message : In get.dfcom(object, dfcom) : Infinite sample size assumed.
(forestGT_imp_psm) # See picture GT_plot2. The plot is rendered but without 95% IC

    #with survminer
HRforest_imp_psm <- 
  ggforest(mimira_object, data = mydata_imp_m3_psm) # ERROR:in ggforest(mimira_object, data = mydata_imp_m3_psm) : inherits(model, "coxph") is not TRUE
(HRforest_imp_psm)

#7 The lucky and providential step
# your solution/advise