Date: 2026-01-18

# Group Regression Analysis and Visualization

#### Shixiang Wang

#### 2026-01-18

We are using the same example from the ezcox package vignette.

Load the package and data.

    library(bregr)
    #> Welcome to 'bregr' package!
    #> =======================================================================
    #> You are using bregr version 1.4.0
    #>
    #> Project home : https://github.com/WangLabCSU/bregr
    #> Documentation: https://wanglabcsu.github.io/bregr/
    #> Cite as      : https://doi.org/10.1002/mdr2.70028
    #>   Wang, S., Peng, Y., Shu, C., Wang, C., Yang, Y., Zhao, Y., Cui, Y., Hu, D. and Zhou, J.-G. (2025),
    #>   bregr: An R Package for Streamlined Batch Processing and Visualization of Biomedical Regression Models. Med Research.
    #> =======================================================================
    #>

    data <- survival::lung
    data <- data |>
      dplyr::mutate(
        ph.ecog = factor(ph.ecog),
        sex = ifelse(sex == 1, "Male", "Female")
      )

Construct grouped batch survival models to determine if the variable `ph.ecog` has different survival effects under different sex groups.

    mds <- br_pipeline(
      data,
      y = c("time", "status"),
      x = "ph.ecog",
      group_by = "sex",
      method = "coxph"
    )
    #> exponentiate estimates of model(s) constructed from coxph method at default

We can examine the constructed models.

    br_get_models(mds)
    #> $Female_ph.ecog
    #> Call:
    #> survival::coxph(formula = survival::Surv(time, status) ~ ph.ecog,
    #>     data = data)
    #>
    #>            coef exp(coef) se(coef)     z       p
    #> ph.ecog1 0.4162    1.5161   0.3867 1.076 0.28182
    #> ph.ecog2 1.2190    3.3836   0.4204 2.900 0.00374
    #> ph.ecog3     NA        NA   0.0000    NA      NA
    #>
    #> Likelihood ratio test=9.23  on 2 df, p=0.009894
    #> n= 90, number of events= 53
    #>
    #> $Male_ph.ecog
    #> Call:
    #> survival::coxph(formula = survival::Surv(time, status) ~ ph.ecog,
    #>     data = data)
    #>
    #>            coef exp(coef) se(coef)     z       p
    #> ph.ecog1 0.3641    1.4393   0.2358 1.544 0.12251
    #> ph.ecog2 0.8190    2.2682   0.2696 3.038 0.00238
    #> ph.ecog3 1.8961    6.6596   1.0345 1.833 0.06682
    #>
    #> Likelihood ratio test=10.46  on 3 df, p=0.01503
    #> n= 137, number of events= 111
    #>    (1 observation deleted due to missingness)
    #>
    #> $All_ph.ecog
    #> Call:
    #> survival::coxph(formula = survival::Surv(time, status) ~ ph.ecog,
    #>     data = data)
    #>
    #>            coef exp(coef) se(coef)     z        p
    #> ph.ecog1 0.3688    1.4461   0.1987 1.857   0.0634
    #> ph.ecog2 0.9164    2.5002   0.2245 4.081 4.48e-05
    #> ph.ecog3 2.2080    9.0973   1.0258 2.152   0.0314
    #>
    #> Likelihood ratio test=18.44  on 3 df, p=0.0003561
    #> n= 227, number of events= 164
    #>    (1 observation deleted due to missingness)

Now, display the results using a forest plot.

    br_show_forest(mds)

We can optimize the plot for better visualization, for example, by removing the second column of the table and eliminating the row with `NA` results.

    br_show_forest(
      mds,
      drop = 2,
      subset = !(Group_variable == "Female" & variable == "ph.ecog" & label == 3)
    )

To subset the data rows, we can input an R expression using variables from `br_get_results(mds)`. For example, we can use `Group_variable == "Female" & variable == "ph.ecog" & label == 3` to locate the row we want to remove, and then use `!()` to select the negated rows.

If drop `All` group is necessary, update the `subset` with:

    br_show_forest(
      mds,
      drop = 2,
      subset = !((Group_variable == "Female" & variable == "ph.ecog" & label == 3) |
        (Group_variable == "All"))
    )