# Pooling and Selection of Cox Regression Models

#### Martijn W Heymans

#### 2021-09-23

# 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 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
    #> $`Step 1 - no variables removed -`
    #>                   term     estimate   std.error  statistic       df    p.value
    #> 1             Duration -0.007738082 0.003989357 -1.9396814 178.7494 0.05399243
    #> 2            Radiation -0.074924387 0.153336031 -0.4886287 178.9424 0.62570287
    #> 3                Onset -0.093736647 0.175892468 -0.5329202 179.0136 0.59474977
    #> 4             Function  0.043959177 0.016917684  2.5984159 175.2854 0.01016263
    #> 5                  Age -0.008853266 0.007712097 -1.1479713 177.7066 0.25252344
    #> 6             Previous -0.098349421 0.199199997 -0.4937220 178.0134 0.62211113
    #> 7           Tampascale -0.023267973 0.014089917 -1.6513918 133.5549 0.10100803
    #> 8           JobControl -0.008379513 0.008329969 -1.0059477 178.6831 0.31580101
    #> 9            JobDemand -0.021840998 0.015512484 -1.4079626 178.3078 0.16088320
    #> 10              Social -0.051348486 0.024912588 -2.0611462 178.3036 0.04074068
    #> 11 factor(Expect_cat)2  0.243295714 0.231183479  1.0523923 177.6191 0.29404919
    #> 12 factor(Expect_cat)3  0.227055353 0.200259380  1.1338063 178.6187 0.25839523
    #>           HR lower.EXP upper.EXP
    #> 1  0.9922918 0.9845108 1.0001342
    #> 2  0.9278136 0.6855705 1.2556522
    #> 3  0.9105225 0.6435046 1.2883376
    #> 4  1.0449397 1.0106267 1.0804177
    #> 5  0.9911858 0.9762151 1.0063861
    #> 6  0.9063322 0.6117408 1.3427877
    #> 7  0.9770006 0.9501492 1.0046109
    #> 8  0.9916555 0.9754881 1.0080908
    #> 9  0.9783958 0.9488992 1.0088093
    #> 10 0.9499476 0.9043761 0.9978154
    #> 11 1.2754457 0.8082175 2.0127772
    #> 12 1.2548993 0.8452496 1.8630856
      pool_coxr$multiparm
    #> $`Step 1 - no variables removed -`
    #>                    p-values D1 F-statistic
    #> Duration           0.052418576   3.7623639
    #> Radiation          0.625104588   0.2387580
    #> Onset              0.594088831   0.2840039
    #> Function           0.009371567   6.7517650
    #> Age                0.250982797   1.3178380
    #> Previous           0.621503127   0.2437614
    #> Tampascale         0.099111558   2.7270949
    #> JobControl         0.314440908   1.0119309
    #> JobDemand          0.159143091   1.9823588
    #> Social             0.039289895   4.2483236
    #> factor(Expect_cat) 0.485351826   0.7228839

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## 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")
    #> Warning in mitml::testModels(fit1, fit0, method = method): The 'model' and
    #> 'null.model' arguments appear to include objects of different classes. Results
    #> may not be trustworthy.

    #> Warning in mitml::testModels(fit1, fit0, method = method): The 'model' and
    #> 'null.model' arguments appear to include objects of different classes. Results
    #> may not be trustworthy.

    #> Warning in mitml::testModels(fit1, fit0, method = method): The 'model' and
    #> 'null.model' arguments appear to include objects of different classes. Results
    #> may not be trustworthy.

    #> Warning in mitml::testModels(fit1, fit0, method = method): The 'model' and
    #> 'null.model' arguments appear to include objects of different classes. Results
    #> may not be trustworthy.

    #> Warning in mitml::testModels(fit1, fit0, method = method): The 'model' and
    #> 'null.model' arguments appear to include objects of different classes. Results
    #> may not be trustworthy.

    #> Warning in mitml::testModels(fit1, fit0, method = method): The 'model' and
    #> 'null.model' arguments appear to include objects of different classes. Results
    #> may not be trustworthy.

    #> Warning in mitml::testModels(fit1, fit0, method = method): The 'model' and
    #> 'null.model' arguments appear to include objects of different classes. Results
    #> may not be trustworthy.

    #> Warning in mitml::testModels(fit1, fit0, method = method): The 'model' and
    #> 'null.model' arguments appear to include objects of different classes. Results
    #> may not be trustworthy.

    #> Warning in mitml::testModels(fit1, fit0, method = method): The 'model' and
    #> 'null.model' arguments appear to include objects of different classes. Results
    #> may not be trustworthy.

    #> Warning in mitml::testModels(fit1, fit0, method = method): The 'model' and
    #> 'null.model' arguments appear to include objects of different classes. Results
    #> may not be trustworthy.

    #> Warning in mitml::testModels(fit1, fit0, method = method): The 'model' and
    #> 'null.model' arguments appear to include objects of different classes. Results
    #> may not be trustworthy.
    #> Entered at Step 1 is - Function
    #> Entered at Step 2 is - Social
    #> Entered at Step 3 is - Duration
    #>
    #> Selection correctly terminated,
    #> No new variables entered the model
      pool_coxr$RR_model_final
    #> $`Final model`
    #>       term     estimate   std.error statistic       df      p.value        HR
    #> 1 Duration -0.007605055 0.003742491 -2.032084 188.0122 0.0435521399 0.9924238
    #> 2 Function  0.056490633 0.015414714  3.664722 188.0122 0.0003219893 1.0581167
    #> 3   Social -0.051992380 0.022611457 -2.299382 188.0122 0.0225821316 0.9493361
    #>   lower.EXP upper.EXP
    #> 1 0.9851240 0.9997776
    #> 2 1.0264257 1.0907861
    #> 3 0.9079217 0.9926396
      pool_coxr$RR_model_final
    #> $`Final model`
    #>       term     estimate   std.error statistic       df      p.value        HR
    #> 1 Duration -0.007605055 0.003742491 -2.032084 188.0122 0.0435521399 0.9924238
    #> 2 Function  0.056490633 0.015414714  3.664722 188.0122 0.0003219893 1.0581167
    #> 3   Social -0.051992380 0.022611457 -2.299382 188.0122 0.0225821316 0.9493361
    #>   lower.EXP upper.EXP
    #> 1 0.9851240 0.9997776
    #> 2 1.0264257 1.0907861
    #> 3 0.9079217 0.9926396
      pool_coxr$predictors_in
    #>          Duration Radiation Onset Function Age Previous Tampascale JobControl
    #> Step 1          0         0     0        1   0        0          0          0
    #> Step 2          0         0     0        0   0        0          0          0
    #> Step 3          1         0     0        0   0        0          0          0
    #> Included        1         0     0        1   0        0          0          0
    #>          JobDemand Social factor(Expect_cat)
    #> Step 1           0      0                  0
    #> Step 2           0      1                  0
    #> Step 3           0      0                  0
    #> Included         0      1                  0

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## 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")
    #> Entered at Step 1 is - Function
    #> Entered at Step 2 is - Duration
    #>
    #> Selection correctly terminated,
    #> No new variables entered the model
      pool_coxr$RR_model_final
    #> $`Final model`
    #>         term     estimate   std.error statistic       df     p.value        HR
    #> 1   Duration -0.008309329 0.003753484 -2.213765 187.9452 0.028047868 0.9917251
    #> 2 Tampascale -0.016998788 0.013360456 -1.272321 177.6436 0.204921997 0.9831449
    #> 3   Function  0.050217190 0.016427330  3.056930 187.0360 0.002563622 1.0514994
    #>   lower.EXP upper.EXP
    #> 1 0.9844091 0.9990955
    #> 2 0.9575624 1.0094108
    #> 3 1.0179701 1.0861332
      pool_coxr$RR_model_final
    #> $`Final model`
    #>         term     estimate   std.error statistic       df     p.value        HR
    #> 1   Duration -0.008309329 0.003753484 -2.213765 187.9452 0.028047868 0.9917251
    #> 2 Tampascale -0.016998788 0.013360456 -1.272321 177.6436 0.204921997 0.9831449
    #> 3   Function  0.050217190 0.016427330  3.056930 187.0360 0.002563622 1.0514994
    #>   lower.EXP upper.EXP
    #> 1 0.9844091 0.9990955
    #> 2 0.9575624 1.0094108
    #> 3 1.0179701 1.0861332
      pool_coxr$predictors_in
    #>          Duration Radiation Onset Function Age Previous Tampascale
    #> Step 1          0         0     0        1   0        0          1
    #> Step 2          1         0     0        0   0        0          1
    #> Included        1         0     0        1   0        0          1
    #>          factor(Expect_cat) factor(Satisfaction) Radiation*Tampascale
    #> Step 1                    0                    0                    0
    #> Step 2                    0                    0                    0
    #> Included                  0                    0                    0
    #>          Tampascale*factor(Expect_cat)
    #> Step 1                               0
    #> Step 2                               0
    #> Included                             0

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## 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")
    #> Removed at Step 1 is - factor(Satisfaction)
    #> Removed at Step 2 is - Radiation*rcs(Tampascale,3)
    #> Removed at Step 3 is - Onset
    #> Removed at Step 4 is - Radiation
    #> Removed at Step 5 is - Previous
    #>
    #> Selection correctly terminated,
    #> No more variables removed from the model
      pool_coxr$RR_model_final
    #> $`Step 6`
    #>                            term    estimate  std.error statistic       df
    #> 1                      Duration -0.00834864 0.00375033 -2.226108 186.9008
    #> 2                      Function  0.05529708 0.01668254  3.314668 184.6679
    #> 3  rcs(Tampascale, 3)Tampascale -0.06563800 0.02724068 -2.409558 129.2769
    #> 4 rcs(Tampascale, 3)Tampascale'  0.05942661 0.02982168  1.992732 106.0096
    #>       p.value        HR lower.EXP upper.EXP
    #> 1 0.027201742 0.9916861 0.9843763 0.9990502
    #> 2 0.001104361 1.0568545 1.0226366 1.0922174
    #> 3 0.017380618 0.9364698 0.8873345 0.9883259
    #> 4 0.048860903 1.0612279 1.0003023 1.1258642
      pool_coxr$RR_model_final
    #> $`Step 6`
    #>                            term    estimate  std.error statistic       df
    #> 1                      Duration -0.00834864 0.00375033 -2.226108 186.9008
    #> 2                      Function  0.05529708 0.01668254  3.314668 184.6679
    #> 3  rcs(Tampascale, 3)Tampascale -0.06563800 0.02724068 -2.409558 129.2769
    #> 4 rcs(Tampascale, 3)Tampascale'  0.05942661 0.02982168  1.992732 106.0096
    #>       p.value        HR lower.EXP upper.EXP
    #> 1 0.027201742 0.9916861 0.9843763 0.9990502
    #> 2 0.001104361 1.0568545 1.0226366 1.0922174
    #> 3 0.017380618 0.9364698 0.8873345 0.9883259
    #> 4 0.048860903 1.0612279 1.0003023 1.1258642
      pool_coxr$predictors_in
    #> # A tibble: 3 x 1
    #>   value
    #>   <chr>
    #> 1 Duration
    #> 2 Function
    #> 3 rcs(Tampascale,3)

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## 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")
    #> Entered at Step 1 is - Function
    #> Entered at Step 2 is - Duration
    #>
    #> Selection correctly terminated,
    #> No new variables entered the model
      pool_coxr$RR_model_final
    #> $`Final model`
    #>                            term    estimate  std.error statistic       df
    #> 1                      Duration -0.00834864 0.00375033 -2.226108 186.9008
    #> 2  rcs(Tampascale, 3)Tampascale -0.06563800 0.02724068 -2.409558 129.2769
    #> 3 rcs(Tampascale, 3)Tampascale'  0.05942661 0.02982168  1.992732 106.0096
    #> 4                      Function  0.05529708 0.01668254  3.314668 184.6679
    #>       p.value        HR lower.EXP upper.EXP
    #> 1 0.027201742 0.9916861 0.9843763 0.9990502
    #> 2 0.017380618 0.9364698 0.8873345 0.9883259
    #> 3 0.048860903 1.0612279 1.0003023 1.1258642
    #> 4 0.001104361 1.0568545 1.0226366 1.0922174
      pool_coxr$RR_model_final
    #> $`Final model`
    #>                            term    estimate  std.error statistic       df
    #> 1                      Duration -0.00834864 0.00375033 -2.226108 186.9008
    #> 2  rcs(Tampascale, 3)Tampascale -0.06563800 0.02724068 -2.409558 129.2769
    #> 3 rcs(Tampascale, 3)Tampascale'  0.05942661 0.02982168  1.992732 106.0096
    #> 4                      Function  0.05529708 0.01668254  3.314668 184.6679
    #>       p.value        HR lower.EXP upper.EXP
    #> 1 0.027201742 0.9916861 0.9843763 0.9990502
    #> 2 0.017380618 0.9364698 0.8873345 0.9883259
    #> 3 0.048860903 1.0612279 1.0003023 1.1258642
    #> 4 0.001104361 1.0568545 1.0226366 1.0922174
      pool_coxr$predictors_in
    #>          Duration Radiation Onset Function Previous factor(Satisfaction)
    #> Step 1          0         0     0        1        0                    0
    #> Step 2          1         0     0        0        0                    0
    #> Included        1         0     0        1        0                    0
    #>          rcs(Tampascale,3) Radiation*rcs(Tampascale,3)
    #> Step 1                   1                           0
    #> Step 2                   1                           0
    #> Included                 1                           0

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