On this page you will find information of the psfmi package. The package contains functions to apply pooling or backward selection (BS) for logistic or Cox regression prediction models.
The basic pooling method is Rubin’s Rules. New is that for categorical predictors, different methods to derive pooled p-values are available as: the total covariance matrix (D1 method), pooling Chi-square values (D2 method), pooling Likelihood ratio statistics (method of Meng and Rubin) or pooling the median p-values (MPR rule). Moreover, two-way interaction terms between continuous, dichotomous and categorical predictors are allowed during BS. Also, all type of predictors, interaction terms or a combination, can be forced in the model during BS. For very large datasets and a large number of imputed datasets the D1 and D3 methods may be less efficient than the D2 and MPR methods.
The package also contains functions to generate apparent model performance measures over imputed datasets as ROC/AUC, Nagelkerke R-squares, Hosmer & Lemeshow test values and calibration plots. A wrapper function over Frank Harrell’s validate function is available. Bootstrap internal validation is performed in each imputed dataset and results are pooled. BS as part of internal validation is optional and recommended. A function with the name mivalext_lr can be used to externally validate prediction models in multiple imputed datasets. The following information of the externally validated model is provided: pooled ROC/AUC, (Nagelkerke) R-Square value, Hosmer and Lemeshow Test, pooled coefficients when the model is freely estimated in imputed datasets and the pooled linear predictor (LP), with information about miscalibration in intercept and slope.
The package can be installed from Github by running the following code in the R console window:
install.packages(“devtools”)
library(devtools)
devtools::install_github(“mwheymans/psfmi”)
library(psfmi)
The main functions that are available in the psfmi package are:
psfmi_lr: pooling and selection of Logistic regression models in multiple imputed datasets
psfmi_coxr: pooling and selection of Cox regression models in multiple imputed datasets
miperform_lr: for performance and internal validation of logistic regression models in multiple imputed datasets
mivalext_lr: external validation in multiple imputed datasets
library(psfmi)
#> Registered S3 methods overwritten by 'ggplot2':
#> method from
#> [.quosures rlang
#> c.quosures rlang
#> print.quosures rlang
pool_lr <- psfmi_lr(data=lbpmilr, nimp=5, impvar="Impnr", Outcome="Chronic",
predictors=c("Gender", "Smoking", "Function", "JobControl",
"JobDemands", "SocialSupport"), method="D1")
#>
#> Pooled model correctly estimated
#> using a p-value of 1
pool_lr$RR_Model
#> $`Step 1`
#> est std.err signif lower upper OR L.OR
#> (Intercept) 1.1637 2.2828 0.6102 -3.3104 5.6379 3.2018 0.0365
#> Gender -0.4220 0.4073 0.3001 -1.2202 0.3762 0.6557 0.2952
#> Smoking 0.1164 0.3407 0.7327 -0.5515 0.7842 1.1234 0.5761
#> Function -0.1457 0.0421 0.0005 -0.2282 -0.0632 0.8644 0.7959
#> JobControl 0.0021 0.0191 0.9145 -0.0355 0.0396 1.0021 0.9652
#> JobDemands -0.0087 0.0357 0.8065 -0.0787 0.0612 0.9913 0.9243
#> SocialSupport 0.0249 0.0540 0.6449 -0.0810 0.1308 1.0252 0.9222
#> U.OR
#> (Intercept) 280.8649
#> Gender 1.4568
#> Smoking 2.1907
#> Function 0.9387
#> JobControl 1.0404
#> JobDemands 1.0632
#> SocialSupport 1.1397
pool_lr$multiparm_p
#> $`Step 1`
#> Chi_sq D1 & RR p-values
#> Gender 1.074 0.3001
#> Smoking 0.117 0.7327
#> Function 11.984 0.0005
#> JobControl 0.012 0.9145
#> JobDemands 0.060 0.8065
#> SocialSupport 0.212 0.6449Back to Examples
Pooling Logistic regression models over 5 imputed datasets with backward selection using a p-value of 0.05 and as method D3 (Meng and Rubin likelihood ratio statistics) and forcing the predictor “Smoking” in the models during backward selection
library(psfmi)
pool_lr <- psfmi_lr(data=lbpmilr, nimp=5, impvar="Impnr", Outcome="Chronic",
predictors=c("Gender", "Smoking", "Function", "JobControl",
"JobDemands", "SocialSupport"), keep.predictors = "Smoking",
p.crit = 0.05, method="D3")
#> Variable excluded at Step 1 is - JobControl
#> Variable excluded at Step 2 is - JobDemands
#> Variable excluded at Step 3 is - SocialSupport
#> Variable excluded at Step 4 is - Gender
#>
#> Pooled model correctly estimated
#> using a p-value of 0.05 and predictors to keep Smoking
pool_lr$RR_Model
#> $`Step 1`
#> est std.err signif lower upper OR L.OR
#> (Intercept) 1.1637 2.2828 0.6102 -3.3104 5.6379 3.2018 0.0365
#> Gender -0.4220 0.4073 0.3001 -1.2202 0.3762 0.6557 0.2952
#> Smoking 0.1164 0.3407 0.7327 -0.5515 0.7842 1.1234 0.5761
#> Function -0.1457 0.0421 0.0005 -0.2282 -0.0632 0.8644 0.7959
#> JobControl 0.0021 0.0191 0.9145 -0.0355 0.0396 1.0021 0.9652
#> JobDemands -0.0087 0.0357 0.8065 -0.0787 0.0612 0.9913 0.9243
#> SocialSupport 0.0249 0.0540 0.6449 -0.0810 0.1308 1.0252 0.9222
#> U.OR
#> (Intercept) 280.8649
#> Gender 1.4568
#> Smoking 2.1907
#> Function 0.9387
#> JobControl 1.0404
#> JobDemands 1.0632
#> SocialSupport 1.1397
#>
#> $`Step 2`
#> est std.err signif lower upper OR L.OR
#> (Intercept) 1.3088 1.8407 0.4770 -2.2988 4.9164 3.7019 0.1004
#> Gender -0.4296 0.4012 0.2843 -1.2158 0.3567 0.6508 0.2965
#> Smoking 0.1173 0.3406 0.7305 -0.5502 0.7849 1.1245 0.5768
#> Function -0.1450 0.0415 0.0005 -0.2262 -0.0637 0.8651 0.7975
#> JobDemands -0.0089 0.0357 0.8019 -0.0788 0.0610 0.9911 0.9242
#> SocialSupport 0.0239 0.0532 0.6532 -0.0804 0.1282 1.0242 0.9228
#> U.OR
#> (Intercept) 136.5169
#> Gender 1.4286
#> Smoking 2.1922
#> Function 0.9383
#> JobDemands 1.0628
#> SocialSupport 1.1367
#>
#> $`Step 3`
#> est std.err signif lower upper OR L.OR U.OR
#> (Intercept) 0.9989 1.3631 0.4637 -1.6728 3.6705 2.7152 0.1877 39.2713
#> Gender -0.4128 0.3955 0.2966 -1.1880 0.3624 0.6618 0.3048 1.4367
#> Smoking 0.1177 0.3406 0.7298 -0.5499 0.7852 1.1249 0.5770 2.1929
#> Function -0.1448 0.0414 0.0005 -0.2260 -0.0636 0.8652 0.7977 0.9384
#> SocialSupport 0.0236 0.0532 0.6575 -0.0806 0.1278 1.0239 0.9225 1.1363
#>
#> $`Step 4`
#> est std.err signif lower upper OR L.OR U.OR
#> (Intercept) 1.5547 0.5442 0.0043 0.4882 2.6212 4.7337 1.6293 13.7527
#> Gender -0.4212 0.3944 0.2855 -1.1943 0.3518 0.6562 0.3029 1.4216
#> Smoking 0.1133 0.3402 0.7392 -0.5535 0.7800 1.1199 0.5749 2.1815
#> Function -0.1445 0.0414 0.0005 -0.2257 -0.0633 0.8654 0.7980 0.9386
#>
#> $`Step 5`
#> est std.err signif lower upper OR L.OR U.OR
#> (Intercept) 1.2804 0.4737 0.0069 0.3519 2.2089 3.5981 1.4218 9.1058
#> Smoking 0.1087 0.3389 0.7485 -0.5555 0.7728 1.1148 0.5738 2.1658
#> Function -0.1486 0.0412 0.0003 -0.2293 -0.0679 0.8619 0.7951 0.9343
pool_lr$multiparm_p
#> $`Step 1`
#> D3 p-values
#> Gender 0.3001
#> Smoking 0.7327
#> Function 0.0005
#> JobControl 0.9145
#> JobDemands 0.8065
#> SocialSupport 0.6449
#>
#> $`Step 2`
#> D3 p-values
#> Gender 0.2843
#> Smoking 0.7305
#> Function 0.0005
#> JobDemands 0.8019
#> SocialSupport 0.6532
#>
#> $`Step 3`
#> D3 p-values
#> Gender 0.2966
#> Smoking 0.7298
#> Function 0.0005
#> SocialSupport 0.6575
#>
#> $`Step 4`
#> D3 p-values
#> Gender 0.2855
#> Smoking 0.7392
#> Function 0.0005
#>
#> $`Step 5`
#> D3 p-values
#> Smoking 0.7485
#> Function 0.0003Back to Examples
Pooling Logistic regression models over 5 imputed datasets with BS using a p-value of 0.05 and as method D1. Several interaction terms, including with a categorical predictor, are part of the selection procedure.
library(psfmi)
pool_lr <- psfmi_lr(data=lbpmilr, nimp=5, impvar="Impnr", Outcome="Chronic",
predictors=c("Gender", "Smoking", "Function", "JobControl", "JobDemands",
"SocialSupport"), p.crit = 0.05, cat.predictors = c("Carrying", "Satisfaction"),
int.predictors = c("Carrying:Smoking", "Gender:Smoking"), method="D2")
#> Registered S3 methods overwritten by 'lme4':
#> method from
#> cooks.distance.influence.merMod car
#> influence.merMod car
#> dfbeta.influence.merMod car
#> dfbetas.influence.merMod car
#> Variable excluded at Step 1 is - JobDemands
#> Variable excluded at Step 2 is - JobControl
#> Variable excluded at Step 3 is - SocialSupport
#> Variable excluded at Step 4 is - Smoking:factor(Carrying)
#> Variable excluded at Step 5 is - Gender:Smoking
#> Variable excluded at Step 6 is - Smoking
#> Variable excluded at Step 7 is - Gender
#> Variable excluded at Step 8 is - factor(Satisfaction)
#> Variable excluded at Step 9 is - Function
pool_lr$RR_Model
#> $`Step 1`
#> est std.err signif lower upper OR
#> (Intercept) 0.2658 2.7332 0.9226 -5.0995 5.6311 1.3045
#> Gender -0.9613 0.6239 0.1238 -2.1862 0.2636 0.3824
#> Smoking -2.4473 1.5169 0.1115 -5.4769 0.5823 0.0865
#> Function -0.0792 0.0504 0.1173 -0.1783 0.0200 0.9239
#> JobControl -0.0071 0.0213 0.7399 -0.0488 0.0347 0.9930
#> JobDemands 0.0019 0.0400 0.9616 -0.0765 0.0803 1.0019
#> SocialSupport 0.0473 0.0593 0.4254 -0.0690 0.1635 1.0484
#> factor(Carrying)2 0.4834 0.6996 0.4898 -0.8908 1.8577 1.6216
#> factor(Carrying)3 1.6765 0.6702 0.0126 0.3603 2.9928 5.3471
#> factor(Satisfaction)2 -0.5596 0.5426 0.3069 -1.6473 0.5280 0.5714
#> factor(Satisfaction)3 -1.0948 0.5989 0.0682 -2.2718 0.0822 0.3346
#> Smoking:factor(Carrying)2 2.1009 1.3864 0.1330 -0.6509 4.8526 8.1731
#> Smoking:factor(Carrying)3 1.9226 1.4998 0.2064 -1.0981 4.9432 6.8384
#> Gender:Smoking 1.1125 0.8993 0.2161 -0.6504 2.8753 3.0418
#> L.OR U.OR
#> (Intercept) 0.0061 278.9559
#> Gender 0.1123 1.3016
#> Smoking 0.0042 1.7902
#> Function 0.8367 1.0202
#> JobControl 0.9523 1.0353
#> JobDemands 0.9264 1.0837
#> SocialSupport 0.9333 1.1777
#> factor(Carrying)2 0.4103 6.4091
#> factor(Carrying)3 1.4338 19.9412
#> factor(Satisfaction)2 0.1926 1.6955
#> factor(Satisfaction)3 0.1031 1.0857
#> Smoking:factor(Carrying)2 0.5216 128.0779
#> Smoking:factor(Carrying)3 0.3335 140.2179
#> Gender:Smoking 0.5218 17.7305
#>
#> $`Step 2`
#> est std.err signif lower upper OR
#> (Intercept) 0.3385 2.2237 0.8790 -4.0229 4.6998 1.4028
#> Gender -0.9650 0.6179 0.1187 -2.1778 0.2478 0.3810
#> Smoking -2.4450 1.5196 0.1126 -5.4813 0.5912 0.0867
#> Function -0.0793 0.0505 0.1173 -0.1786 0.0200 0.9238
#> JobControl -0.0071 0.0212 0.7374 -0.0487 0.0345 0.9929
#> SocialSupport 0.0473 0.0593 0.4251 -0.0690 0.1636 1.0485
#> factor(Carrying)2 0.4799 0.6966 0.4911 -0.8881 1.8479 1.6159
#> factor(Carrying)3 1.6721 0.6657 0.0123 0.3648 2.9794 5.3232
#> factor(Satisfaction)2 -0.5588 0.5421 0.3072 -1.6455 0.5278 0.5719
#> factor(Satisfaction)3 -1.0935 0.5985 0.0683 -2.2696 0.0827 0.3350
#> Smoking:factor(Carrying)2 2.1003 1.3873 0.1334 -0.6539 4.8546 8.1690
#> Smoking:factor(Carrying)3 1.9227 1.4953 0.2051 -1.0887 4.9341 6.8396
#> Gender:Smoking 1.1100 0.8987 0.2168 -0.6517 2.8717 3.0344
#> L.OR U.OR
#> (Intercept) 0.0179 109.9297
#> Gender 0.1133 1.2812
#> Smoking 0.0042 1.8062
#> Function 0.8364 1.0202
#> JobControl 0.9524 1.0351
#> SocialSupport 0.9334 1.1777
#> factor(Carrying)2 0.4114 6.3462
#> factor(Carrying)3 1.4402 19.6753
#> factor(Satisfaction)2 0.1929 1.6952
#> factor(Satisfaction)3 0.1034 1.0862
#> Smoking:factor(Carrying)2 0.5200 128.3243
#> Smoking:factor(Carrying)3 0.3367 138.9520
#> Gender:Smoking 0.5212 17.6674
#>
#> $`Step 3`
#> est std.err signif lower upper OR
#> (Intercept) -0.1239 1.7169 0.9425 -3.4897 3.2419 0.8835
#> Gender -0.9389 0.6130 0.1260 -2.1420 0.2643 0.3911
#> Smoking -2.4290 1.5155 0.1138 -5.4547 0.5967 0.0881
#> Function -0.0824 0.0493 0.0952 -0.1793 0.0144 0.9209
#> SocialSupport 0.0501 0.0587 0.3932 -0.0649 0.1651 1.0514
#> factor(Carrying)2 0.4708 0.6969 0.4997 -0.8982 1.8397 1.6012
#> factor(Carrying)3 1.6785 0.6644 0.0118 0.3735 2.9834 5.3573
#> factor(Satisfaction)2 -0.5587 0.5417 0.3069 -1.6445 0.5270 0.5719
#> factor(Satisfaction)3 -1.0769 0.5945 0.0707 -2.2451 0.0913 0.3407
#> Smoking:factor(Carrying)2 2.1063 1.3887 0.1327 -0.6511 4.8637 8.2176
#> Smoking:factor(Carrying)3 1.8711 1.4732 0.2101 -1.0897 4.8319 6.4956
#> Gender:Smoking 1.1070 0.8996 0.2185 -0.6565 2.8705 3.0253
#> L.OR U.OR
#> (Intercept) 0.0305 25.5826
#> Gender 0.1174 1.3025
#> Smoking 0.0043 1.8161
#> Function 0.8359 1.0145
#> SocialSupport 0.9371 1.1796
#> factor(Carrying)2 0.4073 6.2949
#> factor(Carrying)3 1.4528 19.7549
#> factor(Satisfaction)2 0.1931 1.6939
#> factor(Satisfaction)3 0.1059 1.0956
#> Smoking:factor(Carrying)2 0.5215 129.5012
#> Smoking:factor(Carrying)3 0.3363 125.4551
#> Gender:Smoking 0.5187 17.6463
#>
#> $`Step 4`
#> est std.err signif lower upper OR
#> (Intercept) 1.0947 0.9510 0.2498 -0.7700 2.9593 2.9882
#> Gender -0.9403 0.6077 0.1221 -2.1327 0.2522 0.3905
#> Smoking -2.4432 1.5069 0.1097 -5.4515 0.5652 0.0869
#> Function -0.0825 0.0491 0.0939 -0.1790 0.0141 0.9208
#> factor(Carrying)2 0.4225 0.6862 0.5384 -0.9249 1.7698 1.5257
#> factor(Carrying)3 1.6197 0.6573 0.0140 0.3291 2.9103 5.0517
#> factor(Satisfaction)2 -0.5694 0.5452 0.3014 -1.6648 0.5259 0.5658
#> factor(Satisfaction)3 -1.0750 0.5964 0.0722 -2.2475 0.0976 0.3413
#> Smoking:factor(Carrying)2 2.1715 1.3808 0.1191 -0.5694 4.9123 8.7714
#> Smoking:factor(Carrying)3 1.9067 1.4703 0.2008 -1.0478 4.8611 6.7307
#> Gender:Smoking 1.0662 0.8899 0.2309 -0.6783 2.8107 2.9044
#> L.OR U.OR
#> (Intercept) 0.4630 19.2841
#> Gender 0.1185 1.2868
#> Smoking 0.0043 1.7598
#> Function 0.8361 1.0142
#> factor(Carrying)2 0.3966 5.8697
#> factor(Carrying)3 1.3897 18.3630
#> factor(Satisfaction)2 0.1892 1.6920
#> factor(Satisfaction)3 0.1057 1.1025
#> Smoking:factor(Carrying)2 0.5659 135.9577
#> Smoking:factor(Carrying)3 0.3507 129.1689
#> Gender:Smoking 0.5075 16.6218
#>
#> $`Step 5`
#> est std.err signif lower upper OR L.OR
#> (Intercept) 0.5748 0.9102 0.5277 -1.2093 2.3589 1.7768 0.2984
#> Gender -0.8525 0.6086 0.1618 -2.0477 0.3427 0.4264 0.1290
#> Smoking -0.5830 0.7527 0.4386 -2.0589 0.8928 0.5582 0.1276
#> Function -0.0844 0.0496 0.0891 -0.1818 0.0130 0.9190 0.8337
#> factor(Carrying)2 1.2044 0.5108 0.0185 0.2027 2.2061 3.3347 1.2247
#> factor(Carrying)3 2.1898 0.5826 0.0002 1.0421 3.3375 8.9336 2.8351
#> factor(Satisfaction)2 -0.5717 0.5181 0.2735 -1.6046 0.4613 0.5646 0.2010
#> factor(Satisfaction)3 -1.1332 0.5855 0.0535 -2.2836 0.0172 0.3220 0.1019
#> Gender:Smoking 0.8071 0.8661 0.3514 -0.8907 2.5050 2.2415 0.4104
#> U.OR
#> (Intercept) 10.5790
#> Gender 1.4088
#> Smoking 2.4419
#> Function 1.0130
#> factor(Carrying)2 9.0800
#> factor(Carrying)3 28.1500
#> factor(Satisfaction)2 1.5861
#> factor(Satisfaction)3 1.0174
#> Gender:Smoking 12.2433
#>
#> $`Step 6`
#> est std.err signif lower upper OR L.OR
#> (Intercept) 0.3384 0.8699 0.6973 -1.3666 2.0433 1.4026 0.2550
#> Gender -0.4854 0.4477 0.2786 -1.3643 0.3935 0.6155 0.2556
#> Smoking 0.0198 0.3768 0.9582 -0.7189 0.7584 1.0199 0.4873
#> Function -0.0853 0.0493 0.0841 -0.1820 0.0115 0.9183 0.8336
#> factor(Carrying)2 1.2046 0.5090 0.0180 0.2065 2.2027 3.3354 1.2294
#> factor(Carrying)3 2.1321 0.5681 0.0002 1.0147 3.2495 8.4327 2.7586
#> factor(Satisfaction)2 -0.5885 0.5144 0.2563 -1.6135 0.4364 0.5551 0.1992
#> factor(Satisfaction)3 -1.1106 0.5800 0.0561 -2.2500 0.0288 0.3294 0.1054
#> U.OR
#> (Intercept) 7.7163
#> Gender 1.4821
#> Smoking 2.1349
#> Function 1.0116
#> factor(Carrying)2 9.0495
#> factor(Carrying)3 25.7780
#> factor(Satisfaction)2 1.5472
#> factor(Satisfaction)3 1.0292
#>
#> $`Step 7`
#> est std.err signif lower upper OR L.OR
#> (Intercept) 0.3455 0.8629 0.6889 -1.3459 2.0369 1.4127 0.2603
#> Gender -0.4844 0.4473 0.2792 -1.3626 0.3938 0.6161 0.2560
#> Function -0.0851 0.0490 0.0832 -0.1813 0.0112 0.9185 0.8342
#> factor(Carrying)2 1.2067 0.5043 0.0168 0.2179 2.1954 3.3423 1.2435
#> factor(Carrying)3 2.1311 0.5681 0.0002 1.0136 3.2487 8.4243 2.7554
#> factor(Satisfaction)2 -0.5907 0.5112 0.2514 -1.6087 0.4272 0.5539 0.2001
#> factor(Satisfaction)3 -1.1123 0.5762 0.0541 -2.2442 0.0196 0.3288 0.1060
#> U.OR
#> (Intercept) 7.6664
#> Gender 1.4826
#> Function 1.0113
#> factor(Carrying)2 8.9837
#> factor(Carrying)3 25.7561
#> factor(Satisfaction)2 1.5330
#> factor(Satisfaction)3 1.0198
#>
#> $`Step 8`
#> est std.err signif lower upper OR L.OR
#> (Intercept) -0.0227 0.8026 0.9774 -1.5966 1.5511 0.9775 0.2026
#> Function -0.0905 0.0483 0.0610 -0.1853 0.0042 0.9134 0.8309
#> factor(Carrying)2 1.2844 0.4951 0.0095 0.3136 2.2551 3.6124 1.3684
#> factor(Carrying)3 2.1134 0.5610 0.0002 1.0105 3.2163 8.2766 2.7471
#> factor(Satisfaction)2 -0.5466 0.5106 0.2880 -1.5649 0.4717 0.5789 0.2091
#> factor(Satisfaction)3 -1.0463 0.5745 0.0693 -2.1757 0.0830 0.3512 0.1135
#> U.OR
#> (Intercept) 4.7166
#> Function 1.0042
#> factor(Carrying)2 9.5361
#> factor(Carrying)3 24.9363
#> factor(Satisfaction)2 1.6028
#> factor(Satisfaction)3 1.0866
#>
#> $`Step 9`
#> est std.err signif lower upper OR L.OR
#> (Intercept) -0.6220 0.7120 0.3825 -2.0184 0.7745 0.5369 0.1329
#> Function -0.0764 0.0466 0.1013 -0.1677 0.0150 0.9265 0.8456
#> factor(Carrying)2 1.2674 0.4852 0.0090 0.3163 2.2184 3.5515 1.3720
#> factor(Carrying)3 1.9358 0.5387 0.0004 0.8773 2.9943 6.9297 2.4044
#> U.OR
#> (Intercept) 2.1694
#> Function 1.0151
#> factor(Carrying)2 9.1928
#> factor(Carrying)3 19.9721
#>
#> $`Step 10`
#> est std.err signif lower upper OR L.OR
#> (Intercept) -1.6153 0.3860 0.0000 -2.3718 -0.8588 0.1988 0.0933
#> factor(Carrying)2 1.3988 0.4780 0.0034 0.4618 2.3358 4.0503 1.5869
#> factor(Carrying)3 2.2781 0.4959 0.0000 1.3044 3.2518 9.7582 3.6854
#> U.OR
#> (Intercept) 0.4237
#> factor(Carrying)2 10.3376
#> factor(Carrying)3 25.8378
pool_lr$multiparm_p
#> $`Step 1`
#> D2 D2 & RR p-values
#> Gender 0.96757 0.12380
#> Smoking -0.00466 0.11150
#> Function 2.36408 0.11730
#> JobControl 0.07576 0.73990
#> JobDemands 0.01154 0.96160
#> SocialSupport 0.63015 0.42540
#> factor(Carrying) 8.80222 0.00019
#> factor(Satisfaction) 1.73519 0.17817
#> Smoking:factor(Carrying) 1.23220 0.29991
#> Gender:Smoking 1.52850 0.21610
#>
#> $`Step 2`
#> D2 D2 & RR p-values
#> Gender 1.00674 0.11870
#> Smoking -0.00475 0.11260
#> Function 2.35820 0.11730
#> JobControl 0.08102 0.73740
#> SocialSupport 0.63067 0.42510
#> factor(Carrying) 8.80250 0.00019
#> factor(Satisfaction) 1.73504 0.17818
#> Smoking:factor(Carrying) 1.22667 0.30166
#> Gender:Smoking 1.52142 0.21680
#>
#> $`Step 3`
#> D2 D2 & RR p-values
#> Gender 0.92737 0.12600
#> Smoking -0.00429 0.11380
#> Function 2.72027 0.09520
#> SocialSupport 0.72702 0.39320
#> factor(Carrying) 8.77607 0.00019
#> factor(Satisfaction) 1.70729 0.18307
#> Smoking:factor(Carrying) 1.22698 0.30110
#> Gender:Smoking 1.51076 0.21850
#>
#> $`Step 4`
#> D2 D2 & RR p-values
#> Gender 1.02704 0.12210
#> Smoking -0.00301 0.10970
#> Function 2.74984 0.09390
#> factor(Carrying) 8.43910 0.00027
#> factor(Satisfaction) 1.67024 0.19043
#> Smoking:factor(Carrying) 1.35436 0.26601
#> Gender:Smoking 1.42920 0.23090
#>
#> $`Step 5`
#> D2 D2 & RR p-values
#> Gender 1.12819 0.16180
#> Smoking -0.00301 0.43860
#> Function 2.83200 0.08910
#> factor(Carrying) 8.43910 0.00027
#> factor(Satisfaction) 1.93016 0.14695
#> Gender:Smoking 0.86119 0.35140
#>
#> $`Step 6`
#> D2 D2 & RR p-values
#> Gender 1.12819 0.27860
#> Smoking -0.00301 0.95820
#> Function 2.93787 0.08410
#> factor(Carrying) 8.28623 0.00029
#> factor(Satisfaction) 1.87953 0.15447
#>
#> $`Step 7`
#> D2 D2 & RR p-values
#> Gender 1.12511 0.27920
#> Function 2.95688 0.08320
#> factor(Carrying) 8.28522 0.00029
#> factor(Satisfaction) 1.91007 0.14982
#>
#> $`Step 8`
#> D2 D2 & RR p-values
#> Function 3.49747 0.06184
#> factor(Carrying) 8.50477 0.00022
#> factor(Satisfaction) 1.68816 0.18690
#>
#> $`Step 9`
#> D2 D2 & RR p-values
#> Function 2.66474 0.10284
#> factor(Carrying) 7.57188 0.00056
#>
#> $`Step 10`
#> D2 D2 & RR p-values
#> factor(Carrying) 12.95233 0Back to Examples
Same as above but now forcing several predictors, including interaction terms, in the model during BS.
library(psfmi)
pool_lr <- psfmi_lr(data=lbpmilr, nimp=5, impvar="Impnr", Outcome="Chronic",
predictors=c("Gender", "Smoking", "Function", "JobControl", "JobDemands",
"SocialSupport"), p.crit = 0.05, cat.predictors = c("Carrying", "Satisfaction"),
int.predictors = c("Carrying:Smoking", "Gender:Smoking"),
keep.predictors = c("Smoking:Carrying", "JobControl"), method="D1")
#> Variable excluded at Step 1 is - JobDemands
#> Variable excluded at Step 2 is - SocialSupport
#> Variable excluded at Step 3 is - factor(Satisfaction)
#> Variable excluded at Step 4 is - Gender:Smoking
#> Variable excluded at Step 5 is - Gender
#> Variable excluded at Step 6 is - Function
#>
#> Selection correctly terminated.
#> Variable(s) to keep - Smoking:factor(Carrying) JobControl - last variable(s) in model
pool_lr$RR_Model
#> $`Step 1`
#> est std.err signif lower upper OR
#> (Intercept) 0.2658 2.7332 0.9226 -5.0995 5.6311 1.3045
#> Gender -0.9613 0.6239 0.1238 -2.1862 0.2636 0.3824
#> Smoking -2.4473 1.5169 0.1115 -5.4769 0.5823 0.0865
#> Function -0.0792 0.0504 0.1173 -0.1783 0.0200 0.9239
#> JobControl -0.0071 0.0213 0.7399 -0.0488 0.0347 0.9930
#> JobDemands 0.0019 0.0400 0.9616 -0.0765 0.0803 1.0019
#> SocialSupport 0.0473 0.0593 0.4254 -0.0690 0.1635 1.0484
#> factor(Carrying)2 0.4834 0.6996 0.4898 -0.8908 1.8577 1.6216
#> factor(Carrying)3 1.6765 0.6702 0.0126 0.3603 2.9928 5.3471
#> factor(Satisfaction)2 -0.5596 0.5426 0.3069 -1.6473 0.5280 0.5714
#> factor(Satisfaction)3 -1.0948 0.5989 0.0682 -2.2718 0.0822 0.3346
#> Smoking:factor(Carrying)2 2.1009 1.3864 0.1330 -0.6509 4.8526 8.1731
#> Smoking:factor(Carrying)3 1.9226 1.4998 0.2064 -1.0981 4.9432 6.8384
#> Gender:Smoking 1.1125 0.8993 0.2161 -0.6504 2.8753 3.0418
#> L.OR U.OR
#> (Intercept) 0.0061 278.9559
#> Gender 0.1123 1.3016
#> Smoking 0.0042 1.7902
#> Function 0.8367 1.0202
#> JobControl 0.9523 1.0353
#> JobDemands 0.9264 1.0837
#> SocialSupport 0.9333 1.1777
#> factor(Carrying)2 0.4103 6.4091
#> factor(Carrying)3 1.4338 19.9412
#> factor(Satisfaction)2 0.1926 1.6955
#> factor(Satisfaction)3 0.1031 1.0857
#> Smoking:factor(Carrying)2 0.5216 128.0779
#> Smoking:factor(Carrying)3 0.3335 140.2179
#> Gender:Smoking 0.5218 17.7305
#>
#> $`Step 2`
#> est std.err signif lower upper OR
#> (Intercept) 0.3385 2.2237 0.8790 -4.0229 4.6998 1.4028
#> Gender -0.9650 0.6179 0.1187 -2.1778 0.2478 0.3810
#> Smoking -2.4450 1.5196 0.1126 -5.4813 0.5912 0.0867
#> Function -0.0793 0.0505 0.1173 -0.1786 0.0200 0.9238
#> JobControl -0.0071 0.0212 0.7374 -0.0487 0.0345 0.9929
#> SocialSupport 0.0473 0.0593 0.4251 -0.0690 0.1636 1.0485
#> factor(Carrying)2 0.4799 0.6966 0.4911 -0.8881 1.8479 1.6159
#> factor(Carrying)3 1.6721 0.6657 0.0123 0.3648 2.9794 5.3232
#> factor(Satisfaction)2 -0.5588 0.5421 0.3072 -1.6455 0.5278 0.5719
#> factor(Satisfaction)3 -1.0935 0.5985 0.0683 -2.2696 0.0827 0.3350
#> Smoking:factor(Carrying)2 2.1003 1.3873 0.1334 -0.6539 4.8546 8.1690
#> Smoking:factor(Carrying)3 1.9227 1.4953 0.2051 -1.0887 4.9341 6.8396
#> Gender:Smoking 1.1100 0.8987 0.2168 -0.6517 2.8717 3.0344
#> L.OR U.OR
#> (Intercept) 0.0179 109.9297
#> Gender 0.1133 1.2812
#> Smoking 0.0042 1.8062
#> Function 0.8364 1.0202
#> JobControl 0.9524 1.0351
#> SocialSupport 0.9334 1.1777
#> factor(Carrying)2 0.4114 6.3462
#> factor(Carrying)3 1.4402 19.6753
#> factor(Satisfaction)2 0.1929 1.6952
#> factor(Satisfaction)3 0.1034 1.0862
#> Smoking:factor(Carrying)2 0.5200 128.3243
#> Smoking:factor(Carrying)3 0.3367 138.9520
#> Gender:Smoking 0.5212 17.6674
#>
#> $`Step 3`
#> est std.err signif lower upper OR
#> (Intercept) 1.6174 1.5170 0.2865 -1.3582 4.5930 5.0400
#> Gender -0.9738 0.6132 0.1126 -2.1771 0.2295 0.3777
#> Smoking -2.4624 1.5121 0.1084 -5.4839 0.5591 0.0852
#> Function -0.0784 0.0503 0.1200 -0.1773 0.0205 0.9246
#> JobControl -0.0094 0.0209 0.6520 -0.0505 0.0316 0.9906
#> factor(Carrying)2 0.4377 0.6870 0.5242 -0.9108 1.7862 1.5492
#> factor(Carrying)3 1.6140 0.6583 0.0145 0.3215 2.9065 5.0228
#> factor(Satisfaction)2 -0.5667 0.5455 0.3039 -1.6624 0.5291 0.5674
#> factor(Satisfaction)3 -1.0950 0.5997 0.0686 -2.2740 0.0840 0.3345
#> Smoking:factor(Carrying)2 2.1590 1.3803 0.1210 -0.5806 4.8985 8.6623
#> Smoking:factor(Carrying)3 1.9709 1.4922 0.1932 -1.0337 4.9756 7.1773
#> Gender:Smoking 1.0719 0.8894 0.2282 -0.6716 2.8154 2.9208
#> L.OR U.OR
#> (Intercept) 0.2571 98.7907
#> Gender 0.1134 1.2580
#> Smoking 0.0042 1.7492
#> Function 0.8375 1.0207
#> JobControl 0.9508 1.0321
#> factor(Carrying)2 0.4022 5.9669
#> factor(Carrying)3 1.3791 18.2930
#> factor(Satisfaction)2 0.1897 1.6974
#> factor(Satisfaction)3 0.1029 1.0876
#> Smoking:factor(Carrying)2 0.5596 134.0921
#> Smoking:factor(Carrying)3 0.3557 144.8308
#> Gender:Smoking 0.5109 16.6994
#>
#> $`Step 4`
#> est std.err signif lower upper OR
#> (Intercept) 0.7701 1.4239 0.5886 -2.0214 3.5617 2.1600
#> Gender -0.8584 0.5976 0.1512 -2.0309 0.3141 0.4238
#> Smoking -2.4178 1.4923 0.1101 -5.3983 0.5628 0.0891
#> Function -0.0675 0.0484 0.1635 -0.1625 0.0275 0.9347
#> JobControl -0.0064 0.0208 0.7602 -0.0472 0.0345 0.9937
#> factor(Carrying)2 0.3705 0.6797 0.5860 -0.9651 1.7060 1.4484
#> factor(Carrying)3 1.3956 0.6461 0.0315 0.1244 2.6668 4.0374
#> Smoking:factor(Carrying)2 2.2636 1.3769 0.1038 -0.4737 5.0008 9.6172
#> Smoking:factor(Carrying)3 2.0195 1.4656 0.1744 -0.9250 4.9640 7.5347
#> Gender:Smoking 1.0219 0.8753 0.2431 -0.6943 2.7381 2.7784
#> L.OR U.OR
#> (Intercept) 0.1325 35.2214
#> Gender 0.1312 1.3690
#> Smoking 0.0045 1.7555
#> Function 0.8500 1.0279
#> JobControl 0.9539 1.0351
#> factor(Carrying)2 0.3810 5.5071
#> factor(Carrying)3 1.1324 14.3944
#> Smoking:factor(Carrying)2 0.6227 148.5392
#> Smoking:factor(Carrying)3 0.3966 143.1655
#> Gender:Smoking 0.4994 15.4571
#>
#> $`Step 5`
#> est std.err signif lower upper OR
#> (Intercept) 0.3976 1.3715 0.7719 -2.2907 3.0859 1.4882
#> Gender -0.4103 0.4514 0.3637 -1.2971 0.4764 0.6634
#> Smoking -1.5458 1.2144 0.2072 -3.9674 0.8758 0.2131
#> Function -0.0692 0.0481 0.1512 -0.1636 0.0253 0.9332
#> JobControl -0.0058 0.0207 0.7805 -0.0463 0.0348 0.9943
#> factor(Carrying)2 0.4824 0.6612 0.4659 -0.8164 1.7812 1.6200
#> factor(Carrying)3 1.3679 0.6286 0.0300 0.1331 2.6027 3.9271
#> Smoking:factor(Carrying)2 2.0573 1.3459 0.1296 -0.6136 4.7282 7.8251
#> Smoking:factor(Carrying)3 1.9385 1.4235 0.1782 -0.9069 4.7839 6.9482
#> L.OR U.OR
#> (Intercept) 0.1012 21.8871
#> Gender 0.2733 1.6103
#> Smoking 0.0189 2.4008
#> Function 0.8490 1.0257
#> JobControl 0.9548 1.0354
#> factor(Carrying)2 0.4420 5.9368
#> factor(Carrying)3 1.1423 13.5003
#> Smoking:factor(Carrying)2 0.5414 113.0939
#> Smoking:factor(Carrying)3 0.4038 119.5660
#>
#> $`Step 6`
#> est std.err signif lower upper OR
#> (Intercept) -0.0240 1.3046 0.9853 -2.5818 2.5339 0.9763
#> Smoking -1.5587 1.2167 0.2045 -3.9866 0.8692 0.2104
#> Function -0.0750 0.0475 0.1150 -0.1683 0.0183 0.9278
#> JobControl -0.0030 0.0205 0.8830 -0.0433 0.0373 0.9970
#> factor(Carrying)2 0.5740 0.6463 0.3748 -0.6950 1.8431 1.7754
#> factor(Carrying)3 1.3464 0.6195 0.0300 0.1305 2.5624 3.8438
#> Smoking:factor(Carrying)2 2.0163 1.3476 0.1381 -0.6604 4.6930 7.5102
#> Smoking:factor(Carrying)3 1.9751 1.4262 0.1713 -0.8786 4.8287 7.2072
#> L.OR U.OR
#> (Intercept) 0.0756 12.6021
#> Smoking 0.0186 2.3849
#> Function 0.8451 1.0185
#> JobControl 0.9576 1.0380
#> factor(Carrying)2 0.4991 6.3162
#> factor(Carrying)3 1.1394 12.9671
#> Smoking:factor(Carrying)2 0.5166 109.1767
#> Smoking:factor(Carrying)3 0.4154 125.0521
#>
#> $`Step 7`
#> est std.err signif lower upper OR
#> (Intercept) -0.6687 1.2641 0.5969 -3.1498 1.8124 0.5124
#> Smoking -1.6170 1.2262 0.1923 -4.0701 0.8362 0.1985
#> JobControl -0.0084 0.0201 0.6752 -0.0478 0.0309 0.9916
#> factor(Carrying)2 0.7445 0.6339 0.2407 -0.5006 1.9896 2.1054
#> factor(Carrying)3 1.6545 0.5920 0.0054 0.4919 2.8170 5.2304
#> Smoking:factor(Carrying)2 1.9635 1.3523 0.1504 -0.7272 4.6542 7.1244
#> Smoking:factor(Carrying)3 2.0487 1.4555 0.1660 -0.8808 4.9782 7.7579
#> L.OR U.OR
#> (Intercept) 0.0429 6.1251
#> Smoking 0.0171 2.3075
#> JobControl 0.9534 1.0314
#> factor(Carrying)2 0.6062 7.3123
#> factor(Carrying)3 1.6355 16.7272
#> Smoking:factor(Carrying)2 0.4833 105.0290
#> Smoking:factor(Carrying)3 0.4144 145.2174
pool_lr$multiparm_p
#> $`Step 1`
#> Chi_sq D1 & RR p-values
#> Gender 2.374 0.1238
#> Smoking 2.603 0.1115
#> Function 2.464 0.1173
#> JobControl 0.110 0.7399
#> JobDemands 0.002 0.9616
#> SocialSupport 0.635 0.4254
#> factor(Carrying) 3.435 0.0334
#> factor(Satisfaction) 1.493 0.2315
#> Smoking:factor(Carrying) 1.135 0.3304
#> Gender:Smoking 1.530 0.2161
#>
#> $`Step 2`
#> Chi_sq D1 & RR p-values
#> Gender 2.439 0.1187
#> Smoking 2.589 0.1126
#> Function 2.465 0.1173
#> JobControl 0.112 0.7374
#> SocialSupport 0.636 0.4251
#> factor(Carrying) 3.467 0.0323
#> factor(Satisfaction) 1.492 0.2317
#> Smoking:factor(Carrying) 1.136 0.3301
#> Gender:Smoking 1.526 0.2168
#>
#> $`Step 3`
#> Chi_sq D1 & RR p-values
#> Gender 2.522 0.1126
#> Smoking 2.652 0.1084
#> Function 2.427 0.1200
#> JobControl 0.203 0.6520
#> factor(Carrying) 3.342 0.0363
#> factor(Satisfaction) 1.484 0.2342
#> Smoking:factor(Carrying) 1.208 0.3082
#> Gender:Smoking 1.452 0.2282
#>
#> $`Step 4`
#> Chi_sq D1 & RR p-values
#> Gender 2.063 0.1512
#> Smoking 2.625 0.1101
#> Function 1.946 0.1635
#> JobControl 0.093 0.7602
#> factor(Carrying) 2.634 0.0739
#> Smoking:factor(Carrying) 1.338 0.2725
#> Gender:Smoking 1.363 0.2431
#>
#> $`Step 5`
#> Chi_sq D1 & RR p-values
#> Gender 0.826 0.3637
#> Smoking 1.620 0.2072
#> Function 2.064 0.1512
#> JobControl 0.078 0.7805
#> factor(Carrying) 2.539 0.0805
#> Smoking:factor(Carrying) 1.202 0.3085
#>
#> $`Step 6`
#> Chi_sq D1 & RR p-values
#> Smoking 1.641 0.2045
#> Function 2.489 0.1150
#> JobControl 0.022 0.8830
#> factor(Carrying) 2.444 0.0880
#> Smoking:factor(Carrying) 1.183 0.3146
#>
#> $`Step 7`
#> Chi_sq D1 & RR p-values
#> Smoking 1.739 0.1923
#> JobControl 0.176 0.6752
#> factor(Carrying) 4.004 0.0190
#> Smoking:factor(Carrying) 1.159 0.3234Back to Examples
Pooling Logistic regression models over 5 imputed datasets with BS using a p-value of 0.05 and as method D1. A spline predictor and interaction term are part of the selection procedure.
library(psfmi)
pool_lr <- psfmi_lr(data=lbpmilr, nimp=5, impvar="Impnr", Outcome="Chronic",
predictors=c("Gender", "Smoking", "JobControl", "JobDemands",
"SocialSupport"), p.crit = 0.05, cat.predictors = c("Carrying", "Satisfaction"),
spline.predictors=c("Function"), int.predictors = c("Carrying:Smoking"),
knots=3, method="D1")
#> Variable excluded at Step 1 is - JobDemands
#> Variable excluded at Step 2 is - JobControl
#> Variable excluded at Step 3 is - SocialSupport
#> Variable excluded at Step 4 is - Smoking:factor(Carrying)
#> Variable excluded at Step 5 is - Smoking
#> Variable excluded at Step 6 is - Gender
#> Variable excluded at Step 7 is - factor(Satisfaction)
#> Variable excluded at Step 8 is - rcs(Function,3)
pool_lr$RR_Model
#> $`Step 1`
#> est std.err signif lower upper OR
#> (Intercept) 0.2274 2.7430 0.9339 -5.1568 5.6117 1.2554
#> Gender -0.4878 0.4777 0.3077 -1.4267 0.4510 0.6140
#> Smoking -1.4641 1.2432 0.2434 -3.9488 1.0206 0.2313
#> JobControl -0.0047 0.0216 0.8276 -0.0471 0.0377 0.9953
#> JobDemands -0.0006 0.0398 0.9881 -0.0786 0.0774 0.9994
#> SocialSupport 0.0461 0.0587 0.4316 -0.0688 0.1611 1.0472
#> factor(Carrying)2 0.6094 0.6835 0.3730 -0.7331 1.9518 1.8392
#> factor(Carrying)3 1.6225 0.6547 0.0133 0.3380 2.9071 5.0659
#> factor(Satisfaction)2 -0.5859 0.5366 0.2796 -1.6609 0.4891 0.5566
#> factor(Satisfaction)3 -1.0620 0.5922 0.0735 -2.2253 0.1013 0.3458
#> rcs(Function, 3)Function -0.1294 0.1126 0.2509 -0.3503 0.0916 0.8787
#> rcs(Function, 3)Function' 0.0643 0.1288 0.6175 -0.1882 0.3168 1.0664
#> Smoking:factor(Carrying)2 1.8495 1.3569 0.1756 -0.8385 4.5376 6.3567
#> Smoking:factor(Carrying)3 1.8415 1.4604 0.2127 -1.0853 4.7683 6.3060
#> L.OR U.OR
#> (Intercept) 0.0058 273.6051
#> Gender 0.2401 1.5699
#> Smoking 0.0193 2.7748
#> JobControl 0.9539 1.0384
#> JobDemands 0.9244 1.0805
#> SocialSupport 0.9335 1.1748
#> factor(Carrying)2 0.4804 7.0413
#> factor(Carrying)3 1.4021 18.3035
#> factor(Satisfaction)2 0.1900 1.6309
#> factor(Satisfaction)3 0.1080 1.1066
#> rcs(Function, 3)Function 0.7045 1.0959
#> rcs(Function, 3)Function' 0.8285 1.3728
#> Smoking:factor(Carrying)2 0.4323 93.4631
#> Smoking:factor(Carrying)3 0.3378 117.7217
#>
#> $`Step 2`
#> est std.err signif lower upper OR
#> (Intercept) 0.2034 2.2241 0.9271 -4.1582 4.5650 1.2256
#> Gender -0.4867 0.4680 0.2989 -1.4062 0.4329 0.6147
#> Smoking -1.4605 1.2390 0.2429 -3.9358 1.0147 0.2321
#> JobControl -0.0047 0.0215 0.8283 -0.0469 0.0376 0.9953
#> SocialSupport 0.0462 0.0587 0.4311 -0.0688 0.1611 1.0473
#> factor(Carrying)2 0.6083 0.6793 0.3708 -0.7254 1.9421 1.8374
#> factor(Carrying)3 1.6224 0.6494 0.0126 0.3482 2.8965 5.0650
#> factor(Satisfaction)2 -0.5860 0.5362 0.2792 -1.6601 0.4882 0.5566
#> factor(Satisfaction)3 -1.0602 0.5917 0.0737 -2.2226 0.1022 0.3464
#> rcs(Function, 3)Function -0.1294 0.1126 0.2505 -0.3502 0.0914 0.8786
#> rcs(Function, 3)Function' 0.0643 0.1287 0.6175 -0.1881 0.3167 1.0664
#> Smoking:factor(Carrying)2 1.8471 1.3551 0.1755 -0.8369 4.5312 6.3416
#> Smoking:factor(Carrying)3 1.8350 1.4514 0.2114 -1.0723 4.7422 6.2648
#> L.OR U.OR
#> (Intercept) 0.0156 96.0630
#> Gender 0.2451 1.5417
#> Smoking 0.0195 2.7586
#> JobControl 0.9542 1.0383
#> SocialSupport 0.9335 1.1749
#> factor(Carrying)2 0.4841 6.9730
#> factor(Carrying)3 1.4165 18.1111
#> factor(Satisfaction)2 0.1901 1.6294
#> factor(Satisfaction)3 0.1083 1.1076
#> rcs(Function, 3)Function 0.7045 1.0957
#> rcs(Function, 3)Function' 0.8286 1.3725
#> Smoking:factor(Carrying)2 0.4331 92.8668
#> Smoking:factor(Carrying)3 0.3422 114.6889
#>
#> $`Step 3`
#> est std.err signif lower upper OR
#> (Intercept) -0.0756 1.7959 0.9664 -3.5964 3.4452 0.9272
#> Gender -0.4717 0.4630 0.3089 -1.3815 0.4381 0.6240
#> Smoking -1.4445 1.2324 0.2453 -3.9044 1.0154 0.2359
#> SocialSupport 0.0482 0.0578 0.4051 -0.0652 0.1615 1.0493
#> factor(Carrying)2 0.6042 0.6793 0.3741 -0.7297 1.9381 1.8298
#> factor(Carrying)3 1.6255 0.6487 0.0123 0.3528 2.8982 5.0811
#> factor(Satisfaction)2 -0.5858 0.5362 0.2793 -1.6601 0.4885 0.5567
#> factor(Satisfaction)3 -1.0481 0.5874 0.0749 -2.2019 0.1056 0.3506
#> rcs(Function, 3)Function -0.1355 0.1085 0.2120 -0.3484 0.0774 0.8733
#> rcs(Function, 3)Function' 0.0697 0.1262 0.5806 -0.1777 0.3171 1.0722
#> Smoking:factor(Carrying)2 1.8439 1.3546 0.1761 -0.8392 4.5270 6.3211
#> Smoking:factor(Carrying)3 1.7993 1.4319 0.2137 -1.0645 4.6630 6.0453
#> L.OR U.OR
#> (Intercept) 0.0274 31.3511
#> Gender 0.2512 1.5498
#> Smoking 0.0202 2.7605
#> SocialSupport 0.9369 1.1753
#> factor(Carrying)2 0.4821 6.9457
#> factor(Carrying)3 1.4231 18.1420
#> factor(Satisfaction)2 0.1901 1.6299
#> factor(Satisfaction)3 0.1106 1.1114
#> rcs(Function, 3)Function 0.7058 1.0804
#> rcs(Function, 3)Function' 0.8372 1.3732
#> Smoking:factor(Carrying)2 0.4321 92.4786
#> Smoking:factor(Carrying)3 0.3449 105.9569
#>
#> $`Step 4`
#> est std.err signif lower upper OR
#> (Intercept) 1.0821 1.1254 0.3364 -1.1245 3.2886 2.9508
#> Gender -0.4863 0.4577 0.2885 -1.3855 0.4129 0.6149
#> Smoking -1.5000 1.2279 0.2261 -3.9506 0.9506 0.2231
#> factor(Carrying)2 0.5504 0.6692 0.4110 -0.7630 1.8638 1.7340
#> factor(Carrying)3 1.5689 0.6409 0.0145 0.3117 2.8261 4.8012
#> factor(Satisfaction)2 -0.5925 0.5395 0.2773 -1.6758 0.4908 0.5530
#> factor(Satisfaction)3 -1.0480 0.5894 0.0760 -2.2063 0.1102 0.3506
#> rcs(Function, 3)Function -0.1318 0.1087 0.2256 -0.3450 0.0814 0.8765
#> rcs(Function, 3)Function' 0.0642 0.1261 0.6111 -0.1831 0.3114 1.0663
#> Smoking:factor(Carrying)2 1.9216 1.3469 0.1563 -0.7455 4.5888 6.8319
#> Smoking:factor(Carrying)3 1.8381 1.4288 0.2032 -1.0193 4.6956 6.2848
#> L.OR U.OR
#> (Intercept) 0.3248 26.8047
#> Gender 0.2502 1.5112
#> Smoking 0.0192 2.5873
#> factor(Carrying)2 0.4663 6.4482
#> factor(Carrying)3 1.3657 16.8787
#> factor(Satisfaction)2 0.1872 1.6336
#> factor(Satisfaction)3 0.1101 1.1165
#> rcs(Function, 3)Function 0.7082 1.0848
#> rcs(Function, 3)Function' 0.8327 1.3654
#> Smoking:factor(Carrying)2 0.4745 98.3715
#> Smoking:factor(Carrying)3 0.3609 109.4590
#>
#> $`Step 5`
#> est std.err signif lower upper OR
#> (Intercept) 0.7297 1.1158 0.5131 -1.4575 2.9170 2.0745
#> Gender -0.4998 0.4488 0.2658 -1.3808 0.3812 0.6067
#> Smoking 0.0421 0.3792 0.9116 -0.7013 0.7855 1.0430
#> factor(Carrying)2 1.2022 0.5089 0.0182 0.2044 2.1999 3.3274
#> factor(Carrying)3 2.1161 0.5690 0.0002 0.9971 3.2351 8.2988
#> factor(Satisfaction)2 -0.5877 0.5169 0.2595 -1.6189 0.4436 0.5556
#> factor(Satisfaction)3 -1.0991 0.5823 0.0596 -2.2431 0.0448 0.3332
#> rcs(Function, 3)Function -0.1404 0.1086 0.1962 -0.3534 0.0726 0.8690
#> rcs(Function, 3)Function' 0.0732 0.1262 0.5620 -0.1742 0.3205 1.0759
#> L.OR U.OR
#> (Intercept) 0.2328 18.4850
#> Gender 0.2514 1.4641
#> Smoking 0.4959 2.1935
#> factor(Carrying)2 1.2268 9.0245
#> factor(Carrying)3 2.7104 25.4097
#> factor(Satisfaction)2 0.1981 1.5582
#> factor(Satisfaction)3 0.1061 1.0458
#> rcs(Function, 3)Function 0.7023 1.0753
#> rcs(Function, 3)Function' 0.8402 1.3778
#>
#> $`Step 6`
#> est std.err signif lower upper OR
#> (Intercept) 0.7357 1.1143 0.5091 -1.4486 2.9201 2.0870
#> Gender -0.4978 0.4483 0.2672 -1.3778 0.3823 0.6079
#> factor(Carrying)2 1.2083 0.5039 0.0165 0.2203 2.1962 3.3477
#> factor(Carrying)3 2.1166 0.5691 0.0002 0.9975 3.2358 8.3032
#> factor(Satisfaction)2 -0.5921 0.5133 0.2525 -1.6155 0.4313 0.5532
#> factor(Satisfaction)3 -1.1044 0.5786 0.0568 -2.2410 0.0322 0.3314
#> rcs(Function, 3)Function -0.1389 0.1076 0.1969 -0.3499 0.0721 0.8703
#> rcs(Function, 3)Function' 0.0717 0.1255 0.5677 -0.1743 0.3178 1.0744
#> L.OR U.OR
#> (Intercept) 0.2349 18.5426
#> Gender 0.2521 1.4656
#> factor(Carrying)2 1.2465 8.9909
#> factor(Carrying)3 2.7114 25.4267
#> factor(Satisfaction)2 0.1988 1.5392
#> factor(Satisfaction)3 0.1064 1.0327
#> rcs(Function, 3)Function 0.7048 1.0748
#> rcs(Function, 3)Function' 0.8400 1.3740
#>
#> $`Step 7`
#> est std.err signif lower upper OR
#> (Intercept) 0.3161 1.0559 0.7647 -1.7548 2.3871 1.3718
#> factor(Carrying)2 1.2877 0.4952 0.0093 0.3169 2.2585 3.6244
#> factor(Carrying)3 2.1007 0.5620 0.0002 0.9960 3.2054 8.1719
#> factor(Satisfaction)2 -0.5453 0.5124 0.2911 -1.5685 0.4779 0.5797
#> factor(Satisfaction)3 -1.0365 0.5763 0.0728 -2.1693 0.0963 0.3547
#> rcs(Function, 3)Function -0.1388 0.1071 0.1951 -0.3488 0.0712 0.8704
#> rcs(Function, 3)Function' 0.0643 0.1253 0.6075 -0.1812 0.3099 1.0665
#> L.OR U.OR
#> (Intercept) 0.1729 10.8815
#> factor(Carrying)2 1.3728 9.5684
#> factor(Carrying)3 2.7074 24.6654
#> factor(Satisfaction)2 0.2084 1.6127
#> factor(Satisfaction)3 0.1143 1.1011
#> rcs(Function, 3)Function 0.7055 1.0738
#> rcs(Function, 3)Function' 0.8343 1.3633
#>
#> $`Step 8`
#> est std.err signif lower upper OR
#> (Intercept) -0.2344 0.9852 0.8120 -2.1664 1.6977 0.7911
#> factor(Carrying)2 1.2727 0.4851 0.0087 0.3218 2.2236 3.5706
#> factor(Carrying)3 1.9252 0.5400 0.0004 0.8643 2.9861 6.8566
#> rcs(Function, 3)Function -0.1312 0.1055 0.2136 -0.3380 0.0756 0.8770
#> rcs(Function, 3)Function' 0.0729 0.1237 0.5558 -0.1696 0.3154 1.0756
#> L.OR U.OR
#> (Intercept) 0.1146 5.4613
#> factor(Carrying)2 1.3796 9.2407
#> factor(Carrying)3 2.3734 19.8082
#> rcs(Function, 3)Function 0.7132 1.0785
#> rcs(Function, 3)Function' 0.8440 1.3708
#>
#> $`Step 9`
#> est std.err signif lower upper OR L.OR
#> (Intercept) -1.6153 0.3860 0.0000 -2.3718 -0.8588 0.1988 0.0933
#> factor(Carrying)2 1.3988 0.4780 0.0034 0.4618 2.3358 4.0503 1.5869
#> factor(Carrying)3 2.2781 0.4959 0.0000 1.3044 3.2518 9.7582 3.6854
#> U.OR
#> (Intercept) 0.4237
#> factor(Carrying)2 10.3376
#> factor(Carrying)3 25.8378
pool_lr$multiparm_p
#> $`Step 1`
#> Chi_sq D1 & RR p-values
#> Gender 1.043 0.3077
#> Smoking 1.387 0.2434
#> JobControl 0.047 0.8276
#> JobDemands 0.000 0.9881
#> SocialSupport 0.618 0.4316
#> factor(Carrying) 3.211 0.0412
#> factor(Satisfaction) 1.450 0.2409
#> rcs(Function,3) 1.471 0.2300
#> Smoking:factor(Carrying) 0.982 0.3813
#>
#> $`Step 2`
#> Chi_sq D1 & RR p-values
#> Gender 1.081 0.2989
#> Smoking 1.390 0.2429
#> JobControl 0.047 0.8283
#> SocialSupport 0.620 0.4311
#> factor(Carrying) 3.262 0.0391
#> factor(Satisfaction) 1.447 0.2416
#> rcs(Function,3) 1.475 0.2291
#> Smoking:factor(Carrying) 0.985 0.3801
#>
#> $`Step 3`
#> Chi_sq D1 & RR p-values
#> Gender 1.038 0.3089
#> Smoking 1.374 0.2453
#> SocialSupport 0.693 0.4051
#> factor(Carrying) 3.287 0.0382
#> factor(Satisfaction) 1.432 0.2451
#> rcs(Function,3) 1.652 0.1919
#> Smoking:factor(Carrying) 0.976 0.3831
#>
#> $`Step 4`
#> Chi_sq D1 & RR p-values
#> Gender 1.129 0.2885
#> Smoking 1.492 0.2261
#> factor(Carrying) 3.176 0.0424
#> factor(Satisfaction) 1.420 0.2486
#> rcs(Function,3) 1.639 0.1945
#> Smoking:factor(Carrying) 1.052 0.3560
#>
#> $`Step 5`
#> Chi_sq D1 & RR p-values
#> Gender 1.240 0.2658
#> Smoking 0.012 0.9116
#> factor(Carrying) 7.168 0.0009
#> factor(Satisfaction) 1.629 0.2019
#> rcs(Function,3) 1.716 0.1801
#>
#> $`Step 6`
#> Chi_sq D1 & RR p-values
#> Gender 1.233 0.2672
#> factor(Carrying) 7.183 0.0008
#> factor(Satisfaction) 1.672 0.1935
#> rcs(Function,3) 1.716 0.1801
#>
#> $`Step 7`
#> Chi_sq D1 & RR p-values
#> factor(Carrying) 7.255 0.0008
#> factor(Satisfaction) 1.480 0.2336
#> rcs(Function,3) 1.915 0.1476
#>
#> $`Step 8`
#> Chi_sq D1 & RR p-values
#> factor(Carrying) 6.626 0.0014
#> rcs(Function,3) 1.523 0.2182
#>
#> $`Step 9`
#> Chi_sq D1 & RR p-values
#> factor(Carrying) 10.851 0Back to Examples
library(psfmi)
pool_coxr <- psfmi_coxr(data=lbpmicox, nimp=5, impvar="Impnr", time="Time", status="Status",
predictors=c("Duration", "Radiation", "Onset", "Function", "Age",
"Previous", "Tampascale", "JobControl", "JobDemand", "Social"), p.crit=1,
cat.predictors=c("Expect_cat"), method="D1")
#>
#> Pooled model correctly estimated
#> using a p-value of 1
pool_coxr$RR_Model
#> $`Step 1`
#> est std.err signif lower upper HR L.HR
#> Duration -0.0077 0.0040 0.0524 -0.0156 0.0001 0.9923 0.9845
#> Radiation -0.0749 0.1533 0.6251 -0.3755 0.2256 0.9278 0.6869
#> Onset -0.0937 0.1759 0.5941 -0.4385 0.2510 0.9106 0.6450
#> Function 0.0440 0.0169 0.0094 0.0108 0.0771 1.0450 1.0109
#> Age -0.0089 0.0077 0.2510 -0.0240 0.0063 0.9911 0.9763
#> Previous -0.0983 0.1992 0.6215 -0.4888 0.2921 0.9064 0.6134
#> Tampascale -0.0233 0.0141 0.0991 -0.0509 0.0044 0.9770 0.9504
#> JobControl -0.0084 0.0083 0.3144 -0.0247 0.0079 0.9916 0.9756
#> JobDemand -0.0218 0.0155 0.1591 -0.0522 0.0086 0.9784 0.9491
#> Social -0.0513 0.0249 0.0393 -0.1002 -0.0025 0.9500 0.9047
#> factor(Expect_cat)2 0.2433 0.2312 0.2926 -0.2098 0.6964 1.2755 0.8107
#> factor(Expect_cat)3 0.2271 0.2003 0.2569 -0.1654 0.6196 1.2550 0.8476
#> U.HR
#> Duration 1.0001
#> Radiation 1.2531
#> Onset 1.2853
#> Function 1.0802
#> Age 1.0063
#> Previous 1.3392
#> Tampascale 1.0044
#> JobControl 1.0079
#> JobDemand 1.0086
#> Social 0.9975
#> factor(Expect_cat)2 2.0065
#> factor(Expect_cat)3 1.8582
pool_coxr$multiparm_p
#> $`Step 1`
#> Chi_sq D1 & RR p-values
#> Duration 3.762 0.0524
#> Radiation 0.239 0.6251
#> Onset 0.284 0.5941
#> Function 6.752 0.0094
#> Age 1.318 0.2510
#> Previous 0.244 0.6215
#> Tampascale 2.727 0.0991
#> JobControl 1.012 0.3144
#> JobDemand 1.982 0.1591
#> Social 4.248 0.0393
#> factor(Expect_cat) 0.722 0.4858Back to Examples
library(psfmi)
pool_coxr <- psfmi_coxr(data=lbpmicox, nimp=5, impvar="Impnr", time="Time", status="Status",
predictors=c("Duration", "Radiation", "Onset", "Function", "Age",
"Previous", "Tampascale", "JobControl", "JobDemand", "Social"), p.crit=0.05,
cat.predictors=c("Expect_cat"), method="MPR")
#> Variable excluded at Step 1 is - Radiation
#> Variable excluded at Step 2 is - Previous
#> Variable excluded at Step 3 is - Onset
#> Variable excluded at Step 4 is - factor(Expect_cat)
#> Variable excluded at Step 5 is - JobControl
#> Variable excluded at Step 6 is - Age
#> Variable excluded at Step 7 is - JobDemand
#> Variable excluded at Step 8 is - Tampascale
pool_coxr$RR_Model
#> $`Step 1`
#> est std.err signif lower upper HR L.HR
#> Duration -0.0077 0.0040 0.0524 -0.0156 0.0001 0.9923 0.9845
#> Radiation -0.0749 0.1533 0.6251 -0.3755 0.2256 0.9278 0.6869
#> Onset -0.0937 0.1759 0.5941 -0.4385 0.2510 0.9106 0.6450
#> Function 0.0440 0.0169 0.0094 0.0108 0.0771 1.0450 1.0109
#> Age -0.0089 0.0077 0.2510 -0.0240 0.0063 0.9911 0.9763
#> Previous -0.0983 0.1992 0.6215 -0.4888 0.2921 0.9064 0.6134
#> Tampascale -0.0233 0.0141 0.0991 -0.0509 0.0044 0.9770 0.9504
#> JobControl -0.0084 0.0083 0.3144 -0.0247 0.0079 0.9916 0.9756
#> JobDemand -0.0218 0.0155 0.1591 -0.0522 0.0086 0.9784 0.9491
#> Social -0.0513 0.0249 0.0393 -0.1002 -0.0025 0.9500 0.9047
#> factor(Expect_cat)2 0.2433 0.2312 0.2926 -0.2098 0.6964 1.2755 0.8107
#> factor(Expect_cat)3 0.2271 0.2003 0.2569 -0.1654 0.6196 1.2550 0.8476
#> U.HR
#> Duration 1.0001
#> Radiation 1.2531
#> Onset 1.2853
#> Function 1.0802
#> Age 1.0063
#> Previous 1.3392
#> Tampascale 1.0044
#> JobControl 1.0079
#> JobDemand 1.0086
#> Social 0.9975
#> factor(Expect_cat)2 2.0065
#> factor(Expect_cat)3 1.8582
#>
#> $`Step 2`
#> est std.err signif lower upper HR L.HR
#> Duration -0.0079 0.0040 0.0484 -0.0157 -0.0001 0.9921 0.9844
#> Onset -0.0907 0.1756 0.6054 -0.4349 0.2535 0.9133 0.6473
#> Function 0.0456 0.0165 0.0057 0.0133 0.0780 1.0467 1.0134
#> Age -0.0088 0.0077 0.2520 -0.0240 0.0063 0.9912 0.9763
#> Previous -0.0882 0.1981 0.6561 -0.4766 0.3001 0.9156 0.6209
#> Tampascale -0.0233 0.0140 0.0979 -0.0508 0.0043 0.9770 0.9505
#> JobControl -0.0087 0.0083 0.2923 -0.0249 0.0075 0.9913 0.9754
#> JobDemand -0.0216 0.0155 0.1621 -0.0520 0.0087 0.9786 0.9493
#> Social -0.0508 0.0249 0.0412 -0.0995 -0.0020 0.9505 0.9053
#> factor(Expect_cat)2 0.2665 0.2266 0.2397 -0.1777 0.7106 1.3054 0.8372
#> factor(Expect_cat)3 0.2432 0.1977 0.2187 -0.1443 0.6306 1.2753 0.8656
#> U.HR
#> Duration 0.9999
#> Onset 1.2885
#> Function 1.0811
#> Age 1.0063
#> Previous 1.3500
#> Tampascale 1.0043
#> JobControl 1.0075
#> JobDemand 1.0087
#> Social 0.9980
#> factor(Expect_cat)2 2.0352
#> factor(Expect_cat)3 1.8787
#>
#> $`Step 3`
#> est std.err signif lower upper HR L.HR
#> Duration -0.0076 0.0039 0.0534 -0.0154 0.0001 0.9924 0.9847
#> Onset -0.0860 0.1752 0.6234 -0.4295 0.2574 0.9176 0.6508
#> Function 0.0455 0.0165 0.0058 0.0132 0.0778 1.0466 1.0133
#> Age -0.0092 0.0077 0.2301 -0.0243 0.0058 0.9908 0.9760
#> Tampascale -0.0229 0.0140 0.1021 -0.0503 0.0046 0.9774 0.9509
#> JobControl -0.0091 0.0082 0.2663 -0.0252 0.0070 0.9909 0.9751
#> JobDemand -0.0221 0.0154 0.1525 -0.0524 0.0082 0.9781 0.9489
#> Social -0.0515 0.0248 0.0380 -0.1001 -0.0029 0.9498 0.9047
#> factor(Expect_cat)2 0.2639 0.2262 0.2434 -0.1795 0.7073 1.3020 0.8357
#> factor(Expect_cat)3 0.2520 0.1965 0.1998 -0.1332 0.6372 1.2866 0.8753
#> U.HR
#> Duration 1.0001
#> Onset 1.2936
#> Function 1.0809
#> Age 1.0058
#> Tampascale 1.0046
#> JobControl 1.0070
#> JobDemand 1.0082
#> Social 0.9971
#> factor(Expect_cat)2 2.0285
#> factor(Expect_cat)3 1.8912
#>
#> $`Step 4`
#> est std.err signif lower upper HR L.HR
#> Duration -0.0077 0.0039 0.0496 -0.0155 0.0000 0.9923 0.9846
#> Function 0.0454 0.0165 0.0059 0.0131 0.0777 1.0464 1.0132
#> Age -0.0090 0.0077 0.2430 -0.0240 0.0061 0.9910 0.9763
#> Tampascale -0.0231 0.0140 0.0980 -0.0505 0.0043 0.9772 0.9508
#> JobControl -0.0088 0.0082 0.2802 -0.0248 0.0072 0.9912 0.9755
#> JobDemand -0.0220 0.0154 0.1539 -0.0522 0.0082 0.9782 0.9491
#> Social -0.0523 0.0247 0.0340 -0.1007 -0.0039 0.9490 0.9042
#> factor(Expect_cat)2 0.2655 0.2262 0.2406 -0.1779 0.7089 1.3041 0.8370
#> factor(Expect_cat)3 0.2514 0.1964 0.2006 -0.1336 0.6363 1.2858 0.8749
#> U.HR
#> Duration 1.0000
#> Function 1.0808
#> Age 1.0061
#> Tampascale 1.0043
#> JobControl 1.0072
#> JobDemand 1.0082
#> Social 0.9961
#> factor(Expect_cat)2 2.0318
#> factor(Expect_cat)3 1.8895
#>
#> $`Step 5`
#> est std.err signif lower upper HR L.HR U.HR
#> Duration -0.0074 0.0039 0.0564 -0.0151 0.0002 0.9926 0.9850 1.0002
#> Function 0.0476 0.0165 0.0040 0.0152 0.0800 1.0488 1.0153 1.0833
#> Age -0.0081 0.0076 0.2831 -0.0229 0.0067 0.9919 0.9774 1.0067
#> Tampascale -0.0209 0.0137 0.1272 -0.0478 0.0060 0.9793 0.9533 1.0060
#> JobControl -0.0084 0.0082 0.3053 -0.0244 0.0076 0.9916 0.9759 1.0076
#> JobDemand -0.0210 0.0154 0.1736 -0.0511 0.0092 0.9792 0.9502 1.0092
#> Social -0.0524 0.0244 0.0316 -0.1002 -0.0046 0.9489 0.9047 0.9954
#>
#> $`Step 6`
#> est std.err signif lower upper HR L.HR U.HR
#> Duration -0.0070 0.0039 0.0702 -0.0146 0.0006 0.9930 0.9855 1.0006
#> Function 0.0476 0.0166 0.0041 0.0151 0.0801 1.0488 1.0152 1.0834
#> Age -0.0079 0.0076 0.2955 -0.0228 0.0069 0.9921 0.9775 1.0069
#> Tampascale -0.0208 0.0138 0.1309 -0.0479 0.0062 0.9794 0.9532 1.0062
#> JobDemand -0.0193 0.0154 0.2102 -0.0494 0.0109 0.9809 0.9518 1.0110
#> Social -0.0608 0.0229 0.0079 -0.1057 -0.0160 0.9410 0.8997 0.9841
#>
#> $`Step 7`
#> est std.err signif lower upper HR L.HR U.HR
#> Duration -0.0078 0.0038 0.0393 -0.0153 -0.0004 0.9922 0.9848 0.9996
#> Function 0.0481 0.0166 0.0038 0.0155 0.0807 1.0493 1.0156 1.0840
#> Tampascale -0.0183 0.0134 0.1730 -0.0447 0.0080 0.9819 0.9563 1.0080
#> JobDemand -0.0178 0.0153 0.2438 -0.0477 0.0121 0.9824 0.9534 1.0122
#> Social -0.0602 0.0229 0.0086 -0.1051 -0.0153 0.9416 0.9002 0.9848
#>
#> $`Step 8`
#> est std.err signif lower upper HR L.HR U.HR
#> Duration -0.0080 0.0038 0.0333 -0.0154 -0.0006 0.9920 0.9847 0.9994
#> Function 0.0484 0.0164 0.0032 0.0162 0.0806 1.0496 1.0163 1.0839
#> Tampascale -0.0209 0.0133 0.1168 -0.0470 0.0052 0.9793 0.9541 1.0052
#> Social -0.0564 0.0227 0.0130 -0.1010 -0.0119 0.9452 0.9039 0.9882
#>
#> $`Step 9`
#> est std.err signif lower upper HR L.HR U.HR
#> Duration -0.0076 0.0037 0.0421 -0.0149 -0.0003 0.9924 0.9852 0.9997
#> Function 0.0565 0.0154 0.0002 0.0263 0.0867 1.0581 1.0266 1.0906
#> Social -0.0520 0.0226 0.0215 -0.0963 -0.0077 0.9493 0.9082 0.9923
pool_coxr$multiparm_p
#> $`Step 1`
#> MPR & RR P-values
#> Duration 0.0524
#> Radiation 0.6251
#> Onset 0.5941
#> Function 0.0094
#> Age 0.2510
#> Previous 0.6215
#> Tampascale 0.0991
#> JobControl 0.3144
#> JobDemand 0.1591
#> Social 0.0393
#> factor(Expect_cat) 0.4786
#>
#> $`Step 2`
#> MPR & RR P-values
#> Duration 0.0484
#> Onset 0.6054
#> Function 0.0057
#> Age 0.2520
#> Previous 0.6561
#> Tampascale 0.0979
#> JobControl 0.2923
#> JobDemand 0.1621
#> Social 0.0412
#> factor(Expect_cat) 0.4044
#>
#> $`Step 3`
#> MPR & RR P-values
#> Duration 0.0534
#> Onset 0.6234
#> Function 0.0058
#> Age 0.2301
#> Tampascale 0.1021
#> JobControl 0.2663
#> JobDemand 0.1525
#> Social 0.0380
#> factor(Expect_cat) 0.3887
#>
#> $`Step 4`
#> MPR & RR P-values
#> Duration 0.0496
#> Function 0.0059
#> Age 0.2430
#> Tampascale 0.0980
#> JobControl 0.2802
#> JobDemand 0.1539
#> Social 0.0340
#> factor(Expect_cat) 0.3883
#>
#> $`Step 5`
#> MPR & RR P-values
#> Duration 0.0509
#> Function 0.0052
#> Age 0.2781
#> Tampascale 0.1126
#> JobControl 0.3116
#> JobDemand 0.1742
#> Social 0.0305
#>
#> $`Step 6`
#> MPR & RR P-values
#> Duration 0.0638
#> Function 0.0053
#> Age 0.2892
#> Tampascale 0.1219
#> JobDemand 0.2105
#> Social 0.0075
#>
#> $`Step 7`
#> MPR & RR P-values
#> Duration 0.0349
#> Function 0.0050
#> Tampascale 0.1710
#> JobDemand 0.2438
#> Social 0.0083
#>
#> $`Step 8`
#> MPR & RR P-values
#> Duration 0.0288
#> Function 0.0042
#> Tampascale 0.1155
#> Social 0.0124
#>
#> $`Step 9`
#> MPR & RR P-values
#> Duration 0.0374
#> Function 0.0005
#> Social 0.0206Back to Examples
Pooling Cox regression models over 5 imputed datasets with backward selection using a p-value of 0.05 and as method D2 including interaction terms with a categorical predictor and forcing the predictor “Tampascale” in the models during backward selection
library(psfmi)
pool_coxr <- psfmi_coxr(data=lbpmicox, nimp=5, impvar="Impnr", time="Time", status="Status",
predictors=c("Duration", "Previous", "Radiation", "Onset",
"Function", "Tampascale" ), p.crit=0.05, cat.predictors=c("Satisfaction",
"Expect_cat"), int.predictors=c("Tampascale:Radiation",
"Expect_cat:Tampascale"), keep.predictors = "Tampascale", method="D2" )
#> Variable excluded at Step 1 is - factor(Satisfaction)
#> Variable excluded at Step 2 is - Onset
#> Variable excluded at Step 3 is - Previous
#>
#> Pooled model correctly estimated
#> using a p-value of 0.05 and predictors to keep Tampascale
pool_coxr$RR_Model
#> $`Step 1`
#> est std.err signif lower upper
#> Duration -0.0084 0.0038 0.0273 -0.0159 -0.0009
#> Previous -0.1772 0.2005 0.3766 -0.5701 0.2157
#> Radiation -1.6902 1.0884 0.1206 -3.8250 0.4446
#> Onset -0.0975 0.1780 0.5838 -0.4464 0.2514
#> Function 0.0425 0.0178 0.0168 0.0077 0.0773
#> Tampascale -0.0834 0.0376 0.0265 -0.1571 -0.0097
#> factor(Satisfaction)1 -0.1900 0.3181 0.5527 -0.8276 0.4475
#> factor(Satisfaction)2 -0.0607 0.3992 0.8796 -0.8586 0.7371
#> factor(Expect_cat)2 -2.1919 1.6183 0.1759 -5.3671 0.9833
#> factor(Expect_cat)3 -1.4635 1.4236 0.3039 -4.2536 1.3266
#> Radiation:Tampascale 0.0393 0.0266 0.1394 -0.0128 0.0915
#> Tampascale:factor(Expect_cat)2 0.0603 0.0403 0.1341 -0.0186 0.1393
#> Tampascale:factor(Expect_cat)3 0.0423 0.0365 0.2458 -0.0291 0.1138
#> HR L.HR U.HR
#> Duration 0.9916 0.9842 0.9991
#> Previous 0.8376 0.5655 1.2407
#> Radiation 0.1845 0.0218 1.5599
#> Onset 0.9071 0.6399 1.2858
#> Function 1.0434 1.0077 1.0804
#> Tampascale 0.9200 0.8546 0.9903
#> factor(Satisfaction)1 0.8270 0.4371 1.5644
#> factor(Satisfaction)2 0.9411 0.4238 2.0899
#> factor(Expect_cat)2 0.1117 0.0047 2.6733
#> factor(Expect_cat)3 0.2314 0.0142 3.7682
#> Radiation:Tampascale 1.0401 0.9873 1.0958
#> Tampascale:factor(Expect_cat)2 1.0622 0.9816 1.1495
#> Tampascale:factor(Expect_cat)3 1.0432 0.9713 1.1205
#>
#> $`Step 2`
#> est std.err signif lower upper
#> Duration -0.0085 0.0038 0.0265 -0.0160 -0.0010
#> Previous -0.1895 0.1963 0.3344 -0.5742 0.1953
#> Radiation -1.5692 1.0634 0.1401 -3.6540 0.5157
#> Onset -0.1119 0.1771 0.5274 -0.4591 0.2352
#> Function 0.0429 0.0173 0.0135 0.0089 0.0768
#> Tampascale -0.0811 0.0371 0.0289 -0.1539 -0.0083
#> factor(Expect_cat)2 -2.0401 1.5897 0.1995 -5.1578 1.0776
#> factor(Expect_cat)3 -1.4691 1.4155 0.2993 -4.2435 1.3053
#> Radiation:Tampascale 0.0367 0.0261 0.1594 -0.0144 0.0878
#> Tampascale:factor(Expect_cat)2 0.0571 0.0397 0.1500 -0.0207 0.1349
#> Tampascale:factor(Expect_cat)3 0.0422 0.0363 0.2443 -0.0289 0.1134
#> HR L.HR U.HR
#> Duration 0.9915 0.9841 0.9990
#> Previous 0.8274 0.5632 1.2157
#> Radiation 0.2082 0.0259 1.6748
#> Onset 0.8941 0.6319 1.2652
#> Function 1.0438 1.0089 1.0798
#> Tampascale 0.9221 0.8574 0.9917
#> factor(Expect_cat)2 0.1300 0.0058 2.9376
#> factor(Expect_cat)3 0.2301 0.0144 3.6888
#> Radiation:Tampascale 1.0374 0.9857 1.0918
#> Tampascale:factor(Expect_cat)2 1.0588 0.9795 1.1444
#> Tampascale:factor(Expect_cat)3 1.0431 0.9715 1.1201
#>
#> $`Step 3`
#> est std.err signif lower upper
#> Duration -0.0087 0.0038 0.0232 -0.0161 -0.0012
#> Previous -0.1820 0.1959 0.3529 -0.5659 0.2020
#> Radiation -1.4856 1.0514 0.1577 -3.5468 0.5757
#> Function 0.0431 0.0173 0.0130 0.0091 0.0771
#> Tampascale -0.0815 0.0371 0.0279 -0.1541 -0.0088
#> factor(Expect_cat)2 -2.0787 1.5854 0.1900 -5.1880 1.0307
#> factor(Expect_cat)3 -1.5132 1.4132 0.2843 -4.2830 1.2565
#> Radiation:Tampascale 0.0347 0.0258 0.1789 -0.0159 0.0852
#> Tampascale:factor(Expect_cat)2 0.0583 0.0395 0.1403 -0.0192 0.1358
#> Tampascale:factor(Expect_cat)3 0.0435 0.0362 0.2299 -0.0275 0.1145
#> HR L.HR U.HR
#> Duration 0.9913 0.9840 0.9988
#> Previous 0.8336 0.5678 1.2238
#> Radiation 0.2264 0.0288 1.7784
#> Function 1.0440 1.0091 1.0802
#> Tampascale 0.9217 0.8572 0.9912
#> factor(Expect_cat)2 0.1251 0.0056 2.8030
#> factor(Expect_cat)3 0.2202 0.0138 3.5131
#> Radiation:Tampascale 1.0353 0.9842 1.0889
#> Tampascale:factor(Expect_cat)2 1.0600 0.9810 1.1455
#> Tampascale:factor(Expect_cat)3 1.0445 0.9729 1.1213
#>
#> $`Step 4`
#> est std.err signif lower upper
#> Duration -0.0083 0.0038 0.0290 -0.0157 -0.0008
#> Radiation -1.5430 1.0557 0.1439 -3.6128 0.5268
#> Function 0.0428 0.0173 0.0131 0.0090 0.0767
#> Tampascale -0.0818 0.0369 0.0269 -0.1542 -0.0094
#> factor(Expect_cat)2 -2.1396 1.5729 0.1738 -5.2237 0.9445
#> factor(Expect_cat)3 -1.5389 1.4141 0.2765 -4.3105 1.2326
#> Radiation:Tampascale 0.0364 0.0259 0.1602 -0.0144 0.0871
#> Tampascale:factor(Expect_cat)2 0.0596 0.0392 0.1287 -0.0173 0.1365
#> Tampascale:factor(Expect_cat)3 0.0445 0.0362 0.2192 -0.0265 0.1155
#> HR L.HR U.HR
#> Duration 0.9917 0.9844 0.9992
#> Radiation 0.2137 0.0270 1.6935
#> Function 1.0437 1.0090 1.0797
#> Tampascale 0.9215 0.8571 0.9906
#> factor(Expect_cat)2 0.1177 0.0054 2.5715
#> factor(Expect_cat)3 0.2146 0.0134 3.4301
#> Radiation:Tampascale 1.0371 0.9857 1.0910
#> Tampascale:factor(Expect_cat)2 1.0614 0.9828 1.1463
#> Tampascale:factor(Expect_cat)3 1.0455 0.9738 1.1224
pool_coxr$multiparm_p
#> $`Step 1`
#> D2 D2 & RR p-values
#> Duration 5.1434 0.0273
#> Previous 0.7474 0.3766
#> Radiation 0.3867 0.1206
#> Onset 0.2946 0.5838
#> Function 5.3610 0.0168
#> Tampascale 2.0895 0.0265
#> factor(Satisfaction) 0.3359 0.7149
#> factor(Expect_cat) 0.5157 0.5971
#> Radiation:Tampascale 2.1497 0.1394
#> Tampascale:factor(Expect_cat) 1.0764 0.3412
#>
#> $`Step 2`
#> D2 D2 & RR p-values
#> Duration 5.1963 0.0265
#> Previous 0.8984 0.3344
#> Radiation 0.3298 0.1401
#> Onset 0.3915 0.5274
#> Function 5.7454 0.0135
#> Tampascale 2.1280 0.0289
#> factor(Expect_cat) 0.5517 0.5759
#> Radiation:Tampascale 1.9506 0.1594
#> Tampascale:factor(Expect_cat) 1.0241 0.3593
#>
#> $`Step 3`
#> D2 D2 & RR p-values
#> Duration 5.4502 0.0232
#> Previous 0.8330 0.3529
#> Radiation 0.3214 0.1577
#> Function 5.8119 0.0130
#> Tampascale 2.2151 0.0279
#> factor(Expect_cat) 0.6014 0.5481
#> Radiation:Tampascale 1.7753 0.1789
#> Tampascale:factor(Expect_cat) 1.0767 0.3409
#>
#> $`Step 4`
#> D2 D2 & RR p-values
#> Duration 5.0329 0.0290
#> Radiation 0.2419 0.1439
#> Function 5.7889 0.0131
#> Tampascale 1.9562 0.0269
#> factor(Expect_cat) 0.6180 0.5390
#> Radiation:Tampascale 1.9369 0.1602
#> Tampascale:factor(Expect_cat) 1.1495 0.3169Back to Examples
Pooling Cox regression models over 5 imputed datasets with backward selection using a p-value of 0.05 and as method D1 including a spline predictor and forcing these in the models during backward selection
library(psfmi)
pool_coxr <- psfmi_coxr(data=lbpmicox, nimp=5, impvar="Impnr", time="Time", status="Status",
predictors=c("Duration", "Previous", "Radiation", "Onset", "Function"),
p.crit=0.05, cat.predictors=c("Satisfaction"), spline.predictors=c("Tampascale"),
int.predictors=c("Tampascale:Radiation"), keep.predictors = "Tampascale",
knots=3, method="D1")
#> Variable excluded at Step 1 is - factor(Satisfaction)
#> Variable excluded at Step 2 is - Onset
#> Variable excluded at Step 3 is - Previous
#>
#> Pooled model correctly estimated
#> using a p-value of 0.05 and predictors to keep rcs(Tampascale,3)
pool_coxr$RR_Model
#> $`Step 1`
#> est std.err signif lower
#> Duration -0.0087 0.0038 0.0227 -0.0161
#> Previous -0.1683 0.2013 0.4030 -0.5629
#> Radiation -1.5246 1.9303 0.4297 -5.3087
#> Onset -0.1231 0.1783 0.4897 -0.4725
#> Function 0.0487 0.0183 0.0077 0.0129
#> factor(Satisfaction)1 -0.1715 0.3109 0.5832 -0.7930
#> factor(Satisfaction)2 -0.0078 0.3960 0.9844 -0.7987
#> rcs(Tampascale, 3)Tampascale -0.0811 0.0355 0.0223 -0.1507
#> rcs(Tampascale, 3)Tampascale' 0.0634 0.0439 0.1487 -0.0226
#> Radiation:rcs(Tampascale, 3)Tampascale 0.0367 0.0539 0.4954 -0.0689
#> Radiation:rcs(Tampascale, 3)Tampascale' -0.0182 0.0587 0.7563 -0.1332
#> upper HR L.HR U.HR
#> Duration -0.0012 0.9913 0.9840 0.9988
#> Previous 0.2262 0.8451 0.5696 1.2538
#> Radiation 2.2594 0.2177 0.0049 9.5773
#> Onset 0.2263 0.8842 0.6234 1.2540
#> Function 0.0845 1.0499 1.0130 1.0882
#> factor(Satisfaction)1 0.4500 0.8424 0.4525 1.5683
#> factor(Satisfaction)2 0.7831 0.9922 0.4499 2.1882
#> rcs(Tampascale, 3)Tampascale -0.0116 0.9221 0.8601 0.9885
#> rcs(Tampascale, 3)Tampascale' 0.1494 1.0655 0.9777 1.1611
#> Radiation:rcs(Tampascale, 3)Tampascale 0.1424 1.0374 0.9334 1.1530
#> Radiation:rcs(Tampascale, 3)Tampascale' 0.0968 0.9820 0.8753 1.1016
#>
#> $`Step 2`
#> est std.err signif lower
#> Duration -0.0086 0.0038 0.0242 -0.0160
#> Previous -0.1753 0.1970 0.3735 -0.5614
#> Radiation -1.4583 1.9217 0.4480 -5.2255
#> Onset -0.1412 0.1770 0.4251 -0.4881
#> Function 0.0489 0.0178 0.0061 0.0140
#> rcs(Tampascale, 3)Tampascale -0.0786 0.0351 0.0252 -0.1474
#> rcs(Tampascale, 3)Tampascale' 0.0615 0.0437 0.1588 -0.0241
#> Radiation:rcs(Tampascale, 3)Tampascale 0.0356 0.0538 0.5085 -0.0699
#> Radiation:rcs(Tampascale, 3)Tampascale' -0.0188 0.0588 0.7490 -0.1340
#> upper HR L.HR U.HR
#> Duration -0.0011 0.9914 0.9841 0.9989
#> Previous 0.2108 0.8392 0.5704 1.2347
#> Radiation 2.3090 0.2326 0.0054 10.0644
#> Onset 0.2057 0.8683 0.6138 1.2284
#> Function 0.0838 1.0501 1.0141 1.0874
#> rcs(Tampascale, 3)Tampascale -0.0098 0.9244 0.8629 0.9902
#> rcs(Tampascale, 3)Tampascale' 0.1471 1.0634 0.9762 1.1585
#> Radiation:rcs(Tampascale, 3)Tampascale 0.1410 1.0362 0.9325 1.1514
#> Radiation:rcs(Tampascale, 3)Tampascale' 0.0964 0.9814 0.8746 1.1012
#>
#> $`Step 3`
#> est std.err signif lower
#> Duration -0.0088 0.0038 0.0201 -0.0162
#> Previous -0.1652 0.1968 0.4013 -0.5510
#> Radiation -1.3271 1.9232 0.4902 -5.0972
#> Function 0.0495 0.0179 0.0056 0.0145
#> rcs(Tampascale, 3)Tampascale -0.0775 0.0353 0.0284 -0.1467
#> rcs(Tampascale, 3)Tampascale' 0.0612 0.0440 0.1644 -0.0251
#> Radiation:rcs(Tampascale, 3)Tampascale 0.0323 0.0539 0.5494 -0.0734
#> Radiation:rcs(Tampascale, 3)Tampascale' -0.0179 0.0591 0.7626 -0.1337
#> upper HR L.HR U.HR
#> Duration -0.0014 0.9912 0.9839 0.9986
#> Previous 0.2206 0.8477 0.5764 1.2468
#> Radiation 2.4431 0.2652 0.0061 11.5087
#> Function 0.0844 1.0507 1.0146 1.0881
#> rcs(Tampascale, 3)Tampascale -0.0082 0.9254 0.8636 0.9918
#> rcs(Tampascale, 3)Tampascale' 0.1475 1.0631 0.9752 1.1589
#> Radiation:rcs(Tampascale, 3)Tampascale 0.1379 1.0328 0.9292 1.1479
#> Radiation:rcs(Tampascale, 3)Tampascale' 0.0980 0.9823 0.8749 1.1030
#>
#> $`Step 4`
#> est std.err signif lower
#> Duration -0.0085 0.0038 0.0247 -0.0159
#> Radiation -1.1657 1.9282 0.5455 -4.9462
#> Function 0.0495 0.0178 0.0055 0.0146
#> rcs(Tampascale, 3)Tampascale -0.0767 0.0356 0.0314 -0.1466
#> rcs(Tampascale, 3)Tampascale' 0.0608 0.0444 0.1703 -0.0261
#> Radiation:rcs(Tampascale, 3)Tampascale 0.0278 0.0540 0.6068 -0.0781
#> Radiation:rcs(Tampascale, 3)Tampascale' -0.0118 0.0589 0.8411 -0.1272
#> upper HR L.HR U.HR
#> Duration -0.0011 0.9915 0.9842 0.9989
#> Radiation 2.6147 0.3117 0.0071 13.6631
#> Function 0.0844 1.0507 1.0147 1.0881
#> rcs(Tampascale, 3)Tampascale -0.0068 0.9262 0.8636 0.9932
#> rcs(Tampascale, 3)Tampascale' 0.1478 1.0627 0.9742 1.1593
#> Radiation:rcs(Tampascale, 3)Tampascale 0.1338 1.0282 0.9249 1.1432
#> Radiation:rcs(Tampascale, 3)Tampascale' 0.1036 0.9883 0.8806 1.1092
pool_coxr$multiparm_p
#> $`Step 1`
#> Chi_sq D1 & RR p-values
#> Duration 5.190 0.0227
#> Previous 0.699 0.4030
#> Radiation 0.624 0.4297
#> Onset 0.477 0.4897
#> Function 7.113 0.0077
#> factor(Satisfaction) 0.294 0.7467
#> rcs(Tampascale,3) 3.103 0.0451
#> Radiation:rcs(Tampascale,3) 0.422 0.6558
#>
#> $`Step 2`
#> Chi_sq D1 & RR p-values
#> Duration 5.082 0.0242
#> Previous 0.792 0.3735
#> Radiation 0.576 0.4480
#> Onset 0.636 0.4251
#> Function 7.530 0.0061
#> rcs(Tampascale,3) 2.959 0.0520
#> Radiation:rcs(Tampascale,3) 0.373 0.6887
#>
#> $`Step 3`
#> Chi_sq D1 & RR p-values
#> Duration 5.405 0.0201
#> Previous 0.704 0.4013
#> Radiation 0.476 0.4902
#> Function 7.674 0.0056
#> rcs(Tampascale,3) 2.839 0.0586
#> Radiation:rcs(Tampascale,3) 0.298 0.7423
#>
#> $`Step 4`
#> Chi_sq D1 & RR p-values
#> Duration 5.048 0.0247
#> Radiation 0.366 0.5455
#> Function 7.716 0.0055
#> rcs(Tampascale,3) 2.745 0.0644
#> Radiation:rcs(Tampascale,3) 0.281 0.7550Back to Examples