Frailty Cox model using survey data

General
,
1

Hello,
Please I'm still new in R, and don't understand how to fix some of the error messages in the reproducible data.

I need to run a multilevel analysis (on two levels) using Cox frailty survival model on a survey data. My problems are how to write the survey design using the two weights needed and apply them to the analysis.

I have identified the needed variables for the survey design, which are; primary sampling unit (psu) =~v021, level-1 weight=~wt1_1, level-2 weight=~wt2_1 ,strata=~v022.

Please can someone help me with the svydesign code and how to include it and the weights in a model.

I understand I'm supposed to use svycoxph instead of coxph in my analysis, but I'm lost as to how to write the survey design using the two level weights I need and actually including them in the frailty model below

library(survey)
#> Warning: package 'survey' was built under R version 4.0.5
#> Loading required package: grid
#> Loading required package: Matrix
#> Loading required package: survival
#> Warning: package 'survival' was built under R version 4.0.5
#> 
#> Attaching package: 'survey'
#> The following object is masked from 'package:graphics':
#> 
#>     dotchart
library(survival)
library(frailtypack)
#> Warning: package 'frailtypack' was built under R version 4.0.5
#> Loading required package: boot
#> 
#> Attaching package: 'boot'
#> The following object is masked from 'package:survival':
#> 
#>     aml
#> Loading required package: MASS
#> Loading required package: survC1
#> Warning: package 'survC1' was built under R version 4.0.5
#> Loading required package: doBy
#> Warning: package 'doBy' was built under R version 4.0.5
#> 
#> Attaching package: 'frailtypack'
#> The following object is masked from 'package:survival':
#> 
#>     cluster

datapasta::df_paste (head(rcom, 100)[, c('pid', 'study_time', 'died', 'v021', 'v022', 'v012', 'wt2_1', 'wt1_1', 'v024', 'v025', 'mat_edu')])
#> Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'head': object 'rcom' not found
data.frame(
         pid = c(1,2,3,4,5,6,7,8,9,10,11,12,13,
                 14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,
                 30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,
                 46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,
                 61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,
                 77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,
                 93,94,95,96,97,98,99,100),
  study_time = c(13,9,17,31,39,22,24,0,23,12,9,35,
                 18,20,60,18,5,46,26,54,37,51,31,55,27,15,39,6,
                 29,0,9,40,23,12,35,56,14,40,57,42,5,42,39,39,
                 54,19,52,42,7,28,53,5,28,13,37,0,23,33,27,36,20,
                 24,58,34,12,44,3,34,14,5,10,40,12,36,19,58,17,
                 40,39,58,53,53,1,50,2,28,24,13,13,50,46,46,19,6,
                 32,59,9,30,30,43),
        died = c(0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,
                 0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,
                 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,
                 0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
                 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
                 0),
        v021 = c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
                 2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,3,3,
                 3,3,3,3,3,3,3,3,3,3,3,3,4,4,4,4,4,4,4,4,4,
                 4,4,4,4,4,4,4,4,4,4,5,5,5,5,5,5,5,5,5,5,5,
                 5,5,5,5,5,5,5,5,5,5,5,5,6,6,6,6,6,6,6,6,6,
                 6),
        v022 = c("1","1","1","1","1","1","1","1","1",
                 "1","1","1","1","1","1","1","1","1","1","1","1",
                 "1","1","1","1","1","1","1","1","1","1","1","1","1",
                 "1","1","1","1","1","1","1","1","1","1","1","1",
                 "1","1","1","1","1","1","1","1","1","1","1","1","1",
                 "1","1","1","1","1","1","1","1","1","1","1","1","1",
                 "1","1","1","1","1","1","1","1","1","1","1","1",
                 "1","1","1","1","1","1","2","2","2","2","2","2","2",
                 "2","2","2"),
        v012 = c(40,37,27,27,24,32,35,35,34,20,28,
                 28,26,24,24,25,26,26,26,26,28,27,25,25,27,26,26,
                 21,21,31,36,36,27,23,32,32,33,33,33,28,25,37,
                 33,34,33,28,28,29,33,33,33,39,38,38,38,38,24,27,
                 35,40,22,38,38,21,30,30,30,39,43,18,23,23,25,25,
                 30,45,26,26,35,35,35,35,32,32,40,25,27,30,30,30,
                 28,28,18,27,30,30,27,21,21,30),
       wt2_1 = c(401.200012207031,401.200012207031,
                 401.200012207031,401.200012207031,401.200012207031,401.200012207031,
                 401.200012207031,401.200012207031,401.200012207031,
                 401.200012207031,401.200012207031,401.200012207031,401.200012207031,
                 401.200012207031,401.200012207031,401.200012207031,
                 401.200012207031,401.200012207031,401.200012207031,401.200012207031,
                 401.200012207031,401.200012207031,401.200012207031,
                 401.200012207031,401.200012207031,401.200012207031,401.200012207031,
                 401.200012207031,401.200012207031,401.200012207031,
                 401.200012207031,401.200012207031,401.200012207031,401.200012207031,
                 401.200012207031,401.200012207031,401.200012207031,
                 401.200012207031,401.200012207031,401.200012207031,401.200012207031,
                 401.200012207031,401.200012207031,401.200012207031,
                 401.200012207031,401.200012207031,401.200012207031,401.200012207031,
                 401.200012207031,401.200012207031,401.200012207031,
                 401.200012207031,401.200012207031,401.200012207031,401.200012207031,
                 401.200012207031,401.200012207031,401.200012207031,
                 401.200012207031,401.200012207031,401.200012207031,401.200012207031,
                 401.200012207031,401.200012207031,401.200012207031,
                 401.200012207031,401.200012207031,401.200012207031,401.200012207031,
                 401.200012207031,401.200012207031,401.200012207031,
                 401.200012207031,401.200012207031,401.200012207031,401.200012207031,
                 401.200012207031,401.200012207031,401.200012207031,
                 401.200012207031,401.200012207031,401.200012207031,401.200012207031,
                 401.200012207031,401.200012207031,401.200012207031,
                 401.200012207031,401.200012207031,401.200012207031,401.200012207031,
                 631.818176269531,631.818176269531,631.818176269531,
                 631.818176269531,631.818176269531,631.818176269531,631.818176269531,
                 631.818176269531,631.818176269531,631.818176269531),
       wt1_1 = c(2.5074667930603,2.5074667930603,
                 2.5074667930603,2.5074667930603,2.5074667930603,2.5074667930603,
                 2.5074667930603,2.5074667930603,2.5074667930603,2.5074667930603,
                 2.5074667930603,2.5074667930603,2.5074667930603,
                 2.5074667930603,2.5074667930603,5.1194109916687,5.1194109916687,
                 5.1194109916687,5.1194109916687,5.1194109916687,5.1194109916687,
                 5.1194109916687,5.1194109916687,5.1194109916687,
                 5.1194109916687,5.1194109916687,5.1194109916687,5.1194109916687,
                 5.1194109916687,5.1194109916687,5.1194109916687,5.1194109916687,
                 5.1194109916687,5.1194109916687,2.40910983085632,
                 2.40910983085632,2.40910983085632,2.40910983085632,2.40910983085632,
                 2.40910983085632,2.40910983085632,2.40910983085632,
                 2.40910983085632,2.40910983085632,2.40910983085632,2.40910983085632,
                 2.40910983085632,2.40910983085632,1.06203985214233,
                 1.06203985214233,1.06203985214233,1.06203985214233,1.06203985214233,
                 1.06203985214233,1.06203985214233,1.06203985214233,
                 1.06203985214233,1.06203985214233,1.06203985214233,1.06203985214233,
                 1.06203985214233,1.06203985214233,1.06203985214233,
                 1.06203985214233,1.06203985214233,1.06203985214233,1.06203985214233,
                 2.80098295211792,2.80098295211792,2.80098295211792,
                 2.80098295211792,2.80098295211792,2.80098295211792,2.80098295211792,
                 2.80098295211792,2.80098295211792,2.80098295211792,
                 2.80098295211792,2.80098295211792,2.80098295211792,2.80098295211792,
                 2.80098295211792,2.80098295211792,2.80098295211792,
                 2.80098295211792,2.80098295211792,2.80098295211792,2.80098295211792,
                 2.80098295211792,2.80098295211792,1.24210178852081,
                 1.24210178852081,1.24210178852081,1.24210178852081,1.24210178852081,
                 1.24210178852081,1.24210178852081,1.24210178852081,
                 1.24210178852081,1.24210178852081),
        v024 = c("1","1","1","1","1","1","1","1","1",
                 "1","1","1","1","1","1","1","1","1","1","1","1",
                 "1","1","1","1","1","1","1","1","1","1","1","1","1",
                 "1","1","1","1","1","1","1","1","1","1","1","1",
                 "1","1","1","1","1","1","1","1","1","1","1","1","1",
                 "1","1","1","1","1","1","1","1","1","1","1","1","1",
                 "1","1","1","1","1","1","1","1","1","1","1","1",
                 "1","1","1","1","1","1","1","1","1","1","1","1","1",
                 "1","1","1"),
        v025 = c("1","1","1","1","1","1","1","1","1",
                 "1","1","1","1","1","1","1","1","1","1","1","1",
                 "1","1","1","1","1","1","1","1","1","1","1","1","1",
                 "1","1","1","1","1","1","1","1","1","1","1","1",
                 "1","1","1","1","1","1","1","1","1","1","1","1","1",
                 "1","1","1","1","1","1","1","1","1","1","1","1","1",
                 "1","1","1","1","1","1","1","1","1","1","1","1",
                 "1","1","1","1","1","1","2","2","2","2","2","2","2",
                 "2","2","2"),
     mat_edu = c("5","5","5","4","4","5","4","4","4",
                 "4","4","4","5","5","5","5","5","5","4","4","5",
                 "4","4","4","5","3","3","4","4","5","5","5","5","4",
                 "2","2","0","0","0","5","5","0","1","5","5","3",
                 "3","5","5","5","5","5","5","5","5","5","5","4","5",
                 "5","4","5","5","3","4","4","5","3","1","3","3","3",
                 "1","3","2","1","3","3","4","4","0","0","2","2",
                 "1","0","4","4","4","4","0","0","3","4","2","2","3",
                 "3","3","0")
)
#>     pid study_time died v021 v022 v012    wt2_1    wt1_1 v024 v025 mat_edu
#> 1     1         13    0    1    1   40 401.2000 2.507467    1    1       5
#> 2     2          9    0    1    1   37 401.2000 2.507467    1    1       5
#> 3     3         17    0    1    1   27 401.2000 2.507467    1    1       5
#> 4     4         31    0    1    1   27 401.2000 2.507467    1    1       4
#> 5     5         39    0    1    1   24 401.2000 2.507467    1    1       4
#> 6     6         22    0    1    1   32 401.2000 2.507467    1    1       5
#> 7     7         24    0    1    1   35 401.2000 2.507467    1    1       4
#> 8     8          0    1    1    1   35 401.2000 2.507467    1    1       4
#> 9     9         23    0    1    1   34 401.2000 2.507467    1    1       4
#> 10   10         12    0    1    1   20 401.2000 2.507467    1    1       4
#> 11   11          9    0    1    1   28 401.2000 2.507467    1    1       4
#> 12   12         35    0    1    1   28 401.2000 2.507467    1    1       4
#> 13   13         18    0    1    1   26 401.2000 2.507467    1    1       5
#> 14   14         20    0    1    1   24 401.2000 2.507467    1    1       5
#> 15   15         60    0    1    1   24 401.2000 2.507467    1    1       5
#> 16   16         18    0    2    1   25 401.2000 5.119411    1    1       5
#> 17   17          5    0    2    1   26 401.2000 5.119411    1    1       5
#> 18   18         46    0    2    1   26 401.2000 5.119411    1    1       5
#> 19   19         26    0    2    1   26 401.2000 5.119411    1    1       4
#> 20   20         54    0    2    1   26 401.2000 5.119411    1    1       4
#> 21   21         37    0    2    1   28 401.2000 5.119411    1    1       5
#> 22   22         51    0    2    1   27 401.2000 5.119411    1    1       4
#> 23   23         31    0    2    1   25 401.2000 5.119411    1    1       4
#> 24   24         55    0    2    1   25 401.2000 5.119411    1    1       4
#> 25   25         27    0    2    1   27 401.2000 5.119411    1    1       5
#> 26   26         15    0    2    1   26 401.2000 5.119411    1    1       3
#> 27   27         39    0    2    1   26 401.2000 5.119411    1    1       3
#> 28   28          6    0    2    1   21 401.2000 5.119411    1    1       4
#> 29   29         29    0    2    1   21 401.2000 5.119411    1    1       4
#> 30   30          0    1    2    1   31 401.2000 5.119411    1    1       5
#> 31   31          9    0    2    1   36 401.2000 5.119411    1    1       5
#> 32   32         40    0    2    1   36 401.2000 5.119411    1    1       5
#> 33   33         23    0    2    1   27 401.2000 5.119411    1    1       5
#> 34   34         12    0    2    1   23 401.2000 5.119411    1    1       4
#> 35   35         35    0    3    1   32 401.2000 2.409110    1    1       2
#> 36   36         56    0    3    1   32 401.2000 2.409110    1    1       2
#> 37   37         14    0    3    1   33 401.2000 2.409110    1    1       0
#> 38   38         40    0    3    1   33 401.2000 2.409110    1    1       0
#> 39   39         57    0    3    1   33 401.2000 2.409110    1    1       0
#> 40   40         42    0    3    1   28 401.2000 2.409110    1    1       5
#> 41   41          5    0    3    1   25 401.2000 2.409110    1    1       5
#> 42   42         42    0    3    1   37 401.2000 2.409110    1    1       0
#> 43   43         39    0    3    1   33 401.2000 2.409110    1    1       1
#> 44   44         39    0    3    1   34 401.2000 2.409110    1    1       5
#> 45   45         54    0    3    1   33 401.2000 2.409110    1    1       5
#> 46   46         19    0    3    1   28 401.2000 2.409110    1    1       3
#> 47   47         52    0    3    1   28 401.2000 2.409110    1    1       3
#> 48   48         42    0    3    1   29 401.2000 2.409110    1    1       5
#> 49   49          7    0    4    1   33 401.2000 1.062040    1    1       5
#> 50   50         28    0    4    1   33 401.2000 1.062040    1    1       5
#> 51   51         53    0    4    1   33 401.2000 1.062040    1    1       5
#> 52   52          5    1    4    1   39 401.2000 1.062040    1    1       5
#> 53   53         28    0    4    1   38 401.2000 1.062040    1    1       5
#> 54   54         13    0    4    1   38 401.2000 1.062040    1    1       5
#> 55   55         37    0    4    1   38 401.2000 1.062040    1    1       5
#> 56   56          0    1    4    1   38 401.2000 1.062040    1    1       5
#> 57   57         23    0    4    1   24 401.2000 1.062040    1    1       5
#> 58   58         33    0    4    1   27 401.2000 1.062040    1    1       4
#> 59   59         27    0    4    1   35 401.2000 1.062040    1    1       5
#> 60   60         36    1    4    1   40 401.2000 1.062040    1    1       5
#> 61   61         20    0    4    1   22 401.2000 1.062040    1    1       4
#> 62   62         24    0    4    1   38 401.2000 1.062040    1    1       5
#> 63   63         58    0    4    1   38 401.2000 1.062040    1    1       5
#> 64   64         34    0    4    1   21 401.2000 1.062040    1    1       3
#> 65   65         12    0    4    1   30 401.2000 1.062040    1    1       4
#> 66   66         44    0    4    1   30 401.2000 1.062040    1    1       4
#> 67   67          3    0    4    1   30 401.2000 1.062040    1    1       5
#> 68   68         34    0    5    1   39 401.2000 2.800983    1    1       3
#> 69   69         14    0    5    1   43 401.2000 2.800983    1    1       1
#> 70   70          5    0    5    1   18 401.2000 2.800983    1    1       3
#> 71   71         10    0    5    1   23 401.2000 2.800983    1    1       3
#> 72   72         40    0    5    1   23 401.2000 2.800983    1    1       3
#> 73   73         12    0    5    1   25 401.2000 2.800983    1    1       1
#> 74   74         36    0    5    1   25 401.2000 2.800983    1    1       3
#> 75   75         19    0    5    1   30 401.2000 2.800983    1    1       2
#> 76   76         58    0    5    1   45 401.2000 2.800983    1    1       1
#> 77   77         17    0    5    1   26 401.2000 2.800983    1    1       3
#> 78   78         40    0    5    1   26 401.2000 2.800983    1    1       3
#> 79   79         39    0    5    1   35 401.2000 2.800983    1    1       4
#> 80   80         58    0    5    1   35 401.2000 2.800983    1    1       4
#> 81   81         53    0    5    1   35 401.2000 2.800983    1    1       0
#> 82   82         53    0    5    1   35 401.2000 2.800983    1    1       0
#> 83   83          1    0    5    1   32 401.2000 2.800983    1    1       2
#> 84   84         50    0    5    1   32 401.2000 2.800983    1    1       2
#> 85   85          2    0    5    1   40 401.2000 2.800983    1    1       1
#> 86   86         28    0    5    1   25 401.2000 2.800983    1    1       0
#> 87   87         24    0    5    1   27 401.2000 2.800983    1    1       4
#> 88   88         13    0    5    1   30 401.2000 2.800983    1    1       4
#> 89   89         13    0    5    1   30 401.2000 2.800983    1    1       4
#> 90   90         50    0    5    1   30 401.2000 2.800983    1    1       4
#> 91   91         46    0    6    2   28 631.8182 1.242102    1    2       0
#> 92   92         46    0    6    2   28 631.8182 1.242102    1    2       0
#> 93   93         19    0    6    2   18 631.8182 1.242102    1    2       3
#> 94   94          6    0    6    2   27 631.8182 1.242102    1    2       4
#> 95   95         32    0    6    2   30 631.8182 1.242102    1    2       2
#> 96   96         59    0    6    2   30 631.8182 1.242102    1    2       2
#> 97   97          9    0    6    2   27 631.8182 1.242102    1    2       3
#> 98   98         30    0    6    2   21 631.8182 1.242102    1    2       3
#> 99   99         30    0    6    2   21 631.8182 1.242102    1    2       3
#> 100 100         43    0    6    2   30 631.8182 1.242102    1    2       0

Frailty <- coxph (Surv(study_time, died) ~ factor(mat_edu) + v025 + frailty(v021,distribution="gamma"), data=rcom)
#> Error in terms.formula(formula, specials = ss, data = data): object 'rcom' not found

Created on 2022-01-18 by the reprex package (v2.0.1)

Thank you for the anticipated help.

2

Hi, with your example data, there are not enough factor levels and gives this error:

Error in contrasts<-(*tmp*, value = contr.funs[1 + isOF[nn]]) :
contrasts can be applied only to factors with 2 or more levels

Which could be related to this: r - How to debug "contrasts can be applied only to factors with 2 or more levels" error? - Stack Overflow

Not sure if that is the case with your full dataset though.

1 Like
3

The example data arguments to Surv() don't seem promising. The two died values of 1 both match study_time of 0. All died values of 0 match non-zero study_time values. This makes it difficult to trace through the arguments to coxph to see where the error is thrown.

This S/O post explains that the source of this error is not always in the data frame, such as rcom, and provides debugging advice.

rcom[,c(3,2)]
   died study_time
1     0         13
2     0          9
3     0         17
4     0         31
5     0         39
6     0         22
7     0         24
8     1          0
9     0         23
10    0         12
11    0          9
12    0         35
13    0         18
14    0         20
15    0         60
16    0         18
17    0          5
18    0         46
19    0         26
20    0         54
21    0         37
22    0         51
23    0         31
24    0         55
25    0         27
26    0         15
27    0         39
28    0          6
29    0         29
30    1          0
31    0          9
32    0         40
33    0         23
34    0         12
35    0         35
36    0         56
37    0         14
38    0         40
39    0         57
40    0         42
41    0          5
42    0         42
43    0         39
44    0         39
45    0         54
46    0         19
47    0         52
48    0         42
49    0          7
50    0         28
4

Hi,
I have increased the example data to 100, providing both categories for died variable. I hope that provides the relevant information needed.
My dataset is quite large with about 33924 observations.

Thank you.

5

This seems to work. The warning message can be ignored.

library(survival)
library(frailtypack)
#> Loading required package: boot
#> 
#> Attaching package: 'boot'
#> The following object is masked from 'package:survival':
#> 
#>     aml
#> Loading required package: MASS
#> Loading required package: survC1
#> Loading required package: doBy
#> 
#> Attaching package: 'frailtypack'
#> The following object is masked from 'package:survival':
#> 
#>     cluster

rcom <- data.frame(
  data.frame(
    pid = c(
      1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
      14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
      30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,
      46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
      61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76,
      77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92,
      93, 94, 95, 96, 97, 98, 99, 100
    ),
    study_time = c(
      13, 9, 17, 31, 39, 22, 24, 0, 23, 12, 9, 35,
      18, 20, 60, 18, 5, 46, 26, 54, 37, 51, 31, 55, 27, 15, 39, 6,
      29, 0, 9, 40, 23, 12, 35, 56, 14, 40, 57, 42, 5, 42, 39, 39,
      54, 19, 52, 42, 7, 28, 53, 5, 28, 13, 37, 0, 23, 33, 27, 36, 20,
      24, 58, 34, 12, 44, 3, 34, 14, 5, 10, 40, 12, 36, 19, 58, 17,
      40, 39, 58, 53, 53, 1, 50, 2, 28, 24, 13, 13, 50, 46, 46, 19, 6,
      32, 59, 9, 30, 30, 43
    ),
    died = c(
      0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0,
      0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
      0
    ),
    v021 = c(
      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
      2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3,
      3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4,
      4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
      5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6,
      6
    ),
    v022 = c(
      "1", "1", "1", "1", "1", "1", "1", "1", "1",
      "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
      "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
      "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
      "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
      "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
      "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
      "1", "1", "1", "1", "1", "1", "2", "2", "2", "2", "2", "2", "2",
      "2", "2", "2"
    ),
    v012 = c(
      40, 37, 27, 27, 24, 32, 35, 35, 34, 20, 28,
      28, 26, 24, 24, 25, 26, 26, 26, 26, 28, 27, 25, 25, 27, 26, 26,
      21, 21, 31, 36, 36, 27, 23, 32, 32, 33, 33, 33, 28, 25, 37,
      33, 34, 33, 28, 28, 29, 33, 33, 33, 39, 38, 38, 38, 38, 24, 27,
      35, 40, 22, 38, 38, 21, 30, 30, 30, 39, 43, 18, 23, 23, 25, 25,
      30, 45, 26, 26, 35, 35, 35, 35, 32, 32, 40, 25, 27, 30, 30, 30,
      28, 28, 18, 27, 30, 30, 27, 21, 21, 30
    ),
    wt2_1 = c(
      401.200012207031, 401.200012207031,
      401.200012207031, 401.200012207031, 401.200012207031, 401.200012207031,
      401.200012207031, 401.200012207031, 401.200012207031,
      401.200012207031, 401.200012207031, 401.200012207031, 401.200012207031,
      401.200012207031, 401.200012207031, 401.200012207031,
      401.200012207031, 401.200012207031, 401.200012207031, 401.200012207031,
      401.200012207031, 401.200012207031, 401.200012207031,
      401.200012207031, 401.200012207031, 401.200012207031, 401.200012207031,
      401.200012207031, 401.200012207031, 401.200012207031,
      401.200012207031, 401.200012207031, 401.200012207031, 401.200012207031,
      401.200012207031, 401.200012207031, 401.200012207031,
      401.200012207031, 401.200012207031, 401.200012207031, 401.200012207031,
      401.200012207031, 401.200012207031, 401.200012207031,
      401.200012207031, 401.200012207031, 401.200012207031, 401.200012207031,
      401.200012207031, 401.200012207031, 401.200012207031,
      401.200012207031, 401.200012207031, 401.200012207031, 401.200012207031,
      401.200012207031, 401.200012207031, 401.200012207031,
      401.200012207031, 401.200012207031, 401.200012207031, 401.200012207031,
      401.200012207031, 401.200012207031, 401.200012207031,
      401.200012207031, 401.200012207031, 401.200012207031, 401.200012207031,
      401.200012207031, 401.200012207031, 401.200012207031,
      401.200012207031, 401.200012207031, 401.200012207031, 401.200012207031,
      401.200012207031, 401.200012207031, 401.200012207031,
      401.200012207031, 401.200012207031, 401.200012207031, 401.200012207031,
      401.200012207031, 401.200012207031, 401.200012207031,
      401.200012207031, 401.200012207031, 401.200012207031, 401.200012207031,
      631.818176269531, 631.818176269531, 631.818176269531,
      631.818176269531, 631.818176269531, 631.818176269531, 631.818176269531,
      631.818176269531, 631.818176269531, 631.818176269531
    ),
    wt1_1 = c(
      2.5074667930603, 2.5074667930603,
      2.5074667930603, 2.5074667930603, 2.5074667930603, 2.5074667930603,
      2.5074667930603, 2.5074667930603, 2.5074667930603, 2.5074667930603,
      2.5074667930603, 2.5074667930603, 2.5074667930603,
      2.5074667930603, 2.5074667930603, 5.1194109916687, 5.1194109916687,
      5.1194109916687, 5.1194109916687, 5.1194109916687, 5.1194109916687,
      5.1194109916687, 5.1194109916687, 5.1194109916687,
      5.1194109916687, 5.1194109916687, 5.1194109916687, 5.1194109916687,
      5.1194109916687, 5.1194109916687, 5.1194109916687, 5.1194109916687,
      5.1194109916687, 5.1194109916687, 2.40910983085632,
      2.40910983085632, 2.40910983085632, 2.40910983085632, 2.40910983085632,
      2.40910983085632, 2.40910983085632, 2.40910983085632,
      2.40910983085632, 2.40910983085632, 2.40910983085632, 2.40910983085632,
      2.40910983085632, 2.40910983085632, 1.06203985214233,
      1.06203985214233, 1.06203985214233, 1.06203985214233, 1.06203985214233,
      1.06203985214233, 1.06203985214233, 1.06203985214233,
      1.06203985214233, 1.06203985214233, 1.06203985214233, 1.06203985214233,
      1.06203985214233, 1.06203985214233, 1.06203985214233,
      1.06203985214233, 1.06203985214233, 1.06203985214233, 1.06203985214233,
      2.80098295211792, 2.80098295211792, 2.80098295211792,
      2.80098295211792, 2.80098295211792, 2.80098295211792, 2.80098295211792,
      2.80098295211792, 2.80098295211792, 2.80098295211792,
      2.80098295211792, 2.80098295211792, 2.80098295211792, 2.80098295211792,
      2.80098295211792, 2.80098295211792, 2.80098295211792,
      2.80098295211792, 2.80098295211792, 2.80098295211792, 2.80098295211792,
      2.80098295211792, 2.80098295211792, 1.24210178852081,
      1.24210178852081, 1.24210178852081, 1.24210178852081, 1.24210178852081,
      1.24210178852081, 1.24210178852081, 1.24210178852081,
      1.24210178852081, 1.24210178852081
    ),
    v024 = c(
      "1", "1", "1", "1", "1", "1", "1", "1", "1",
      "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
      "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
      "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
      "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
      "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
      "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
      "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
      "1", "1", "1"
    ),
    v025 = c(
      "1", "1", "1", "1", "1", "1", "1", "1", "1",
      "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
      "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
      "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
      "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
      "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
      "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
      "1", "1", "1", "1", "1", "1", "2", "2", "2", "2", "2", "2", "2",
      "2", "2", "2"
    ),
    mat_edu = c(
      "5", "5", "5", "4", "4", "5", "4", "4", "4",
      "4", "4", "4", "5", "5", "5", "5", "5", "5", "4", "4", "5",
      "4", "4", "4", "5", "3", "3", "4", "4", "5", "5", "5", "5", "4",
      "2", "2", "0", "0", "0", "5", "5", "0", "1", "5", "5", "3",
      "3", "5", "5", "5", "5", "5", "5", "5", "5", "5", "5", "4", "5",
      "5", "4", "5", "5", "3", "4", "4", "5", "3", "1", "3", "3", "3",
      "1", "3", "2", "1", "3", "3", "4", "4", "0", "0", "2", "2",
      "1", "0", "4", "4", "4", "4", "0", "0", "3", "4", "2", "2", "3",
      "3", "3", "0"
    )
  )
)

Frailty <- coxph(Surv(study_time, died) ~ factor(mat_edu) + v025 + frailty(v021, distribution = "gamma"), data = rcom)
#> Warning in coxpenal.fit(X, Y, istrat, offset, init = init, control, weights =
#> weights, : Inner loop failed to coverge for iterations 2 3 4

Frailty
#> Call:
#> coxph(formula = Surv(study_time, died) ~ factor(mat_edu) + v025 + 
#>     frailty(v021, distribution = "gamma"), data = rcom)
#> 
#>                                coef  se(coef)       se2     Chisq DF    p
#> factor(mat_edu)1          -4.82e-01  1.15e+05  1.15e+05  1.76e-11  1 1.00
#> factor(mat_edu)2          -2.00e-02  9.81e+04  9.81e+04  4.17e-14  1 1.00
#> factor(mat_edu)3          -1.82e-01  7.97e+04  7.97e+04  5.20e-12  1 1.00
#> factor(mat_edu)4           2.09e+01  5.92e+04  5.92e+04  1.25e-07  1 1.00
#> factor(mat_edu)5           2.20e+01  5.92e+04  5.92e+04  1.38e-07  1 1.00
#> v0252                     -1.83e+01  5.32e+04  5.32e+04  1.18e-07  1 1.00
#> frailty(v021, distributio                                1.25e-06  0 0.97
#> 
#> Iterations: 6 outer, 83 Newton-Raphson
#>      Variance of random effect= 5e-07   I-likelihood = -18.8 
#> Degrees of freedom for terms= 4 1 0 
#> Likelihood ratio test=6.42  on 5 df, p=0.3
#> n= 100, number of events= 5
6

Good to know that it worked.
Can I get help on writing the survey design with the two weights provided above (individual-level and cluster-level), and using them in a frailty model?

Thank you

7

That's something I can't help with. I suggest using the reprex in my answer to pose that question to StackOverFlow

8

Thanks, doing that now.

9

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