Add transition probabilities

Add (cumulative) hazard based on the provided data set and model. If ci=TRUE confidence intervals (CI) are also added. Their width can be controlled via the se_mult argument. The method by which the CI are calculated can be specified by ci_type. This is a wrapper around predict.gam. When reference is specified, the (log-)hazard ratio is calculated.

add_trans_prob(
  newdata,
  object,
  overwrite = FALSE,
  ci = FALSE,
  alpha = 0.05,
  nsim = 100L,
  time_var = NULL,
  interval_length = "intlen",
  ...
)

Arguments

newdata: A data frame or list containing the values of the model covariates at which predictions are required. If this is not provided then predictions corresponding to the original data are returned. If newdata is provided then it should contain all the variables needed for prediction: a warning is generated if not. See details for use with link{linear.functional.terms}.
object: a fitted gam object as produced by gam().
overwrite: Should hazard columns be overwritten if already present in the data set? Defaults to FALSE. If TRUE, columns with names c("hazard", "se", "lower", "upper") will be overwritten.
ci: logical. Indicates if confidence intervals should be calculated. Defaults to TRUE.
alpha: The alpha level for confidence/credible intervals.
nsim: Number of simulations (draws from posterior of estimated coefficients) on which estimation of CIFs and their confidence/credible intervals will be based on.
time_var: Name of the variable used for the baseline hazard. If not given, defaults to "tend" for gam fits, else "interval". The latter is assumed to be a factor, the former numeric.
interval_length: The variable in newdata containing the interval lengths. Can be either bare unquoted variable name or character. Defaults to "intlen".
...: Further arguments passed to predict.gam and get_hazard

Examples

ped <- tumor[1:50,] %>% as_ped(Surv(days, status)~ age)
pam <- mgcv::gam(ped_status ~ s(tend)+age, data = ped, family=poisson(), offset=offset)
ped_info(ped) %>% add_hazard(pam, type="link")
#> # A tibble: 22 × 10
#>    tstart  tend intlen intmid interval    age hazard    se ci_lower ci_upper
#>     <dbl> <dbl>  <dbl>  <dbl> <fct>     <dbl>  <dbl> <dbl>    <dbl>    <dbl>
#>  1      0    27     27   13.5 (0,27]     59.7  -7.05 0.391    -7.83    -6.27
#>  2     27    33      6   30   (27,33]    59.7  -7.06 0.386    -7.83    -6.28
#>  3     33    55     22   44   (33,55]    59.7  -7.09 0.367    -7.82    -6.35
#>  4     55    62      7   58.5 (55,62]    59.7  -7.10 0.361    -7.82    -6.37
#>  5     62   139     77  100.  (62,139]   59.7  -7.20 0.310    -7.82    -6.58
#>  6    139   209     70  174   (139,209]  59.7  -7.29 0.280    -7.85    -6.73
#>  7    209   214      5  212.  (209,214]  59.7  -7.30 0.279    -7.86    -6.74
#>  8    214   257     43  236.  (214,257]  59.7  -7.35 0.269    -7.89    -6.82
#>  9    257   304     47  280.  (257,304]  59.7  -7.41 0.265    -7.94    -6.88
#> 10    304   308      4  306   (304,308]  59.7  -7.42 0.265    -7.95    -6.89
#> # ℹ 12 more rows
ped_info(ped) %>% add_hazard(pam, type = "response")
#> # A tibble: 22 × 10
#>    tstart  tend intlen intmid interval    age   hazard    se ci_lower ci_upper
#>     <dbl> <dbl>  <dbl>  <dbl> <fct>     <dbl>    <dbl> <dbl>    <dbl>    <dbl>
#>  1      0    27     27   13.5 (0,27]     59.7 0.000869 0.391 0.000397  0.00190
#>  2     27    33      6   30   (27,33]    59.7 0.000862 0.386 0.000398  0.00187
#>  3     33    55     22   44   (33,55]    59.7 0.000837 0.367 0.000402  0.00174
#>  4     55    62      7   58.5 (55,62]    59.7 0.000829 0.361 0.000402  0.00171
#>  5     62   139     77  100.  (62,139]   59.7 0.000747 0.310 0.000402  0.00139
#>  6    139   209     70  174   (139,209]  59.7 0.000681 0.280 0.000389  0.00119
#>  7    209   214      5  212.  (209,214]  59.7 0.000676 0.279 0.000387  0.00118
#>  8    214   257     43  236.  (214,257]  59.7 0.000640 0.269 0.000373  0.00110
#>  9    257   304     47  280.  (257,304]  59.7 0.000602 0.265 0.000355  0.00102
#> 10    304   308      4  306   (304,308]  59.7 0.000599 0.265 0.000353  0.00102
#> # ℹ 12 more rows
ped_info(ped) %>% add_cumu_hazard(pam)
#> # A tibble: 22 × 9
#>    tstart  tend intlen intmid interval    age cumu_hazard cumu_lower cumu_upper
#>     <dbl> <dbl>  <dbl>  <dbl> <fct>     <dbl>       <dbl>      <dbl>      <dbl>
#>  1      0    27     27   13.5 (0,27]     59.7      0.0235     0.0107     0.0513
#>  2     27    33      6   30   (27,33]    59.7      0.0286     0.0131     0.0625
#>  3     33    55     22   44   (33,55]    59.7      0.0470     0.0220     0.101 
#>  4     55    62      7   58.5 (55,62]    59.7      0.0528     0.0248     0.113 
#>  5     62   139     77  100.  (62,139]   59.7      0.110      0.0557     0.220 
#>  6    139   209     70  174   (139,209]  59.7      0.158      0.0829     0.303 
#>  7    209   214      5  212.  (209,214]  59.7      0.161      0.0849     0.309 
#>  8    214   257     43  236.  (214,257]  59.7      0.189      0.101      0.356 
#>  9    257   304     47  280.  (257,304]  59.7      0.217      0.118      0.404 
#> 10    304   308      4  306   (304,308]  59.7      0.220      0.119      0.408 
#> # ℹ 12 more rows

Arguments

See also

Examples