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Calculate likelihoods for three three-state model structures

Source: R/lhoods.R
calc_likes.Rd

Calculate likelihood values and other summary output for the following three state models structures: partitioned survival, clock forward state transition, and clock reset state transition. The function requires appropriately formatted patient-level data, a set of fitted survival regressions, and the time cut-off (if two-piece modeling is used).

Usage

calc_likes(ptdata, dpam, cuttime = 0)

Arguments

ptdata

Dataset of patient level data. Must be a tibble with columns named:

  • ptid: patient identifier

  • pfs.durn: duration of PFS from baseline

  • pfs.flag: event flag for PFS (=1 if progression or death occurred, 0 for censoring)

  • os.durn: duration of OS from baseline

  • os.flag: event flag for OS (=1 if death occurred, 0 for censoring)

  • ttp.durn: duration of TTP from baseline (usually should be equal to pfs.durn)

  • ttp.flag: event flag for TTP (=1 if progression occurred, 0 for censoring).

Survival data for all other endpoints (time to progression, pre-progression death, post-progression survival) are derived from PFS and OS.

dpam

List of survival regressions for each endpoint:

  • pre-progression death (PPD)

  • time to progression (TTP)

  • progression-free survival (PFS)

  • overall survival (OS)

  • post-progression survival clock forward (PPS-CF) and

  • post-progression survival clock reset (PPS-CR).

cuttime

Time cutoff - this is nonzero for two-piece models.

Value

A list of three tibbles: all is a tibble of results for all patients:

  • methname: the model structure or method.

  • npar: is the number of parameters used by that method.

  • npts_1 to npts_4 are the number of patients experiencing outcomes 1-4 respectively (see below), and npts_tot the total.

  • ll_1 to ll_4 are the log-likelihood values for patients experiencing outcomes 1-4 respectively (see below), and ll_tot the total. valid is a tibble of the same design as all but only in patients with valid likelihoods for all 4 methods sum is a tibble in respect of patients with valid likelihoods for all 4 methods providing:

  • npts: number of patients contributing results for this method.

  • npar: number of parameters used by that method.

  • ll: total log-likelihood

  • AIC: Akaike Information Criterion value for this model

  • BIC: Bayesian Information Criterion value for this model

The four outcomes are as follows:

  • (1) refers to patients who remain alive and progression-free during the follow-up;

  • (2) refers to patients who die without prior progression during the follow-up;

  • (3) refers to patients who progress and then remain alive for the remaining follow-up, and

  • (4) refers to patients who progress and die within the follow-up.

Examples

# \donttest{
bosonc <- create_dummydata("flexbosms")
fits <- fit_ends_mods_spl(bosonc)
# Pick out best distribution according to min AIC
params <- list(
  ppd = find_bestfit(fits$ppd, "aic")$fit,
  ttp = find_bestfit(fits$ttp, "aic")$fit,
  pfs = find_bestfit(fits$pfs, "aic")$fit,
  os = find_bestfit(fits$os, "aic")$fit,
  pps_cf = find_bestfit(fits$pps_cf, "aic")$fit,
  pps_cr = find_bestfit(fits$pps_cr, "aic")$fit
  )
calc_likes(bosonc, dpam=params)
#> $all
#> # A tibble: 4 × 12
#>   methname     npar npts_1 npts_2 npts_3 npts_4 npts_tot  ll_1  ll_2  ll_3  ll_4
#>   <chr>       <dbl>  <int>  <int>  <int>  <int>    <int> <dbl> <dbl> <dbl> <dbl>
#> 1 psm_simple      7     72     29     35     68      204 -64.8 -152. -151.   NA 
#> 2 psm_complex     9     72     29     35     68      204 -64.8 -147. -150. -473.
#> 3 stm_cf          9     72     29     35     68      204 -65.2 -147. -148. -473.
#> 4 stm_cr         10     72     29     35     68      204 -65.2 -147. -148. -470.
#> # ℹ 1 more variable: ll_tot <dbl>
#> 
#> $valid
#> # A tibble: 4 × 12
#>   methname     npar npts_1 npts_2 npts_3 npts_4 npts_tot  ll_1  ll_2  ll_3  ll_4
#>   <chr>       <dbl>  <int>  <int>  <int>  <int>    <int> <dbl> <dbl> <dbl> <dbl>
#> 1 psm_simple      7     72     29     35     67      203 -64.8 -152. -151. -469.
#> 2 psm_complex     9     72     29     35     67      203 -64.8 -147. -150. -468.
#> 3 stm_cf          9     72     29     35     67      203 -65.2 -147. -148. -468.
#> 4 stm_cr         10     72     29     35     67      203 -65.2 -147. -148. -464.
#> # ℹ 1 more variable: ll_tot <dbl>
#> 
#> $sum
#> # A tibble: 4 × 8
#>   methname     npts  npar    ll   aic   bic rank_aic rank_bic
#>   <chr>       <int> <dbl> <dbl> <dbl> <dbl>    <dbl>    <dbl>
#> 1 psm_simple    203     7 -836. 1685. 1709.        4        4
#> 2 psm_complex   203     9 -830. 1679. 1708.        3        3
#> 3 stm_cf        203     9 -828. 1673. 1703.        2        2
#> 4 stm_cr        203    10 -824. 1668. 1701.        1        1
#> 
# }