predict.JointFPM.md
In JointFPM: A Parametric Model for Estimating the Mean Number of Events

Mean number of events in bladder did not change

Code
  bldr_model <- JointFPM(Surv(time = start, time2 = stop, event = event, type = "counting") ~
    1, re_model = ~ pyridoxine + thiotepa, ce_model = ~ pyridoxine + thiotepa,
  re_indicator = "re", ce_indicator = "ce", df_ce = 3, df_re = 3, cluster = "id",
  data = bladder1_stacked)
  predict(bldr_model, newdata = data.frame(pyridoxine = 1, thiotepa = 0), t = c(
    50), ci_fit = FALSE)
Output
    stop      fit
  1   50 2.430327

Parallel: Check calc CIs for mean number

Code
  bldr_model <- JointFPM(Surv(time = start, time2 = stop, event = event, type = "counting") ~
    1, re_model = ~ pyridoxine + thiotepa, ce_model = ~ pyridoxine + thiotepa,
  re_indicator = "re", ce_indicator = "ce", df_ce = 3, df_re = 3, cluster = "id",
  data = bladder1_stacked)
  print(predict(bldr_model, newdata = data.frame(pyridoxine = 1, thiotepa = 0),
  t = c(50), ci_fit = TRUE), digits = 4)
Output
    stop  fit   lci   uci
  1   50 2.43 1.096 3.764

Parallel: Check calc CIs for diff in mean number

Code
  bldr_model <- JointFPM(Surv(time = start, time2 = stop, event = event, type = "counting") ~
    1, re_model = ~ pyridoxine + thiotepa, ce_model = ~ pyridoxine + thiotepa,
  re_indicator = "re", ce_indicator = "ce", df_ce = 3, df_re = 3, cluster = "id",
  data = bladder1_stacked)
  print(predict(bldr_model, type = "diff", newdata = data.frame(pyridoxine = 1,
    thiotepa = 0), exposed = function(x) transform(x, thiotepa = 1), t = c(50),
  ci_fit = TRUE), digits = 4)
Output
    stop   fit     lci   uci
  1   50 0.878 -0.1133 1.869

Parallel: Check calc CIs for marg mean number

Code
  bldr_model <- JointFPM(Surv(time = start, time2 = stop, event = event, type = "counting") ~
    1, re_model = ~ pyridoxine + thiotepa + size, ce_model = ~ pyridoxine +
    thiotepa + size, re_indicator = "re", ce_indicator = "ce", df_ce = 3, df_re = 3,
  cluster = "id", data = bladder1_stacked)
  print(predict(bldr_model, newdata = data.frame(pyridoxine = 1, thiotepa = 0),
  t = c(10), type = "marg_mean_no", ci_fit = TRUE), digits = 4)
Output
    stop    fit    lci   uci
  1   10 0.6101 0.2782 0.942

Parallel: Check calc CIs for diff in marg mean number

Code
  bldr_model <- JointFPM(Surv(time = start, time2 = stop, event = event, type = "counting") ~
    1, re_model = ~ pyridoxine + thiotepa + size, ce_model = ~ pyridoxine +
    thiotepa + size, re_indicator = "re", ce_indicator = "ce", df_ce = 3, df_re = 3,
  cluster = "id", data = bladder1_stacked)
  print(predict(bldr_model, type = "marg_diff", newdata = data.frame(pyridoxine = 1,
    thiotepa = 0), exposed = function(x) transform(x, thiotepa = 1), t = c(50),
  ci_fit = TRUE), digits = 4)
Output
    stop    fit     lci   uci
  1   50 0.8813 -0.1132 1.876

Integration with GQ works

Code
  bldr_model <- JointFPM(Surv(time = start, time2 = stop, event = event, type = "counting") ~
    1, re_model = ~ pyridoxine + thiotepa, ce_model = ~ pyridoxine + thiotepa,
  re_indicator = "re", ce_indicator = "ce", df_ce = 3, df_re = 3, cluster = "id",
  data = bladder1_stacked)
  predict(bldr_model, newdata = data.frame(pyridoxine = 1, thiotepa = 0), t = c(1,
    50, 100), method = "gq", ngq = 30, ci_fit = FALSE)
Output
    stop        fit
  1    1 0.03450826
  2   50 2.42961444
  3  100 3.95690620