Calibration-curves-estimated-with-loess-smoothers
Calibration-curves-estimated-with-loess-smoothers.RmdData preperation
This vignette showcases how to estimate BLR-IPCW and pseudo-value calibration curves when using loess smoothers for the model to estimate the observed event probabilities. This is in contrast to using restricted cubic splines, which was done so in the Overview vignette. Both approaches are viable and we refer to the literature for a discussion of each method (Austin, Harrell, and Klaveren 2020; Harrell 2015). We mimic exactly the analysis from the Overview vignette, specifying loess smoother rather than restricted cubic splines, and assess whether the conclusions would be any different. This is primarily to showcase the utility of the package.
We use data from the European Society for Blood and Marrow
Transplantation (EBMT 2023), which
contains multistate survival data after a transplant for patients with
blood cancer. The start of follow up is the day of the transplant and
the initial state is alive and in remission. There are three
intermediate events (\(2\): recovery,
\(3\): adverse event, or \(4\): recovery + adverse event), and two
absorbing states (\(5\): relapse and
\(6\): death). This data was originally
made available from the mstate package (Wreede, Fiocco, and Putter 2011).
We start by reminding ourselves of the EBMT (Wreede, Fiocco, and Putter 2011; EBMT 2023)
validation datasets ebmtcal and msebmtcal, and
the predicted transition probabilities out of state j = 1
made at time s = 0 from the multistate model
(tps0). We also set the time at which we want to evaluate
the transition probabilities t. Please refer to the Overview
vignette for a more detailed description of this data.
set.seed(101)
library(calibmsm)
data("ebmtcal")
head(ebmtcal)
#> id rec rec.s ae ae.s recae recae.s rel rel.s srv srv.s year agecl
#> 1 1 22 1 995 0 995 0 995 0 995 0 1995-1998 20-40
#> 2 2 29 1 12 1 29 1 422 1 579 1 1995-1998 20-40
#> 3 3 1264 0 27 1 1264 0 1264 0 1264 0 1995-1998 20-40
#> 4 4 50 1 42 1 50 1 84 1 117 1 1995-1998 20-40
#> 5 5 22 1 1133 0 1133 0 114 1 1133 0 1995-1998 >40
#> 6 6 33 1 27 1 33 1 1427 0 1427 0 1995-1998 20-40
#> proph match dtcens dtcens.s
#> 1 no no gender mismatch 995 1
#> 2 no no gender mismatch 422 0
#> 3 no no gender mismatch 1264 1
#> 4 no gender mismatch 84 0
#> 5 no gender mismatch 114 0
#> 6 no no gender mismatch 1427 1
data("msebmtcal")
head(msebmtcal)
#> id from to trans Tstart Tstop time status
#> 1 1 1 2 1 0 22 22 1
#> 2 1 1 3 2 0 22 22 0
#> 3 1 1 5 3 0 22 22 0
#> 4 1 1 6 4 0 22 22 0
#> 5 1 2 4 5 22 995 973 0
#> 6 1 2 5 6 22 995 973 0
data("tps0")
head(tps0)
#> id pstate1 pstate2 pstate3 pstate4 pstate5 pstate6 se1
#> 1 1 0.1139726 0.2295006 0.08450376 0.2326861 0.1504855 0.1888514 0.01291133
#> 2 2 0.1140189 0.2316569 0.08442692 0.2328398 0.1481977 0.1888598 0.01291552
#> 3 3 0.1136646 0.2317636 0.08274331 0.2325663 0.1504787 0.1887834 0.01289444
#> 4 4 0.1383878 0.1836189 0.07579429 0.2179331 0.1538475 0.2304185 0.01857439
#> 5 5 0.1233226 0.1609740 0.05508100 0.1828176 0.1425950 0.3352099 0.01944967
#> 6 6 0.1136646 0.2317636 0.08462424 0.2305854 0.1505534 0.1888087 0.01289444
#> se2 se3 se4 se5 se6 j
#> 1 0.02369584 0.01257251 0.02323376 0.01648630 0.01601795 1
#> 2 0.02374329 0.01256056 0.02324869 0.01632797 0.01603703 1
#> 3 0.02375770 0.01245752 0.02322375 0.01647890 0.01601525 1
#> 4 0.03004447 0.01462570 0.03018673 0.02124071 0.02416121 1
#> 5 0.03419721 0.01367768 0.03423941 0.02329644 0.03688586 1
#> 6 0.02375770 0.01257276 0.02317348 0.01649531 0.01602438 1
t.eval <- 1826Estimation of calibration curves out of state j = 1 at time s = 0 using loess smoothers
We now produce calibration curves for the predicted transition probabilities out of state \(j = 1\) at time \(s = 0\). Given all individuals start in state \(1\), there is no need to consider the transition probabilities out of states \(j \neq 1\) at \(s = 0\). Calibration is assessed at follow up time (\(t = 1826\) days). We start by extracting the predicted transition probabilities from state \(j = 1\) at time \(s = 0\) from the object . These are the transition probabilities we aim to assess the calibration of.
tp.pred.s0 <- tps0 |>
dplyr::filter(j == 1) |>
dplyr::select(any_of(paste("pstate", 1:6, sep = "")))We now produce calibration curves using loess smoothers for the regression model where the observed event probabilities are estimated. This is specified by . Behaviour of the curve can be controlled using and , which we leave as default in this example. Please refere to documentation for the function from the package for details of these parameters. A smaller span and/or degree will create a more smoother line. This is done for both the BLR-IPCW and pseudo-value calibration curves.
dat.calib.blr <-
calib_msm(data.mstate = msebmtcal,
data.raw = ebmtcal,
j = 1,
s = 0,
t = t.eval,
tp.pred = tp.pred.s0,
calib.type = 'blr',
curve.type = "loess",
w.covs = c("year", "agecl", "proph", "match"),
CI = 95,
CI.type = "bootstrap",
CI.R.boot = 200)
#> Warning in calib_blr_ipcw(data.raw = data.raw, data.mstate = data.mstate, : WARNING, SOME BOOTSTRAPPED OBSERVED EVENT PROBABILITIES WERE NA FOR STATE 1
#> THERE ARE 92 ITERATIONS WITH NA's
#> THE MEAN NUMBER OF NA's IN EACH ITERATION IS 0.96
#> Warning in calib_blr_ipcw(data.raw = data.raw, data.mstate = data.mstate, : WARNING, SOME BOOTSTRAPPED OBSERVED EVENT PROBABILITIES WERE NA FOR STATE 2
#> THERE ARE 113 ITERATIONS WITH NA's
#> THE MEAN NUMBER OF NA's IN EACH ITERATION IS 1.075
#> Warning in calib_blr_ipcw(data.raw = data.raw, data.mstate = data.mstate, : WARNING, SOME BOOTSTRAPPED OBSERVED EVENT PROBABILITIES WERE NA FOR STATE 3
#> THERE ARE 120 ITERATIONS WITH NA's
#> THE MEAN NUMBER OF NA's IN EACH ITERATION IS 1.135
#> Warning in calib_blr_ipcw(data.raw = data.raw, data.mstate = data.mstate, : WARNING, SOME BOOTSTRAPPED OBSERVED EVENT PROBABILITIES WERE NA FOR STATE 4
#> THERE ARE 123 ITERATIONS WITH NA's
#> THE MEAN NUMBER OF NA's IN EACH ITERATION IS 1.23
#> Warning in calib_blr_ipcw(data.raw = data.raw, data.mstate = data.mstate, : WARNING, SOME BOOTSTRAPPED OBSERVED EVENT PROBABILITIES WERE NA FOR STATE 5
#> THERE ARE 111 ITERATIONS WITH NA's
#> THE MEAN NUMBER OF NA's IN EACH ITERATION IS 1.155
#> Warning in calib_blr_ipcw(data.raw = data.raw, data.mstate = data.mstate, : WARNING, SOME BOOTSTRAPPED OBSERVED EVENT PROBABILITIES WERE NA FOR STATE 6
#> THERE ARE 121 ITERATIONS WITH NA's
#> THE MEAN NUMBER OF NA's IN EACH ITERATION IS 1.185
dat.calib.pv <-
calib_msm(data.mstate = msebmtcal,
data.raw = ebmtcal,
j = 1,
s = 0,
t = t.eval,
tp.pred = tp.pred.s0,
calib.type = 'pv',
curve.type = "loess",
pv.group.vars = c("year"),
pv.n.pctls = 3,
CI = 95,
CI.type = 'parametric')We now produce calibration plots using the function.
plot(dat.calib.blr)
Figure 1: BLR-IPCW calibration plots out of state j = 1 at time s = 0 estimated using loess smoothers
plot(dat.calib.pv)
Figure 2: pseudo-value calibration plots out of state j = 1 at time s = 0 estimated using loess smoothers
The equivalent Figures from the Overview vignette, produced using restricted cubic splines, are Figures 2 and 3.
Write a short comparison… XXXX
Estimation of calibration curves out of state j = 1 at time s = 100 using loess smoothers
We now produce calibration curves for the predicted transition probabilities out of state \(j = 1\) at time \(s = 100\). Calibration is assessed at follow up time (\(t = 1826\) days). We start by extracting the predicted transition probabilities from state \(j = 1\) at time \(s = 100\) from the object . These are the transition probabilities we aim to assess the calibration of.
tp.pred.s100 <- tps100 |>
dplyr::filter(j == 1) |>
dplyr::select(any_of(paste("pstate", 1:6, sep = "")))We now produce calibration curves using loess smoothers for the regression model where the observed event probabilities are estimated. This is specified by . Behaviour of the curve can be controlled using and , which we leave as default in this example. Please refere to documentation for the function from the package for details of these parameters. A smaller span and/or degree will create a more smoother line. This is done for both the BLR-IPCW and pseudo-value calibration curves.
dat.calib.blr <-
calib_msm(data.mstate = msebmtcal,
data.raw = ebmtcal,
j = 1,
s = 100,
t = t.eval,
tp.pred = tp.pred.s100,
calib.type = 'blr',
curve.type = "loess",
w.covs = c("year", "agecl", "proph", "match"),
CI = 95,
CI.type = "bootstrap",
CI.R.boot = 200)
#> Warning in calib_msm(data.mstate = msebmtcal, data.raw = ebmtcal, j = 1, : In the landmark cohort of individuals uncensored and in state j at time s,
#> there are some states have less than 50 people at the time at which calibration is being assessed (t).
#> Warnings and errors may occur when the models are fitted to estimate the calibration curves due to small sample size.
#> This warning has been written to try and intercept some uninformative error messages when the underlying statistical models fail.
#> The number to flag this warning (50) has been chosen arbitrarily, and does not constitute a sufficient sample size from a statistical point of view.
#> Warning in calib_blr_ipcw(data.raw = data.raw, data.mstate = data.mstate, : WARNING, SOME BOOTSTRAPPED OBSERVED EVENT PROBABILITIES WERE NA FOR STATE 1
#> THERE ARE 113 ITERATIONS WITH NA's
#> THE MEAN NUMBER OF NA's IN EACH ITERATION IS 1.07
#> Warning in calib_blr_ipcw(data.raw = data.raw, data.mstate = data.mstate, : WARNING, SOME BOOTSTRAPPED OBSERVED EVENT PROBABILITIES WERE NA FOR STATE 2
#> THERE ARE 132 ITERATIONS WITH NA's
#> THE MEAN NUMBER OF NA's IN EACH ITERATION IS 1.165
#> Warning in calib_blr_ipcw(data.raw = data.raw, data.mstate = data.mstate, : WARNING, SOME BOOTSTRAPPED OBSERVED EVENT PROBABILITIES WERE NA FOR STATE 5
#> THERE ARE 113 ITERATIONS WITH NA's
#> THE MEAN NUMBER OF NA's IN EACH ITERATION IS 1.115
#> Warning in calib_blr_ipcw(data.raw = data.raw, data.mstate = data.mstate, : WARNING, SOME BOOTSTRAPPED OBSERVED EVENT PROBABILITIES WERE NA FOR STATE 6
#> THERE ARE 125 ITERATIONS WITH NA's
#> THE MEAN NUMBER OF NA's IN EACH ITERATION IS 1.18
dat.calib.pv <-
calib_msm(data.mstate = msebmtcal,
data.raw = ebmtcal,
j = 1,
s = 100,
t = t.eval,
tp.pred = tp.pred.s100,
calib.type = 'pv',
curve.type = "loess",
pv.group.vars = c("year"),
CI = 95,
CI.type = 'parametric')
#> Warning in calib_msm(data.mstate = msebmtcal, data.raw = ebmtcal, j = 1, : In the landmark cohort of individuals uncensored and in state j at time s,
#> there are some states have less than 50 people at the time at which calibration is being assessed (t).
#> Warnings and errors may occur when the models are fitted to estimate the calibration curves due to small sample size.
#> This warning has been written to try and intercept some uninformative error messages when the underlying statistical models fail.
#> The number to flag this warning (50) has been chosen arbitrarily, and does not constitute a sufficient sample size from a statistical point of view.We now produce calibration plots using the function.
plot(dat.calib.blr)
Figure 3: BLR-IPCW calibration plots out of state j = 1 at time s = 100 estimated using loess smoothers
plot(dat.calib.pv)
Figure 4: pseudo-value calibration plots out of state j = 1 at time s = 100 estimated using loess smoothers
The equivalent Figures from the Overview vignette, produced using restricted cubic splines, are Figures 5 and 6.
Write a short comparison…
Estimation of calibration curves out of state j = 3 at time s = 100 using loess smoothers
We now produce calibration curves for the predicted transition probabilities out of state \(j = 3\) at time \(s = 100\). Calibration is assessed at follow up time (\(t = 1826\) days). We start by extracting the predicted transition probabilities from state \(j = 3\) at time \(s = 100\) from the object . These are the transition probabilities we aim to assess the calibration of.
tp.pred.s100 <- tps100 |>
dplyr::filter(j == 3) |>
dplyr::select(any_of(paste("pstate", 1:6, sep = "")))We now produce calibration curves using loess smoothers for the regression model where the observed event probabilities are estimated. This is specified by . Behaviour of the curve can be controlled using and , which we leave as default in this example. Please refere to documentation for the function from the package for details of these parameters. A smaller span and/or degree will create a more smoother line. This is done for both the BLR-IPCW and pseudo-value calibration curves.
dat.calib.blr <-
calib_msm(data.mstate = msebmtcal,
data.raw = ebmtcal,
j = 3,
s = 100,
t = t.eval,
tp.pred = tp.pred.s100,
calib.type = 'blr',
curve.type = "loess",
w.covs = c("year", "agecl", "proph", "match"),
CI = 95,
CI.type = "bootstrap",
CI.R.boot = 200)
#> Warning in calib_msm(data.mstate = msebmtcal, data.raw = ebmtcal, j = 3, : In the landmark cohort of individuals uncensored and in state j at time s,
#> there are some states have less than 50 people at the time at which calibration is being assessed (t).
#> Warnings and errors may occur when the models are fitted to estimate the calibration curves due to small sample size.
#> This warning has been written to try and intercept some uninformative error messages when the underlying statistical models fail.
#> The number to flag this warning (50) has been chosen arbitrarily, and does not constitute a sufficient sample size from a statistical point of view.
#> Warning in calib_blr_ipcw(data.raw = data.raw, data.mstate = data.mstate, : WARNING, SOME BOOTSTRAPPED OBSERVED EVENT PROBABILITIES WERE NA FOR STATE 3
#> THERE ARE 107 ITERATIONS WITH NA's
#> THE MEAN NUMBER OF NA's IN EACH ITERATION IS 0.995
#> Warning in calib_blr_ipcw(data.raw = data.raw, data.mstate = data.mstate, : WARNING, SOME BOOTSTRAPPED OBSERVED EVENT PROBABILITIES WERE NA FOR STATE 4
#> THERE ARE 133 ITERATIONS WITH NA's
#> THE MEAN NUMBER OF NA's IN EACH ITERATION IS 1.265
#> Warning in calib_blr_ipcw(data.raw = data.raw, data.mstate = data.mstate, : WARNING, SOME BOOTSTRAPPED OBSERVED EVENT PROBABILITIES WERE NA FOR STATE 5
#> THERE ARE 126 ITERATIONS WITH NA's
#> THE MEAN NUMBER OF NA's IN EACH ITERATION IS 1.26
#> Warning in calib_blr_ipcw(data.raw = data.raw, data.mstate = data.mstate, : WARNING, SOME BOOTSTRAPPED OBSERVED EVENT PROBABILITIES WERE NA FOR STATE 6
#> THERE ARE 129 ITERATIONS WITH NA's
#> THE MEAN NUMBER OF NA's IN EACH ITERATION IS 1.19
dat.calib.pv <-
calib_msm(data.mstate = msebmtcal,
data.raw = ebmtcal,
j = 3,
s = 100,
t = t.eval,
tp.pred = tp.pred.s100,
calib.type = 'pv',
curve.type = "loess",
pv.group.vars = c("year"),
CI = 95,
CI.type = 'parametric')
#> Warning in calib_msm(data.mstate = msebmtcal, data.raw = ebmtcal, j = 3, : In the landmark cohort of individuals uncensored and in state j at time s,
#> there are some states have less than 50 people at the time at which calibration is being assessed (t).
#> Warnings and errors may occur when the models are fitted to estimate the calibration curves due to small sample size.
#> This warning has been written to try and intercept some uninformative error messages when the underlying statistical models fail.
#> The number to flag this warning (50) has been chosen arbitrarily, and does not constitute a sufficient sample size from a statistical point of view.We now produce calibration plots using the function.
plot(dat.calib.blr)
Figure 5: BLR-IPCW calibration plots out of state j = 3 at time s = 100 estimated using loess smoothers
plot(dat.calib.pv)
Figure 6: pseudo-value calibration plots out of state j = 3 at time s = 100 estimated using loess smoothers
The equivalent Figures from the Overview vignette, produced using restricted cubic splines, are Figures 7 and 8.
Write a short comparison… XXXX
A note on the fact that confidence intervals may be lower than 0 or bigger than 1
Given the standard error is calculated on the probability scale here, as opposed to the logit scale, the confidence intervals may exceed 0 and 1. I have checked and the confidence intervals are calculated in the same way in the CalibrationCurves package: https://cran.r-project.org/web/packages/CalibrationCurves/CalibrationCurves.pdf . In particular see code for val.prob.ci.2: https://github.com/BavoDC/CalibrationCurves/blob/master/R/val.prob.ci.2.R . There parametric confidence intervals are calculated in the same way we calculate parametric confidence intervals for the pseudo value approach. I am therefore confident this is correct, but is something i’d like to come back to and consider the implications of. It’s also intriguing that even for the bootstrapped BLR-IPCW confidence intervals, we are seeing confidence intervals well outside 0 and 1. I thought this would be an issue for the pseudo-value parametric confidence intervals as we are just adding on 1.96*se on the probability scale. However, I didnt actually think that would be possible for the bootstrapped confidence intervals, as in any given bootstrapped dataset, predicted-observed values would still lie between 0 and 1, so this is something to explore in more detail.
Having done a bit more research (see test_loess_bootstrap.R in the test folder), the values below 0 are driven by a small number of individuals with the smallest risks. Lots of their predicted-observed values are NAs, and they have some negative values too. It is evident these small number of individuals are driving all the NA values we are finding in each bootstrap iteration. Going to try increasing the span to see if that helps. Now i know the mechanism, although not something I’m particularly worried about, as seems to be driven by small sample size when assessing calibration at time s = 100.
Compare the following graphs to figures 1 and 2 above. First do .
dat.calib.blr <-
calib_msm(data.mstate = msebmtcal,
data.raw = ebmtcal,
j = 1,
s = 0,
t = t.eval,
tp.pred = tp.pred.s0,
calib.type = 'blr',
curve.type = "loess",
loess.span = 0.9,
w.covs = c("year", "agecl", "proph", "match"),
CI = 95,
CI.type = "bootstrap",
CI.R.boot = 200)
#> Warning in calib_blr_ipcw(data.raw = data.raw, data.mstate = data.mstate, : WARNING, SOME BOOTSTRAPPED OBSERVED EVENT PROBABILITIES WERE NA FOR STATE 1
#> THERE ARE 92 ITERATIONS WITH NA's
#> THE MEAN NUMBER OF NA's IN EACH ITERATION IS 0.96
#> Warning in calib_blr_ipcw(data.raw = data.raw, data.mstate = data.mstate, : WARNING, SOME BOOTSTRAPPED OBSERVED EVENT PROBABILITIES WERE NA FOR STATE 2
#> THERE ARE 113 ITERATIONS WITH NA's
#> THE MEAN NUMBER OF NA's IN EACH ITERATION IS 1.075
#> Warning in calib_blr_ipcw(data.raw = data.raw, data.mstate = data.mstate, : WARNING, SOME BOOTSTRAPPED OBSERVED EVENT PROBABILITIES WERE NA FOR STATE 3
#> THERE ARE 120 ITERATIONS WITH NA's
#> THE MEAN NUMBER OF NA's IN EACH ITERATION IS 1.135
#> Warning in calib_blr_ipcw(data.raw = data.raw, data.mstate = data.mstate, : WARNING, SOME BOOTSTRAPPED OBSERVED EVENT PROBABILITIES WERE NA FOR STATE 4
#> THERE ARE 123 ITERATIONS WITH NA's
#> THE MEAN NUMBER OF NA's IN EACH ITERATION IS 1.23
#> Warning in calib_blr_ipcw(data.raw = data.raw, data.mstate = data.mstate, : WARNING, SOME BOOTSTRAPPED OBSERVED EVENT PROBABILITIES WERE NA FOR STATE 5
#> THERE ARE 111 ITERATIONS WITH NA's
#> THE MEAN NUMBER OF NA's IN EACH ITERATION IS 1.155
#> Warning in calib_blr_ipcw(data.raw = data.raw, data.mstate = data.mstate, : WARNING, SOME BOOTSTRAPPED OBSERVED EVENT PROBABILITIES WERE NA FOR STATE 6
#> THERE ARE 121 ITERATIONS WITH NA's
#> THE MEAN NUMBER OF NA's IN EACH ITERATION IS 1.185
dat.calib.pv <-
calib_msm(data.mstate = msebmtcal,
data.raw = ebmtcal,
j = 1,
s = 0,
t = t.eval,
tp.pred = tp.pred.s0,
calib.type = 'pv',
curve.type = "loess",
loess.span = 0.9,
pv.group.vars = c("year"),
pv.n.pctls = 3,
CI = 95,
CI.type = 'parametric')We now produce calibration plots using the function.
plot(dat.calib.blr)
Figure 7: BLR-IPCW calibration plots out of state j = 1 at time s = 0 estimated using loess smoothers, span = 0.9
plot(dat.calib.pv)
Figure 8: pseudo-value calibration plots out of state j = 1 at time s = 0 estimated using loess smoothers, span = 0.9
Then do .
dat.calib.blr <-
calib_msm(data.mstate = msebmtcal,
data.raw = ebmtcal,
j = 1,
s = 0,
t = t.eval,
tp.pred = tp.pred.s0,
calib.type = 'blr',
curve.type = "loess",
loess.span = 1,
w.covs = c("year", "agecl", "proph", "match"),
CI = 95,
CI.type = "bootstrap",
CI.R.boot = 200)
#> Warning in calib_blr_ipcw(data.raw = data.raw, data.mstate = data.mstate, : WARNING, SOME BOOTSTRAPPED OBSERVED EVENT PROBABILITIES WERE NA FOR STATE 1
#> THERE ARE 92 ITERATIONS WITH NA's
#> THE MEAN NUMBER OF NA's IN EACH ITERATION IS 0.96
#> Warning in calib_blr_ipcw(data.raw = data.raw, data.mstate = data.mstate, : WARNING, SOME BOOTSTRAPPED OBSERVED EVENT PROBABILITIES WERE NA FOR STATE 2
#> THERE ARE 113 ITERATIONS WITH NA's
#> THE MEAN NUMBER OF NA's IN EACH ITERATION IS 1.075
#> Warning in calib_blr_ipcw(data.raw = data.raw, data.mstate = data.mstate, : WARNING, SOME BOOTSTRAPPED OBSERVED EVENT PROBABILITIES WERE NA FOR STATE 3
#> THERE ARE 120 ITERATIONS WITH NA's
#> THE MEAN NUMBER OF NA's IN EACH ITERATION IS 1.135
#> Warning in calib_blr_ipcw(data.raw = data.raw, data.mstate = data.mstate, : WARNING, SOME BOOTSTRAPPED OBSERVED EVENT PROBABILITIES WERE NA FOR STATE 4
#> THERE ARE 123 ITERATIONS WITH NA's
#> THE MEAN NUMBER OF NA's IN EACH ITERATION IS 1.23
#> Warning in calib_blr_ipcw(data.raw = data.raw, data.mstate = data.mstate, : WARNING, SOME BOOTSTRAPPED OBSERVED EVENT PROBABILITIES WERE NA FOR STATE 5
#> THERE ARE 111 ITERATIONS WITH NA's
#> THE MEAN NUMBER OF NA's IN EACH ITERATION IS 1.155
#> Warning in calib_blr_ipcw(data.raw = data.raw, data.mstate = data.mstate, : WARNING, SOME BOOTSTRAPPED OBSERVED EVENT PROBABILITIES WERE NA FOR STATE 6
#> THERE ARE 121 ITERATIONS WITH NA's
#> THE MEAN NUMBER OF NA's IN EACH ITERATION IS 1.185
dat.calib.pv <-
calib_msm(data.mstate = msebmtcal,
data.raw = ebmtcal,
j = 1,
s = 0,
t = t.eval,
tp.pred = tp.pred.s0,
calib.type = 'pv',
curve.type = "loess",
loess.span = 1,
pv.group.vars = c("year"),
pv.n.pctls = 3,
CI = 95,
CI.type = 'parametric')We now produce calibration plots using the function.
plot(dat.calib.blr)
Figure 9: BLR-IPCW calibration plots out of state j = 1 at time s = 0 estimated using loess smoothers, span = 1
plot(dat.calib.pv)
Figure 10: pseudo-value calibration plots out of state j = 1 at time s = 0 estimated using loess smoothers, span = 1