Regression models for censored time-to-event data using infinitesimal jack-knife pseudo-observations, with applications to left-truncation
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Regression models for censored time-to-event data using infinitesimal jack-knife pseudo-observations, with applications to left-truncation. / Parner, Erik T.; Andersen, Per K.; Overgaard, Morten.
I: Lifetime Data Analysis, Bind 29, 2023, s. 654–671.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Regression models for censored time-to-event data using infinitesimal jack-knife pseudo-observations, with applications to left-truncation
AU - Parner, Erik T.
AU - Andersen, Per K.
AU - Overgaard, Morten
PY - 2023
Y1 - 2023
N2 - Jack-knife pseudo-observations have in recent decades gained popularity in regression analysis for various aspects of time-to-event data. A limitation of the jack-knife pseudo-observations is that their computation is time consuming, as the base estimate needs to be recalculated when leaving out each observation. We show that jack-knife pseudo-observations can be closely approximated using the idea of the infinitesimal jack-knife residuals. The infinitesimal jack-knife pseudo-observations are much faster to compute than jack-knife pseudo-observations. A key assumption of the unbiasedness of the jack-knife pseudo-observation approach is on the influence function of the base estimate. We reiterate why the condition on the influence function is needed for unbiased inference and show that the condition is not satisfied for the Kaplan-Meier base estimate in a left-truncated cohort. We present a modification of the infinitesimal jack-knife pseudo-observations that provide unbiased estimates in a left-truncated cohort. The computational speed and medium and large sample properties of the jack-knife pseudo-observations and infinitesimal jack-knife pseudo-observation are compared and we present an application of the modified infinitesimal jack-knife pseudo-observations in a left-truncated cohort of Danish patients with diabetes.
AB - Jack-knife pseudo-observations have in recent decades gained popularity in regression analysis for various aspects of time-to-event data. A limitation of the jack-knife pseudo-observations is that their computation is time consuming, as the base estimate needs to be recalculated when leaving out each observation. We show that jack-knife pseudo-observations can be closely approximated using the idea of the infinitesimal jack-knife residuals. The infinitesimal jack-knife pseudo-observations are much faster to compute than jack-knife pseudo-observations. A key assumption of the unbiasedness of the jack-knife pseudo-observation approach is on the influence function of the base estimate. We reiterate why the condition on the influence function is needed for unbiased inference and show that the condition is not satisfied for the Kaplan-Meier base estimate in a left-truncated cohort. We present a modification of the infinitesimal jack-knife pseudo-observations that provide unbiased estimates in a left-truncated cohort. The computational speed and medium and large sample properties of the jack-knife pseudo-observations and infinitesimal jack-knife pseudo-observation are compared and we present an application of the modified infinitesimal jack-knife pseudo-observations in a left-truncated cohort of Danish patients with diabetes.
KW - Competing risks
KW - Cumulative incidence
KW - Cumulative risk
KW - Left-truncation
KW - Pseudo-observations
KW - GENERALIZED LINEAR-MODELS
KW - COMPETING RISKS
KW - SUBDISTRIBUTION
KW - LIFE
U2 - 10.1007/s10985-023-09597-5
DO - 10.1007/s10985-023-09597-5
M3 - Journal article
C2 - 37157038
VL - 29
SP - 654
EP - 671
JO - Lifetime Data Analysis
JF - Lifetime Data Analysis
SN - 1380-7870
ER -
ID: 346804675