On logistic regression with right censored data, with or without competing risks, and its use for estimating treatment effects
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On logistic regression with right censored data, with or without competing risks, and its use for estimating treatment effects. / Blanche, Paul Frederic; Holt, Anders; Scheike, Thomas.
In: Lifetime Data Analysis, Vol. 29, 2023, p. 441–482.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - On logistic regression with right censored data, with or without competing risks, and its use for estimating treatment effects
AU - Blanche, Paul Frederic
AU - Holt, Anders
AU - Scheike, Thomas
PY - 2023
Y1 - 2023
N2 - Simple logistic regression can be adapted to deal with right-censoring by inverse probability of censoring weighting (IPCW). We here compare two such IPCW approaches, one based on weighting the outcome, the other based on weighting the estimating equations. We study the large sample properties of the two approaches and show that which of the two weighting methods is the most efficient depends on the censoring distribution. We show by theoretical computations that the methods can be surprisingly different in realistic settings. We further show how to use the two weighting approaches for logistic regression to estimate causal treatment effects, for both observational studies and randomized clinical trials (RCT). Several estimators for observational studies are compared and we present an application to registry data. We also revisit interesting robustness properties of logistic regression in the context of RCTs, with a particular focus on the IPCW weighting. We find that these robustness properties still hold when the censoring weights are correctly specified, but not necessarily otherwise.
AB - Simple logistic regression can be adapted to deal with right-censoring by inverse probability of censoring weighting (IPCW). We here compare two such IPCW approaches, one based on weighting the outcome, the other based on weighting the estimating equations. We study the large sample properties of the two approaches and show that which of the two weighting methods is the most efficient depends on the censoring distribution. We show by theoretical computations that the methods can be surprisingly different in realistic settings. We further show how to use the two weighting approaches for logistic regression to estimate causal treatment effects, for both observational studies and randomized clinical trials (RCT). Several estimators for observational studies are compared and we present an application to registry data. We also revisit interesting robustness properties of logistic regression in the context of RCTs, with a particular focus on the IPCW weighting. We find that these robustness properties still hold when the censoring weights are correctly specified, but not necessarily otherwise.
KW - Average treatment effect
KW - Competing risks
KW - Ipcw adjustment
KW - Logistic regression
KW - COVARIATE ADJUSTMENT
KW - CAUSAL INFERENCE
KW - MODELS
KW - TRIALS
KW - EFFICIENCY
KW - FAILURE
KW - TESTS
U2 - 10.1007/s10985-022-09564-6
DO - 10.1007/s10985-022-09564-6
M3 - Journal article
C2 - 35799026
VL - 29
SP - 441
EP - 482
JO - Lifetime Data Analysis
JF - Lifetime Data Analysis
SN - 1380-7870
ER -
ID: 313862253