2020
DOI: 10.1002/sim.8704
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Regression with a right‐censored predictor using inverse probability weighting methods

Abstract: Summary In a longitudinal study, measures of key variables might be incomplete or partially recorded due to drop‐out, loss to follow‐up, or early termination of the study occurring before the advent of the event of interest. In this paper, we focus primarily on the implementation of a regression model with a randomly censored predictor. We examine, particularly, the use of inverse probability weighting methods in a generalized linear model (GLM), when the predictor of interest is right‐censored, to adjust for … Show more

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Cited by 14 publications
(13 citation statements)
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“…The gradient boosted model (GBM) was used to estimate the propensity score (PS) of patients undergoing TTE examination, so as to minimize the imbalance of variables between the TTE group and the non-TTE group. Using PS as the weight, the inverse probabilities weighting (IPW) model was used to generate a weighted cohort (15). P<0.05 indicates that the difference is statistically significant.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…The gradient boosted model (GBM) was used to estimate the propensity score (PS) of patients undergoing TTE examination, so as to minimize the imbalance of variables between the TTE group and the non-TTE group. Using PS as the weight, the inverse probabilities weighting (IPW) model was used to generate a weighted cohort (15). P<0.05 indicates that the difference is statistically significant.…”
Section: Discussionmentioning
confidence: 99%
“…In clinical practice, these indicators often prompt doctors to perform TTE examinations on patients. Based on this result, IPW was used to standardize the difference between the TTE group and the non-TTE group (15). The results are shown in Table 1.…”
Section: Double Robust Analysismentioning
confidence: 99%
“…The gradient boosted model (GBM) was used to estimate the propensity score (PS) of the patient's sedation and analgesia program to minimize the imbalance of variables between the two sedation and analgesia programs. With PS as the weight, a weighted queue was generated using an inverse probability weighting (IPW) model (11). A two-sided P<0.05 indicated a statistically significant difference.…”
Section: Discussionmentioning
confidence: 99%
“…However, less attention has been paid to regression problems where the response Y is observed and the covariates are censored. Even though a few semiparametric or nonparametric methods have been proposed to deal with censored covariates, most of the existing methods were designed for linear models 11,20,23,26 or models with only Type I censoring. 5,16,18,19 For estimation under our settings by means of multiple imputation, we construct the nonparametric estimator mMI (u) by extending the idea of conditional multiple imputation of Atem et al 21 The algorithm is summarized as follows:…”
Section: Conditional Multiple Imputation (Mi) Methodsmentioning
confidence: 99%
“…However, less attention has been paid to regression problems where the response Y is observed and the covariates are censored. Even though a few semiparametric or nonparametric methods have been proposed to deal with censored covariates, most of the existing methods were designed for linear models 11,20,23,26 or models with only Type I censoring 5,16,18,19 …”
Section: Methodsmentioning
confidence: 99%