The Experts below are selected from a list of 310602 Experts worldwide ranked by ideXlab platform
Michal Abrahamowicz - One of the best experts on this subject based on the ideXlab platform.
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using categorical markers as auxiliary variables in log rank tests and Hazard Ratio estimation
Canadian Journal of Statistics-revue Canadienne De Statistique, 2005Co-Authors: Todd A Mackenzie, Michal AbrahamowiczAbstract:Markers, which are prognostic longitudinal variables, can be used to replace some of the information lost due to right censoring. They may also be used to remove or reduce bias due to informative censoring. In this paper, the authors propose novel methods for using markers to increase the efficiency of log-rank tests and Hazard Ratio estimation, as well as parametric estimation. They propose a «plug-in» methodology that consists of writing the test statistic or estimate of interest as a functional of Kaplan–Meier estimators. The latter are then replaced by an efficient estimator of the survival curve that incorporates information from markers. Using simulations, the authors show that the resulting estimators and tests can be up to 30% more efficient than the usual procedures, provided that the marker is highly prognostic and that the frequency of censoring is high.
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marginal and Hazard Ratio specific random data geneRation applications to semi parametric bootstrapping
Statistics and Computing, 2002Co-Authors: Todd Mackenzie, Michal AbrahamowiczAbstract:Cox's partial likelihood for censored time-to-event data can be interpreted as a permutation probability, whereby covariate values are permuted to the observed times-to-event and censoring times. This interpretation facilitates a simple method for jointly generating times-to-event and covariate tuples with considerable flexibility, including time dependence of the Hazard Ratio and specification of both the marginal time-to-event and covariate distributions. This interpretation also facilitates a method for semi-parametric bootstrapping of Hazard Ratio estimators.
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To Semi-Parametric Bootstrapping
Statistics and Computing, 2002Co-Authors: Todd Mackenzie, Michal AbrahamowiczAbstract:Cox’s partial likelihood for censored time-to-event data can be interpreted as a permutation probability, whereby covariate values are permuted to the observed times-to-event and censoring times. This interpretation facilitates a simple method for jointly generating times-to-event and covariate tuples with considerable flexibility, including time dependence of the Hazard Ratio and specification of both the marginal time-to-event and covariate distributions. This interpretation also facilitates a method for semi-parametric bootstrapping of Hazard Ratio estimators.
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time dependent Hazard Ratio modeling and hypothesis testing with application in lupus nephritis
Journal of the American Statistical Association, 1996Co-Authors: Michal Abrahamowicz, Todd Mackenzie, John M EsdaileAbstract:Abstract We investigate the association between duRation of untreated disease and survival in lupus nephritis, a rare rheumatologic disease. In this case, as in many other studies of survival, a priori consideRations suggest that the effect of the predictor on Hazard may change with increasing follow-up time. To accommodate such situations, we use regression splines to model the Hazard Ratio as a flexible function of time. We propose model-based tests of the hypotheses of Hazards proportionality and of no association. We evaluate the accuracy of estimation and inference in simulations and also present analysis of a larger medical data set.
Todd Mackenzie - One of the best experts on this subject based on the ideXlab platform.
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Time Dependent Hazard Ratio Estimation Using Instrumental Variables Without Conditioning on an Omitted Covariate
2019Co-Authors: Todd Mackenzie, Pablo Martinez-camblor, James O'malleyAbstract:Abstract Estimation that employs instrumental variables (IV) can reduce or eliminate bias due to confounding. In observational studies instruments result from natural experiments such as the effect of clinician preference or geographic distance on treatment selection. In randomized studies the randomization indicator is an instrument, especially if the study is blinded, e.g. no placebo effect. Estimation via instruments is a highly developed field for linear models but the use of instruments in time-to-event analysis is far from established. Various IV-based estimators of the Hazard Ratio (HR) from Cox's regression models have been proposed. We extend IV based estimation of Cox's models beyond proportionality of Hazards, and address estimation of a log-linear time dependent Hazard Ratio and a piecewise constant HR. We estimate the marginal time-dependent Hazard Ratio unlike other approaches that estimate the Hazard Ratio conditional on the omitted covariates. Due to the non-collapsibility of the Cox's models these two estimands are not identical. We report the results of simulations that includes the use of copulas to generate potential times-to-event that have a given marginal structural time dependent Hazard Ratio but are dependent on omitted covariates. We demonstrate the method to estimate the time dependent Hazard Ratio for two vascular interventions.
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marginal and Hazard Ratio specific random data geneRation applications to semi parametric bootstrapping
Statistics and Computing, 2002Co-Authors: Todd Mackenzie, Michal AbrahamowiczAbstract:Cox's partial likelihood for censored time-to-event data can be interpreted as a permutation probability, whereby covariate values are permuted to the observed times-to-event and censoring times. This interpretation facilitates a simple method for jointly generating times-to-event and covariate tuples with considerable flexibility, including time dependence of the Hazard Ratio and specification of both the marginal time-to-event and covariate distributions. This interpretation also facilitates a method for semi-parametric bootstrapping of Hazard Ratio estimators.
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To Semi-Parametric Bootstrapping
Statistics and Computing, 2002Co-Authors: Todd Mackenzie, Michal AbrahamowiczAbstract:Cox’s partial likelihood for censored time-to-event data can be interpreted as a permutation probability, whereby covariate values are permuted to the observed times-to-event and censoring times. This interpretation facilitates a simple method for jointly generating times-to-event and covariate tuples with considerable flexibility, including time dependence of the Hazard Ratio and specification of both the marginal time-to-event and covariate distributions. This interpretation also facilitates a method for semi-parametric bootstrapping of Hazard Ratio estimators.
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time dependent Hazard Ratio modeling and hypothesis testing with application in lupus nephritis
Journal of the American Statistical Association, 1996Co-Authors: Michal Abrahamowicz, Todd Mackenzie, John M EsdaileAbstract:Abstract We investigate the association between duRation of untreated disease and survival in lupus nephritis, a rare rheumatologic disease. In this case, as in many other studies of survival, a priori consideRations suggest that the effect of the predictor on Hazard may change with increasing follow-up time. To accommodate such situations, we use regression splines to model the Hazard Ratio as a flexible function of time. We propose model-based tests of the hypotheses of Hazards proportionality and of no association. We evaluate the accuracy of estimation and inference in simulations and also present analysis of a larger medical data set.
Yutaka Matsuyama - One of the best experts on this subject based on the ideXlab platform.
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on Hazard Ratio estimators by proportional Hazards models in matched pair cohort studies
Emerging Themes in Epidemiology, 2017Co-Authors: Tomohiro Shinozaki, Mohammad Ali Mansournia, Yutaka MatsuyamaAbstract:Background In matched-pair cohort studies with censored events, the Hazard Ratio (HR) may be of main interest. However, it is lesser known in epidemiologic literature that the partial maximum likelihood estimator of a common HR conditional on matched pairs is written in a simple form, namely, the Ratio of the numbers of two pair-types. Moreover, because HR is a noncollapsible measure and its constancy across matched pairs is a restrictive assumption, marginal HR as “average” HR may be targeted more than conditional HR in analysis.
Nandita Mitra - One of the best experts on this subject based on the ideXlab platform.
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bias in estimating the causal Hazard Ratio when using two stage instrumental variable methods
Statistics in Medicine, 2015Co-Authors: Dylan S Small, Justin E Bekelman, Nandita MitraAbstract:Two-stage instrumental variable methods are commonly used to estimate the causal effects of treatments on survival in the presence of measured and unmeasured confounding. Two-stage residual inclusion (2SRI) has been the method of choice over two-stage predictor substitution (2SPS) in clinical studies. We directly compare the bias in the causal Hazard Ratio estimated by these two methods. Under a principal stratification framework, we derive a closed-form solution for asymptotic bias of the causal Hazard Ratio among compliers for both the 2SPS and 2SRI methods when survival time follows the Weibull distribution with random censoring. When there is no unmeasured confounding and no always takers, our analytic results show that 2SRI is generally asymptotically unbiased, but 2SPS is not. However, when there is substantial unmeasured confounding, 2SPS performs better than 2SRI with respect to bias under certain scenarios. We use extensive simulation studies to confirm the analytic results from our closed-form solutions. We apply these two methods to prostate cancer treatment data from Surveillance, Epidemiology and End Results-Medicare and compare these 2SRI and 2SPS estimates with results from two published randomized trials. Copyright © 2015 John Wiley & Sons, Ltd.
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Bias in estimating the causal Hazard Ratio when using two‐stage instrumental variable methods
Statistics in Medicine, 2015Co-Authors: Dylan S Small, Justin E Bekelman, Nandita MitraAbstract:Two-stage instrumental variable methods are commonly used to estimate the causal effects of treatments on survival in the presence of measured and unmeasured confounding. Two-stage residual inclusion (2SRI) has been the method of choice over two-stage predictor substitution (2SPS) in clinical studies. We directly compare the bias in the causal Hazard Ratio estimated by these two methods. Under a principal stratification framework, we derive a closed-form solution for asymptotic bias of the causal Hazard Ratio among compliers for both the 2SPS and 2SRI methods when survival time follows the Weibull distribution with random censoring. When there is no unmeasured confounding and no always takers, our analytic results show that 2SRI is generally asymptotically unbiased, but 2SPS is not. However, when there is substantial unmeasured confounding, 2SPS performs better than 2SRI with respect to bias under certain scenarios. We use extensive simulation studies to confirm the analytic results from our closed-form solutions. We apply these two methods to prostate cancer treatment data from Surveillance, Epidemiology and End Results-Medicare and compare these 2SRI and 2SPS estimates with results from two published randomized trials. Copyright © 2015 John Wiley & Sons, Ltd.
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sensitivity of the Hazard Ratio to nonignorable treatment assignment in an observational study
Statistics in Medicine, 2007Co-Authors: Nandita Mitra, Daniel F HeitjanAbstract:In non-randomized studies, estimation of treatment effects generally requires adjustment for imbalances in observed covariates. One such method, based on the propensity score, is useful in many applications but may be biased when the assumption of strongly ignorable treatment assignment is violated. Because it is not possible to evaluate this assumption from the data, it is advisable to assess the sensitivity of conclusions to violations of strong ignorability. Lin et al. (Biomet. 1998; 54:948–963) have implemented this idea by investigating how an unmeasured covariate may affect the conclusions of an observational study. We extend their method to assess sensitivity of the treatment Hazard Ratio to hidden bias under a range of covariate distributions. We derive simple formulas for approximating the true from the apparent treatment Hazard Ratio estimated under a specific survival model, and assess the validity of these formulas in simulation studies. We demonstrate the method in an analysis of SEER-Medicare data on the effects of chemotherapy in elderly colon cancer patients.Copyright © 2006 John Wiley & Sons, Ltd.
Tomohiro Shinozaki - One of the best experts on this subject based on the ideXlab platform.
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Hazard Ratio Estimators after Terminating Observation within Matched Pairs in Sibling and Propensity Score Matched Designs
The International Journal of Biostatistics, 2019Co-Authors: Tomohiro Shinozaki, Mohammad Ali MansourniaAbstract:Similar to unmatched cohort studies, matched cohort studies may suffer from the censoring of events prior to the end of follow-up. Moreover, in some matched-pair cohort studies, observation time is prematurely terminated immediately after the follow-up of his/her matched member is completed by an event or censoring. Although the follow-up termination within matched pairs may or may not change the Hazard Ratio estimators, when and how the change occurs has not been clarified. We study the change in the estimates of the Hazard Ratio conditional on matched pairs and/or covariates by considering two types of matched-pair designs in cohort studies-sibling pair matching and propensity score matching-in which termination can be naturally considered. If all possible confounders are shared within the matched pairs, after termination, a wide range of Hazard Ratio estimators coincides with that obtained from a stratified Cox model. If unshared confounders should be adjusted for in the analysis, however, such coincidence is not observed. Simulation studies on sibling designs with unshared confounders suggested that the pair-stratified covariate-adjusted Cox model for the Hazard Ratio conditional on matched pairs and covariates is generally preferred, for which termination does not deteriorate the estimation. Conversely, the comparison between stratifying or not stratifying on pair is a more subtle issue in propensity score matching which targets a marginal or covariate-conditional Hazard Ratio. Based on simulation studies considering Cox models after matching based on estimated propensity scores, we discourage pair-stratified analysis and termination, particularly after data collection.
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on Hazard Ratio estimators by proportional Hazards models in matched pair cohort studies
Emerging Themes in Epidemiology, 2017Co-Authors: Tomohiro Shinozaki, Mohammad Ali Mansournia, Yutaka MatsuyamaAbstract:Background In matched-pair cohort studies with censored events, the Hazard Ratio (HR) may be of main interest. However, it is lesser known in epidemiologic literature that the partial maximum likelihood estimator of a common HR conditional on matched pairs is written in a simple form, namely, the Ratio of the numbers of two pair-types. Moreover, because HR is a noncollapsible measure and its constancy across matched pairs is a restrictive assumption, marginal HR as “average” HR may be targeted more than conditional HR in analysis.
