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Likelihood Method for Randomized Time-to-Event Studies with All-or-None Compliance: Causal Inference in Survival analysis - Brossura
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Research Paper (postgraduate) from the year 2009 in the subject Mathematics - Statistics, grade: A, University of Canterbury (Department of Mathematics and Statistics), course: Statistics, language: English, abstract: Estimating causal effects in clinical trials often suffers from treatment non-compliance and missing outcomes. In time-to-event studies, it is more complicated because of censoring, the mechanism of which may be non-ignorable. While new estimators have recently been proposed to account for non-compliance and missing outcomes, few papers have specifically considered time-to-event outcomes, where even the intention-to-treat (ITT) estimator is potentially biased for estimating causal effects of assigned treatment. In this paper we develop a likelihood based method for randomized clinical trials (RCTs) with non-compliance for time-to-event data and adapt the EM algorithm according to derive the maximum likelihood estimators (MLEs). In addition, we give formulations of the average causal effect (ACE) and compliers average causal effect (CACE) to suit survival analysis. To illustrate the likelihood-based method (EM algorithm), a breast cancer trial data was re-analysed using a model, which assumes that the failure times and censored times both follow Weibull and Lognormal distributions, respectively (termed the NIGN-WW model and NIGN-LL model).
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Likelihood Method for Randomized Time-to-Event Studies with All-or-None Compliance: Causal Inference in Survival analysis
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Likelihood Method for Randomized Time-to-Event Studies with All-or-None Compliance
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Research Paper (postgraduate) from the year 2009 in the subject Mathematics - Statistics, grade: A, University of Canterbury (Department of Mathematics and Statistics), course: Statistics, language: English, abstract: Estimating causal effects in clinical trials often suffers from treatment non-compliance and missing outcomes. In time-to-event studies, it is more complicated because of censoring, the mechanism of which may be non-ignorable. While new estimators have recently been proposed to account for non-compliance and missing outcomes, few papers have specifically considered time-to-event outcomes, where even the intention-to-treat (ITT) estimator is potentially biased for estimating causal effects of assigned treatment.In this paper we develop a likelihood based method for randomized clinical trials (RCTs) with non-compliance for time-to-event data and adapt the EM algorithm according to derive the maximum likelihood estimators (MLEs). In addition, we give formulations of the average causal effect (ACE) and compliers average causal effect (CACE) to suit survival analysis.To illustrate the likelihood-based method (EM algorithm), a breast cancer trial data was re-analysed using a model, which assumes that the failure times and censored times both follow Weibull and Lognormal distributions, respectively (termed the NIGN-WW model and NIGN-LL model). 160 pp. Englisch. Codice articolo 9783668438637
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Likelihood Method for Randomized Time-to-Event Studies with All-or-None Compliance : Causal Inference in Survival analysis
Da: AHA-BUCH GmbH, Einbeck, Germania
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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Research Paper (postgraduate) from the year 2009 in the subject Mathematics - Statistics, grade: A, University of Canterbury (Department of Mathematics and Statistics), course: Statistics, language: English, abstract: Estimating causal effects in clinical trials often suffers from treatment non-compliance and missing outcomes. In time-to-event studies, it is more complicated because of censoring, the mechanism of which may be non-ignorable. While new estimators have recently been proposed to account for non-compliance and missing outcomes, few papers have specifically considered time-to-event outcomes, where even the intention-to-treat (ITT) estimator is potentially biased for estimating causal effects of assigned treatment.In this paper we develop a likelihood based method for randomized clinical trials (RCTs) with non-compliance for time-to-event data and adapt the EM algorithm according to derive the maximum likelihood estimators (MLEs). In addition, we give formulations of the average causal effect (ACE) and compliers average causal effect (CACE) to suit survival analysis.To illustrate the likelihood-based method (EM algorithm), a breast cancer trial data was re-analysed using a model, which assumes that the failure times and censored times both follow Weibull and Lognormal distributions, respectively (termed the NIGN-WW model and NIGN-LL model). Codice articolo 9783668438637
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Likelihood Method for Randomized Time-to-Event Studies with All-or-None Compliance | Causal Inference in Survival analysis
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Taschenbuch. Condizione: Neu. Likelihood Method for Randomized Time-to-Event Studies with All-or-None Compliance | Causal Inference in Survival analysis | Irene Hudson (u. a.) | Taschenbuch | 160 S. | Englisch | 2017 | GRIN Verlag | EAN 9783668438637 | Verantwortliche Person für die EU: GRIN Publishing GmbH, Waltherstr. 23, 80337 München, info[at]grin[dot]com | Anbieter: preigu Print on Demand. Codice articolo 108965302
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