Predicting analysis time in events-driven clinical trials using accumulating time-to-event surrogate information.

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Predicting analysis time in events-driven clinical trials using accumulating time-to-event surrogate information.

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2015 nî lūn-bûn

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2015年の論文

@ja

2015年論文

@yue

2015年論文

@zh-hant

2015年論文

@zh-hk

2015年論文

@zh-mo

2015年論文

@zh-tw

2015年论文

@wuu

2015年论文

@zh

2015年论文

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Predicting analysis time in ev ...... o-event surrogate information.

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label

Predicting analysis time in ev ...... o-event surrogate information.

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prefLabel

Predicting analysis time in ev ...... o-event surrogate information.

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P1476

Predicting analysis time in ev ...... o-event surrogate information.

@en

P2093

Bruce Dornseif

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Chunlei Ke

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Jianming Wang

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Lei Fu

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Zhinuan Yu

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P2860

Time-dependent endpoints as predictors of overall survival in multiple myeloma

On the use and utility of the Weibull model in the analysis of survival data.

Joint modeling of progression-free survival and death in advanced cancer clinical trials.

Prediction of event times in the REMATCH Trial.

Joint modeling of progression-free survival and overall survival by a Bayesian normal induced copula estimation model.

A hybrid approach to predicting events in clinical trials with time-to-event outcomes.

Interim futility analysis with intermediate endpoints.

Predicting analysis times in randomized clinical trials.

Evaluation of sample size and power for analyses of survival with allowance for nonuniform patient entry, losses to follow-up, noncompliance, and stratification.

Sample size and the probability of a successful trial.

P304

198-207

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P31

scholarly article

P356

10.1002/PST.1732

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P433

http://www.w3.org/2001/XMLSchema#string

P478

http://www.w3.org/2001/XMLSchema#string

P577

2015-12-22T00:00:00Z

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P698

26689725

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