A Parametric Survival Model When a Covariate is Subject to Left-Censoring. - Wikidata - wikimedia - TriplyDB

A Parametric Survival Model When a Covariate is Subject to Left-Censoring.

A Parametric Survival Model When a Covariate is Subject to Left-Censoring.

http://www.wikidata.org/entity/Q37366615

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Flexible Modeling of Survival Data with Covariates Subject to Detection Limits via Multiple Imputation A Fast EM Algorithm for Fitting Joint Models of a Binary Response and Multiple Longitudinal Covariates Subject to Detection Limits.

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P2860

A Parametric Survival Model When a Covariate is Subject to Left-Censoring.

http://www.wikidata.org/entity/Q37366615

description

article científic

@ca

article scientifique

@fr

articolo scientifico

@it

artigo científico

@pt

bilimsel makale

@tr

scientific article published on January 2012

@en

vedecký článok

@sk

vetenskaplig artikel

@sv

videnskabelig artikel

@da

vědecký článek

@cs

name

A Parametric Survival Model When a Covariate is Subject to Left-Censoring.

@en

A Parametric Survival Model When a Covariate is Subject to Left-Censoring.

@nl

type

label

A Parametric Survival Model When a Covariate is Subject to Left-Censoring.

@en

A Parametric Survival Model When a Covariate is Subject to Left-Censoring.

@nl

prefLabel

A Parametric Survival Model When a Covariate is Subject to Left-Censoring.

@en

A Parametric Survival Model When a Covariate is Subject to Left-Censoring.

@nl

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2155-6180.S3-002

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Journal of biometrics & biostatistics

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A Parametric Survival Model When a Covariate is Subject to Left-Censoring.

@en

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Abdus Sattar

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Sanjoy K Sinha

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P2860

Fabricating data: how substituting values for nondetects can ruin results, and what can be done about it.

Analysis of non-ignorable missing and left-censored longitudinal data using a weighted random effects tobit model.

Opening the black box of biomarker measurement error.

Linear regression with an independent variable subject to a detection limit.

Understanding the inflammatory cytokine response in pneumonia and sepsis: results of the Genetic and Inflammatory Markers of Sepsis (GenIMS) Study.

Maximum likelihood estimation in generalized linear models with multiple covariates subject to detection limits

Inflammatory markers at hospital discharge predict subsequent mortality after pneumonia and sepsis.

Quantitative detection of microbial genes by using DNA microarrays.

A parametric survival model with an interval-censored covariate.

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scholarly article

P356

10.4172/2155-6180.S3-002

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2

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Suppl 3

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Nathan J Morris

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2012-01-01T00:00:00Z

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24319625

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3852406

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