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Bayesian Semi-parametric G-computation For Causal Inference in a Cohort Study with Mnar Dropout and Death

Journal of the Royal Statistical Society Series C (Applied Statistics)(2021)10.1111/rssc.12464Source: DataRank Database

Bayesian Semi-parametric G-computation For Causal Inference in a Cohort Study with Mnar Dropout and Death is a research paper published in Journal of the Royal Statistical Society Series C (Applied Statistics) (2021). On theSindex it has a DataRank of 0.

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (2 grants)Impact (FWCI 0.00)OA: hybridTopics (29)Linked Datasets (4)DataCite Reuse (4)IDs (PubMed, PMC, OpenAlex)

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Context only (not used in score)

Findable (2/2)

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Accessible (1/2)

Open Access

Interoperable (2/2)

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Authors (2)

Michael J. Daniels ORCID,Maria Josefsson ORCID

Live enrichment

PubMed:33692597 PMC:PMC7939177 OpenAlex:https://openalex.org/W2917439997

NCI NIH HHS

Grant: R01 CA183854

NIGMS NIH HHS

Grant: R01 GM112327

FWCI

0.00

Citation Percentile

0.0%

hybridLicense: cc-by

journalhttps://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/rssc.12464

publisherhttps://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/rssc.12464

publisherhttps://onlinelibrary.wiley.com/doi/pdf/10.1111/rssc.12464

publisherhttps://onlinelibrary.wiley.com/doi/full-xml/10.1111/rssc.12464

publisherhttps://academic.oup.com/jrsssc/article-pdf/70/2/398/49155007/rssc_70_2_398.pdf

Fields of Study

Advanced Causal Inference TechniquesStatistical Methods and InferenceStatistical Methods and Bayesian Inference

Keywords

Causal inferenceMissing dataMarginal structural modelDropout (neural networks)Parametric statisticsBayesian probabilityConfoundingEconometricsObservational studyCausal modelInferenceComputer sciencePsychologyStatisticsMathematicsArtificial intelligenceMachine learningSensitivity analysisLongitudinal dataCognitive AgingBartObservational DataTime-varying ConfoundingTime-varying ExposureNon-ignorable MissingSurvivor Average Causal Effect

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