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Statistical Methods and Strategies for Working with Large Data Bases

Dados Bibliográficos

ID9104962
AutoresGuillermo Marshall (0000-0002-2902-014X, University of Chile, autor correspondente), William G Henderson, Thomas E Moritz, Thomas Moritz (0000-0001-5976-9226), A Laurie Shroyer (0000-0001-6461-0623, Denver VA Medical Center), Frederick L Grover (Denver VA Medical Center), Karl E Hammermeister (University of Chile, autor correspondente)
Ano1995
Volume33
FascículoSupplement
PáginasOS35-OS42
Data de publicação1995-10-01
Peer ReviewedSim
Open AccessNão
TipoARTICLE
PeriódicoMedical Care (JOURNAL)
Identificadores do periódicoISSN: 0025-7079 • E-ISSN: 1537-1948
EditoraOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/00005650-199510001-00005
PMID7475410
OpenAlexW1995792599
IdiomaEN
Citações recebidas1
Referências citadas1

This article describes the statistical methods and strategies to be used in establishing the linkages between processes and structures of care with risk-adjusted outcomes in a large multicenter Veterans Affairs cooperative study in health services of patients undergoing cardiac surgery. The statistical analyses consist of test involving nine specific hypotheses related to the effect of processes and structures of care on risk-adjusted outcomes. From the statistical point of view, the major obstacles of this study are the need for data reduction and imputation of missing data. The former obstacle is addressed through the use of data-reduction techniques, such as principal components and cluster of variables. The latter is addressed through the use of classic and new techniques for imputation of missing data, such as MISSGEN, principal components for qualitative data, and the expectation and maximization algorithm. Data reduction and imputation of missing data are done with clinically derived variable groups called "dimensions" or "subdimensions." The effect of processes and structures of care is assessed by a two-step process. First, outcomes are modeled using only patient risk factors. The selection of risk factors in the modeling process is discussed in detail. Second, these risk-adjusted outcomes are modeled using one of the nine process or structure subhypotheses. The relationship of the processes and structures of care dimensions and/or subdimensions that are linked to risk-adjusted outcomes are identified

Data mining · Econometrics · Health care · Imputation (statistics) · Machine learning · Missing data · Statistical hypothesis testing · Statistics · Computer Science · Mathematics · Statistical Methods and Bayesian Inference · Statistical Methods and Inference · Statistical Methods in Clinical Trials

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Obras citantes distintas1
Citações por ano0,05
Intervalo de citações2004 - 2004 (1)
Velocidade de citaçãohistorical
Altamente citadoNão
Tipos de citaçãoNeutras: 1
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