Results 241 to 250 of about 764,403 (277)

The promise and peril of database research: Common pitfalls and how to avoid them. [PDF]

JTCVS Open
Rossetti NE, Seyoum N, Puri V.
europepmc   +1 more source

Machine learning prediction of metabolic-associated fatty liver disease in type 2 diabetes: Emphasizing data imputation and feature selection. [PDF]

PLoS One
Khosravi Z, Barzinpour F, Rabizadeh S, Nakhjavani M, Esteghamati A.   +4 more
europepmc   +1 more source

Evaluating the Combined Effects of an Adverse Childhood Experiences-Focused Family Advocate Model and the Strengthening Families Program: Study Protocol for a Hybrid Type 1 Effectiveness-Implementation Trial in 36 New Jersey Communities. [PDF]

JMIR Res Protoc
Elgin D, Graham PW, Johnson K, Khan S, Mills M, Wondimagegnehu F, Gibbons B, Williams J, Jiang X, Zapcic A, Simon B.   +10 more
europepmc   +1 more source

Validity and Reliability of the Fatigue Severity Scale in an Adult Swedish Burn Population. [PDF]

Eur Burn J
Enblom S, Huss F.
europepmc   +1 more source

Missingness in Eligibility Criteria for Target Trial Emulation in EHR With Survival Outcomes. [PDF]

Stat Med
Shen J, Linn KA, Clark AS, Mamtani R, Hubbard RA.   +4 more
europepmc   +1 more source

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Distinguishing “Missing at Random” and “Missing Completely at Random”

American Statistician, 1996
Abstract Missing at random (MAR) and missing completely at random (MCAR) are ignorability conditions—when they hold, they guarantee that certain kinds of inferences may be made without recourse to complicated missing-data modeling. In this article we review the definitions of MAR, MCAR, and their recent generalizations.
Daniel F Heitjan
exaly   +2 more sources

A Test of Missing Completely at Random for Multivariate Data with Missing Values

Journal of the American Statistical Association, 1988
Abstract A common concern when faced with multivariate data with missing values is whether the missing data are missing completely at random (MCAR); that is, whether missingness depends on the variables in the data set. One way of assessing this is to compare the means of recorded values of each variable between groups defined by whether other ...
exaly   +4 more sources

A test of missing completely at random for longitudinal data with missing observations

Statistics in Medicine, 1997
Liang and Zeger proposed a generalized estimating equations approach to the analysis of longitudinal data. Their models assume that missing observations are missing completely at random in the sense of Rubin. However, when this assumption does not hold, their analysis may yield biased results.
Taesung Park
exaly   +3 more sources

Missing value imputation on missing completely at random data using multilayer perceptrons

Neural Networks, 2011
Data mining is based on data files which usually contain errors in the form of missing values. This paper focuses on a methodological framework for the development of an automated data imputation model based on artificial neural networks. Fifteen real and simulated data sets are exposed to a perturbation experiment, based on the random generation of ...
Esther-Lydia Silva-Ramí­Rez, Rafael Pino-Mejias, María-Dolores Cubiles-De-La-Vega   +2 more
exaly   +3 more sources