Estimating the bias in meta analysis estimates for continuous data with non-random missing study variance

QR Code

Estimating the bias in meta analysis estimates for continuous data with non-random missing study variance

This paper examines, analytically, the biases introduced in the meta analysis estimates when the study-level variances are missing with non-random missing mechanism (MNAR). Two common approaches in handling this problem is considered, namely, the missing variances are imputed, and the studies with m...

Full description

Bibliographic Details
Main Author:	Nik Idris, Nik Ruzni
Format:	Article
Language:	English
Published:	Universiti Teknologi Malaysia 2011
Subjects:	QA Mathematics
Online Access:	http://irep.iium.edu.my/7219/ http://irep.iium.edu.my/7219/4/estimating_the_bias_in_meta_analysis_estimates_for_continuous_data.pdf

Similar Items

Estimating the bias in meta analysis estimates for continuous data with non-random missing study variance
by: Nik Idris, Nik Ruzni
Published: (2010)

Estimating the bias in meta analysis estimates based on fixed effect model for data with missing variability measures
by: Nik Idris, Nik Ruzni
Published: (2012)

Performance of selected imputation techniques for missing variances in meta-analysis
by: Nik Idris, Nik Ruzni, et al.
Published: (2013)

A comparison of methods to detect publication bias for meta-analysis of continuous data
by: Nik Idris, Nik Ruzni
Published: (2012)

The effects of the choice of meta analysis model on the overall estimates for continuous data with missing standard deviations
by: Nik Ruzni, Nik Idris, et al.
Published: (2010)

An empirical study of the choice of meta analysis model on the overall estimates for continuous data with missing standard deviations
by: Nik Idris, Nik Ruzni, et al.
Published: (2010)

An empirical study of the choice of meta analysis model on the overall estimates for continuous data with missing standard deviations
by: Nik Idris, Nik Ruzni, et al.
Published: (2010)

Performance of selected imputation techniques for missing variances in meta-analysis
by: Nik Idris, Nik Ruzni, et al.
Published: (2012)

On pooling different levels of data in estimating parameters of continuous meta-analysis
by: Nik Idris, Nik Ruzni, et al.
Published: (2014)

On pooling different levels of data in estimating parameters of continuous meta-analysis
by: Nik Idris, Nik Ruzni, et al.
Published: (2014)

Performance of the trim and fill method in adjusting for the publication bias in meta-analysis of continuous data
by: Nik Idris, Nik Ruzni
Published: (2012)

A study on the effects of different levels of data on the overall meta-analysis estimates
by: Nik Idris, Nik Ruzni, et al.
Published: (2015)

Quantifying and adjusting the effects of publication bias in continuous meta-analysis
by: Nik Idris, Nik Ruzni
Published: (2012)

Quantifying and adjusting the effects of publication bias in continuous meta-analysis
by: Nik Idris, Nik Ruzni
Published: (2012)

An empirical comparison of meta analysis models for continuous data with missing standard deviations
by: Nik Idris, Nik Ruzni, et al.
Published: (2011)

The effects of imputing the missing standard deviations on the standard error of meta analysis estimates
by: Nik Idris, Nik Ruzni, et al.
Published: (2009)

An empirical study on the efficiency of the trim and fill method in correcting publication bias in meta analysis
by: Mohd Nasir, Norizzati, et al.
Published: (2011)

Estimating bias and variances in bootstrap logistic regression for Umaru and impact data
by: Fitrianto, Anwar, et al.
Published: (2014)

Missing variability in meta-analysis : is imputing always good?
by: Nik Idris, Nik Ruzni, et al.
Published: (2006)

A case study on the effect of imputing the missing variability measures in meta analysis
by: Nik Idris, Nik Ruzni
Published: (2007)

A study on the effect of publication bias in meta analysis
by: Mohd Nasir, Nurina, et al.
Published: (2011)

An empirical assessment of meta-analysis estimates from multi-level studies
by: Misran, Nurul Afiqah, et al.
Published: (2015)

Assessing the efficacy of the trim and fill method in adjusting for publication bias in meta analysis
by: Nik Idris, Nik Ruzni, et al.
Published: (2012)

Modelling repeated measures data in meta analysis: an alternative approach
by: Nik Idris, Nik Ruzni
Published: (2008)

Analysis of missing data on estimable parameter function
by: Daoud, Jamal Ibrahim, et al.
Published: (2006)

Estimation of Missing Rainfall Data in Pahang using Modified Spatial Interpolation Weighting Methods
by: Roslinazairimah, Zakaria, et al.
Published: (2015)

Estimating missing daily rainfall data via artificial neural network over peninsular Malaysia
by: Loh, Wing Son
Published: (2021)

Simulation study of adjusted spatial weighting method to estimate missing rainfall data
by: Muhammad Az-Zuhri, Azman, et al.
Published: (2018)

Combining aggregate data and individual patient data in meta-analysis : an alternative method
by: Nik Idris, Nik Ruzni, et al.
Published: (2015)

Combining aggregate data and individual patient data in meta-analysis: an alternative method
by: Nik Idris, Nik Ruzni, et al.
Published: (2015)

On selection of models for continuos meta analysis data with incomplete variability measures
by: Nik Idris, Nik Ruzni, et al.
Published: (2011)

Statistical analysis in estimating missing rainfall data / Norlaila Hamzah
by: Hamzah, Norlaila
Published: (2006)

Least-squares variance component estimation
by: Teunissen, Peter, et al.
Published: (2008)

A modified two-stage method for combining the aggregate-data and individual-patient-data in meta-analysis
by: Nik Idris, Nik Ruzni, et al.
Published: (2015)

Mean-variance model with fuzzy random data
by: Othman, Mohammad Haris Haikal, et al.
Published: (2020)

Variance Component Estimation by the Method of Least-Squares
by: Teunissen, Peter, et al.
Published: (2008)

Integrating aggregate-data and individual-patient-data in meta-analysis: an empirical assessment and an alternative methodfor the two-stage approach
by: Nik Idris, Nik Ruzni, et al.
Published: (2015)

An integrated fuzzy regression–analysis of variance algorithm for improvement of electricity consumption estimation in uncertain environments
by: Azadeh, A., et al.
Published: (2011)

Integer estimation in the presence of biases
by: Teunissen, Peter
Published: (2001)

Revised spatial weighting methods for estimation of missing rainfall data
by: Suhaila, Jamaludin, et al.
Published: (2008)