Imputation of missing variance data using non-linear mixed effects modelling to enable an inverse variance weighted meta-analysis of summary-level longitudinal data: a case study-Reference-Cited by-同舟云学术

Imputation of missing variance data using non-linear mixed effects modelling to enable an inverse variance weighted meta-analysis of summary-level longitudinal data: a case study

Published:2012-05-07 Issue:4 Volume:11 Page:318-324
ISSN:1539-1604
Container-title:Pharmaceutical Statistics
language:en
Short-container-title:Pharmaceut. Statist.

Author:

Boucher Martin¹

Affiliation:

1. Pharmacometrics; Pfizer Ltd.; Sandwich, Kent UK

Publisher

Wiley

Subject

Pharmacology (medical),Pharmacology,Statistics and Probability

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1002/pst.1515/fullpdf

Reference13 articles.

1. A systematic review identifies a lack of standardization in methods for handling missing variance data;Wiebe;Journal of Clinical Epidemiology,2006

2. A note on dealing with missing standard errors in meta-analyses of continuous outcome measures in WinBUGS;Stevens;Pharmaceutical Statistics,2011

3. Imputing missing standard deviations in meta-analyses can provide accurate results;Furukawa;Journal of Clinical Epidemiology,2006

4. Meta-analyses of therapies for postmenopausal osteoporosis: I. Systematic reviews of randomized trials in osteoporosis: introduction and methodology;Cranney;Endocrine Reviews,2002

5. Compensating for missing data from longitudinal studies using WinBUGS;Carrigan;Journal of Statistical Software,2007

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