Generalized Least Squares for Trend Estimation of Summarized Dose–response Data

By Nicola Orsini, R. Bellocco, Sander Greenland. 2006

1,234 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/35aebb58d90e64c10d76f3854b3f0f4ef7dee0a6

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In Statistics and Probability

Citations in the corpus

Daily Sitting Time and All-Cause Mortality: A Meta-Analysis (2013, methodology): “Using the generalized least squares for trend (GLST) procedure we then estimated the trend for each study sample and pooled the estimates to produce a forest plot and random effects trend[25].”
Red and Processed Meat and Colorectal Cancer Incidence: Meta-Analysis of Prospective Studies (2011, methodology): “In linear dose-response meta-analyses, we pooled the relative risk estimates per unit of intake increase (with its standard error) reported in the studies, or computed by us from the categorical data using generalized least-squares for trend estimation [32].”
Body mass index and survival in women with breast cancer—systematic literature review and meta-analysis of 82 follow-up studies (2014, neutral): “generalised least-squares for trend estimation[25].”
Sedentary behaviour and risk of all-cause, cardiovascular and cancer mortality, and incident type 2 diabetes: a systematic review and dose response meta-analysis (2018, methodology): “Estimates of the linear association for each contributing study were calculated using Generalized Least-Squares regression [26, 27].”
Meta-analyses of Colorectal Cancer Risk Factors (2013, methodology): “We assessed the goodness of fit and heterogeneity using Q statistics [146].”
Obesity and colon and rectal cancer risk: a meta-analysis of prospective studies. (2007, methodology): “We used the method described by Greenland et al (7, 8), which takes into account that the level-specific risk estimates are correlated.”
Adiponectin levels and risk of type 2 diabetes: a systematic review and meta-analysis. (2009, methodology): “We used the 2-stage GLST method that estimates studyspecific slope lines first and then derives an overall average slope.(15) We chose the 2-stage method because this allowed us to combine the GLST-estimated studyspecificslopeswiththeresults fromstudiesnoteligible forGLSTbutthatreported effect estim”
BMI and all-cause mortality in older adults: a meta-analysis. (2014, methodology): “Restricted cubic spline models were initially computed for each study taking into account the within study correlation; afterward, a random effects meta analysis was performed by using the regression coefficients and the variance covariance matrix from each individual study (17, 18).”
Facilitating meta‐analyses by deriving relative effect and precision estimates for alternative comparisons from a set of estimates presented by exposure level or disease category (2008, methodology): “Practical examples of the method were presented in papers published in 1993 [8], and, much more recently [9], the latter paper also providing a command, glst, written for Stata 9.”
Food groups and risk of type 2 diabetes mellitus: a systematic review and meta-analysis of prospective studies (2017, methodology): “The method described by Greenland and Longnecker [16, 17] was applied for the dose–response analysis and computed study-specific slopes (linear trends) and 95% CIs from the natural logs of the RRs and CIs across intake categories of the 12 pre-defined food groups.”