Multivariate meta-analysis for data consortia, individual patient meta-analysis, and pooling projects

Ritz, John; Demidenko, Eugene; Spiegelman, Donna

doi:10.1016/j.jspi.2007.07.004

Cited by 32 publications

(30 citation statements)

References 45 publications

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“…In our results we report statistics that provide simple summaries on the extent of heterogeneity. Specifically, the Cochran Q test for (residual) heterogeneity (Berkey et al 1996; Ritz et al 2008) and the I 2 that measures the proportion of total variation attributable to heterogeneity (Rücker et al 2008). Statistical analysis was performed in R version 3.0.1 (R Development Core Team (2012), Vienna, Austria).…”

Section: Methodsmentioning

confidence: 99%

Changing patterns of the temperature–mortality association by time and location in the US, and implications for climate change

Nordio

Zanobetti²,

Colicino³

et al. 2015

Environment International

117

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The shape of the non-linear relationship between temperature and mortality varies among cities with different climatic conditions. There has been little examination of how these curves change over space and time. We evaluated the short-term effects of hot and cold temperatures on daily mortality over six 7-years periods in 211 US cities, comprising over 42 million deaths. Cluster analysis was used to group the cities according to similar temperatures and relative humidity. Temperature-mortality functions were calculated using B-splines to model the heat effect (lag 0) and the cold effect on mortality (moving average lag 1-5). The functions were then combined through meta-smoothing and subsequently analyzed by meta-regression. We identified eight clusters. At lag 0, Cluster 5 (West Coast) had a RR of 1.14 (95% CI: 1.11,1.17) for temperatures of 27°C vs 15.6 °C, and Cluster 6 (Gulf Coast) has a RR of 1.04 (95% CI: 1.03,1.05), suggesting that people are acclimated to their respective climates. Controlling for cluster effect in the multivariate-meta regression we found that across the US, the excess mortality from a 24-hr temperature of 27°C decreased over time from 10.6% to 0.9%. We found that the overall risk due to the heat effect is significantly affected by summer temperature mean and air condition usage, which could be a potential predictor in building climate-change scenarios.

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Section: Methodsmentioning

confidence: 99%

Changing patterns of the temperature–mortality association by time and location in the US, and implications for climate change

Nordio

Zanobetti²,

Colicino³

et al. 2015

Environment International

117

View full text Add to dashboard Cite

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“…The test is based on the statistic:

Q MathClass-rel= {MathClass-op\sum}_{i MathClass-rel= 1}^{m} ([], {((), {\overset{bold-italicθ}{true}}_{i} MathClass-bin- {bold-italicX}_{i} \overset{bold-italicβ}{true})}^{sans-serifT} {bold-italicS}_{i}^{MathClass-bin- 1} ((), {\overset{bold-italicθ}{true}}_{i} MathClass-bin- {bold-italicX}_{i} \overset{bold-italicβ}{true})) MathClass-punc,

where

\overset{bold-italicβ}{true}

are estimated by the correspondent fixed‐effect model. An extension of this heterogeneity test for a subset of β has also been proposed .…”

Section: Multivariate Meta‐analysismentioning

confidence: 99%

Multivariate meta‐analysis for non‐linear and other multi‐parameter associations

Gasparrini

Armstrong

Kenward

2012

Statistics in Medicine

559

511

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In this paper, we formalize the application of multivariate meta-analysis and meta-regression to synthesize estimates of multi-parameter associations obtained from different studies. This modelling approach extends the standard two-stage analysis used to combine results across different sub-groups or populations. The most straightforward application is for the meta-analysis of non-linear relationships, described for example by regression coefficients of splines or other functions, but the methodology easily generalizes to any setting where complex associations are described by multiple correlated parameters. The modelling framework of multivariate meta-analysis is implemented in the package mvmeta within the statistical environment R. As an illustrative example, we propose a two-stage analysis for investigating the non-linear exposure–response relationship between temperature and non-accidental mortality using time-series data from multiple cities. Multivariate meta-analysis represents a useful analytical tool for studying complex associations through a two-stage procedure. Copyright © 2012 John Wiley & Sons, Ltd.

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“…To relax the assumption of normality, many of these methods can be cast in an unbiased estimating equations (EE) format (Ritz et al . ). These EE methods are asymptotically ( K → ∞ ) equivalent to ML for distributions with zero third moment and are much simpler computationally.…”

Section: Multivariate Meta‐analysis and Meta‐regressionmentioning

confidence: 97%

“…The simplest way to achieve this is to use a projection of c † b on the set of non-negative definite matrices found by f † b D Pƒ C P 0 , where P is the matrix of eigenvectors and ƒ C is the matrix of truncated at 0 eigenvalues of matrix c † b , Demidenko (2004, Section 5.3.3). To relax the assumption of normality, many of these methods can be cast in an unbiased estimating equations (EE) format (Ritz et al, 2008). These EE methods are asymptotically (K !…”

Section: Multivariate Meta-analysismentioning

confidence: 99%

Combining Statistical Evidence

Kulinskaya

Morgenthaler

Staudte

2014

Int Statistical Rev

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SUMMARYThe combination of evidence from independent studies has a curious history. The origins reach back at least to the beginning of the twentieth century. Since the mid-seventies the term meta-analysis (G. Glass, 1976 presidential address to the American EducationalResearch Association) has become popular in several fields, among them medical statistics and the behavioral sciences. The most widely used procedures were perfected in early papers and subsequently a kind of groupthink has taken hold of meta-analysis. This explains the need for a review in a statistics journal, destined for a statistical audience.Meta-analysis is not a hot research topic among graduate students in statistics and by writing this article we hope to change this. We wish to point out the shortcomings of the mainstream view and exhibit some of the open problems that await the attention of statistical researchers.A host of competent reviews of meta-analysis have been published and several booklength treatments are also available. We have listed many of these in the bibliography, but cannot guarantee completeness.

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Multivariate meta-analysis for data consortia, individual patient meta-analysis, and pooling projects

Cited by 32 publications

References 45 publications

Changing patterns of the temperature–mortality association by time and location in the US, and implications for climate change

Changing patterns of the temperature–mortality association by time and location in the US, and implications for climate change

Multivariate meta‐analysis for non‐linear and other multi‐parameter associations

Combining Statistical Evidence

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