A new synthesis analysis method for building logistic regression prediction models

Sheng, Elisa; Zhou, Xiao Hua; Chen, Hua; Hu, Guizhou; Duncan, Ashlee W.

doi:10.1002/sim.6125

Cited by 6 publications

(5 citation statements)

References 18 publications

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“…However, such difference depends on various factors, such as the number of patients per site, the ratio between the within-site heterogeneity and the between-site heterogeneity, and the number of sites. Meta-analysis-based model aggregation is extensively studied in the literature for prediction purposes 28 – 30 . A comprehensive comparison between LMM and meta-analysis is however beyond the scope of this paper.…”

Section: Discussionmentioning

confidence: 99%

DLMM as a lossless one-shot algorithm for collaborative multi-site distributed linear mixed models

et al. 2022

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Linear mixed models are commonly used in healthcare-based association analyses for analyzing multi-site data with heterogeneous site-specific random effects. Due to regulations for protecting patients’ privacy, sensitive individual patient data (IPD) typically cannot be shared across sites. We propose an algorithm for fitting distributed linear mixed models (DLMMs) without sharing IPD across sites. This algorithm achieves results identical to those achieved using pooled IPD from multiple sites (i.e., the same effect size and standard error estimates), hence demonstrating the lossless property. The algorithm requires each site to contribute minimal aggregated data in only one round of communication. We demonstrate the lossless property of the proposed DLMM algorithm by investigating the associations between demographic and clinical characteristics and length of hospital stay in COVID-19 patients using administrative claims from the UnitedHealth Group Clinical Discovery Database. We extend this association study by incorporating 120,609 COVID-19 patients from 11 collaborative data sources worldwide.

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Section: Discussionmentioning

confidence: 99%

DLMM as a lossless one-shot algorithm for collaborative multi-site distributed linear mixed models

et al. 2022

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“…Yet, as stated, these methods do not allow for combining data with partial overlap. The closest candidate is a specific synthesis regression approach [19], which is only applicable in low-dimensional settings. In contrast, the current proposal is a synthesis regression approach that can deal with partial overlap in high-dimensional data.…”

Section: Discussionmentioning

confidence: 99%

“…Specifically for SNP data, approaches such as the lasso [12] or componentwise likelihood-based boosting [13] have been suggested. We use the latter as a basis for a synthesis regression approach [19] that can deal with partial overlap of the molecular data to address a challenge likely encountered when data are pooled from several studies, such as in the context of the InterLymph Consortium.…”

Section: Methodsmentioning

confidence: 99%

A multivariable approach for risk markers from pooled molecular data with only partial overlap

Stelzer¹,

Maccioni

Gerhold‐Ay

et al. 2019

BMC Med Genet

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Background Increasingly, molecular measurements from multiple studies are pooled to identify risk scores, with only partial overlap of measurements available from different studies. Univariate analyses of such markers have routinely been performed in such settings using meta-analysis techniques in genome-wide association studies for identifying genetic risk scores. In contrast, multivariable techniques such as regularized regression, which might potentially be more powerful, are hampered by only partial overlap of available markers even when the pooling of individual level data is feasible for analysis. This cannot easily be addressed at a preprocessing level, as quality criteria in the different studies may result in differential availability of markers – even after imputation. Methods Motivated by data from the InterLymph Consortium on risk factors for non-Hodgkin lymphoma, which exhibits these challenges, we adapted a regularized regression approach, componentwise boosting, for dealing with partial overlap in SNPs. This synthesis regression approach is combined with resampling to determine stable sets of single nucleotide polymorphisms, which could feed into a genetic risk score. The proposed approach is contrasted with univariate analyses, an application of the lasso, and with an analysis that discards studies causing the partial overlap. The question of statistical significance is faced with an approach called stability selection. Results Using an excerpt of the data from the InterLymph Consortium on two specific subtypes of non-Hodgkin lymphoma, it is shown that componentwise boosting can take into account all applicable information from different SNPs, irrespective of whether they are covered by all investigated studies and for all individuals in the single studies. The results indicate increased power, even when studies that would be discarded in a complete case analysis only comprise a small proportion of individuals. Conclusions Given the observed gains in power, the proposed approach can be recommended more generally whenever there is only partial overlap of molecular measurements obtained from pooled studies and/or missing data in single studies. A corresponding software implementation is available upon request. Trial registration All involved studies have provided signed GWAS data submission certifications to the U.S. National Institute of Health and have been retrospectively registered. Electronic supplementary material The online version of this article (10.1186/s12881-019-0849-0) contains supplementary material, which is available to authorized users.

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“…Other fixed‐effects approaches that account for the missing confounders are available from the literature (e.g. ).…”

Section: Discussionmentioning

confidence: 99%

A graphical model approach to systematically missing data in meta‐analysis of observational studies

Kovačić

Varnai

2016

Statistics in Medicine

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When studies in meta-analysis include different sets of confounders, simple analyses can cause a bias (omitting confounders that are missing in certain studies) or precision loss (omitting studies with incomplete confounders, i.e. a complete-case meta-analysis). To overcome these types of issues, a previous study proposed modelling the high correlation between partially and fully adjusted regression coefficient estimates in a bivariate meta-analysis. When multiple differently adjusted regression coefficient estimates are available, we propose exploiting such correlations in a graphical model. Compared with a previously suggested bivariate meta-analysis method, such a graphical model approach is likely to reduce the number of parameters in complex missing data settings by omitting the direct relationships between some of the estimates. We propose a structure-learning rule whose justification relies on the missingness pattern being monotone. This rule was tested using epidemiological data from a multi-centre survey. In the analysis of risk factors for early retirement, the method showed a smaller difference from a complete data odds ratio and greater precision than a commonly used complete-case meta-analysis. Three real-world applications with monotone missing patterns are provided, namely, the association between (1) the fibrinogen level and coronary heart disease, (2) the intima media thickness and vascular risk and (3) allergic asthma and depressive episodes. The proposed method allows for the inclusion of published summary data, which makes it particularly suitable for applications involving both microdata and summary data. Copyright © 2016 John Wiley & Sons, Ltd.

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A new synthesis analysis method for building logistic regression prediction models

Cited by 6 publications

References 18 publications

DLMM as a lossless one-shot algorithm for collaborative multi-site distributed linear mixed models

DLMM as a lossless one-shot algorithm for collaborative multi-site distributed linear mixed models

A multivariable approach for risk markers from pooled molecular data with only partial overlap

A graphical model approach to systematically missing data in meta‐analysis of observational studies

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