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One-stage dose-response meta-analysis for aggregated data.

Alessio Crippa1, Andrea Discacciati2, Matteo Bottai2

  • 11 Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden.

Statistical Methods in Medical Research
|May 11, 2018
PubMed
Summary
This summary is machine-generated.

A new one-stage method for meta-analysis of non-linear dose-response curves includes all studies, unlike traditional methods. This approach enhances the estimation of complex curves and heterogeneity across studies.

Keywords:
Meta-analysisdose–responseflexible modelmixed modelrandom-effects

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Pharmacology

Background:

  • Standard two-stage meta-analysis methods for non-linear dose-response curves often exclude studies with limited exposure groups.
  • This exclusion can lead to incomplete data utilization and potentially biased estimations of dose-response relationships.
  • Complex dose-response patterns may not be adequately captured by traditional approaches.

Purpose of the Study:

  • To develop and present a novel one-stage method for estimating non-linear dose-response curves from aggregated data.
  • To compare the performance of the one-stage method against the traditional two-stage approach.
  • To highlight the advantages of the one-stage method in handling complex curves and quantifying heterogeneity.

Main Methods:

  • A one-stage approach was developed using a linear mixed model framework.
  • The methodology encompasses model specification, estimation, hypothesis testing, prediction, goodness-of-fit assessment, model comparison, and heterogeneity quantification.
  • The proposed method was illustrated using both simulated and real-world data from a published meta-analysis.

Main Results:

  • The one-stage method successfully estimates pooled non-linear dose-response curves and between-study heterogeneity using all available studies.
  • Complex curve shapes, including splines and spikes at zero exposure, can be effectively modeled.
  • The one-stage approach facilitates advanced applications such as quantifying exposure-specific heterogeneity and predicting marginal/conditional curves.

Conclusions:

  • The developed one-stage method offers a more comprehensive approach to dose-response meta-analysis of aggregated data.
  • It overcomes limitations of the two-stage method by including all studies, thus improving the estimation of complex dose-response relationships.
  • The method is implemented in the 'dosresmeta' R package, providing a valuable tool for researchers in the field.