論文

査読有り 国際誌
2017年11月30日

Meta-analytical synthesis of regression coefficients under different categorization scheme of continuous covariates.

Statistics in medicine
  • Daisuke Yoneoka
  • ,
  • Masayuki Henmi

36
27
開始ページ
4336
終了ページ
4352
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1002/sim.7434

Recently, the number of clinical prediction models sharing the same regression task has increased in the medical literature. However, evidence synthesis methodologies that use the results of these regression models have not been sufficiently studied, particularly in meta-analysis settings where only regression coefficients are available. One of the difficulties lies in the differences between the categorization schemes of continuous covariates across different studies. In general, categorization methods using cutoff values are study specific across available models, even if they focus on the same covariates of interest. Differences in the categorization of covariates could lead to serious bias in the estimated regression coefficients and thus in subsequent syntheses. To tackle this issue, we developed synthesis methods for linear regression models with different categorization schemes of covariates. A 2-step approach to aggregate the regression coefficient estimates is proposed. The first step is to estimate the joint distribution of covariates by introducing a latent sampling distribution, which uses one set of individual participant data to estimate the marginal distribution of covariates with categorization. The second step is to use a nonlinear mixed-effects model with correction terms for the bias due to categorization to estimate the overall regression coefficients. Especially in terms of precision, numerical simulations show that our approach outperforms conventional methods, which only use studies with common covariates or ignore the differences between categorization schemes. The method developed in this study is also applied to a series of WHO epidemiologic studies on white blood cell counts.

リンク情報
DOI
https://doi.org/10.1002/sim.7434
PubMed
https://www.ncbi.nlm.nih.gov/pubmed/28815681

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