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A novel approach for identifying and addressing case‐mix heterogeneity in individual participant data meta‐analysis

(2019) RESEARCH SYNTHESIS METHODS. 10(4). p.582-596
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Abstract
Case-mix heterogeneity across studies complicates meta-analyses. As a result of this, treatments that are equally effective on patient subgroups may appear to have different effectiveness on patient populations with different case mix. It is therefore important that meta-analyses be explicit for what patient population they describe the treatment effect. To achieve this, we develop a new approach for meta-analysis of randomized clinical trials, which use individual patient data (IPD) from all trials to infer the treatment effect for the patient population in a given trial, based on direct standardization using either outcome regression (OCR) or inverse probability weighting (IPW). Accompanying random-effect meta-analysis models are developed. The new approach enables disentangling heterogeneity due to case mix from that due to beyond case-mix reasons.
Keywords
causal inference, direct standardization, inverse probability weighting, meta-analysis, outcome regression, transportability, CAUSAL INFERENCE, MODELS, IPD

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Citation

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MLA
Vo, Tẩt Thẳng, et al. “A Novel Approach for Identifying and Addressing Case‐mix Heterogeneity in Individual Participant Data Meta‐analysis.” RESEARCH SYNTHESIS METHODS, vol. 10, no. 4, 2019, pp. 582–96.
APA
Vo, T. T., Porcher, R., Chaimani, A., & Vansteelandt, S. (2019). A novel approach for identifying and addressing case‐mix heterogeneity in individual participant data meta‐analysis. RESEARCH SYNTHESIS METHODS, 10(4), 582–596.
Chicago author-date
Vo, Tẩt Thẳng, Raphael Porcher, Anna Chaimani, and Stijn Vansteelandt. 2019. “A Novel Approach for Identifying and Addressing Case‐mix Heterogeneity in Individual Participant Data Meta‐analysis.” RESEARCH SYNTHESIS METHODS 10 (4): 582–96.
Chicago author-date (all authors)
Vo, Tẩt Thẳng, Raphael Porcher, Anna Chaimani, and Stijn Vansteelandt. 2019. “A Novel Approach for Identifying and Addressing Case‐mix Heterogeneity in Individual Participant Data Meta‐analysis.” RESEARCH SYNTHESIS METHODS 10 (4): 582–596.
Vancouver
1.
Vo TT, Porcher R, Chaimani A, Vansteelandt S. A novel approach for identifying and addressing case‐mix heterogeneity in individual participant data meta‐analysis. RESEARCH SYNTHESIS METHODS. 2019;10(4):582–96.
IEEE
[1]
T. T. Vo, R. Porcher, A. Chaimani, and S. Vansteelandt, “A novel approach for identifying and addressing case‐mix heterogeneity in individual participant data meta‐analysis,” RESEARCH SYNTHESIS METHODS, vol. 10, no. 4, pp. 582–596, 2019.
@article{8641248,
  abstract     = {Case-mix heterogeneity across studies complicates meta-analyses. As a result of this, treatments that are equally effective on patient subgroups may appear to have different effectiveness on patient populations with different case mix. It is therefore important that meta-analyses be explicit for what patient population they describe the treatment effect. To achieve this, we develop a new approach for meta-analysis of randomized clinical trials, which use individual patient data (IPD) from all trials to infer the treatment effect for the patient population in a given trial, based on direct standardization using either outcome regression (OCR) or inverse probability weighting (IPW). Accompanying random-effect meta-analysis models are developed. The new approach enables disentangling heterogeneity due to case mix from that due to beyond case-mix reasons.},
  author       = {Vo, Tẩt Thẳng and Porcher, Raphael and Chaimani, Anna and Vansteelandt, Stijn},
  issn         = {1759-2879},
  journal      = {RESEARCH SYNTHESIS METHODS},
  keywords     = {causal inference,direct standardization,inverse probability weighting,meta-analysis,outcome regression,transportability,CAUSAL INFERENCE,MODELS,IPD},
  language     = {eng},
  number       = {4},
  pages        = {582--596},
  title        = {A novel approach for identifying and addressing case‐mix heterogeneity in individual participant data meta‐analysis},
  url          = {http://dx.doi.org/10.1002/jrsm.1382},
  volume       = {10},
  year         = {2019},
}

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