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@ARTICLE{Matsushita:127909,
      author       = {K. Matsushita and S. H. Ballew and B. C. Astor and P. E. d.
                      Jong and R. T. Gansevoort and B. R. Hemmelgarn and A. S.
                      Levey and A. Levin and C.-P. Wen and M. Woodward and J.
                      Coresh and H. Brenner$^*$ and Müller$^*$ and B.
                      Schöttker$^*$},
      collaboration = {C. K. D. P. Consortium},
      title        = {{C}ohort profile: the chronic kidney disease prognosis
                      consortium.},
      journal      = {International journal of epidemiology},
      volume       = {42},
      number       = {6},
      issn         = {1464-3685},
      address      = {Oxford},
      publisher    = {Oxford Univ. Press},
      reportid     = {DKFZ-2017-03931},
      pages        = {1660 - 1668},
      year         = {2013},
      abstract     = {The Chronic Kidney Disease Prognosis Consortium (CKD-PC)
                      was established in 2009 to provide comprehensive evidence
                      about the prognostic impact of two key kidney measures that
                      are used to define and stage CKD, estimated glomerular
                      filtration rate (eGFR) and albuminuria, on mortality and
                      kidney outcomes. CKD-PC currently consists of 46 cohorts
                      with data on these kidney measures and outcomes from >2
                      million participants spanning across 40 countries/regions
                      all over the world. CKD-PC published four meta-analysis
                      articles in 2010-11, providing key evidence for an
                      international consensus on the definition and staging of CKD
                      and an update for CKD clinical practice guidelines. The
                      consortium continues to work on more detailed analysis
                      (subgroups, different eGFR equations, other exposures and
                      outcomes, and risk prediction). CKD-PC preferably collects
                      individual participant data but also applies a novel
                      distributed analysis model, in which each cohort runs
                      statistical analysis locally and shares only analysed
                      outputs for meta-analyses. This distributed model allows
                      inclusion of cohorts which cannot share individual
                      participant level data. According to agreement with cohorts,
                      CKD-PC will not share data with third parties, but is open
                      to including further eligible cohorts. Each cohort can opt
                      in/out for each topic. CKD-PC has established a productive
                      and effective collaboration, allowing flexible participation
                      and complex meta-analyses for studying CKD.},
      cin          = {C070},
      ddc          = {610},
      cid          = {I:(DE-He78)C070-20160331},
      pnm          = {313 - Cancer risk factors and prevention (POF3-313)},
      pid          = {G:(DE-HGF)POF3-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:23243116},
      doi          = {10.1093/ije/dys173},
      url          = {https://inrepo02.dkfz.de/record/127909},
}