Selected article for: "logistic regression and sample size"

Author: A.J.W. Haasnoot; M.W. Schilham; S.S.M. Kamphuis; P.C.E. Hissink Muller; A. Heiligenhaus; D. Foell; R.A. Ophoff; T.R.D.J. Radstake; A.I. Den Hollander; T.H.C.M. Reinards; S. Hiddingh; N. Schalij-Delfos; E.P.A.H. Hoppenreijs; M.A.J. van Rossum; C. Wouters; R.K. Saurenmann; N. Wulffraat; R. ten Cate; J.H. de Boer; S.L. Pulit; J.J.W. Kuiper
Title: An amino acid motif in HLA-DRß1 distinguishes patients with uveitis in juvenile idiopathic arthritis
  • Document date: 2017_5_22
  • ID: 4it5c9n2_43
    Snippet: We dropped failing samples from the data and then proceeded to perform SNP QC. All SNPs with missingness >5% were removed, as were SNPs with frequency <1% (too rare to be analyzed in the logistic regression framework, given the sample size). SNPs out of Hardy-Weinberg equilibrium in controls or across the full dataset were removed (p < 1 x 10 -6 in Phase 1; p < 1 x 10 -3 in Phase 2), as were SNPs with significant differential missingness between .....
    Document: We dropped failing samples from the data and then proceeded to perform SNP QC. All SNPs with missingness >5% were removed, as were SNPs with frequency <1% (too rare to be analyzed in the logistic regression framework, given the sample size). SNPs out of Hardy-Weinberg equilibrium in controls or across the full dataset were removed (p < 1 x 10 -6 in Phase 1; p < 1 x 10 -3 in Phase 2), as were SNPs with significant differential missingness between cases and controls (p < 5 x 10 -2 in Phase 1; p < 1 x 10 -3 in Phase 2).

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