Selected article for: "logistic regression and multivariable model"

Author: Jones, Bryony A.; Sauter-Louis, Carola; Henning, Joerg; Stoll, Alexander; Nielen, Mirjam; Van Schaik, Gerdien; Smolenaars, Anja; Schouten, Matthijs; den Uijl, Ingrid; Fourichon, Christine; Guatteo, Raphael; Madouasse, Aurélien; Nusinovici, Simon; Deprez, Piet; De Vliegher, Sarne; Laureyns, Jozef; Booth, Richard; Cardwell, Jackie M.; Pfeiffer, Dirk U.
Title: Calf-Level Factors Associated with Bovine Neonatal Pancytopenia – A Multi-Country Case-Control Study
  • Document date: 2013_12_2
  • ID: 0dpm35dd_10
    Snippet: Due to the matched design, conditional logistic regression with farm as the matching variable was used for univariable analysis to obtain matched odds ratios (mOR), 95% confidence intervals (ci) and Wald test p values. It was first conducted on the dataset of 1559 calves, but due to missing observations the sample size for each variable was different. Variables with greater than 30% missing observations were excluded. The number of calves in the .....
    Document: Due to the matched design, conditional logistic regression with farm as the matching variable was used for univariable analysis to obtain matched odds ratios (mOR), 95% confidence intervals (ci) and Wald test p values. It was first conducted on the dataset of 1559 calves, but due to missing observations the sample size for each variable was different. Variables with greater than 30% missing observations were excluded. The number of calves in the final multivariable model was 1296 due to missing observations in the retained variables, so univariable analysis was repeated with the smaller dataset. Variables with p values greater than 0.2 in univariable analysis were excluded from multivariable analysis. Pair-wise associations between the exposure variables were examined by the chi-squared test, and polychoric correlation was used to check for collinearity.

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