Problems due to Small Samples and Sparse Data in Conditional ...

Problems due to small samples and sparse data in conditional ...

Conditional logistic regression was developed to avoid "sparse-data" biases that can arise in ordinary logistic regression analysis.

Problems due to Small Samples and Sparse Data in Conditional Logistic. Regression Analysis. Sander Greenland,1 Judith A. Schwartzbaum,2 and William D. Finkle3.

Problems Due to Small Samples and Sparse Data in Conditional ...

PDF | Conditional logistic regression was developed to avoid "sparse-data" biases that can arise in ordinary logistic regression analysis. Nonetheless,.

[PDF] Problems due to small samples and sparse data in conditional ...

These problems can arise in any likelihood-based analysis, including ordinary logistic regression, and are described in the context of matched case-control ...

Problems due to small samples and sparse data in conditional ...

Sparse-data bias can cause misleading inferences about confounding, effect modification, dose response, and induction periods, and can interact with other ...

Problems due to small samples and sparse data in conditional ...

Problems due to small samples and sparse data in conditional logistic regression analysis. Sander Greenland, Judith A. Schwartzbaum, William D. Finkle.

RE: “PROBLEMS DUE TO SMALL SAMPLES AND SPARSE DATA ...

Greenland et al. (1) offer a critical discussion of problems arising in the conditional logistic regression model when information in the data is weak relative ...

Re: "Problems due to small samples and sparse data in conditional ...

Re: "Problems due to small samples and sparse data in conditional logistic regression analysis"

Unconditional or Conditional Logistic Regression Model for Age ...

The sparse data problem, however, may not be a concern for loose-matching data when the matching between cases and controls is not unique, and ...

Sparse data bias: a problem hiding in plain sight - jstor

Problems due to small samples and sparse data in conditional logistic regression analysis. Am J Epidemiol 2000;151:531-9. doi:10.1093/ oxfordjournals.aje ...

Problems due to small samples and sparse data in conditional ...

Pascal and Francis Bibliographic Databases · Problems due to small samples and sparse data in conditional logistic regression analysis.

Noncollapsibility, confounding, and sparse-data bias. Part 2

Greenland, S ∙ Schwartzbaum, JA ∙ Finkle, WD. Problems from small samples and sparse data in conditional logistic regression analysis. Am J ...

Bias-reduced and separatio-proof conditional logistic regression ...

Request PDF | Bias-reduced and separatio-proof conditional logistic regression with small or sparse data sets | Conditional logistic regression is used for ...

Sparse data bias: a problem hiding in plain sight

Problems due to small samples and sparse data in conditional logistic regression analysis. Am J Epidemiol 2000;151:531-9. doi:10.1093 ...

Sparse data bias: a problem hiding in plain sight - Semantic Scholar

32 References ; Problems due to small samples and sparse data in conditional logistic regression analysis. S. GreenlandJ. SchwartzbaumW. ; Bayesian perspectives ...

Sparse data and use of logistic regression - Wiley Online Library

The degree of this “small-sample bias” (a misleading term, because this problem can happen with very large datasets) is mainly dependent on the ...

Sensitivity Analyses for Sparse-Data Problems—Using ... - Lippincott

... small sample sizes these estimators may be highly biased.3 Despite the fact that conditional maximum likelihood estimators were developed to deal with sparse ...

12.4 - Inference for Log-linear Models: Sparse Data | STAT 504

The bias problem due to sparseness Section · When a model does not converge, try adding a tiny number to all zero cells in the table, such as 0.000000001. Some ...

Sparse conditional logistic regression for analyzing large-scale ...

This paper considers the problem of estimation and variable selection for large high-dimensional data (high number of predictors p and large ...

Bias-reduced and separation-proof conditional logistic regression ...

Title not supplied. Breslow. 1980 ; Problems due to small samples and sparse data in conditional logistic regression analysis. Greenland S · Am J Epidemiol, (5): ...