When a combined inference is aimed at, as in a multi-centre analysis with lesser number of tables (Freidlin and Gastwirth, 1999), then deletion of tables may lead to loss of valuable information. Combining tables in a typical 2 x 2 data would also have limitations in that it could be resulted with Simpson's paradox. Also, Agresti (2000) has discussed the effect of adding very small constants or pooling tables in sparse 2 x 2 data sets with zero cells. However, addition of a constant could influence the interpretation of a summary measure like the odds ratio and a sensitivity analysis needs to be carried out before making a final decision on choosing an appropriate constant. Subbiah and Srinivasan (2008) have proposed a method classifying sparsity based on the odds ratio and to obtain the bounds for classification as a means for studying the sensitivity
Package: |
nose |
Type: |
Package |
Version: |
1.0 |
Date: |
2012-12-04 |
License: |
GPL-2 |
Freidlin, B., Gastwirth, J.L., 1999. Unconditional versions of several tests commonly used in the analysis of contingency tables. Biometrics 55, 264-267.
Agresti, A., 2000. Strategies for comparing treatments on a binary response with multi-centre data. Statistics in Medicine 19, 1115-1139.
Subbiah, M., Srinivasan, M.R., 2008 Classification of 2 x 2 sparse data sets with zero cells. Statistics and Probability Letters 78, 3212-3215.