The score test for independence in R x C contingency tables with missing data.
In this paper, the score test statistic for testing independence in R x C contingency tables with missing data is proposed. Under the null hypothesis of independence, the statistic has an approximate chi-squared distribution with (R - 1)(C - 1) degrees of freedom. The proposed test statistic is quite similar to the Pearson chi-squared statistic with complete data and, unlike the likelihood ratio statistic for testing independence, its computation is simple and noniterative. In addition, a score test statistic is proposed for testing independence when the rows and columns of the R x C table are ordinal. Finally, extensions of the score statistics to test for conditional independence in a set of (R x C) contingency tables with missing data are described. This yields score test statistics that are natural extensions of the Mantel-Haenszel statistic. An example, using a subset of data from the Six Cities Study, is presented to illustrate the methods.
Lipsitz
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SR
Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA.
Fitzmaurice
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GM
D000397:Air Pollution / Q000009:adverse effects; D001699:Biometry; D016009:Chi-Square Distribution; D002648:Child; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D009035:Mothers; D012135:Respiratory Sounds / Q000209:etiology; D012140:Respiratory Tract Diseases / Q000209:etiology; D012907:Smoking / Q000009:adverse effects