A software for performing exact logistic regression - Isingo, Remuli R.
Logistic regression is a widely used and accepted statistical method for analysis of clinical and epidemiological studies to investigate simultaneous relationship between several covariatis and one binary outcome variable. Conventional methods for statistical inference in logistic regression may fail to derive valid results when the normality assumptions are not met, typically when the sample sizes are small or unbalanced. Exact methods, which were made feasible by developments in computing algorithms, have to be performed in these situations.With this in mind, this study developed software for performing exact logistic regression. This was then applied to analyze a data set of a study to determine the association of piperaquine resistance to P. falciparum malaria with sex, age, duration of stay in malaria endemic area and recent history of anti-malaria use in Hainan province in China. Due to small sample size, the investigator could not analyze simultaneously all covariates. However, using the study software, we found that all covariates were not statistically significant at 5 per cent level. Exact two-tailed p-values for age, sex, residence and drug history were 0.1238, 0.3696, 1.0000, 0.4042 respectively.For the purpose of evaluating the reliability of the study software, the results from the study software was compared with the results using LogExact, a commercially available but relatively expensive software. The comparisons were done using varying samples and number of covariates from the data described above. The results of both softwares agreed to large extents. Point and median unbiased estimates are exactly the same up to the three decimal places. However, there are slight differences when we compared the confidence intervals that can be attributed to differences in the algorithms for searching solutions of linear equations.
Thesis (Master of Science in Public Health, Biostatistics)--University of the Philippines Manila.
Keywords: Logistic regression