Abstract
Correlated or matched data is frequently collected under many study designs in applied sciences such as the social, behavioral, economic, biological, medical, epidemiologic, health, public health, and drug developmental sciences. Challenges with respect to availability and cost commonly occur with matching observational or experimental study subjects, thus researchers frequently encounter situations where the observed sample consists of a combination of correlated and uncorrelated data. This paper discusses and proposes testing procedures to handle data when partially correlated data is available. Theoretical as well as numerical investigation will be provided. The proposed testing procedures will be applied to real data. These procedures will be of special importance in meta-analysis where partially correlated data is a concern when combining results of various studies.
| Original language | American English |
|---|---|
| State | Published - Mar 1 2009 |
| Event | Eastern North American Region International Biometric Society Annual Conference (ENAR) - Duration: Mar 15 2015 → … |
Conference
| Conference | Eastern North American Region International Biometric Society Annual Conference (ENAR) |
|---|---|
| Period | 03/15/15 → … |
Keywords
- McNemar test
- Pearson chi-square test
- Inverse chi-square method
- Weighted chi-square test
- Tippett method
- Partially matched-pair
- Case–control and matching studies
- T-test
- Z-test
- Power of the test
- p-Value of the test
- Efficiency
- Matched pairs sign test
- Sign test
- Wilcoxon signed-rank test
- Correlated and uncorrelated data
DC Disciplines
- Biostatistics
- Public Health
