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dc.contributor.authorMwaro, Joshua Owuori
dc.date.accessioned2021-04-27T09:11:14Z
dc.date.available2021-04-27T09:11:14Z
dc.date.issued2020
dc.identifier.urihttp://hdl.handle.net/11071/10233
dc.descriptionA Thesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Statistical Sciences (MSc. SS) at Strathmore Universityen_US
dc.description.abstractHaving missing information is almost inevitable in research, but many researchers only report on complete cases. Here we review the missing data theory, missingness characteristics, look at the background information, importance of studying missing data, the most common ways of correcting for missing data then extend to Kenyan HIV/ TB co-infection setting. We review most of the existing methods of dealing with missing data and what other scholars have done in the missing data area. In the methodology section, we outline and give characteristics and features of the four methods for dealing with missing data (Analysis of complete cases only, Mean/Single imputation method, MLE method, and Multiple Imputation method.) which our study is focused on. We also test the four methods on the simulated data then apply the same procedure on the real Kenyan HIV/TB co-infection data. Results showed that analysis of data that was corrected for missingness using: complete cases only, weighted method, likelihood-based, and multiple imputation estimated the Kenyan HIV/TB co-infection rate to be 29%, 27%, 26%, and 21% respectively. The results indicate that MI is the best approach to correct for missing data as it does not overestimate the HIV & TB co-infection rate.en_US
dc.language.isoenen_US
dc.publisherStrathmore Universityen_US
dc.subjectMissing dataen_US
dc.subjectHIV/TB co-infectionen_US
dc.subjectImputationen_US
dc.subjectMissing completely at randomen_US
dc.subjectMissing at randomen_US
dc.subjectMissing not at randomen_US
dc.titleIdentifying the best method to correct for missing data, a case of HIV/TB co-infection in Kenyaen_US
dc.typeThesisen_US


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