Estimating appropriate sample size for research on malaria data: a case study of Afigya-Sekyere District

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Sample size determination is often important steps in planning any statistical study and is usually not easy calculating. To determine appropriate sample size it is important to use detail approach than to use short cuts. This thesis work offers distinct approaches for calculating successful and meaningful sample size for different study designs. Additionally, there are also different procedures for calculating sample size for two (2) approaches of drawing statistical inference from the study results. That is, confidence interval and test of significance approach. Also discussed is the relationship between power and sample size estimation. Power and sample size estimations are critical steps in the design of clinical trials. Power characterizes the ability of a study to detect a meaningful significant effect if indeed it exists. Usually these tasks can be accomplished by a statistician by using estimates of treatment effect and variance or sample standard deviation from past trials or from pilot studies. However, when exact power computations are not possible or when there is no effect size of clinical data, then simulation base approach must be adopted. This helps to recruit as many patients as required by the study than more or less patients that are not required.
A thesis submitted to the School of Graduate Studies, Kwame Nkrumah University of Science and Technology, Kumasi, in partial fulfilment of the requirements for the award of the Degree of Master of Philosophy