Tails of a test-<\/strong><\/p>\n\n\n\nWhen two group comparisons are made, the p value is reported as a one-sided or one-tailed or a two-sided or a two-tailed p value (the words \u201ctail\u201d and \u201cside\u201d are used synonymously). This primarily refers to the \u201cdirection\u201d of movement of the outcome of interest.<\/p>\n\n\n\n
example. Let us say that we are evaluating a new drug for the prevention of mother to child transmission.<\/p>\n\n\n\n
We know that this new drug can either decrease mother to-child transmission or not have an effect but certainly<\/p>\n\n\n\n
cannot \u201cincrease\u201d it.<\/p>\n\n\n\n
Thus, in this case, we use a \u201cone-sided\u201d test.<\/p>\n\n\n\n
On the other hand, if we were evaluating a new antidiabetic drug, we start by saying that the blood sugar in the study, which is the outcome of interest, can stay the same, increase or decrease after treatment. It does not<\/p>\n\n\n\n
mean that the anti-diabetic drug will increase the blood sugar by itself, but we allow for the fact that the drug may<\/p>\n\n\n\n
not work and hence the blood sugar can rise. We thus use \u201ctwo-sided \u201c tests here.<\/p>\n\n\n\n
Prevalence-<\/strong><\/p>\n\n\n\nprimarily refers to the percentage or proportion of patients or participants who have the characteristic of interest.<\/p>\n\n\n\n
Estimated design effect (DEFF)<\/strong><\/p>\n\n\n\nMost statistical test are applied under the assumption that the data has been collected by Simple Random Sampling where no respondent declines participation. However, this rarely happens in real life and we have to \u201ccorrect\u201d for this by multiplying the sample variance by a constant. This constant will correct for the<\/p>\n\n\n\n
departure of the value of the actual variance obtained through non-random sampling from that which would bed<\/p>\n\n\n\n
obtained classically through random sampling . This constant is called the design effect<\/p>\n\n\n\n
Effect size<\/strong><\/p>\n\n\n\nis essentially the magnitude of the difference between groups.<\/p>\n\n\n\n
Hazard ratio<\/strong> \u2013<\/p>\n\n\n\nHazard is a measure that is classically used in studies that involve Survival analysis, also called as time to event analysis [e.g.,time to discharge from the hospital, time to metastases, time to disease recurrence].<\/p>\n\n\n\n
Hazard ratio can be defined as the chance of occurrence of an event over time.<\/p>\n\n\n\n
To give an example, in a two-group comparison, a hazard ratio of 2 for death can be interpreted as one group having twice the chance of dying relative to the other group, over a certain time-period.<\/p>\n\n\n\n