Suppose we eventually plan to accumulate 50 MI cases during the period 2010–2015. Assume that the 24-hour case-fatality rate is truly 20% during this period. How much power would such a study have in distinguishing between case-fatality rates in 2000 and 2010–2015 if a two-sided test with significance level .05 is planned?
Continuous Probability Distributions
Probability distributions are of two types, which are continuous probability distributions and discrete probability distributions. A continuous probability distribution contains an infinite number of values. For example, if time is infinite: you could count from 0 to a trillion seconds, billion seconds, so on indefinitely. A discrete probability distribution consists of only a countable set of possible values.
Normal Distribution
Suppose we had to design a bathroom weighing scale, how would we decide what should be the range of the weighing machine? Would we take the highest recorded human weight in history and use that as the upper limit for our weighing scale? This may not be a great idea as the sensitivity of the scale would get reduced if the range is too large. At the same time, if we keep the upper limit too low, it may not be usable for a large percentage of the population!
Cardiovascular Disease
Suppose the incidence rate of myocardial infarction (MI)
was 5 per 1000 among 45- to 54-year-old men in 2000.
To look at changes in incidence over time, 5000 men in this
age group were followed for 1 year starting in 2010. Fifteen
new cases of MI were found.
7.15 Suppose we eventually plan to accumulate 50 MI
cases during the period 2010–2015. Assume that the
24-hour case-fatality rate is truly 20% during this period.
How much power would such a study have in distinguishing
between case-fatality rates in 2000 and 2010–2015 if a
two-sided test with significance level .05 is planned?
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