Consider the following 2 × 2 table from a hypothetical cohort study. Calculate the relative risk of CVD comparing those with high fasting glucose (≥126 mg/dL) with those with normal fasting glucose (<126 mg/dL) Serum fasting glucose Developed CVD Did not develop CVD Total ≥126 mg/dL (exposed) 125 325 450 <126 mg/dL (not exposed) 50 500 550 Total 175 825 1000 Group of answer choices 0.33 0.75 1.02 3.08 5.34
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!
Consider the following 2 × 2 table from a hypothetical cohort study. Calculate the relative risk of CVD comparing those with high fasting glucose (≥126 mg/dL) with those with normal fasting glucose (<126 mg/dL)
|
Serum fasting glucose
|
Developed CVD
|
Did not develop CVD
|
Total
|
| ≥126 mg/dL (exposed) |
125
|
325
|
450
|
| <126 mg/dL (not exposed) |
50
|
500
|
550
|
| Total |
175
|
825
|
1000
|
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