Reliable causal inference based on observational studies is seriously threatened by unmeasured confounding.
What is unmeasured cofounding?
- By definition, an unmeasured confounder is a variable that is connected to both the exposed and the result and could explain the apparent observed link.
- The validity of interpretation in observational studies is threatened by unmeasured confounding. The use of negative control group to reduce unmeasured confounding has grown in acceptance and popularity in recent years.
Although they've been utilised mostly for bias detection, negative controls have a long history in laboratory sciences and epidemiology of ruling out non-causal causes. A pair of negative control exposure and outcome variables can be utilised to non-parametrically determine the average treatment effect (ATE) from observational data that is vulnerable to uncontrolled confounding, according to a recent study by Miao and colleagues.
Reliable causal inference based on observational studies is seriously threatened by unmeasured confounding.
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Answer:
20%
Step-by-step explanation:
4/20 = x/100
100*4 =400
400/20 = 20
so
4/20 = 20/100
Answer:
Option B is correct.
The value of x is, 0
Step-by-step explanation:
Given the system of equation:
-x+2y =6 .....[1]
6y =x+18 ......[2]
We can write equation [1] as;
-x = 6-2y or
x = -6 + 2y ......[3]
Substitute equation [3] in [2] we get;
6y =-6 + 2y + 18
or
6y = 2y +12
Subtract 2y from both sides we get;
6y -2y = 2y+ 12 -2y
Simplify:
4y = 12
Divide both sides by 4 we get;
Simplify:
y =3
Substitute the value of y in equation [3] to solve for x;
x = -6 + 2(3)
x = -6 +6 = 0
Therefore, the value of x is, 0
