Causality based on conflicting data

In summary: The remaining 40% could be attributed to other risk factors. However, this calculation is not definitive and further research is needed to fully understand the relationship between PFO and cryptogenic stroke.
  • #1
JakeA
12
0
There's a common heart defect called a PFO, Patent Foreman Ovale.

http://www.google.com/search?hl=en&q=pfo+stroke+risk&btnG=Google+Search&aq=f&oq=pfo+stroke+risk

It's thought to cause stroke and migraines in some people. 25% of the general population has a PFO, and 40% of people who have a had a cryptogenic stroke, one where the cause is unknown, have a PFO. The link between PFO and strokes is that a PFO will open a flap between the chambers of your heart periodically allowing venous blood clots that would otherwise be filtered through the lungs to go to your brain.

My question is what percentage of people who have a PFO and a cryptogenic stroke should be assumed to have it caused by the PFO?

At a minimum you could conclude that it's at least 37.5%, which is 15/40, the increment between the general population of 25% and the crypto stoke population of 40%.

The problem is that PFOs don't increase the risk of stroke for the general population, or at least don't do it significantly. The data is somewhat at conflict, I think because there are other risk factors for people with PFOs and strokes, i.e. you have to have something else that is pathogenic besides a PFO to be at increased risk of stroke.

But back to my question, what would you do with the other 62.5% of the people with PFO and stroke? Would you, in absence of any valid data take 50% and assume another increment of roughly 30%?

Thanks.
 
Physics news on Phys.org
  • #2
What you did is statistically invalid. What you can do is ask whether the presence of PFO increases your risk of getting a cryptogenic stroke. By Bayes' Law,

[tex]P(\text{cryptogenic stroke}|\text{PFO}) =
\frac {P(\text{PFO}|\text{cryptogenic stroke})\,P(\text{cryptogenic stroke})} {P(\text{PFO})}[/tex]

where
  • [itex]P(\text{cryptogenic stroke}|\text{PFO})[/itex] is the probability of having a cryptogenic stroke given that one has PFO
  • [itex]P(\text{PFO}|\text{cryptogenic stroke})[/itex] is the probability that someone who has had a cryptogenic stroke also has PFO
  • [itex]P(\text{cryptogenic stroke})[/itex] is the probability of having a cryptogenic stroke by any cause
  • [itex]P(\text{PFO})[/itex] is the probability of having PFO.

Using [itex]P(\text{PFO}|\text{cryptogenic stroke})=0.4[/itex] and [itex]P(\text{PFO})=0.25[/itex] yields

[tex]P(\text{cryptogenic stroke}|\text{PFO}) = 1.6 \times P(\text{cryptogenic stroke})[/tex]

In other words, people with PFO have a 60% increased chance of having a cryptogenic stroke than the population at large.
 

Related to Causality based on conflicting data

1. What is causality and how is it determined?

Causality refers to the relationship between an event (cause) and a resulting event (effect). It is determined through the use of scientific methods, such as experiments and statistical analysis, which aim to establish a cause-and-effect relationship between variables.

2. Can conflicting data be used to establish causality?

Conflicting data can make it difficult to establish causality, as it may suggest different explanations for the observed relationship between variables. However, by carefully examining the data and considering alternative explanations, it is possible to determine the most likely cause-and-effect relationship.

3. How does correlation differ from causation?

Correlation refers to a relationship between two variables, while causation refers to a cause-and-effect relationship between those variables. While a correlation may suggest a potential relationship between variables, it does not necessarily mean that one variable causes the other.

4. What are some limitations of using causality to explain phenomena?

Causality can be limited by factors such as confounding variables, which may influence the relationship between the variables being studied. Additionally, causality may be difficult to establish in complex systems where there are multiple potential causes and effects.

5. How can causality based on conflicting data be used to inform decision-making?

Causality based on conflicting data can provide important insights into the relationships between variables, allowing for a more informed decision-making process. By carefully evaluating the data and considering alternative explanations, decisions can be made based on the most likely cause-and-effect relationship.

Similar threads

  • Biology and Medical
Replies
2
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
21
Views
3K
  • Set Theory, Logic, Probability, Statistics
Replies
8
Views
1K
Replies
19
Views
2K
  • Biology and Medical
Replies
6
Views
4K
  • Beyond the Standard Models
Replies
1
Views
2K
  • Biology and Medical
Replies
2
Views
3K
Replies
10
Views
2K
  • Quantum Physics
2
Replies
69
Views
5K
Replies
6
Views
1K
Back
Top