Synthesis: Causal Inference

Name: Synthesis: Causal Inference
Uploaded: 2017-12-17T16:21:46+00:00
Duration: PTM23S30
Channel: Lucas Sutherland
Description: Synthesis: Causal Inference

Synthesis: Causal Inference
Kassiani Mellou, based on EPIET material EPIET Introductory Course, Lazareto, Menorca 2011 Welcome A short synthesis to close off our discussions today Papers will be provided to you – along with complete lecture at the end.

How do we understand causality?
Intuitively? My son loves to turn on the light switch This is how he understands causality – but he doesn’t think about what lies underneath - wires, electricity, paying the power bill – what really causes the light to come on

The famous map… TO what does it refer?
What Snow did was very revolutionary – especially as illness such as cholera and chlamydia were thought to come from MIASMA or “bad air” Snow did a study and felt that it was connected to the pump. He made a leap of faith to identify water

Can someone describe this epi curve?
Does anyone know the story behind this? Ignac Semmelweis noted that the incidence of puerperal fever could be drastically cut by handwashing with chlorinated lime solution. This was unknown and physicians protested. Physicians would go directly from dissecting cadavers to delivering babies.

How is cause defined? “Antecedent event, condition, or characteristic that was necessary for the occurrence of the disease event and without, the disease event either would not have occurred at all or until some time later.” One of many definitions Rothman KJ, Greenland: Causation and Causal Inference in Epidemiology, (Am J PH, 2005)

Cause in the context of epidemiology
Count and compare AND Search for cause and effect Source of the outbreak? Risk factor for disease? Why? Implement control measures Give recommendations Epidemiologists use gathered data and a broad range of biomedical and psychosocial theories in an iterative way to generate or expand theory, to test hypotheses, and to make educated, informed assertions about which relationships are causal, and about exactly how they are causal. We are putting together a body of evidence to lead us to think about possible causality. We provide evidence for an against a hypothses

RR = 89.7 95%CI = 82.5 – 91.4 p<0.001 Does a statistical association automatically mean that there is a causal relationship? Significance!!!! What we all look for in our studies Do statistical associations automatically mean that there´s a causal relationship? NO – can be chance, bias, confounding, faulty study design etc We use the language of epidemiologists that get us into trouble in the media – Cancer cluster example and speaking with the media Many have tried to provide criteria or postulates for causality over the years... And it is part of an ongoing debate

Statistical association
Causal ? Result of chance selection bias information bias confounding

Henle-Koch-Postulates (1890)
Pathogen must identified in ill person/animal Pathogen must be culturable Cultured pathogen should cause illness in test animal Pathogen must be reisolated and found identical to original Robert Koch 1n microbiologist Koch's postulates are four criteria designed to establish a causal relationship between a causative microbe and a disease. However, Koch abandoned the universalist requirement of the first postulate altogether when he discovered asymptomatic carriers of cholera[1] and, later, of typhoid fever. The second postulate may also be suspended for certain microorganisms or entities that cannot (at the present time) be grown in pure culture, such as prions responsible for Creutzfeld Jacob disease The third postulate specifies "should", not "must", because as Koch himself proved in regard to both tuberculosis and cholera,[2] not all organisms exposed to an infectious agent will acquire the infection. Noninfection may be due to such factors as general health and proper immune functioning; acquired immunity from previous exposure or vaccination; or genetic immunity, as with the resistance to malaria conferred by possessing at least one sickle cell allele.

Bradford Hill’s criteria (1965)
1. Strength of Association 2. Consistency 3. Specificity 4. Temporality 5. Biological gradient (dose response) 6. Plausibility 7. Coherence 8. Experimental Evidence 9. Analogy Sir Austin Bradford Hill (1987 – 1991) – (a man with two last names...) British epidemiologist ´50 ies: Doll & Hill: „Smoking and Lung Cancer“ Decision making on basis of epidemiological data Criteria are very important AB Hill: The Environment and Disease: Association or Causation? Proc Royal Soc Med 1965;58:

Criteria for a Causal Relationship
Temporal relationship Strength of the association Biologic plausibility Dose–response relationship Replication of the findings Effect of removing the exposure Extent to which alternate explanations have been considered Specificity of the association Consistency with other knowledge L Gordis: Epidemiology 4th revised edition, W. Saunders publishers July 2008

Temporal relationship Strength of the association Biologic plausibility Dose–response relationship Replication of the findings Effect of removing the exposure Extent to which alternate explanations have been considered Specificity of the association Consistency with other knowledge L Gordis: Epidemiology 4th revised edition, W. Saunder publishers July 2008

Temporal Relationship
Exposure must precede disease Essential criterion for causality Knowledge of: Latency period Incubation period Most important criterion that must always be met Exposure precedes disease development with adequate elapsed time Incubation period and latency period can be synonymous: Incubation period is the time elapsed between exposure to a pathogenic organism, a chemical or radiation, and when symptoms and signs are first apparent. The period may be as short as minutes to as long as thirty years in the case of variant Creutzfeldt-Jakob disease. While Latent or Latency period may be synonymous, a distinction is sometimes made between Incubation period, the period between infection and clinical onset of the disease, and Latent period, the time from infection to infectiousness. Latency period:􀀀time from initial exposure to an agent to the onset of disease (diseases with long latency it can be very difficult to determine cause… cancer) more rapid when a shorter latency period. Study designs with temporal relationship Cohort, case-control, and RCT

Strength of Association
Strong associations are more likely being causal than weak ones. Smoking > 20 cigarettes/day  laryngeal carcinoma (RR 20) BUT not all strong associations are causal… Stronger association is more likely to be causal, but a weak association can also be causal Example here from smoking and laryngeal carcinoma – very high RR For example we see a strong association in the following example.

Cases of Down Syndrome by Birth Order
Researcher noticed that you were more likely to be born with with Down Syndrome if you were later born – with a noticeable trend effect – 1, 2…. Hypothesized about why this might be…

Cases of Down Syndrome by Maternal Age Groups
Thought to look at maternal age groups And saw a very strong trend that there was increasing incidence of down syndrome after aged 30 and more noticeably after 35 and 40+

Strength of Association
Strong associations are more likely being causal than weak ones. Smoking > 20 cigarettes/day  laryngeal carcinoma (RR 20) BUT: Not all strong associations are causal… And weak associations do not rule out causality… 􀂄Stronger association is more likely to be causal, but a weak association can also be causal 􀂄ExamplesRR for lung cancer and cigarette smoking from various studies are around 10 RR for breast cancer and cigarette smoking from various studies are between 1–1.5This suggests that the association between smoking and lung cancer is more likely to be causal than smoking and breast cancer

Smoking and Lung cancer? Breast cancer? Passive smoking
Cigarette smoking and lung cancer RR= ~ 10 Cigarette smoking and breast cancer RR = ~ Passive smoking and lung cancer RR = ~ 1.4 suggests that the association between smoking and lung cancer is more likely to be causal than smoking and breast cancer but because so many people are exposed to smoke and because so many develop breast cancer, this is of interest.

Biologic Plausibility
Is consistent with current biological and medical common knowledge. Smoking Ingesting of chemicals and known carcinogens DNA mutations lung cancer Smoking and lung cancer Histopathology of respiratory epithelium in smokers

Biologic Plausibility
Is consistent with current biological and medical common knowledge. Percivall Pott - scrotum cancer observed in chimney sweeps (1775) Peptic ulcers and Helicobacter pylori (1980s) Don’t be quick to dismiss… we are still learning a lot Absence of coherence cannot been taken as evidence against a causal relationship Scrotum cancer and chimney sweeps (first occupational disease) ulcers are caused by an infection with the bacterium, Helicobacter pylori - nobel prize for Warren and Marshall in 2005 Some modern ones would also be a link between MS and CCVI - opening veins to allow iron to drain from the brain… Scientists say that the constriction of veins in the neck might actually be caused by MS, and until Dr. Zamboni's results have been replicated, successfully reversing MS in patients, CCSVI is only an idea well worth exploring.

Dose-response Relationship
Risk increases with more intense/more frequent exposure But: High dose at which any further increase has no effect Low dose may be that no response occurs or can be measured Paralleling association implies causality: The more cigarettes are smoked, the greater the risk of lung cancer. Low dose – chemical exposure (minimum dose – certain number of bugs) High dose – saturation point (doesn’t matter if you have one or one million…) Beneficial dose – vaccination + red wine!!! , the absence of dose-response does not preclude causal association

Replication of findings
Findings found in: different populations by using different study designs Jan Hendrik Schön – organic electronics Hwang Woo-suk – stem cell research We look to repeat and confirm these results – and explore how widely generalizable it might be. Seeing some interesting things now as many studies used to include only men and then were generalized to women – courses of some chronic disease very different… JHS – German physicist - organic electronics – single cell microconductors - falsified HWS - Now disgraced from his falsification of stem cell work – findings could not be replicated. But did clone the first dog. (not all scientists who are crooked are crooked in every sense… although I have some doubts about snuppy!)

Effect of removing the exposure
A decrease in the outcome of interest is seen when the exposure is removed. Similar to the dose-response relationship, the presence of this criterion supports the notion of causal association. However, the absence does not preclude it Saw examples of this in an outbreak Let’s look at a few community level examples mostly related to policy changes..

Law came in in 87 but no until late 1993 did they have fines associated with violation – and also allow officers to stop them and give a citation - increase way up

Extent to which alternate explanations have been considered
Has adjustment been made for possible confounding? Down’s and birth order example illustrates this nicely… Have you considered and adjusted for possible confounders. What is a confounding variable? Statistical relationship between ice cream sales and incidence of heat stroke (confounder – summer time) By definition, a confounding variable is associated with both the probable cause and the outcome. The confounder is not allowed to lie in the causal pathway between the cause and the outcome: In addition, a confounder is always a risk factor that has a different prevalence in two risk groups (e.g. females/males).

”The Norwegian comedian Marve Fleksnes once stated: I am probably allergic to leather because every time I go to bed with my shoes on, I wake up with a headache the next morning.”

Specificity of the association
One cause has one effect. specificity of the association suggests that one exposure is specific to one disease This criterion is not applicable to all exposure-disease associations because a disease may be caused by several exposures, and an exposure may cause several diseases. The more micro you go, the more specific you can be. Specificity strengthens the body of evidence for causality, however absence of specificity does not rule out causality An exposure is likely to have a deleterious effect on a specific mechanism (at a cellular or molecular level) that may then lead to one or more diseases An exposure, such as smoke from cigarette smoking, is comprised of many smaller chemical components abestosis Asbestos exposure mesothelioma lung cancer

Rothman and Greenland One cause – one effect – simplistic and not true
Most outcomes are as the result of many contributing causes Necessary Sufficient Probabilistic Epidemiologists Rothman and Greenland emphasize that the "one cause - one effect" understanding is a simplistic mis-belief. Most outcomes, whether disease or death, are caused by a chain or web consisting of many component causes. Causes can be distinguished as necessary, sufficient or probabilistic conditions. necessary condition of a statement must be satisfied for the statement to be true (to breathe is necessary for life…) A sufficient condition is one that, if satisfied, assures the statement's truth. (to leave the ground is sufficent to be considered for jumping) If a necessary condition can be identified and controlled (e.g., antibodies to a disease agent), the harmful outcome can be avoided

Condition of the sidewalk
Earlier head trauma leading to equilibrium problems Use of cane to support walking Type of footwear Weather conditions Several exposures cause the outcome Fall on the ice leading to hip fracture… other contributing issues – head trauma interacted with the other conditions and resulted in a falllaeding to hip fracture Everything can be taken to be multi causal – philosophical discussion brittle bones Source: Rothman KJ, Greenland: Causation and Causal Inference in Epidemiology, (Am J PH, 2005)

Consistency with other knowledge
If an association is supported by the results of different disciplines Animal studies – we still do animal research Other studies such as ecological studies, cross-sectional studies Other types of data such as sales data – this is used Absence of consistency does not rule out a causal relationship

Summary Not a checklist! (don’t stop thinking)
Beware of biologic plausibility Always aim for better evidence Association is not causality!!! Keep an open mind Remain critical (… especially of your own studies) Epidemiology is an art and a science You are creating a convincing body of evidence for your audience.

Thank you for your attention!

Synthesis: Causal Inference

Similar presentations

Presentation on theme: "Synthesis: Causal Inference"— Presentation transcript:

Similar presentations

About project

Feedback

Log in

Auth with social network:

Synthesis: Causal Inference

Similar presentations

Presentation on theme: "Synthesis: Causal Inference"— Presentation transcript:

Similar presentations

About project

Feedback