Causality Inferences. Objectives: 1. To understand the concept of risk factors and outcome in a scientific way. 2. To understand and comprehend each and.

Slides:



Advertisements
Similar presentations
Causation When do we have enough evidence? Sam Bracebridge.
Advertisements

Deriving Biological Inferences From Epidemiologic Studies.
Causality Causality Hill’s Criteria Cross sectional studies.
Bradford Hill’s Criteria for Inferring Causality
Causal Inference in Epidemiology
The Fundamental Problem of Causal Inference Confounds and The Fundamental Problem of Causal Inference Probabilistic vs. Deterministic Causality Four Criteria.
The burden of proof Causality FETP India. Competency to be gained from this lecture Understand and use Doll and Hill causality criteria.
Epidemiology Kept Simple
Microbiology Koch and establishing causal links between microrganisms and disease.
Study Design Data. Types of studies Design of study determines whether: –an inference to the population can be made –causality can be inferred random.
Epidemiology & Critical Thinking D. Morse st Avenue Tel: Office Hours: 4:00-5:00 (M & W)
Association to Causation. Sequence of Studies Clinical observations Available data Case-control studies Cohort studies Randomized trials.
Causation Abdulaziz Bin Saeed Assistant professor Fam&Com Medicine.
Designing Clinical Research 2009 Confounding and Causal Inference Warren Browner.
STATISTICAL ASSOCIATION AND CAUSALITY Nigel Paneth.
Lecture 8 Objective 20. Describe the elements of design of observational studies: case reports/series.
GerstmanGerstmanChapter 21GerstmanChapter 21 Epidemiology Chapter 2 Causal Concepts.
Dr. Zahid Ahmad Butt. Cause “An antecedent event, condition, or characteristic that was necessary for the occurrence of the disease at the moment it occurred,
1 Causation in Epidemiological Studies Dr. Birgit Greiner Senior Lecturer.
 Correlation and regression are closely connected; however correlation does not require you to choose an explanatory variable and regression does. 
Web of Causation; Exposure and Disease Outcomes Thomas Songer, PhD Basic Epidemiology South Asian Cardiovascular Research Methodology Workshop.
Causal inference Afshin Ostovar Bushehr University of Medical Sciences Bushehr, /4/20151.
Lecture 6 Objective 16. Describe the elements of design of observational studies: (current) cohort studies (longitudinal studies). Discuss the advantages.
CONFOUNDING DEFINITION: A third variable (not the exposure or outcome variable of interest) that distorts the observed relationship between the exposure.
Causation. Associations may be due to Chance (random error) statistics are used to reduce it by appropriate design of the study statistics are used to.
Cause  Clinicians are confronted frequently with information about possible causal relationships. Example: Relationship between the cigarette smoking.
Lecture 7 Objective 18. Describe the elements of design of observational studies: case ‑ control studies (retrospective studies). Discuss the advantages.
Does Association Imply Causation? Sometimes, but not always! What about: –x=mother's BMI, y=daughter's BMI –x=amt. of saccharin in a rat's diet, y=# of.
Causal relationships, bias, and research designs Professor Anthony DiGirolamo.
Warm-Up.
Instructor Resource Chapter 18 Copyright © Scott B. Patten, Permission granted for classroom use with Epidemiology for Canadian Students: Principles,
Causation and association Dr. Salwa Tayel Family and Community Medicine Department King Saud University.
Reading Health Research Critically The first four guides for reading a clinical journal apply to any article, consider: the title the author the summary.
Showing Cause, Introduction to Study Design Principles of Epidemiology.
From association to causation
Chapter 2: General Concepts Chapter 2 Causal Concepts
Psychology Research Methods. Characteristics of Good Psychological Research © 2002 John Wiley & Sons, Inc.
Introduction to epidemiology Mark Dancox Public Health Intelligence Analyst Course – Day 1.
Comprehensive Evaluation Concept & Design Analysis Process Evaluation Outcome Assessment.
Journal Club Curriculum-Study designs. Objectives  Distinguish between the main types of research designs  Randomized control trials  Cohort studies.
Statistics in Clinical Trials: Key Concepts
Lecture notes on epidemiological studies for undergraduates
By Hatim Jaber MD MPH JBCM PhD
Causation Analysis in Occupational and Environmental Medicine
Showing Cause, Introduction to Study Design
Association & Causation in epidemiological studies
SUSSER’S CAUSAL CRITERIA
4.3: Using Studies Wisely.
How can we identify a novel carcinogen?
Chapter 4: Designing Studies
Chapter 4: Designing Studies
CHAPTER 4 Designing Studies
Association to Causation
Critical Appraisal วิจารณญาณ
Chapter 4: Designing Studies
Does Association Imply Causation?
Chapter 4: Designing Studies
CHAPTER 4 Designing Studies
CHAPTER 4 Designing Studies
Relationship Relation: Association: real and spurious Statistical:
Chapter 4: Designing Studies
Chapter 4: Designing Studies
Chapter 4: Designing Studies
Chapter 4: Designing Studies
Chapter 4: Designing Studies
Dr Luis E Cuevas – LSTM Julia Critchley
Chapter 4: Designing Studies
Chapter 4: Designing Studies
Chapter 3 Hernán & Robins Observational Studies
CHAPTER 4 Designing Studies
Presentation transcript:

Causality Inferences

Objectives: 1. To understand the concept of risk factors and outcome in a scientific way. 2. To understand and comprehend each and every causality inference of Hill’s criteria. 3. You students must be capable to identify risk factors and outcome in the research within the scope of causality inference.

Causal Criteria Hill ’ s Criteria (1965)  Strength; the higher the measure of association the stronger the relationship  Consistency; repeated observations of a factor effect give similar outcome  Specificity; the factor is a specific for the outcome (e.g. smoking and lung diseases)

Cont.  Temporality; proof of exposure prior to outcome (e.g. allergy preceded by exposure)  Biological Gradient ; Dose response relationship (e.g. amount of alcohol consumption and rate of car accident)  Plausibility; logically and likely to be true (e.g. the historical suspicion of the relationship between cholera and water drinking)

Cont.  Coherence; the ideas are going together (e.g. spotting SARS cases based on clinical condition rather than diagnostic test)  Experimental Evidence ; matching with previous animal or human studies  Analogy ; similarity using imagination (e.g. imagination links romance with fragrance)

Koch ’ s postulates were an example of deterministic causality. To prove that an organism causes a disease, he required that: 1. The organism must be isolated in every case of the disease (i.e. be necessary) 2. The organism must be grown in pure culture 3. The organism must always cause the disease when inoculated into an experimental animal (i.e. be sufficient) 4. The organism must then be recovered from the experimental animal and identified.

ASSOCIATION VS CAUSATION To decide whether exposure A causes disease B, we must first find out whether the two variables are associated, i.e. whether one is found more commonly in the presence of the other.

MAKING CAUSAL INFERENCES The use of causal criteria in making inferences from data.

WHEN TO APPLY CAUSAL CRITERIA? Causal criteria are principally designed to deal with the problem of confounding. By applying the criteria, we reduce the possibility of falsely assigning cause to the wrong exposure. Causal criteria do not work well in the case of bias.

Objectives: 1. To understand the concept of risk factors and outcome in a scientific way. 2. To understand and comprehend each and every causality inference of Hill’s criteria. 3. You students must be capable to identify risk factors and outcome in the research within the scope of causality inference.