eavluation of evidences jgjihcihxugxjhxig .pptx

eavluation of evidences

1. Evaluation of causation Should I believe my measurement ? 1 1/15/2025

2. Smoking Lung cancer Associated OR = 9.1 Due to, -Chance ? -Confounding ? -Bias ? True association may be: - causal -non- cau s al 2 1/15/2025

3. Judging Observed Association Apply the criteria and make judgment of causality 3 Could it be due to selection or measurement bias? NO Could it be due to confounding? NO Could it be A result of chance? NO ( probability ) Could it be Causal? 1/15/2025

4. Common Problems in observed findings Inadequacy of the observed sample Inappropriate selection of study subjects Inappropriate/unfair data collection methods Comparing unequal 4 1/15/2025

5. Accuracy Accuracy = Validity + Precision .Validity = is the finding a reflection of the truth? . Precision = is the finding due to sampling variation ? . reliability = is the finding an indication of the same result of different rater ? 5 1/15/2025

6. Validity Vs Reliability 6 1/15/2025

7. Precision Precision in measurement and estimation corresponds to the reduction of random error . Mostly related to sampling variation or sampling error. Solution: Increase sample size Improving the efficiency of measurement. 7 1/15/2025

8. Validity Internal : are we measuring what we intend to measure Do we have alternative explanations for the observed findings: Chance Bias Confounding External (generalizability) : can we make inferences beyond the subjects of the study 8 1/15/2025

9. Chance/random error Chance can often be an alternative explanation to observed findings must always be considered. Evaluation of chance is a domain of statistics involving: 1 . Hypothesis Testing ( Test of Statistical Significance) 2 . Estimation of Confidence Interval But , Statistical significance do not provide information on bias and uncontrolled confounding. 9 1/15/2025

10. P-value ≤ 0.05 􀃆 Chance is unlikely explanation ∴ Reject the null hypothesis ∴ There is Statistically significant difference Accept alternative hypothesis Confidence Interval Provide information that p-value gives. – If null value is included in a 95% confidence interval , by definition the corresponding P-value is >0.05 . so , it is not significant 10 1/15/2025

11. Definition of bias Any systematic error in an epidemiological study, that results in an incorrect estimate of the association between exposure and risk of disease An alternative explanation for an observed association is the possibility that some aspect of the design or conduct of a study has introduced a bias into the results. 11 1/15/2025

12. Bias Unlike “chance” and “confounding,” which can be evaluated quantitatively, the effects of bias are far more difficult to evaluate and may even be impossible to take into account in the analysis. General class of Bias Selection Observation bias (Information) bias 12 1/15/2025

13. Types of (important) bias Selection bias Error in selection of study participants Information bias Errors in procedures for gathering relevant information 1/15/2025 13

14. Selection Bias Involves biases arising from the procedures by which the study participants are selected from the source population Can be introduced at any stage of a study ( bad definition of eligible populations, lack of accuracy of sampling frame, uneven diagnostic procedures) and implementations 1/15/2025 14

15. Observation/Information Bias Results from systematic differences in the way data on exposure or outcome are obtained from the various study groups Does data collected correctly? 15 1/15/2025

16. Recall Bias Sick individuals more likely to remember and report exposures than healthy individuals Problematic in case-control studies 16 1/15/2025

17. Control of Bias in the Design Phase Choice of Study Population -to reduce selection bias Use inclusion and exclusion criteria Use appropriate sampling techniques Consider your type of study design Data Collection Methods- to reduce Observation bias Use standardized questionnaires Train data collectors/interviewers Method of data collection should be similar for all study groups 17 1/15/2025

18. A mixing of the effect of the exposure under study on the disease with that of a third factor A factor which is associated with the exposure variable, and an independent of the exposure, is related to the outcome/disease (that is, it’s a risk factor for the outcome) Confounding 18 1/15/2025

19. Criteria of confounder variable It must not intermediate It should be risk factor/cause for outcome/ disease with other main variable It must be risk for the outcome/disease independently 19 1/15/2025

20. Interrelationship EXPOSURE(a) DISEASE CONFOUNDING FACTOR(b) 20 1/15/2025

21. Sir Austin Bradford Hill In 1965 Proceedings of the Royal Society of Medicine Bradford Hill’s listed the following criteria in causality in attempting to distinguish causal and non-causal associations Strength of association Consistency of findings Biological gradient (dose-response) Temporal sequence Biological plausibility Coherence with established facts Specificity of association 21 1/15/2025

22. Strength of the Association The Stronger the association (OR 0.00 or + ∞ ), then less likely the relationship is totally due to the effect of an uncontrolled confounding variable A strong association serves only to rule out hypothesis that association is entirely due to weak unmeasured confounder or other sources of bias But weak association does not rule out a causal association 22 1/15/2025

23. Biological Credibility / Plausibility The belief in the existence of a cause and effect relationship is enhanced if there is a known or postulated biologic mechanism by which the exposure might reasonably alter the risk of developing the disease Alcohol and CHD (HDL) OC use and circulatory disease (platelet adhesiveness; arterial wall changes) Smoking and lung cancer (hundreds of carcinogens and promoters) 23 1/15/2025

24. Since what is considered biologically plausible at any given time depends on the current state of knowledge, the lack of a known or postulated mechanism does not necessarily mean that a particular association is not causal 24 1/15/2025

25. Consistency with Other Investigations Have multiple studies conducted by multiple investigators concluded the same thing? Relationships that are demonstrated in multiple studies are more likely to be causal , i.e., consistent results are found in different populations, in different circumstances, and with different study designs . 25 1/15/2025

26. Time Sequence / Temporality Exposure of interest has to precede the outcome (by a period of time that biologically makes sense) Smoking and lung ca; induction/latency 26 1/15/2025

27. Biological gradient ( Dose-Response) refers to the presence of a unidirectional dose–response curve Smoke more, higher CHD death rates Difficulty: The presence of a dose-response relationship doesn’t mean that the association is one of cause and effect. Could be, for example, due to confounding. Smoking and hepatic cirrhosis (alcohol) Absence of a dose-response relationship does not mean that a cause-effect relationship does not exist. Sometimes there is a convincing association but not a dose-response relationship 27 1/15/2025

28. Coherence Causal mechanism proposed must not contradict what is known about the natural history and biology of the disease, but the causal relationship may be indirect data may not be available to directly support the proposed mechanism 28 1/15/2025

29. Thank you !! 29 1/15/2025