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II. Potential Errors In Epidemiologic Studies Random Error Dr. Sherine Shawky.

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Presentation on theme: "II. Potential Errors In Epidemiologic Studies Random Error Dr. Sherine Shawky."— Presentation transcript:

1 II. Potential Errors In Epidemiologic Studies Random Error Dr. Sherine Shawky

2 Learning Objectives Understand the concept of random error Recognize the methods to prevent random error Know the methods to evaluate the role of chance on results

3 Performance Objectives Improve precision Evaluate the role of chance

4 In most epidemiologic studies, it is impossible to evaluate every member of the entire population. Thus, the relationship between exposure and health-related event is judged from observations on sample of the population

5 Samples n1n1 n2n2 n3n3 n4n4 n5n5 n6n6 N

6 Chance Lack of Precision Random Error

7 Control of Random Error Prevent Study Evaluate

8 Prevention of Random Error Sample size Hypothesis Type of Error

9 Hypothesis H 0 = No difference H 1 = Some difference

10 Types of Error

11 n1n1 n2n2 N Sample Size

12              How many subjects are required ?

13 Sample Size Calculation Assumption Parameters Factors

14 Assumption for Sample Size Calculation H 0 is not true & H 1 is true

15 Factors for Sample Size Calculation Population size Research question Study design Type of data

16 Parameters for Sample Size Calculation Probability of type I error Probability of type II error Proportion of population that are exposed to, or have health- related event Magnitude of the expected effect

17 What is the power of this study if only these subjects are available ? ? Power           

18 Power Calculation Work the appropriate sample size equation in the inverse direction, using the available sample size

19 Evaluation of the Role of Chance Statistical Testing Confidence Interval

20 Statistical Testing Assumption Statistical test P-value

21 Assumption for Statistical Testing H 0 is true

22 Choice of Statistical Test Research question Type of data Characteristics of data

23 P-value The P-value is the estimated value for  issue from results The P-value depends on the sample size and the strength of the association

24 P-value (cont.) Two-tailed for given magnitude and uncertain direction One tailed for given magnitude and known direction

25 Confidence Interval (CI) More informative than P-value Indicates presence or absence of statistical significance Calculated for mean, proportion, relative risk and odds ratio

26 Interpretation of CI

27 Conclusion When a research is performed on a sample of the population, the researcher has to minimize the role of chance before initiating the study. Also, he should evaluate its impact on the results before making decisions.

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