QUALITY CONTROL IN MEDICAL RESEARCH grp 6.pptx

QUALITY CONTROL IN MEDICAL RESEARCH

1. QUALITY CONTROL IN RESEARCH 1. APIKU JOSEPH MALU 2. KIVUMBI Jamilu 3. TUGABIRWE SANDRA 4. WOMAYI Ivaan TABAN Abdu Salaam

2. Objectives By the end of this presentation, the students should be able to; Define the term quality control. Define the term data quality. State the goals of quality control measures. Explain how to ensure quality control under validity and reliability. Discuss the importance of quality control in research.

3. Introduction what is Data Quality? General speaking, data is of high quality when it satisfies the requirements of its intended use for clients, decision-makers, downstream applications and processes.

4. What is quality control? Quality does not have a singular definition , despite the relative meaning of ‘ value’. Quality control is the process by which data is tested and measured to ensure that it meets the standard. Or are the measures that a researcher employs or observes during the process of data collection to ensure its quality. Through this process, researchers can evaluate , maintain and improve products / results quality.

5. Goals of quality control To ensure that the products / results are as uniform as possible To minimize errors and inconsistences within data Note : Q uality control measures are applied before data collection , during data collection and after data collection.

6. The quality control measures. T he main aim of to esnsure validity and reliability. V alididty It is defined as the ability to measure what is supposed to be measured. Validity measurement types • Face – It is a method of deciding on the ability of the instrument to do what it should based on the face value. It is subjective

7. Cont. Content – It refers to the comprehensiveness of the instrument, the ability of the measuring instrument to cover all the relevant areas, and is usually determined by expert opinion. • Concurrent – It shows how valid an instrument is by comparing it with an already valid instrument. • Predictive – This implies the ability of the measure to predict expected outcomes. Correlation is used to compute this and the higher the correlation, the more evident the predictive validity.

8. Cont. Reliability refers to how consistent, stable or predictable what we measure is. A clear example could be made with the bathroom scale. It could measure your weight (valid, that is what a scale measures), but should it give you 60kg at the first time, and 80kg the second time, you will question that value. It shows that the scale is not reliable, though valid.

9. Types of reliability Inter rater or inter observer reliability – This estimation is used to assess the degree to which different raters/observers give consistent estimates of the same event. For example in observing a student perform a task in the clinical setting, two observers using a checklist may rate the student. At the end the ratings of the two observers could be correlated to give an estimate of the reliability or consistency between the two raters.

10. Cont. Test retest reliability – This is used to assess the consistency of a measure from one time to another. The same test could be administered to the same sample on two different occasions. It is assumed that there is no substantial change in what is being measured in the two occasions. The interval between the two tests matters and the shorter the gap, the higher the correlation. Different estimates may therefore be obtained depending on the interval.

11. Cont. Parallel form reliability – It is used to assess the consistency of results of two tests constructed in the same way from the same areas. The researcher constructs large number of test items from for example human biology course, Respiratory system, and randomly divides them into two equal halves. Both tests are administered to the same group, and the scores correlated to estimate the reliability.

12. Cont. Internal consistency reliability – It is used to assess the consistency of results across items within a test. A single measurement is used to estimate how the items yield similar results. The most commonly used is the split half method, where the total items are divided into two sets. The entire instrument is then administered to a group of people, and the total score for each randomly divided half is calculated. The split half reliability will be the correlation between the total scores. This is often called the odd-even method due to the way the split is made for the two halves.

13. How to ensure quality control under validity and reliability Sample size determination : It is important to have an appropriate sample size to ensure that the results are statistically significant and representative of the population being studied. Randomization: This involves random selection of participants or subjects to eliminate bias and increase the accuracy of the study.

14. Cont. Control group: A control group is a group that does not receive the treatment being tested, and is used to provide a baseline for comparison to the group that does receive the treatment. Double-blind study: This is a type of study where neither the researchers nor the participants know who is receiving the treatment being tested. This eliminates bias and ensures that the results are objective.

15. cont. Standardization of procedures : All procedures and methods used in the study should be standardized to ensure consistency and accuracy of results. Data validation and verification : All data should be validated and verified to ensure accuracy and consistency. This can be done through data entry validation, data cleaning, and data verification by independent researchers. Statistical analysis: Statistical analysis is used to determine the significance of the results obtained in the study.

16. Cont. Pilot Study / pretesting of data collection methods Pilot study is the trial run or piloting of the instrument of data collection usually undertaken on subjects that are similar to the real subjects for the study. The pilot study is important because it enables the researcher to correct and modify the instrument based on the responses from the field. The actual data collection should not begin until the pilot study has been completed, and all the necessary deficiencies corrected .

17. Cont. Ethical considerations This is also one of the quality control measures in research. In methodology most especially during data collection , ethical issues are anticipated to ensure that the researcher produces quality results. Peer review: Peer review involves having the study reviewed by other experts in the field to ensure that the research is of high quality and meets the standards of the scientific community.

18. The importance of quality control in research It prevents data collected or results from being unreliable and increases the trust on the side of institutions or bodies to utilize it. Quality control in researcher is important because it ensures that the researcher presents evidenced based data and is standardized. It ensures that data collection errors are eliminated. It prevents biasness of results.