class: center, middle, inverse, title-slide .title[ # Introduction to Biomedical Research ] .subtitle[ ## Validity of Epidemiological Data ] .author[ ### Dr. Zulfiqar Ali (Assistant Professor) ] .institute[ ### College of Statistcal Sciences, University of the Punjab, Lahore ] .date[ ### 21 June 2023 ] --- --- # Validity in Epidemiological Studies - **Validity**: Extent to which collected data accurately measures what it intends to measure. -- - **Key Aspects**: Internal validity, external validity, construct validity, measurement validity. -- --- # Internal Validity - **Definition**: Associations within a study attributed to exposure or risk factor under investigation. -- - **Strategies**: Randomization, control groups, blinding, study design considerations. -- - Minimizing biases and confounding variables for greater confidence. -- --- # External Validity - **Definition**: Generalizability of study findings to the target population or other settings. -- - **Factors to consider**: Representativeness of the study sample, similarity of the study setting to the target population, generalizability of the findings. -- --- # Construct Validity - **Definition**: Accuracy of measurements representing underlying theoretical constructs. -- - **Ensuring validity**: Valid and reliable measurement tools, assessing reliability, using standardized instruments, conducting pilot testing. -- --- # Measurement Validity - **Definition**: Accuracy of data collection methods and instruments. -- - **Importance**: Reliability of measurements reflecting true values. -- - **Minimizing measurement errors**: Recall bias, social desirability bias, errors in data collection techniques. -- - **Techniques**: Questionnaires, interviews, laboratory tests. -- --- # Selection Bias - **Definition**: Selection of participants not representative of the target population, leading to a distorted sample. -- - **Mitigating selection bias**: Appropriate sampling techniques, e.g., random sampling, stratified sampling. -- --- # Information Bias - **Definition**: Errors in data collection or interpretation. -- - **Sources**: Recall bias, reporting bias, misclassification of exposure or outcome variables. -- - **Minimizing information bias**: Standardized data collection protocols, training of data collectors, validated measurement tools. -- --- ## Confounding - **Definition**: Extraneous factors associated with both exposure and outcome, leading to a distortion of observed relationship. -- - **Addressing confounding**: Study design strategies (randomization, matching), statistical adjustment techniques (regression analysis). -- --- ## Conclusion - Validity of epidemiological data is crucial for accurate and reliable conclusions. -- - Internal validity, external validity, construct validity, measurement validity, selection bias, information bias, and confounding all play significant roles. -- - Addressing these factors through rigorous study design, careful data collection, and appropriate statistical analyses enhances validity. --