Epidemiology Sampling Error
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are three primary challenges to achieving an accurate estimate of the association: Bias Confounding, and Random error. Random error occurs because the estimates we produce are based on samples, and samples may not accurately reflect random error vs systematic error epidemiology what is really going on in the population at large. . There are differences of opinion random error epidemiology among various disciplines regarding how to conceptualize and evaluate random error. In this module the focus will be on evaluating the precision of differential misclassification the estimates obtained from samples. Learning Objectives After successfully completing this unit, the student will be able to: Explain the effects of sample size on the precision of an estimate Define and interpret 95% confidence intervals for confounding by indication measures of frequency and measures of association Define and interpret p-values Discuss common mistakes in the interpretation of measures of random error Random Error Consider two examples in which samples are to be used to estimate some parameter in a population: Suppose I wish to estimate the mean weight of the freshman class entering Boston University in the fall, and I select the first five freshmen who agree to be weighed. Their mean weight is 153 pounds.
Misclassification Bias Example
Is this an accurate estimate of the mean value for the entire freshman class? Intuitively, you know that the estimate might be off by a considerable amount, because the sample size is very small and may not be representative of the mean for the entire class. In addition, if I were to repeat this process and take multiple samples of five students and compute the mean for each of these samples, I would likely find that the estimates varied from one another by quite a bit. This also implies that some of the estimates are very inaccurate, i.e. far from the true mean for the class. Suppose I have a box of colored marbles and I want you to estimate the proportion of blue marbles without looking into the box. I shake up the box and allow you to select 4 marbles and examine them to compute the proportion of blue marbles in your sample. Again, you know intuitively that the estimate might be very inaccurate, because the sample size is so small. If you were to repeat this process and take multiple samples of 4 marbles to estimate of the proportion of blue marbles, you would likely find that the estimates varied from one another by quite a bit, and many of the estimates would be very inaccurate. The parameters being estimated differed in these two examples. The fi
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Chance In Epidemiology
activate BMA members Sign in via OpenAthens Sign in via your non differential misclassification bias towards the null institution Edition: International US UK South Asia Toggle navigation The BMJ logo Site map Search Search differential error form SearchSearch Advanced search Search responses Search blogs Toggle top menu ResearchAt a glance Research papers Research methods and reporting Minerva Research news EducationAt a glance Clinical http://sphweb.bumc.bu.edu/otlt/MPH-Modules/EP/EP713_RandomError/EP713_RandomError_print.html reviews Practice Minerva Endgames State of the art News & ViewsAt a glance News Features Editorials Analysis Observations Head to head Editor's choice Letters Obituaries Views and reviews Rapid responses Campaigns Archive For authors Jobs Hosted About The BMJ Resources for online and print readers Publications Epidemiology for the uninitiated Chapter 4. Measurement error http://www.bmj.com/about-bmj/resources-readers/publications/epidemiology-uninitiated/4-measurement-error-and-bias and bias Chapter 4. Measurement error and bias More chapters in Epidemiology for the uninitiated Epidemiological studies measure characteristics of populations. The parameter of interest may be a disease rate, the prevalence of an exposure, or more often some measure of the association between an exposure and disease. Because studies are carried out on people and have all the attendant practical and ethical constraints, they are almost invariably subject to bias. Selection bias Selection bias occurs when the subjects studied are not representative of the target population about which conclusions are to be drawn. Suppose that an investigator wishes to estimate the prevalence of heavy alcohol consumption (more than 21 units a week) in adult residents of a city. He might try to do this by selecting a random sample from all the adults registered with local general practitioners, and sending them a postal questionnaire about their drinking habits. With this design, one source of error would be the exclusion f
Epidemiological Studies 5:53 AM Sulav Shrestha 2 comments Email This BlogThis! Share to Twitter Share to Facebook Concept of Error: In epidemiology: refers to a phenomenon in which the result or finding of the study does http://community.medchrome.com/2011/06/errors-and-bias-in-epidemiological.html not reflect the truth of the fact. Types of Error: Random (chance) Error - associated with precision Systematic Error/Bias - associated with selection Common Sources of Error: Selection bias Absence or inadequacy of controls Unwarranted conclusion Ignoring the periods of exposure to risk Improper interpretation of associations Mixing of non-comparable records Error of measurement Random error/ Chance variation Error that generally occurs in sampling procedure. It is a divergence, due to random error chance alone, of an observation on a sample from the true population value, leading to lack of precision in the measurement of an association. Picture description: Out of a sample of 100 people, 3 consecutive sample drawn randomly may contain: 0% diseased people 10% diseased people 70% diseased people This is called random error where the error is due to chance. The only way to reduce it is to increase the size epidemiology sampling error of sample. Elimination of error is not possible Sources of random error: Individual biological variation Sampling error Measurement error Types of Random Errors Type I Error - alpha error Type II Error - beta error How to reduce Random Error? Increase the size of the study. Systemic Error/Bias Any process or attempts in any stage of the study from designing to its execution to the application of information from the study which produces results or conclusions that differ systematically from truth. A. Selection Bias A distortion in true study finding due to improper selection procedures or it is due to an effect of selection process Most common type of bias. Some potential sources of selection biases: Self selection bias Selection of control group Selection of sampling frame Loss to follow up Improper diagnostic criteria More intensive interview to desired subjects etc. B. Information Bias It is distortion in true study finding due to improper information/lack of information or misclassification. Potential sources of Information Bias: Invalid instrument Incorrect diagnostic criteria Misclassifications Recall laps error Interviewing techniques Losses to follow up, attrition/experimental mortality, etc. C. Confounding Bias Special type of Bias The term "confounding" - effect of extraneous variable that entirely or partially explains the apparent association between the study exposure and the disease.