BIAS AND CONFOUNDING Nigel Paneth. HYPOTHESIS FORMULATION AND ERRORS IN RESEARCH All analytic studies must begin with a clearly formulated hypothesis.

Slides:



Advertisements
Similar presentations
Correlation vs. Causation
Advertisements

CONCEPTS UNDERLYING STUDY DESIGN
SLIDE 1 Confounding and Bias Aya Goto Nguyen Quang Vinh.
Chance, bias and confounding
Bias Thanks to T. Grein.
Evaluating Hypotheses Chapter 9. Descriptive vs. Inferential Statistics n Descriptive l quantitative descriptions of characteristics.
Evaluating Hypotheses Chapter 9 Homework: 1-9. Descriptive vs. Inferential Statistics n Descriptive l quantitative descriptions of characteristics ~
C82MCP Diploma Statistics School of Psychology University of Nottingham 1 Overview of Lecture Independent and Dependent Variables Between and Within Designs.
Chapter 17 Comparing Two Proportions
Bias and errors in epidemiologic studies Manish Chaudhary BPH( IOM) MPH(BPKIHS)
Sample Size and Statistical Power Epidemiology 655 Winter 1999 Jennifer Beebe.
THREE CONCEPTS ABOUT THE RELATIONSHIPS OF VARIABLES IN RESEARCH
Part 3 of 3 By: Danielle Davidov, PhD & Steve Davis, MSW, MPA INTRODUCTION TO RESEARCH: SAMPLING & DESIGN.
Inferential Statistics
Thomas Songer, PhD with acknowledgment to several slides provided by M Rahbar and Moataza Mahmoud Abdel Wahab Introduction to Research Methods In the Internet.
HSS4303B – Intro to Epidemiology Feb 11, JudgeYes They Are HotNo They Are NotTotals Yes They Are Hot41344 No They Are Not42731 Totals Pr(a)
© 2008 McGraw-Hill Higher Education The Statistical Imagination Chapter 9. Hypothesis Testing I: The Six Steps of Statistical Inference.
Estimation and Hypothesis Testing Now the real fun begins.
Multiple Choice Questions for discussion
Lecture 8 Objective 20. Describe the elements of design of observational studies: case reports/series.
Evidence-Based Medicine 4 More Knowledge and Skills for Critical Reading Karen E. Schetzina, MD, MPH.
Epidemiology The Basics Only… Adapted with permission from a class presentation developed by Dr. Charles Lynch – University of Iowa, Iowa City.
+ Chapter 9 Summary. + Section 9.1 Significance Tests: The Basics After this section, you should be able to… STATE correct hypotheses for a significance.
Evidence-Based Medicine 3 More Knowledge and Skills for Critical Reading Karen E. Schetzina, MD, MPH.
Comparing Two Population Means
Research Design. Research is based on Scientific Method Propose a hypothesis that is testable Objective observations are collected Results are analyzed.
T tests comparing two means t tests comparing two means.
1 Statistical Inference Greg C Elvers. 2 Why Use Statistical Inference Whenever we collect data, we want our results to be true for the entire population.
Chapter 8 Introduction to Hypothesis Testing
 Is there a comparison? ◦ Are the groups really comparable?  Are the differences being reported real? ◦ Are they worth reporting? ◦ How much confidence.
Measures of Association
User Study Evaluation Human-Computer Interaction.
Instructor Resource Chapter 5 Copyright © Scott B. Patten, Permission granted for classroom use with Epidemiology for Canadian Students: Principles,
Bias Defined as any systematic error in a study that results in an incorrect estimate of association between exposure and risk of disease. To err is human.
Significance testing and confidence intervals Col Naila Azam.
Sample Size And Power Warren Browner and Stephen Hulley  The ingredients for sample size planning, and how to design them  An example, with strategies.
Study Designs for Clinical and Epidemiological Research Carla J. Alvarado, MS, CIC University of Wisconsin-Madison (608)
System error Biases in epidemiological studies FETP India.
How confident are we in the estimation of mean/proportion we have calculated?
1.1 Statistical Analysis. Learning Goals: Basic Statistics Data is best demonstrated visually in a graph form with clearly labeled axes and a concise.
Issues concerning the interpretation of statistical significance tests.
CHAPTER 9 Testing a Claim
Scientific Methods and Terminology. Scientific methods are The most reliable means to ensure that experiments produce reliable information in response.
Retain H o Refute hypothesis and model MODELS Explanations or Theories OBSERVATIONS Pattern in Space or Time HYPOTHESIS Predictions based on model NULL.
BC Jung A Brief Introduction to Epidemiology - XIII (Critiquing the Research: Statistical Considerations) Betty C. Jung, RN, MPH, CHES.
Health and Disease in Populations 2002 Sources of variation (1) Paul Burton! Jane Hutton.
Aim: What factors must we consider to make an experimental design?
Understanding lack of validity: Bias
Design of Clinical Research Studies ASAP Session by: Robert McCarter, ScD Dir. Biostatistics and Informatics, CNMC
Chapter 8: Introduction to Hypothesis Testing. Hypothesis Testing A hypothesis test is a statistical method that uses sample data to evaluate a hypothesis.
The Practice of Statistics, 5th Edition Starnes, Tabor, Yates, Moore Bedford Freeman Worth Publishers CHAPTER 9 Testing a Claim 9.1 Significance Tests:
1 Causation in epidemiology, confounding and bias Imre Janszky Faculty of Medicine NTNU.
Chapter 2: The Research Enterprise in Psychology.
Inferential Statistics Psych 231: Research Methods in Psychology.
Research design By Dr.Ali Almesrawi asst. professor Ph.D.
The Practice of Statistics, 5th Edition Starnes, Tabor, Yates, Moore Bedford Freeman Worth Publishers CHAPTER 9 Testing a Claim 9.1 Significance Tests:
Methods of Presenting and Interpreting Information Class 9.
Importance of Biostatistics in Biomedical Research
BIAS AND CONFOUNDING Nigel Paneth.
Fukushima Medical University Aya Goto Nguyen Quang Vinh
Understanding Results
BIAS AND CONFOUNDING
CHAPTER 9 Testing a Claim
Evaluating Associations
ERRORS, CONFOUNDING, and INTERACTION
Evaluating the Role of Bias
Critical Appraisal วิจารณญาณ
The objective of this lecture is to know the role of random error (chance) in factor-outcome relation and the types of systematic errors (Bias)
Type I and Type II Errors
Presentation transcript:

BIAS AND CONFOUNDING Nigel Paneth

HYPOTHESIS FORMULATION AND ERRORS IN RESEARCH All analytic studies must begin with a clearly formulated hypothesis. The hypothesis must be quantitative and specific. It must predict a relationship of a specific size.

For example: “Babies who are breast-fed have less illness than babies who are bottle-fed.” Which illnesses? How is feeding type defined? How large a difference in risk? A better example: “Babies who are exclusively breast-fed for three months or more will have a reduction in the incidence of hospital admissions for gastroenteritis of at least 30% over the first year of life.”

Only specific prediction allows one to draw legitimate conclusions from a study which tests a hypothesis. But even with the best formulated hypothesis, two types of errors can occur. Type 1 - observing a difference when in truth there is none. Type 2 - failing to observe a difference when there is one.

These errors are generally produced by one or more of the following: RANDOM ERROR RANDOM MISCLASSIFICATION BIAS CONFOUNDING

RANDOM ERROR Deviation of results and inferences from the truth, occurring only as a result of the operation of chance. Can produce type 1 or type 2 errors.

RANDOM (OR NON- DIFFERENTIAL) MISCLASSIFICATION Random error applied to the measurement of an exposure or outcome. Errors in classification can only produce type 2 errors, except if applied to a confounder or to an exposure gradient.

BIAS Systematic, non-random deviation of results and inferences from the truth, or processes leading to such deviation. Any trend in the collection, analysis, interpretation, publication or review of data that can lead to conclusions which are systematically different from the truth. (Dictionary of Epidemiology, 3rd ed.)

MORE ON BIAS Note that in bias, the focus is on an artifact of some part of the research process (assembling subjects, collecting data, analyzing data) that produces a spurious result. Bias can produce either a type 1 or a type 2 error, but we usually focus on type 1 errors due to bias.

MORE ON BIAS Bias can be either conscious or unconscious. In epidemiology, the word bias does not imply, as in common usage, prejudice or deliberate deviation from the truth.

CONFOUNDING A problem resulting from the fact that one feature of study subjects has not been separated from a second feature, and has thus been confounded with it, producing a spurious result. The spuriousness arises from the effect of the first feature being mistakenly attributed to the second feature. Confounding can produce either a type 1 or a type 2 error, but we usually focus on type 1 errors.

THE DIFFERENCE BETWEEN BIAS AND CONFOUNDING Bias creates an association that is not true, but confounding describes an association that is true, but potentially misleading.

EXAMPLES OF RANDOM ERROR, BIAS, MISCLASSIFICATION AND CONFOUNDING IN THE SAME STUDY: STUDY: In a cohort study, babies of women who bottle feed and women who breast feed are compared, and it is found that the incidence of gastroenteritis, as recorded in medical records, is lower in the babies who are breast-fed.

EXAMPLE OF RANDOM ERROR By chance, there are more episodes of gastroenteritis in the bottle-fed group in the study sample, producing a type 1 error. (When in truth breast feeding is not protective against gastroenteritis). Or, also by chance, no difference in risk was found, producing a type 2 error (When in truth breast feeding is protective against gastroenteritis).

EXAMPLE OF RANDOM MISCLASSIFICATION Lack of good information on feeding history results in some breast- feeding mothers being randomly classified as bottle-feeding, and vice- versa. If this happens, the study finding underestimates the true RR, whichever feeding modality is associated with higher disease incidence, producing a type 2 error.

EXAMPLE OF BIAS The medical records of bottle-fed babies only are less complete (perhaps bottle fed babies go to the doctor less) than those of breast fed babies, and thus record fewer episodes of gastro-enteritis in them only. This is called ias because the observation itself is in error.

EXAMPLE OF CONFOUNDING The mothers of breast-fed babies are of higher social class, and the babies thus have better hygiene, less crowding and perhaps other factors that protect against gastroenteritis. Crowding and hygiene are truly protective against gastroenteritis, but we mistakenly attribute their effects to breast feeding. This is called confounding. because the observation is correct, but its explanation is wrong.

PROTECTION AGAINST RANDOM ERROR AND RANDOM MISCLASSIFICATION Random error can work to falsely produce an association (type 1 error) or falsely not produce an association (type 2 error). We protect ourselves against random misclassification producing a type 2 error by choosing the most precise and accurate measures of exposure and outcome.

PROTECTION AGAINST TYPE 1 ERRORS We protect our study against random type 1 errors by establishing that the result must be unlikely to have occurred by chance (e.g. p <.05). P-values are established entirely to protect against type 1 errors due to chance, and do not guarantee protection against type 1 errors due to bias or confounding. This is the reason we say statistics demonstrate association but not causation.

PROTECTION AGAINST TYPE 2 ERRORS We protect our study against random type 2 errors by providing adequate sample size, and hypothesizing large differences. The larger the sample size, the easier it will be to detect a true difference, and the largest differences will be the easiest to detect. (Imagine how hard it would be to detect a 1% increase in the risk of gastroenteritis with bottle-feeding).

TWO WAYS TO INCREASE POWER The sample size needed to detect a significant difference is called the power of a study. 1. Choosing the most precise and accurate measures of exposure and outcome has the effect of increasing the power of our study, because of variances of the outcome measures, which enter into statistical testing, are decreased. 2.Having an adequate sized sample of study subjects

KEY PRINCIPLE IN BIAS AND CONFOUNDING The factor that creates the bias, or the confounding variable, must be associated with both the independent and dependent variables (i.e. with the exposure and the disease). Association of the bias or confounder with just one of the two variables is not enough to produce a spurious result.

In the example just given: The BIAS, namely incomplete chart recording, has to be associated with feeding type (the independent variable) and also with recording of gastroenteritis (the dependent variable) to produce the false result. The CONFOUNDING VARIABLE (or CONFOUNDER) better hygiene, has to be associated with feeding type and also with gastroenteritis to produce the spurious result.

Were the bias or the confounder associated with just the independent variable or just the dependent variable, they would not produce bias or confounding. This gives a useful rule: If you can show that a potential confounder is NOT associated with either one of the two variables under study (exposure or outcome), confounding can be ruled out.

GOOD STUDY DESIGN PROTECTS AGAINST ALL FORMS OF ERROR

SOME TYPES OF BIAS 1. SELECTION BIAS Any aspect of the way subjects are assembled in the study that creates a systematic difference between the compared populations that is not due to the association under study.

2. INFORMATION BIAS Any aspect of the way information is collected in the study that creates a systematic difference between the compared populations that is not due to the association under study. (some call this measurement bias). The incomplete chart recording in the baby feeding example would be a form of information bias. Other examples - Diagnostic suspicion bias Recall bias Sometimes biases apply to a population of studies, rather than to one study, as in publication bias (tendency to publish papers which show positive results).