Validity and bias are essential aspects of any research—a brief description of internal and external validity and different types of bias related to the epidemiological study.
These annotated slides will help you interpret an OR or RR in clinical terms. Please download these slides and view them in PowerPoint so you can view the annotations describing each slide.
These annotated slides will help you interpret an OR or RR in clinical terms. Please download these slides and view them in PowerPoint so you can view the annotations describing each slide.
Concept of Association, Causation and Correlation
Association - Spurious, Indirect & Direct
Multi-factorial causation
Guidelines for Judging causality
Additional Criteria for Judging causality
Sample size and how to calculate it
- Why sample size is important
- Alpha and beta errors
- Main outcome and Effect size
- Practical examples using Means-Proportions-Correlation- Confidence Interval
Concept of Association, Causation and Correlation
Association - Spurious, Indirect & Direct
Multi-factorial causation
Guidelines for Judging causality
Additional Criteria for Judging causality
Sample size and how to calculate it
- Why sample size is important
- Alpha and beta errors
- Main outcome and Effect size
- Practical examples using Means-Proportions-Correlation- Confidence Interval
Systematic (non-random) error that results in an incorrect estimate of the association between exposure and risk of disease.
Can occur in all stages of a study
Not affected by study sample size
Difficult to adjust for afterwards, but can be reduced by adequate study design.
•Can never be totally avoided, but we must be aware of it and interpret our results accordingly
Evidence based decision making in periodonticsHardi Gandhi
INTRODUCTION TO EVIDENCE BASED DENTISTRY
EVIDENCE BASED PERIODONTOLOGY
NEED, PRINCIPLES, GOALS AND ADVANTAGES OF EBDM
SKILLS NEEDED FOR EBDM
ASSESING THE EVIDENCE
INCORPORATING INTO THE PRACTICE
EpidemiologyUnit 3Bias, Error, Confounding and Effect Modification4hrs
Radha Maharjan
MN(WHD)
Contents
3.1 Bias and Error in Epidemiology
3.1.1 Bias (Researcher and Respondent)
Recall Bias
Information Bias ( sponsor bias, social desirability bias, acquiescence Bias)
Selection Bias
Confirmation Bias
The halo effect.
Contents
3.1.2 Error
Systematic Error
Random Error
Confounding & Effect Modification
Definition of Error
A measure of the estimated difference between the observed or calculated value of a quantity and its true value.
Random error or Chance
It is the by-chance error
It makes observed value different from the true value
May occur through sampling variability or random fluctuation of the event of interest due to
biological variability, sampling error and measurement error (not due to machine)
lack of precision in the measurement of an association
Biological variability:
The natural variability in a lab parameter due to physiologic differences among subjects and within the same subject over time.
Differences between subjects due to differences in diet, genetics or immune status.
Sampling error:
Sampling error is a statistical error that occurs when an analyst does not select a sample that represents the entire population of data.
Measurement error:
Measurement Error (also called Observational Error) is the difference between a measured quantity and its true value.
Random error or Chance
Random error can never be completely eliminated since we can study only a sample of the population.
Random error can be reduced by
careful measurement of exposure and outcome
Proper selection of study
Taking larger sample- increase the size of the study.
Systematic error or Bias
Systematic error (or bias) occurs in epidemiology when results differ in a systematic manner from the true values.
Bias is any difference between the true value and observed value due to all causes other than random fluctuation and sampling variability.
This type of error is generally more insidious and hard to detect.
Systematic error or Bias
For example over-estimate of blood sugar of every subject by 0.05 mmol/l resulted from using inaccurate analyser.
The possible sources of systematic error are many and varied but the important biases are selection bias, measurement bias, confounding, information bias, recall (respondent) bias, etc..
Sources of error in epidemiological study
Common sources of error are
selection bias
absence or inadequacy of controls
unwarranted conclusions
improper interpretation of associations
mixing of non-comparable records
errors of measurement (intra-observer variation, inter-observer variation), etc.
The error can be minimised through
study design (by randomisation, restriction & matching) and
during analysis of the results (by stratification and statistical modelling) ..
Selection bias
The prostate is an exocrine gland of the male mammalian reproductive system
It is a walnut-sized gland that forms part of the male reproductive system and is located in front of the rectum and just below the urinary bladder
Function is to store and secrete a clear, slightly alkaline fluid that constitutes 10-30% of the volume of the seminal fluid that along with the spermatozoa, constitutes semen
A healthy human prostate measures (4cm-vertical, by 3cm-horizontal, 2cm ant-post ).
It surrounds the urethra just below the urinary bladder. It has anterior, median, posterior and two lateral lobes
It’s work is regulated by androgens which are responsible for male sex characteristics
Generalised disease of the prostate due to hormonal derangement which leads to non malignant enlargement of the gland (increase in the number of epithelial cells and stromal tissue)to cause compression of the urethra leading to symptoms (LUTS
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
Couples presenting to the infertility clinic- Do they really have infertility...Sujoy Dasgupta
Dr Sujoy Dasgupta presented the study on "Couples presenting to the infertility clinic- Do they really have infertility? – The unexplored stories of non-consummation" in the 13th Congress of the Asia Pacific Initiative on Reproduction (ASPIRE 2024) at Manila on 24 May, 2024.
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Recomendações da OMS sobre cuidados maternos e neonatais para uma experiência pós-natal positiva.
Em consonância com os ODS – Objetivos do Desenvolvimento Sustentável e a Estratégia Global para a Saúde das Mulheres, Crianças e Adolescentes, e aplicando uma abordagem baseada nos direitos humanos, os esforços de cuidados pós-natais devem expandir-se para além da cobertura e da simples sobrevivência, de modo a incluir cuidados de qualidade.
Estas diretrizes visam melhorar a qualidade dos cuidados pós-natais essenciais e de rotina prestados às mulheres e aos recém-nascidos, com o objetivo final de melhorar a saúde e o bem-estar materno e neonatal.
Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
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Acute scrotum is a general term referring to an emergency condition affecting the contents or the wall of the scrotum.
There are a number of conditions that present acutely, predominantly with pain and/or swelling
A careful and detailed history and examination, and in some cases, investigations allow differentiation between these diagnoses. A prompt diagnosis is essential as the patient may require urgent surgical intervention
Testicular torsion refers to twisting of the spermatic cord, causing ischaemia of the testicle.
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1. Validity & Biases
in epidemiological
studies
Dr. Abhijit Das
PG resident
Govt. Bundelkhand Medical
College, Sagar, M.P
.
2. IF AN ASSOCIATION IS OBSERVED, THE
FIRST QUESTION ASKED MUST ALWAYS BE
…
“Is it REAL?”
“Is it a VALID observation?”
3. • Validity of epidemiologic study concerns whether or
not there are imperfections in the study design, the
methods of data collection, or the methods of data
analysis that might distort the conclusions made
about an exposure-disease relationship.
• No such imperfections Study is VALID.
6. • If not valid (Internal/External)
• There are imperfections
• The extent of the distortion of the results from the
correct conclusions is called Bias
7. BIAS
• “Any systematic error in the design, conduct or analysis of
a study that results in a mistaken estimate of an
exposure’s effect on the risk of disease.”
• Lack of internal validity or incorrect assessment of the
association between an exposure and an effect in the
target population.
Schlesselman JJ. Case-Control Studies: Design, Conduct, and Analysis. New York: Oxford
University Press; 1982.
8. • Classified by the research stage in which they occur or
by the direction of change in a estimate.
• Definition and selection of the study population, data
collection, and the association between different
determinants of an effect in the population.
9. 4
1
1 2 3
Sacket
and
Choi
According to the
stages of research
Maclure
and
Schneew
eiss
Steinec
k and
Ahlbo
m
Applying
the causal
diagram
theory
• Based on the
concept of
study base
4Kleinba
um et al
• Three main
groups
• Selection bias,
Information bias,
and
Confounding
CLASSIFICATION OF BIAS
11. SELECTION BIAS
• The error introduced when the study population does
not represent the target population.
• At any stage of a research
• Design (bad definition of the eligible population, lack
of accuracy of sampling frame, uneven diagnostic
procedures in the target population) and
implementation.
12. EXAMPLES OF SELECTION BIAS
• Select volunteers as exposed group and non-volunteers
as non-exposed group in a study of screening
effectiveness
• Study health of workers in a workplace exposed to some
occupational exposures comparing to health of general
population
₋ Working individuals are likely to be healthier than general
population that includes unemployed people (Healthy
13. CONTROLLING SELECTION BIAS
• Define criteria of selection of diseased and non-
diseased participants independent of exposures in a
case-control study
• Define criteria of selection of exposed and non-
exposed participants independent of disease
outcomes in a cohort study
14. Inappropria
te
definition of
eligible
population
• Ascertainment bias- Competing
risks, Healthcare access bias,
Length-bias sampling, Neyman
bias, Spectrum bias, Survivor
treatment selection bias
• Selection of control- Berkson’s
bias, Exclusion bias, Friend
control bias, Inclusion bias,
Matching bias, Relative control
Lack of
accuracy of
sampling
frame
Non-random
sampling bias
Telephone
random
sampling bias
Citation bias
Dissemination
bias
Publication bias
Uneven
diagnostic
procedures
in the
target
population
During
study
implementati
on
Losses/withdraw
als to follow up
Missing
information in
multivariable
analysis
Non-response
bias
SELECTION
BIAS
15. COMPETING RISKS
• When two or more outputs are mutually exclusive, any
of them competes with each other in the same
subject.
• For example, early death by AIDS can produce a
decrease in liver failure mortality in parenteral drug
users. A proper analysis of this question should take
into account the competing causes of death; for
instance.
16. HEALTHCARE ACCESS BIAS
• Observational study
• When the patients admitted to an institution do not
represent the cases originated in the community.
• Popularity bias
• Centripetal bias
• Referral filter bias
• Diagnostic/treatment access bias
17. NEYMAN BIAS (INCIDENCE-PREVALENCE BIAS/SELECTIVE SURVIVAL
BIAS)
• Where the very sick or very well (or both) are erroneously
excluded from a study. The bias (“error”) in results can be
skewed in two directions
• Excluding patients who have died will make conditions look
less severe.
• Excluding patients who have recovered will make conditions
look more severe.
18. • Both cross sectional and (prevalent) case-control studies.
• Any risk factors we may identify in a study using
prevalent cases may be related more to survival with the
disease than to the development of the disease
(incidence).
• Careful selection of study type can help to lessen the
effects from this bias
19. SPECTRUM BIAS
• In the assessment of validity of a diagnostic test
• When researchers included only ‘‘clear’’ or ‘‘definite’’ cases,
not representing the whole spectrum of disease
presentation, and/or ‘‘clear’’ or healthy controls subjects,
not representing the conditions in which a differential
diagnosis should be carried out.
• Sensitivity and specificity of a diagnostic test are increased.
20. BERKSON’S BIAS
• First described by Berkson in 1946 for case- control
studies.
• Hospital control
• It is produced when the probability of hospitalization
of cases and controls differ, and it is also influenced
by the exposure.
• Hospital patients differ from people in the community
21. FRIEND CONTROL BIAS
• It was assumed that the correlation in exposure status
between cases and their friend controls lead to biased
estimates of the association between exposure and
outcome.
• Problem may be that the controls are too similar to
the cases in regard to many variables, including the
variables that are being investigated in the study.
22. MATCHING
• It is well known that matching, either individual or
frequency matching, introduces a selection bias, which
is controlled for by appropriate statistical analysis
• Overmatching is produced when researchers match by
a non-confounding variable and can underestimate an
association
23. Inappropria
te
definition of
eligible
population
• Ascertainment bias- Competing
risks, Healthcare access bias,
Length-bias sampling, Neyman
bias, Spectrum bias, Survivor
treatment selection bias
• Selection of control- Berkson’s
bias, Exclusion bias, Friend
control bias, Inclusion bias,
Matching bias, Relative control
Lack of
accuracy of
sampling
frame
Non-random
sampling bias
Telephone
random
sampling bias
Citation bias
Dissemination
bias
Publication bias
Uneven
diagnostic
procedures
in the
target
population
During
study
implementati
on
Losses/withdraw
als to follow up
Missing
information in
multivariable
analysis
Non-response
bias
SELECTION
BIAS
24. LACK OF ACCURACY OF SAMPLING FRAME
• Non-random sampling bias: most common bias in this
group
• Telephone random sampling bias: It excludes some
households from the sample, thus producing a
coverage bias. In the US it has been shown that the
differences between participants and non-participants
are generally not large, but the situation can be very
different in less developed countries
• Citation bias
• Dissemination bias
25. UNEVEN DIAGNOSTIC PROCEDURES IN THE TARGET
POPULATION
• In case-control studies
• Detection bias
• Diagnostic suspicion bias: types of detection bias, exposure
is taken as another diagnostic criterion
• Unmasking-detection signal-bias: exposure that, rather than
causing a disease, causes a sign or symptom that
precipitates a search for the disease
• Mimicry bias: exposure may become suspicious if, rather
than causing disease, it causes a benign disorder which
resembles the disease
26. DURING STUDY IMPLEMENTATION
• Losses/withdrawals to follow up: in both cohort and
experimental studies.
• Missing information : multivariable analysis selects
records with complete information on the variables
included in the model. If participants with complete
information do not represent target population, it can
introduce a selection bias.
27. • Non-response bias: A bias that occurs due to
systematic differences between responders and non-
responders
• This bias is common in descriptive, analytic and
experimental research and survey studies
• Results in mistakes in estimating population
characteristics based on the underrepresentation of
phenomena due to non-response
28. INFORMATION BIAS
• Information bias occurs when information is
collected differently between two groups, leading to
an error in the conclusion of the association
• When information is incorrect- there is misclassification
Differential misclassification
Non-differential misclassification
29. EXAMPLES OF INFORMATION BIAS
• Interviewer knows the status of the subjects before the
interview process
• Interviewer may probe differently about exposures in
the past if he or she knows the subjects as cases
• Subjects may recall past exposure better or in more
detail if he or she has the disease (recall bias)
• Surrogates, such as relatives, provide exposure
information for dead cases, but living controls provide
30. CONTROLLING INFORMATION BIAS
• Have a standardized protocol for data collection
• Make sure sources and methods of data collection are
similar for all study groups
• Make sure interviewers and study personnel are
unaware of exposure/disease status
• Adapt a strategy to assess potential information bias
• Blinding
31. Differential misclassification bias
Detection bias
Observer/interviewer bias
Recall bias
Reporting bias
Non-differential misclassification bias
Misclassificatio
n bias
INFORMATION
BIAS
Ecological
fallacy
Regression to
mean
Others
Hawthorne effect
Temporal ambiguity
Will Rogers phenomenon
Work up bias (verification bias)
Lead time bias
Protopathic bias
32. MISCLASSIFICATION BIAS
• When individuals are assigned to a different category
than the one they should be in.
• Non-differential misclassification occurs when the
probability of individuals being misclassified is equal
across all groups in the study.
• Differential misclassification occurs when the
probability of being misclassified differs between
groups in a study
33. OBSERVER/INTERVIEWER BIAS
• Knowledge of the hypothesis, the disease status, or
the exposure status (including the intervention
received) can influence data recording (observer
expectation bias)
• Type of detection bias; affect assessment in
observational and interventional studies
• Blinding/adequate training for observers in how to
record findings
34. RECALL BIAS
• Rumination bias- the presence of disease influences the
perception of its causes
• Exposure suspicion bias- the search for exposure to the
putative cause
• Participant expectation bias- in a trial if the patient
knows what they receive may influence their answers
• More common in case-control studies, although it can
occur in cohort studies and trials.
35. REPORTING BIAS
• Systematic distortion that arises from the selective
disclosure or withholding of information by parties
• Obsequiousness bias
• Family aggregation bias
• Underreporting bias
36. ECOLOGICAL FALLACY
• When analyses realised in an ecological (group level)
analysis are used to make inferences at the individual level.
• For instance, if exposure and disease are measured at the
group level (for example, exposure prevalence and disease
risk in each country), exposure-disease relations can be
biased from those obtained at the individual level (for
example, exposure status and disease status in each
subject).
37. HAWTHORNE EFFECT
• Described in the 1920s in the Hawthorne plant of the
Western Electric Company (Chicago, IL).
• It is an increase in productivity—or other outcome
under study—in participants who are aware of being
observed.
• A study of hand-washing among medical staff found
that when the staff knew they were being watched,
compliance with hand-washing was 55% greater than
when they were not being watched (Eckmanns 2006)
38. LEAD TIME BIAS
• A distortion overestimating the apparent time
surviving with a disease caused by bringing forward
the time of its diagnosis
• The added time of illness produced by the diagnosis
of a condition during its latency period.
• This bias is relevant in the evaluation of the efficacy of
screening, in which the cases detected in the screened
group has a longer duration of disease than those
39. Lead time bias where health outcome is the same in someone whose disease is detected by
screening compared with someone whose disease is detected from symptoms, but survival time
from the time of diagnosis is longer in the screened patient
Lead time bias where the screened patient lives longer than the unscreened patient, but overall
survival time is still exaggerated by the lead time from earlier diagnosis.
40. VERIFICATION BIAS/ WORK-UP BIAS
• Occurs during investigations of diagnostic test accuracy
when there is a difference in testing strategy between
groups of individuals, leading to differing ways of
verifying the disease of interest.
• In the assessment of validity of a diagnostic test, it is
produced when the execution of the gold standard is
influenced by the results of the assessed test, typically the
reference test is less frequently performed when the test
41. CONFOUNDING
• From confounder, i.e. to mix together.
• A distortion that modifies an association
between an exposure and an outcome
because a factor is independently
associated with the exposure and the
outcome.
• Not possible to separate the contribution
that any single causal factor has made.
• Solution: Study design:
Matching/randomization, Stratification
smoking
Ca esophagus
alcohol
42. BIAS AND CONFOUNDING
• Systematic error
in a study
• Cannot be fixed
• May lead to
errors in the
conclusion
• When
confounding
variables are
known, the effect
may be fixed
43. SPECIFIC BIASES IN A TRIAL
• Allocation of intervention bias
• Compliance bias
• Contamination bias
• Lack of intention to treat analysis
44. ALLOCATION OF INTERVENTION BIAS
• Systematic difference in how participants are assigned
to comparison groups in a clinical trial
• More common in non-randomised trials
• Sequentially numbered, opaque, sealed envelopes
(SNOSE); sequentially numbered containers; pharmacy
controlled allocation; and central allocation
45. COMPLIANCE BIAS
• David Sackett, 1979 paper on Biases in Analytic Research
• Participants who are compliant with an intervention may
differ from those who are non-compliant, and in ways that
might affect the risk of the outcome being measured.
• Clinical trials should attempt to collect data on compliance
and while analyses should include the intention to treat
analysis
49. TRIAL
• Allocation of intervention
bias
• Compliance bias
• Hawthorne effect
• Lack of intention to treat
analysis
SRMA
• Citation bias
• Dissemination bias
• Language bias
• Publication bias
50. REFERENCES
• Bias, Miguel Delgado-Rodrı´guez, Javier Llorca, J Epidemiol
Community Health 2004;58:635–641. doi:
10.1136/jech.2003.008466
• Catalogue of Bias, accessible at: https://catalogofbias.org/
• Gordis Epidemiology, Elsevier, 6th edition.
Editor's Notes
Primary objective of epidemiologic research is to obtain a valid estimate of an effect measure of interest
INTERNAL- Study done properly without any methodological flaws and finding is therefore valid for the Study Population.
EXTERNAL- Finding is generalizable from study population to defined population, presumably TARGET population.
Internal validity is more important than External validity, Study could be internally valid but not externally. But if a study is externally valid that means it is internally valid too.
So the concept of bias is lack
1st-Biases can be classified
2nd-The most important biases are those produced in
One-Reading up on the field, specification and selection the study sample, execution of experimental maneuver, measurement of exposures/outcomes, data analysis, results interpretation and publication.
Three- Considered in this order, confounding, misclassification, misrepresentation and analysis deviation
2. It can be introduced
Volunteers could be more health conscious than non- volunteers, thus resulting in less disease
Volunteers could also be at higher risk, such as having a family history of illness, thus resulting in more disease
Ascertainment bias is produced when the kind of patients gathered does not represent the cases originated in the population.
2nd- It is more frequent when dealing with causes of death: as any person only dies once, the risk for a specific cause of death can be affected by an earlier one.
This may be due: (popularity bias) to the own institution if admission is determined by the interest of health personnel on certain kind of cases.
{centripetal bias), to the patients if they are attracted by the prestige of certain clinicians.
((referral filter bias) to the healthcare organization if it is organized in increasing levels of complexity (primary, secondary, and tertiary care) and ‘‘difficult’’ cases are referred to tertiary care.
(diagnostic/treatment access bias)to a web of causes if patients by cultural, geographical, or economic reasons show a differential degree of access to an institution
After 3rd -We may not know which groups (improved/died) we are excluding, making it impossible to adjust for any bias in results.
Before 4th- This type of bias is also called incidence-prevalence bias .
Combining prevalent and incident cases can actually make prevalent-incidence bias worse, obscuring the true relationship between your study variables (i.e. the variables in your experiment or study)
Incident cases are newer cases — like first time admissions. Prevalent cases are pre-existing cases, which are usually sicker with more progressed disease than incident cases.
Before 2nd- preferable to use incident in case-control studies of disease etiology.
If, for example, most people who develop the disease die soon after diagnosis, they will be underrepresented in a study that uses prevalent cases, and such a study is more likely to include longer-term survivors. This would constitute a highly nonrepresentative group of cases, and any risk factors identified with this nonrepresentative group may not be a general characteristic of all patients with the disease, but only of survivors.
after 3rd- Neyman bias is less of a problem with acute, short-lived cases than with long-term diseases like HIV or tuberculosis
Occurs when a diagnostic test is studied in a different range of individuals to the intended population for the test
His original paper showed that two diseases, which have no real relationship, can be what he called ‘spuriously associated‘ in hospital-based case control studies. However, the idea wasn’t widely accepted until 1979.
They represent a sample of an ill-defined reference population that usually cannot be characterized and thus to which results cannot be generalized.
Moreover, hospital patients differ from people in the community.
For example, the prevalence of cigarette smoking is known to be higher in hospitalized patients than in community residents; many of the diagnoses for which people are admitted to the hospital are smoking related.
matched analysis in studies with individual matching and adjusting for the variables used to match in frequency
Ascertainment bias is produced when the kind of patients gathered does not represent the cases originated in the population.
Obviously, this selection procedure can yield a nonrepresentative sample in which a parameter estimate differs from the existing at the target population
In Systematic reviews and meta-analysis
Citation bias: articles more frequently cited are more easily found and included in systematic reviews and meta analysis.
Dissemination bias: the biases associated to the whole publication process, from biases in the retrieval of information (including language bias) to the way the results are reported
Publication bias: regarding an association that is produced when the published reports do not represent the studies carried out on that association. Several factors have been found to influence publication, the most important being statistical significance, size of the study, funding, prestige, type of design, and study quality
Detection bias occurs when exposure influences the diagnosis of the disease.
Unmasking (detection signal) bias- example: If a medication can cause vaginal bleeding, and people with this symptom go sooner to the doctor and receive earlier or more intensive examination and investigations to diagnose cancer, it may appear that the medication caused the cancer. However, all that may have happened is that the medication has prompted an earlier or more intensive search for the disease, leading to an apparently increased rate among those using the medication.
Exposure can trigger the search for the disease; for instance, benign anal lesions increases the diagnosis of anal cancer.
During the study design, try to identify factors that might cause signs or symptoms that are also associated with the disease of interest. Then, during analysis of the data keep in mind any such factors, and carry out a sensitivity analysis if indicated.
Ensure correct diagnoses of the condition by using gold standard diagnostic reference test to confirm the diagnosis. Readers should be wary of studies that rely on symptoms and self-reported measures to infer critical outcomes.
when losses/withdrawals are uneven in both the exposure and outcome categories, the validity of the statistical results may be affected
After 2nd- it is relevant mainly in retrospective study, record based study.
Example= healthy volunteer effect- participants are generally healthy than general population.
Differential misclassification occurs when the level of misclassification differs between the two groups
Non-differential misclassification occurs when the level of misclassification does not differ between the two groups
1. Correct classification of individuals, and of exposures and participant characteristics, is an essential element of any study. Misclassification occurs when
2. Results in Incorrect associations being observed between the assigned categories and the outcomes of interest
The means by which interviewers can introduce error into a questionnaire include administering the interview or helping the respondents in different ways (even with gestures), putting emphases in different questions, and so on.
Last line- how to record findings, identifying any potential conflicts before recordings commence and clearly defining the methods, tools and time frames for collecting data.
Definition: Systematic error due to differences in accuracy or completeness of recall to memory of past events or experiences.
RB-in case-control in which participants know their diseases
EsB-in cohort studies (for example, workers who known their exposure to hazardous substances may show a trend to report more the effects related to them)
PEB-trials without participants’ blinding
1.participants can ‘‘collaborate’’ with researchers and give answers in the direction they perceive are of interest
2. the existence of a case triggers family information
3. is common with socially undesirable behaviours, such as alcohol consumption
Ecological fallacy can be produced by within group (individual level) biases, such as confounding, selection bias, or misclassification, and by confounding by group or effect modification by group
Seen in Trial
premise of screening is that it allows earlier detection and treatment of a disease or health condition, leading to a greater chance of cure or at least longer survival. Individuals with disease detected through population screening receive a diagnosis earlier before signs and symptoms appear.
As a consequence, estimates of differences in survival time between people diagnosed from screening and those whose disease is detected after symptoms develop can be biased, as survival time will appear to be longer in screen-detected people if early detection has no effect on the course of disease (figure 1) or if survival time is extended (figure 2).
Prevention- In randomised control trials evaluating screening, lead time bias can be countered by taking the time origin as the point of randomisation, not the point of diagnosis, or by comparing the number of deaths occurring in a given period of time instead or as well as the number of people surviving.
After 1- when only a proportion of the study group receives confirmation of the diagnosis by the reference standard, or if some patients receive a different reference standard at the time of diagnosis.
At last- Many reference tests are invasive, expensive, or carry a procedural risk (e.g. angiography, biopsy, surgery), and therefore, patients and clinicians may be less likely to pursue further tests if a preliminary test is negative.
Example- A study assessed the accuracy of D-dimer testing for diagnosing pulmonary embolism (PE). Patients that had a positive D-dimer result were further assessed with ventilation–perfusion scans (reference standard 1), whereas patients that had negative D-dimer results were assessed with routine clinical follow up (reference standard 2).
Prevention- Ideally, in a diagnostic accuracy study, all patients should receive the same reference test.
Prevention- Some standard methods of ensuring concealment of group allocation prior to allocation include
This means that compliance is a confounding factor in assessing the effect of the intervention
Intention to treat-