Public Health Flashcards
Systematic errorcan only be decreased by ______.
Eliminating the source ofbias
Whilerandom error (random errorsin data) can be decreased by repeating measurements and/or increasing the sample size.
Random error (random errorsin data) can be decreased by ___________.
Repeating measurements and/or increasing the sample size.
A test with a highprecisionwill have________.
Minimalrandom error
Precisionimproves with _______.
Decreasedstandard deviationand increasedpowerof a statistical test.
Interrater Reliability
The test yields the same results when performed by different researchers.
Parallel-test Reliability
The reliability of a new test is compared with an established test.
The new test determines the reliability of a test in comparison to another test, the reliability of which has already been established.
Similar statistical results imply a similar degree of reliability.
Test-retest Reliability
The test yields the same results when repeated on the same subjects.
A test with high validity will have _________.
Minimal systematic error and bias.
Internal Validity
Extent to which a study is free of error (most often in the form ofbias) and the results are therefore true for the study sample.
Highinternal validitycan be achieved by matching study groups according to age, sex, and other characteristics, and observing measures to reduce systemic errors (bias) to a minimum
Highinternal validitycan be achieved by ________.
Matching study groups according to age, sex, and other characteristics, and observing measures to reduce systemic errors (bias) to a minimum
External Validity
Refers to whether study results can be extrapolated from a sample population to the general population (generalizability).
A test has high external validity if results in the sample group reflect the actual figures (eg, prevalence) in the general population.
A study with highexternal validityresults can be reproduced in different sample groups and has highinternal validity
A test has high external validity if __________.
Results in the sample group reflect the actual figures (eg, prevalence) in the general population.
A study with _____external validityresults can be reproduced in different sample groups and has ____internal validity
high; high
Berkson Bias
Individuals in sample groups drawn from ahospitalpopulation are more likely to be ill than individuals in the general population.
Attrition Bias
Participants lost to follow up have a different prognosis than those who complete the study.
Most commonly seen in prospective studies.
Risk that the remaining participants differ significantly from those lost to follow up.
Susceptibility Bias
One disease predisposes affected individuals to another disease, and the treatment for the first disease is mistakenly interpreted as a predisposing factor for thesecond disease.
Survival biasalso known as__________.
Prevalence-incidencebiasandNeyman bias
Survival bias (also known asprevalence-incidencebiasandNeyman bias)
When observed subjects have more or less severe manifestations than the standard exposed individual.
If individuals with severe disease die before the moment of observation, those with less severe disease are more likely to be observed.
If individuals with less severe disease have a resolution of their disease before the moment of observation, those with more severe disease are more likely to be observed.
Most commonly occurs incase-controlandcross-sectionalstudies.
Sampling bias also known as __________.
Ascertainment bias
Sampling bias (ascertainment bias)
Occurs when certain individuals are more likely to be selected for a study group, resulting in a nonrandomized sample.
This can lead to incorrect conclusionsbeing drawn about the relationship between exposures and outcomes.
Limitsgeneralizability.
Types ofsampling bias:
- Nonresponse bias → Nonresponder characteristics differ significantly from responder characteristics because nonrespondersdo not returninformation during a study (e.g., subjects do not return a call).
- Healthy worker effect → The working population is healthier on average than the general population. Severely ill individuals do not usually work, so any sample consisting of only subjects that work is not representative of the general population.
- Volunteer bias → Individuals who volunteer to participate in a study have different characteristics than the general population.
Types ofSampling Bias (ascertainment bias)
- Nonresponse bias → Nonresponder characteristics differ significantly from responder characteristics because nonrespondersdo not returninformation during a study (e.g., subjects do not return a call).
- Healthy worker effect → The working population is healthier on average than the general population. Severely ill individuals do not usually work, so any sample consisting of only subjects that work is not representative of the general population.
- Volunteer bias → Individuals who volunteer to participate in a study have different characteristics than the general population.
Nonresponse Bias
Nonresponder characteristics differ significantly from responder characteristics because nonrespondersdo not returninformation during a study (e.g., subjects do not return a call).
Healthy Worker Effect
The working population is healthier on average than the general population.
Severely ill individuals do not usually work, so any sample consisting of only subjects that work is not representative of the general population.
Volunteer Bias
Individuals who volunteer to participate in a study have different characteristics than the general population.
Solutions to Selection Bias (Ascertainment Bias)
- Randomization
- Subjects are randomly assigned to the exposure and control groupsto ensure that both groups are roughly equal inbaseline characteristics(often displayed in a table, e.g., inrandomized controlled trials).
- Controls for both known and unknownconfounders
- Successful if possibleconfoundingcharacteristics (e.g., socioeconomic demographics,family history) are approximately equally distributed between the exposure and control groups - Ensure the sample is representative of the population of interest (e.g., incase-control studies).
- Ensure the correct reference group is chosen for comparison.
- Collect as much data on the characteristics of the participants as possible.
- Nonresponder characteristics should not be assumed. Instead, undisclosed characteristics of nonresponders should be recorded as unknown.
Referral Bias
Results when patients are sampled from specialized medical centers and therefore they do not represent the general population.
Recall Bias
Awareness of a conditionby subjectschangestheir recall of relatedrisk factors(recall a certain exposure).
Common inretrospective studies.
Solution → reducing timeto follow upin retrospective studies (e.g.,retrospective cohort studiesorcase-control studies)
Recall Bias Solutions
Reducing timeto follow upin retrospective studies (e.g.,retrospective cohort studiesorcase-control studies)
Allocation Bias
Systematic difference in the way that participants are assigned to treatment and control groups.
Solution → randomization
Allocation Bias Solutions
Randomization
Late-look Bias
Sending a survey out to people diagnosed with a fatal illness five years after diagnosis will preferentially sample those with a low grade disease (or few comorbidities).
Types of Selection Bias
- Berkson bias
- Attrition bias
- Susceptibility bias
- Survival bias (also known asprevalence-incidencebiasand Neyman bias)
- Sampling bias (ascertainment bias)
Types of Information Bias
- Measurementbias
- Reporting bias
- Interviewer bias
Information Bias
Incorrect data collection, measurement, or interpretation that leads to misclassification of groups or exposure.
Information is gathered differently between the treatment and control groups.
Insufficient information about exposure and disease frequency among subjects.
Solution → standardize data collection.
Types ofinformation bias:
1. Measurementbias → anysystematic errorthat occurs when measuring the outcome
- Reporting bias: a distortion of the information from research due to the selective disclosure or suppression of information by the individuals involved in the study. Can involve the study, design, analysis, and/or findings. Results in underreporting or overreporting of exposure or outcome
- Interviewer bias: Different interviewing approaches prompt different responses by interviewees, which results in researchers finding differences between groups when there are none.
Information Bias Solutions
Standardize data collection
MeasurementBias
Anysystematic errorthat occurs when measuring the outcome
Reporting Bias
A distortion of the information from research due to the selective disclosure or suppression of information by the individuals involved in the study.
Can involve the study, design, analysis, and/or findings. Results in underreporting or overreporting of exposure or outcome
Interviewer Bias
Different interviewing approaches prompt different responses by interviewees, which results in researchers finding differences between groups when there are none.
Misclassification Bias
Results from an incorrect categorization of subjects regarding their exposure status, outcome status, or both.
Cognitive Bias
Thepersonal beliefsof the subjects and/or investigators.
Solution → Use placebo, blinding and prolong the time of the observation to monitor long term effects.
Types of cognitive bias:
- Response bias → Study participants do not respond truthfully or accurately because of the manner in which questions are phrased (e.g., leading questions) and/or because subjects interpret certain answer options to be more socially acceptable than others.
- Observer bias(experimenter-expectancyeffect orPygmalion effect) → The measurement of a variable or classification of subjects is influenced by the researcher’s knowledge or expectations.
- Confirmation bias → The researcher includes only those results that support their hypothesis and ignores other results.
- Placebo and nocebo effects → A placebo or nocebo affects subjects’ preconceptions/beliefs about the outcome.
Cognitive Bias Solutions
Use placebo, blinding and prolong the time of the observation to monitor long term effects.
Types of Cognitive Bias
- Response bias
- Observer bias(experimenter-expectancyeffect orPygmalion effect)
- Confirmation bias
- Placebo and nocebo effects
Response Bias
Study participants do not respond truthfully or accurately because of the manner in which questions are phrased (e.g., leading questions) and/or because subjects interpret certain answer options to be more socially acceptable than others.
Observer Bias is also known as ___________.
Experimenter-expectancyeffect orPygmalion effect
Observer Bias(experimenter-expectancyeffect orPygmalion effect)
The measurement of a variable or classification of subjects is influenced by the researcher’s knowledge or expectations.
Confirmation Bias
The researcher includes only those results that support their hypothesis and ignores other results.
Placebo and Nocebo Effects
A placebo or nocebo affects subjects’ preconceptions/beliefs about the outcome.
Performance Bias
Researchers provide different levels of attention/care to different groups, or subjects change their responses when they become aware of which group they are allocated to.
For example:
1. Procedure bias → When patients or investigators decide on the assignment of treatment and this affects the findings. The investigator may consciously or subconsciously assign particular treatments to specific types of patients (e.g., one group receives a higher quality of treatment; patients in treatment group spend more time in highly specialized hospital units ). Solution → blinding
- Hawthorne effect → subjectschange their behavioronce they areawarethat they are being observed. Especially relevant in psychiatric research. This type ofbiasis difficult to eliminate. Solution → blinding
Procedure Bias
When patients or investigators decide on the assignment of treatment and this affects the findings.
The investigator may consciously or subconsciously assign particular treatments to specific types of patients (e.g., one group receives a higher quality of treatment; patients in treatment group spend more time in highly specialized hospital units).
Solution → blinding
Procedure Bias Solution
Blinding
Hawthorne Effect
Subjectschange their behavioronce they areawarethat they are being observed.
Especially relevant in psychiatric research.
This type ofbiasis difficult to eliminate.
Solution → blinding
Hawthorne Effect Solution
Blinding
Cofounding Bias
Anythird variablethat has not been considered in the study but thatcorrelates with the exposure and the outcome. Aconfoundercan be responsible for the observed relationship between the dependent andindependent variables.
Solutions to cofounding:
- Performmultiple studieswith different populations.
- Randomization(randomized controlled trials)
- Crossover studydesign
- Restriction (epidemiology) → a study design in which only individuals who meet certain criteria are included in the study sample (e.g., only male individuals with a particular disease are included in a study to avoid the influence ofgenderon the exposure and outcome). Disadvantages → limit generalizability and makes obtaining a large sample group difficult
- Matching (epidemiology) → A study design in which study participants aregrouped intopairswith similar attributes. Commonlyused incase-control studiesto minimizeconfounding. Disadvantages →Does not completely eliminateconfounding, can introduceconfoundingif the effect of the matching factor on disease occurrence cannot be studied anymore, can introducebias
- Stratified analysis
Solutions to Cofounding
- Performmultiple studieswith different populations.
- Randomization(randomized controlled trials)
- Crossover studydesign
- Restriction (epidemiology) → a study design in which only individuals who meet certain criteria are included in the study sample (e.g., only male individuals with a particular disease are included in a study to avoid the influence ofgenderon the exposure and outcome). Disadvantages → limit generalizability and makes obtaining a large sample group difficult
- Matching (epidemiology) → A study design in which study participants aregrouped intopairswith similar attributes. Commonlyused incase-control studiesto minimizeconfounding. Disadvantages →Does not completely eliminateconfounding, can introduceconfoundingif the effect of the matching factor on disease occurrence cannot be studied anymore, can introducebias
- Stratified analysis
Lead Time Bias
Early detection of disease ismisinterpretedasincreased survival (Lead time is the average length of time between thedetectionof a disease and the expectedoutcome).
Often discussed in the context of cancer screening.
Lead-timebiasoccurs whensurvival timesare chosen as an endpoint ofscreening tests.
Solution → Measure theback-endsurvival by adjusting for the severity of the disease at the time of diagnosis. The gold standard forscreening testeffectiveness is to usemortality ratesinstead of survival times.
Lead Time Bias Solutions
Measure theback-endsurvival by adjusting for the severity of the disease at the time of diagnosis.
The gold standard forscreening testeffectiveness is to use mortality ratesinstead of survival times.
Length Time Bias
Apparent improvementin the duration of survival for aterminal diseasewith along clinical course (e.g.,slow-growingtumor).
Often discussed in the context of cancer screening.
Solution → Arrange patients according to theseverityof the disease. Use arandomized controlled trialto allocate subjects into control and treatment groups.
Length Time Bias Solutions
Arrange patients according to theseverityof the disease.
Use arandomized controlled trialto allocate subjects into control and treatment groups.
Surveillance Bias
Outcome is diagnosed more frequently in a sample group than in the general population because of increased testing and monitoring.
Results in misleadingly high incidenceand prevalence rates.
Solution → Compare the treatment group to an unexposed control groupwith a similar likelihood of screening. Select an outcome that is possible in both the exposed and unexposed groups.
Surveillance Bias Solutions
Compare the treatment group to an unexposedcontrol groupwith a similar likelihood of screening.
Select an outcome that is possible in both the exposed and unexposed groups.
Effect Modification
A third variableinfluences the effect of an exposure on an outcome.
Occurs when the exposure has adifferent effecton the control and treatment groups.
Not considered a type ofbiasin itself, but rather a biological phenomenon (i.e., the exposure has a different impact in different circumstances).
Example:A certain drug works in children, but does not have any effect on adults.
Solution →stratified analysis. Stratifyingparticipants into subgroups according to the third variable results in a stronger relationship in one subgroup. Stratified analysishelps to differentiateeffect modificationfromconfounding. When the population isstratifiedaccording to a factor, different results will be seen depending on whether it is aconfounderor aneffect modifier.
- Well-known examples of effect modification include:
1. Effect of estrogens on the risk of venous thrombosis (modified by smoking)
2. Risk of lung cancer in people exposed to asbestos (modified by smoking)
Effect Modification Solutions
- Stratified analysis.
- Stratifyingparticipants into subgroups according to the third variable results in a stronger relationship in one subgroup.
- Stratified analysishelps to differentiateeffect modification fromconfounding.
- When the population isstratifiedaccording to a factor, different results will be seen depending on whether it is aconfounderor aneffect modifier.
Cognitive biases can lead to diagnostic errors for example:
- Confirmation bias →tendency of an investigator to favor results that support his or her hypothesis or preconceived notions, ignoring other results.
- Anchoring bias → tendency to inappropriately rely on initial perception or information, which hinders laterjudgmentwhen new information becomes available (e.g., favoring a diagnosis proposed earlier despite new evidence)
- Availability bias → tendency to make judgments based on the availability of information in an individual’smemory (e.g., when a physician makes a premature diagnosis that comes to mind easily and quickly due to having seen several patients with a similar clinical presentation)
- Framing bias → the tendency to be influenced by how information is presented (e.g., the order of symptoms and/or emphasis placed on specific findings).
- Visceral bias → a tendency for clinical decisions to be influenced by positive or negative feelings towards the patient (e.g., doubts regarding symptoms when described by a patient withsubstance use disorder)
Confirmation Bias
Tendency of an investigator to favor results that support his or her hypothesis or preconceived notions, ignoring other results.
Anchoring Bias
Tendency to inappropriately rely on initial perception or information, which hinders laterjudgmentwhen new information becomes available (e.g., favoring a diagnosis proposed earlier despite new evidence)
Availability Bias
Tendency to make judgments based on the availability of information in an individual’smemory (e.g., when a physician makes a premature diagnosis that comes to mind easily and quickly due to having seen several patients with a similar clinical presentation)
Framing Bias
Tendency to be influenced by how information is presented (e.g., the order of symptoms and/or emphasis placed on specific findings).
Visceral Bias
Tendency for clinical decisions to be influenced by positive or negative feelings towards the patient (e.g., doubts regarding symptoms when described by a patient withsubstance use disorder)
Intention-to-Teat analysis
All patients who initially enrolled in the study, includingdrop-outs, are included in the analysis of study data.
Advantage:
- Allows the investigator or reader of the study to draw accurate conclusions regarding the effectiveness of an intervention
- Helps to reduceselection bias → Participants who arerandomizedare included in the analysis and analyzed according to the group they were originally assigned to.
- Preservesrandomization(a large dropout rate may be influenced by the treatment itself.Intention-to-treatanalysis preservesrandomizationby limiting theconfounding effects of dropout due to treatment)
Disadvantages
- Noncompliance can lead to a conservative estimate of the treatment effect.
- Heterogeneity might be introduced (e.g., noncompliant,drop-out, and compliant subjects are analyzed together).
- Susceptible totype II error
Per-Protocol Analysis
Treatment and control groups are compared using data from only those study participants who adhered to the study protocol.
Advantages:
- Improves the estimate of the real effect of treatment under optimal conditions
- Can be used innoninferiority trials,phase 1 trials, andphase 2 trials
Disadvantages:
- Loss ofrandomization(increasedselection bias)
- Overestimates the effects of the tested treatments (because it does not takedrop-outsinto account)
- Possibly a significant reduction in sample size
- Increases risk ofbias
- Excludes participants that did not adhere to the protocol or were lost to follow up
Stratified Analysis
Stratifyingparticipants into subgroups according to a third variable to eliminateconfounding(e.g., by age,gender, race).
Evaluation of the association between exposure and disease is performed within each stratum (e.g., crudeodds ratio).
Crudeodds ratioorrelative risk, as well as theirconfidence intervals, are used to measures the strength of the association (how different they are).
Eliminatesconfounding.
Example: evaluating the association betweenobesityand cardiovascular disease afterstratifyingby age.
Evidence Based Medicine
Practice of medicine in which the physician uses clinicaldecision-makingmethods based on the best available current research frompeer-reviewedclinical and epidemiological studies with the aim of producing the most favorable outcome for the patient.
Levels of Evidence
Method used inevidence-based medicineto determine the reliability of findings from a clinical and/or epidemiological study.
Level I –> findings from at least onehigh-qualityrandomized controlled study.
Level III –> Expert opinions
(Level I –> most reliable; Level III –> least reliable).
Level I of Evidence
Findings from at least onehigh-quality randomized controlled study.
(Level I –> most reliable; Level III –> least reliable).
Level III of Evidence
Expert opinions
(Level I –> most reliable; Level III –> least reliable).
Grades of Clinical Recommendation (according to Evidence Based Medicine Guidelines)
System developed by medical societies, healthcare regulatory entities, and governments to rate clinical evidence and create guidelines for clinical practice based on medical evidence.
From A-D (A highly recommended; D not recommended)
I –> evidence is insufficience
Prevalence / (1 - Prevalence) =
Incidence rate (IR) × Average duration of the disease (T)
Incidence Rate (IR) =
[Prevalence / (1 - Prevalence)] / average duration of the disease (T)
Birth rate
Number oflive birthswithin a specific time interval
Fertility Rate
Rate oflive birthsamongwomen of childbearing age(15–44 years) in a population within a specific time interval
Morbidity
Number of individuals in a population with a disease at a specific point in time (i.e., thedisease burdenin a population)
Mortality
Number of deaths in a population within a specific time interval
Standard Deviation
How much variability exists in a set of values, around the mean of these values.
Standard Error
An estimate of how much variability exists in a (theoretical) set of sample means around the true population mean.
Affected by extreme outliers
Mean
Most resistant to outliers. Can be used to describe a qualitative data.
Mode
Positively skewed distribution (right-skeweddistribution)
Data set has a peak on the left side and along tail on the right(positive direction).
Mean > median > mode.
Negatively skewed distribution (left-skeweddistribution)
Data set has a peak on the right side and along tail on the left(negative direction).
Mean < median < mode.
In normal distribution (bell curve, Gaussian distribution) recommended measures of central tendency are _________; recommended measure of spread is __________.
Mean, median and mode; standard deviation.
In skewed (asymmetrical) distribution recommended measure of central tendency is the __________; recommended measure of spread is _________.
Median; range or interquartile range
(Standard deviationshould be avoided because it requires themean. Themeanshould be avoided as ameasure of central tendencyin skewed data sets)
Null hypothesis(H0)
The assumption that there isno relationship between two measured variables(e.g., the exposure and the outcome)or no significant difference between two studied populations.
Alternative hypothesis(H1)
The assumption that there is arelationship between two measured variables (e.g., the exposure and the outcome)or a significant difference between two studied populations. This hypothesis is formulated as a counterpart to thenull hypothesis.
- Directionalalternative hypothesis(one-tailed) → specifies the direction of a tested relationship. It states that one variable is predicted to be larger or smaller than null value.
- Non-directionalalternative hypothesis(two-tailed) → only states that a difference exists in a tested relationship (does not specify the direction)
Directionalalternative hypothesis(one-tailed)
Specifies the direction of a tested relationship. It states that one variable is predicted to be larger or smaller than null value.
Non-directionalalternative hypothesis(two-tailed)
Only states that a difference exists in a tested relationship (does not specify the direction)
P-value
Probabilitythat a statistical test leads to the false conclusion that there is a relationship between two measured variables (e.g., the exposure and the outcome) or that there is a significant difference between two studied populations.
Probability of obtaining test results at least as extreme as those observed during the test, assuming that H0 is correct.
Probability that the study results occurred by chance alone, given that the null hypothesis is true.
- If thep-valueis equal to or less than a predetermined significance level(usually setat 0.05), the association is consideredstatistically significant(i.e., the probabilitythat the result was obtained by chance is< 5%).Thenull hypothesisis rejected and thealternative hypothesisis accepted. If p value > 0.05, null hypothesis is not rejected and the results are not significant.
- It is not possible to prove H1is true, but having ap-value that is lower than thesignificance levelindicates that it is very unlikely that the H0is correct.
If thep-valueis equal to or less than a predetermined significance level(usually setat 0.05), the association is considered________.
statistically significant(i.e., theprobabilitythat the result was obtained by chance is< 5%).
Thenull hypothesisis rejected and thealternative hypothesisis accepted.
If p value > 0.05, null hypothesis is ________.
not rejected and the results are not significant
It is not possible to prove H1is true, but having ap-valuethat is _______ than thesignificance levelindicates that it is very unlikely that the H0is correct.
lower
Type 1 error (α)
Thenull hypothesisis rejectedwhen it is actually trueand, consequently, thealternative hypothesisis accepted, although the observed effect is actually due to chance(false positiveerror).
Stating that there is an effect or difference when none exists (H0 incorrectly rejected in favor of H1).
Significance level (type 1 errorrate) → theprobabilityof atype 1 error(denoted with “α”)
Significance level (type 1 errorrate)
Probabilityof atype 1 error(denoted with “α”)
- Thesignificance levelis determined by the principal investigatorbefore the study is conducted.The higher thesignificance level, the higher the likelihood that a difference between two groups is the result of chance.
- For medical/epidemiological studies, thesignificance level αis usuallyset to 0.05
- If p < α, then assuming H0 is true, the probability of obtaining the test results would be less than the probability of making a type I error. H0 is therefore rejected as false.
The higher thesignificance level, the _____ the likelihood that a difference between two groups is the result of chance.
higher
If p < α, then assuming H0 is true, the probability of obtaining the test results would be _____ than the probability of making a type I error. H0 is therefore ________.
less; rejected as false.
Type 2 error (β)
Thenull hypothesisis acceptedwhen it is actually falseand, consequently,thealternative hypothesisis rejected even though an observed effect did not occur due to chance(false negativeerror).
An existing effect is overlooked.
A small sample size increases the likelihood of atype 2 error.
Stating that there is not an effect or difference when one exists (H0 is not rejected when it is in fact false).
- Type 2 errorrate →theprobabilityof atype 2 error(denoted by “β”)
A small sample size _______ the likelihood of atype 2 error.
increases
Stating that there is not an effect or difference when one exists (H0 is not rejected when it is in fact false).