Please enable JavaScript.
Coggle requires JavaScript to display documents.
EPIDEMIOLOGY and STATISTICS (Types of Study *Bias note that errors can…
EPIDEMIOLOGY and STATISTICS
Screening
Tests
Negative and Positive Predictive Values
NPV and PPV
*NPV = Negative Predictive Value
NPV = Negative predictive Value
example:
Notes
:
note that in the 2x2 table the TP = sensitivity and TN = specificity
--> whenever you are given a sensitivity and specificity fills these in right away
*PPV = Positive Predictive Value
Positive Predictive --> depends on
PREVALENCE
Clinical Cases
Clinical Case
Notes
:
note that
Clinical Case
PPV = Positive Predictive Value example
example:
Notes
:
note that same for PPV and NPV
for PPV you take the True positives and divide by all positive results
PPV = Positive Predictive Value example 2
example:
Notes
:
note that same for PPV and NPV
for PPV it depends on PREVALENCE
Sensitivity and Specificity
think of graph and cutoff point fr a certain marker
right graph = higher marker being screened for
--> positive screen test
--> disease present
left graph = lower marker being screened for
--> negative screen test
--> NO disease
*Sensitivity
sensitivity = TP/FN
--> % of people with the disease picked up by the test
--> trying to get as many people WITH disease to test positive
Clinical Cases
Clinical Case
Notes
:
note that
Clinical Case
Sensitivity
Notes
:
note for extremely rare disease you want a high sensitivity
for very common diseases, you want a higher specificity
example:
*Specificity
Specificity= TN//FP
--> % of people correctly ruled out as not having the disease
False positive rate = FP is related to specificity
FP = 1 - specificity
Clinical Cases
Clinical Case
Notes
:
note that
Clinical Case
Specificity and
FPR = False
Positive Rates
Notes
:
Specifity and FPR = false positive rates do not change and are independent of the disease prevalence in different populations
this means that you find the specificity, then FPR = 1 - specificity
--> you can then apply this FPR to another population with a different prevalence of the disease
specificity = % of healthy people identified by a screening test
--> specificity = 1 - FPR
note when filling out the chart, always fill:
--> total population
--> prevalence - this also gives the non-disease = healthy
--> FP = % of healthy people = 1 - specificity
FPR example:
Risk Reduction Statistics
the
*NNT NNH = Number
Needed to Treat / Harm
NNT ARRRRR
... important
NNT = 1/ARR
NNT for reduction in absolute risk
Clinical Cases
Clinical Case
Notes
:
note that
Clinical Case
NNT = Number
Needed to Treat
Notes
:
NNT is the inverse of the ARR = absolute relative risk
Example
:
Solution
:
NNT example 2
Notes
:
NNT is the inverse of the ARR = absolute relative risk
Example
:
*Odds Ratio vs. RR
the OR always over estimates the RR
the OR tends towards the RR when the prevalence of a disease is low, or the prevalence is close to the incidence
--> you can see this in the picture to the right as the factor prev./inc tends to --> 0 in both the exposed and unexposed groups,
--> the OR tends --> RR
*OR= Odds ratio
odds looks backwards
--> splits up Disease vs no disease
--> looks at odds of exposure vs no exposure
OR =
ADDS BATCIO
= ad / bc
Clinical Cases
Clinical Case
Notes
:
note that
Clinical Case
*RR = Relative Risk
RR looks at exposure vs NO exposure
--> risk of getting the disease
RR = Exp+Disease / total exposed
--> divided by (NOexpose+Disease / total NOexposed)
Clinical Cases
Clinical Case
Notes
:
note that
Clinical Case
RR = Relative Risk
Solution
:
Example
:
Notes
:
important to know the difference between relative risk, absolute risk, and odds ratios
sometimes RR = relative risk can overestimate the risk of n exposure
--> example: RR of UTI from not getting circumcised is high, but the actual AR = absolute risk difference is low because UTI in boys are so extremely rare
Types of Study *Bias
note that errors can either be random or systematic
bias = systematic error
random error = type 1 and types 2 errors
--> type 1 error = alpha = wrong rejection
--> type 2 error = beta = wrong accepting of the null
*Recall Bias
common with interview style studies
*Special Types of Bias
Pygmalian Bias = smart Pigtail Gretchen IQ bias
think of smart kid like Gretchen with Pigtails
type of observer bias where they already have a concieved notion of an outcome
comes from study of students where their IQs given to the teacher made the teacher think they were smarter
--> smart IQ kids = pigtails = Pygmalian bias
Notes
:
note that
Clinical Case
Hawthorne Effect Bias
think Pierce Hawthorne FAKING heart attacks because he KNOWS people are WATCHING and STUDYING him
Hawthorne effect is where people being study become aware and fake their behaviour
Notes
:
note that int his case the people being studied = doctors
Hawthorne effect is they realize this and fake their behaviour like pierce faking a heart attack
Clinical Case
Berkson's Bias
think of Pete Burke at the hospital
Berkson bias is where CONTROL patients are chosen from the hospital
more likely to be sick so are not good controls
Notes
:
note that
Clinical Case
Confounding variables vs Effect modification
note these are both external variables that have an effect on the exposure and the disease
difference is in stratified analysis
--> confounding variables show the RR is about the same when stratified by the outside variable
--> effect modification is where there is a large effect in the RR when you do stratification by the new variable
Confounding Variables Bias
something not accounted for in the study as the cause of an outcome
study finds association with significant p value, but doing stratification (= dividing up the people by age stratification) with an outside variable shows no difference
every study STOPS confounding variables by getting MATCHED groups by baseline when comparing controls to exposed
CASES
Clinical Case
Notes
:
note that
Clinical Case
Effect modification
opposite of confounding
effect modification is where there is a large effect in the RR when you do stratification by the new variable
can be positive or negative
*Lead-time Bias
Clinical Cases
Clinical Case
Notes
:
note that
Clinical Case
Lead-time Bias
Example:
Notes
:
note that lead time bias happens when there appears to be a longer survival rate for times right after the screening process
if a screening test survival rate doesn't hold up at later times that means there is lead time bias
*Observer Bias
Clinical Cases
Clinical Case
Notes
:
note that
Clinical Case
Observer Bias
Example:
Notes
:
*Observer Bias
case 2*
Example:
*Selection Bias
attrition bias = loss of follow up
--> special kind of selection bias
--> loss of one specific group to follow up that skews the results
STATS
Standard Error
Notes
:
note that sample error is inversely related to sample size
Case presentation:
Types of Statistical Tests
T-test
for comparing means of 2 separate samples
--> note that the t test is a special case of an F test ANOVA
T test vs Chi square
Notes
:
note
Clinical Case
Chi-Squared Test
same independent variable (continuous with mean and sd)
comparing two nominal groups (classic example is gender men vs women and comparing a certain variable)
Chi square test example
Notes
:
note that Chi square test is for 2 nominal variables, often comparing men and women as two categories
Clinical Case
*ANOVA = Analysis of Variance
for comparing means of more than 2 sample populations
note that the t test compares means of 2 sample populations
--> the t test is a special case of an F test ANOVA
α = alpha , β = beta , Type 1 / 2 Errors
Type 2 Error = Beta
and Statistical Power
power = 1 - β
failure to detect a difference since the n number not big enough
this is not forgiveable since all need to do is find more people
--> where alpha errors exist no matter what
Clinical Cases
Clinical Case
Notes
:
note that
Clinical Case
Type 2 Error = Beta
and Statistical Power
Notes
:
α = prob of type 1 error
type 1 error = α = prob of rejecting the null when it is actually true
--> think this is the usual error we use as α = 0.05 meaning that for a stat test we assume there is a 5% chance we make a type 1 error and reject the null as false, when there is a small chance it may still be true
β = prob of type 2 error
type 2 error = β = prob of not rejecting the null when it is actually false
--> type 2 error is directly related to statistical power because if you don't have enough power in your study or large enough sample, you may have a type 2 error and not reject the null when it is actually false
statistical power = (1 - β )
Case presentation:
Statistical Power calculation from B error example
Case presentation:
*Type 1 Error = Alpha and 95% confidence level
confidence level = 1 - α
type 1 error is where you detect a difference, but incorrectly
--> actually no difference
--> only 95% sure there is a difference
think alpha errors are understood to happen= built into study since you know there is an alpha = 5% chance you have a type 1 error
P Value
the prob of getting a finding just given to chance, assuming that the null hypothesis is true
EPI Principles and Terms
Incidence
Incidence vs Prevalence
Notes
:
this question simply tries to trick you on incidence vs prevalence
-incidence tells you nothing about the amount of people in population currently who have the disease
prevalence = incidence x survival duration
--> survival duration = 1 / fatality
Example
:
Accumulative Incidence
Accumulative Incidence
Example
:
Notes
:
note that cummulative incidence is over a specified time period like a year in this example
it does NOT count the people in the population who already have the disease so you have to subtract them from the total population first
you don't subtract anything else, even deaths since these people may have gotten the disease in the time period
Accumulation Risk Effect
for both risks and protective factors
some risk factors or protective factors take longer periods to give their risk
SMOKING for example accumulates over years and gets worse and worse with more use
Accumulation Risk Effect example
Notes
:
accumulation effect simply means a risk or protection accumulates over time
this is not true for all risks
classic example is smoking
--> reason why we take a pk year history since it has the accumulation effect
same true here for antioxidant use over a lifetime
Example
:
*Attributable Risk
What percentage of risk can be attributed to smoking?
--> attributed risk % = risk difference / (total risk RR)
--> attributed risk % = (RR -1) / RR
Subtract then divide
Attributable Riske example
Example
:
Notes
:
Attributable risk = RR - 1/ RR
Attributable Risk
Subtract then divide
AR = (RR -1) / RR
AR = (RRe - RRue / RRe
Prevalence
prevalence = incidence x time period
Point Prevalence Example
point prevalence same as normal prevalence but measured at a specific time
Notes
:
Example
:
*Odds Ratio
prob of event happening vs not happening
Odds ratio example
Example
:
Health Promotion and Disease Prevention
Primary Health Prevention
no disease is currently present
but there are evident risks that you can counsel to lower
promotion of health
Pernicious Anemia Case
Notes
:
note that health promotion is the main primary health prevention strategy because you can see very clear risks in a patient that you can cunsel them to lower
but there is no disease present yet and no symptoms
you are trying to lower disease later down the road
Clinical Case
Secondary Health Prevention
disease could be present, but assymptomatic
mainly screening for cancer, blood, hypercholesteremia, etc.
Tertiary Health Prevention
disease is present and there are symptoms as well
trying to lower any extra symptoms
cure the disease or slow its progress
Types of *Studies
*RCTs = Randomized Control Trials
gold standard of studies
*Observational Studies
*Case Control Studies
"CASE is first, risks are 2nd"
good for rare diseases
--> since need to find SACES of the disease first, then test for risks after
only study type that goes in reverse
pick CASES = have disease
--> regardless of exposure = find this after
pick controls as = NON CASES of diseases
--> regardless of exposure = find this after
CASE control studies CONTROL for the CASE of DISEASE 1st
--> figure out the risks exposure after
Clinical Cases
Clinical Case
Notes
:
note that
Clinical Case
Case example
Notes
:
note that CASE control studies are unique because they are the only one where they divide patients into CASES and NON cases of a disease
--> they then look back to see if they have risk + exposure or NOT
this is the opposite of retrospective cohort studies, observational studies, etc.
--> they instead divide into risk exposure vs non risk exposure --> then find whether they get a disease or NOT
Clinical Case
Case example 2
Notes
:
note that CASE control studies controls are NON DISEASE people
for CASE controls, think you control for the CASE of DISEASE
then figure the risks after
Clinical Case
*Ecological Studies
"eco" = environemnt
think of the whole species in the environment
eco studies are at population level, NOT individual
Clinical Cases
Clinical Case
Notes
:
note that
Clinical Case
Case example
Notes
:
note that ecological studies are eco so it is the whole species and the environment they live in
thus eco studies are at the population level, NOT the individual level
ecological fallacy = trying to predict individual outcomes based on eco studies or the entire population
Clinical Case
*Prospective Cohort Studies
find a cohort of people who are either exposed or not exposed to a risk and see if they develop a disease
Prospective cohort example
Clinical Case
Notes
:
*Crossover study
crossover from one treatment to another in the SAME patient to compare the 2 treatments
randomly group patients to AB group or BA group
AB = treatment A / washout period / treatment B
BA group is the oppositie
pros for crossovers = patients are their own controls
cons for crossovers = need washout period long enough to make sure no effect on second treatment
Stats / EPI you should know off-hand
general cancer stats and risks
Cancer Incidence
Women :
Incidence = breast, lung, colon
Mortality = lung, breast, colon
Men
Incidence = prostate, lung, colon
Mortality = lung, prostate, colon
note that lung is second for both in incidence, but switches for the sex cancer for mortality as the number 1
Smoking Risks
smoking is one of the biggest risk factors for most diseases
biggest mortality risk reducer in MI (even more than aspirin, BP etc.)
Smoking = biggest mortality risk for MI, expecially for Diabetics
Notes:
note in the graph to the left smoking is the largest factor for mortality
it is even more pronounced in diabetics for getting an MI
you would think that aspirin would be directly related to CAD and reduce mortality more, but smoking is still bigger, even more in diabetics
Case example
Infectious Diseases
Diarrhea Outbreak Example
Notes
:
NTDs have different severity
severity of a neural tube defect can range from moderate, such as spina bifida, to severe and non-life-compatible, such as anencephaly
the high AchE is because in NTDs it leaks out into the amniotic sac from the CSF of the open neural tube
Example
:
Basics of the Punnet Square
make sure to put either all numbers or ALL percents
NEVER mix them up!
Crytptogenic stroke and ASD and PFO
Notes
:
I put in the 75% into the square by accident
Clinical Case
*Hardy-Weinberg Genetics
prevalence of alleles in the population
assumes there is no evolution, or changes in the population
Notes
:
p = normal allele frequency % in population
q = mutant allele frequency %
all add to 1
q2 = phenotype of disease
2pq = carrier of disease
Clinical Cases
Clinical Case
Notes
:
note that
Clinical Case
*Normal Distribution
68 95 99.7 Rule
--> 1,2,3 SD from the mean
--> % of population in those SDs
*Normal Distribution
68 95 99.7 Rule
--> 1,2,3 SD from the mean
--> % of population in those SDs
actual 95% population =
1.96 SD
actual 99% population =
2.58 SD
*Skewed Normal Distributions
SKEW
= means the MEAN is skewed by a TAIL in either the Positive or NEgative direction
"MEAN, Median, Mode"
= MEAN is always skewed the most by the tail
--> then Median
--> then MODE
makes sense as the MODE hass to be near the HUMP of the Curve
*Home
