Terms  Definitions 

Incidence rate 
It's a measure of the risk. Number of new events/number of persons exposed to risk

Prevalence rate 
It's a measure of the extent. All cases of a disease/total population at risk

Relationship between incidence and prevalence 
Prevalence = Incidence X Duration

What happens to incidence and prevalence if: New effective treatment is initiated 
Prevalence decreases

What happens to incidence and prevalence if: New effective vaccine gains widespread use 
Both incidence and prevalence decrease

What happens to incidence and prevalence if: Number of persons dying from the condition increases 
Prevalence decreases

What happens to incidence and prevalence if: Additional research funds are added 
No change in either incidence nor prevalence

What happens to incidence and prevalence if: Behavioral risk factors are reduced in the poulation 
Both incidence and prevalence decrease

What happens to incidence and prevalence if: Contacts between infected and noninfected persons are reduced 
Both incidence and prevalence decrease

What happens to incidence and prevalence if: Recovery from the disease is more rapid 
Prevalence decreases

What happens to incidence and prevalence if: Longterm survival rates for the disease increase 
Prevalence increases

Morbidity rate 
Rate of disease in a population at risk. Both incident and prevalent cases.

Mortality rate 
Rate of death in a population at risk. Incident cases only.

Attack rate 
A type of incidence in which the denominator is further reduced for some known exposure

Point prevalence 
Prevalence at a specified point in time

Period prevalence 
Prevalence during a span of time

Crude rate 
Measured rate for whole population

Specific rate 
Measured rate for a subgroup of the population

Standardized rate 
Adjustment to make groups equal on some factor

Number needed to treat 
Inverse of incidence rate. Means that I would have to treat X number of people to prevent one case. 1/ARR; ARR = event rate in control group  event rate in treated group

Crude mortality rate 
Deaths/population

Causespecific mortality rate 
Deaths from cause/population

Causefatality rate 
Deaths from cause/number of people with the disease

Proportionate mortality rate (PMR) 
Deaths from cause/all deaths

Sensitivity 
The percentage of sick people for whom the test was positive: TP / TP + FN or a/a+c or 1FN rate

False negative rate 
1  sensitivity

Specificity 
The percentage of healthy people identified as not having the disease: TN / TN + FP or d/(d+b) or 1FN rate

False positive rate 
1  specificity

Positive predictive value 
The probability that a person with a positive test truly has the disease: TP / TP + FP or a/(a+b)

Negative predictive value 
The probability that a person with a negative test doesn’t have the disease: TN / TN + FN

Accuracy 
TP + TN / total screened patients

What is the relationship between positive and negative predictive values and prevalence 
Prevalence is directly proportional to PPP and inversely proportional to NPP

Selective bias 
The sample is not representative of the population

Measurement bias 
The information is gathered in a manner that distorts the information.

Berkson bias 
Selection bias in which hospital records are used to estimate population prevalence

Nonrespondent bias 
Selection bias in which people included in the study are different than those who are not

Hawthorne effect 
Subject's behavior is altered because they are being studied. Only a factor when there's no control group in a prospective study.

Solution to selection bias 
Use a random, independent sample.

Solution to measurement bias 
Set up a control/placebo group

Experimenter expectancy bias 
Experimenter's expectations are passed on to subjects producing the desired effects.

Solution to experimenter expectancy bias 
Doubleblind design  neither the experimenter nor the subject know who receives the intervention.

Leadtime bias 
Gives a false estimate of survival rates. Confuses improved screening with improved survival.

Solution to leadtime survival 
Measure backend survival  measure increased lifeexpectancy

Recall bias 
Subjects fail to accurately recall events in the past. It's a problem in retrospective studies.

Solution to recall bias 
Use multiple sources to confirm information

Latelook bias 
Individuals with severe disease are less likely to be uncovered in a survey because they die first

Solution to latelook bias 
Stratify by severity.

Confounding bias 
Factor being examined is related or influenced by other factors of less interest

Solution to confounding 
Do multiple studies and good research design

Case report 
Clinical characteristic or outcome from a single clinical subject or event

Case series report 
Clinical characteristic or outcome from a group of clinical subjects. Just diseased, no control group.

Crosssectional study 
The presence or absence of disease and other variables in a representative sample at a particular time. Measures prevalence, not incidence. Cause and effect cannot be determined.

Casecontrol study 
People with disease compared to a control group. Almost always retrospective. Doesn't measure incidence or prevalence but determines causality. Qualities of the healthy are compared to qualities of the sick, determines risk factors. Use odds ratio.

Cohort study 
Group with risk factor is compared to group without it  prospective. Oppossite of casecontrol. Measure incidence in each group, determines causality. Most reliable and valid. Use relative risk or attributable risk

Tools used to analyze cohort studies and incidence data 
Relative risk and attributable risk

Relative risk 
Incidence rate of exposed group / incidence rate of the unexposed group. Greater chance of one group of disease compared to the other group. Used for cohort studies.

Attributable risk 
Incidence rate of exposed group  incidence rate of unexposed group. How many more cases in one group. Used for cohort studies.

Odds ratio 
AD/BC; where A is the table cell of the object of study and D is diagonally across from it. Chance of risk given disease. Used for casecontrol studies.

Observational studies 
Case, case series, crosssectional, casecontrol, cohort

Phase 1 clinical trial 
Testing safety of drug in healthy volunteers

Phase 2 clinical trial 
Testing protocol and dose levels in small group of patient volunteers

Phase 3 clinical trial 
Efficacy and occurrence of side effects in large group of patient volunteers.

Intervention studies 
Randomized controlled clinical trial, community trial, crossover study

Randomized controlled clinical trial 
Subjects are randomly allocated into intervention and control groups. Most rigorous study. Doubleblind is when neither patients nor doctors know which group a patient is in. Least subject to bias, expensive.

Community trial 
Entire community is tested

Crossover study 
All subjects receive intervention, but at different times.

Combine probabilities for independent events 
By multiplication

Combine probabilities for nonindependent events 
Multiply the probability of one event by the probability of the second, assuming the first event occurred

Combine probabilities for mutually exclusive events 
By addition

Combine probabilities for events that are not mutually exclusive 
Add the two probabilities and subtract the multiplied probabilities

Central tendency values 
Mean, median, mode

Mean 
Average = sum of the values / number of values

Median 
The 50th percentile. The value which divides the set into an upper half and a lower half.

Mode 
The most frequent value encountered

Positive skew of the distribution curve 
Tail is to the right, mean greater than median

Negative skew of the distribution curve 
Tail is to the left, median is greater than mean

Best central tendency measure for skewed distributions 
Median

Best central tendency measure for normal distribution 
Mean, median and mode are all the same

1 standard deviation 
68% of cases

2 standard deviations 
95.5% of cases

3 standard deviations 
99.7% of cases

Between the mean and 1 standard deviation 
34% of cases

Between 1 standard deviation and 2 standard deviations 
13.5% of cases

Between 2 standard deviations and 3 standard deviations 
2.4%of cases

Above 3 standard deviations 
0.15% of cases

Confidence interval 
A percentage that assures how much up or down from the sample the true population is.

95% confidence 
Z = 2

99% confidence 
Z = 2.5

Confidence interval 
Mean + Z (S/square root of the sample size)

Confidence interval for relative risk and odds ratio 
If the CI range excludes 1 then it is significant. If the range is above one > increased risk; if the range is below one > decreased risk. If the CI range includes 1, then it is not significant

Null hypothesis 
The opposite of what is trying to prove. E.g. hypothesis: the drug works; null hypothesis: the drug doesn’t work

pvalue < 0.05 
Reject the null hypothesis  reached statistical significance

pvalue > 0.05 
Do not reject null hypothesis  has not reached statistical significance

Type I error or alpha error 
Rejecting the null hypothesis when it's really true  asserting the drug works, when it really doesn’t. The pvalue is the chance of a type I error  if p=0.05, then chance of type I error is 5%.

Type II error or beta error 
Failing to reject the null hypothesis when its really false  asserting the drug doesn’t work, when it does. Cannot be estimated from pvalue.

Statistical power 
1  P = beta error

How to increase power 
Increase the sample size, which increases power and decreases type II errors

Types of scales in statistics 
Nominal, ordinal, interval, ratio

Nominal scale 
Puts objects into different groups or categories. Gender, drug Vs. placebo group, etc…

Ordinal scale 
Puts groups into sequence, ranks or in different states of quality. Olympic medals, class rank, etc…

Interval scale 
A group that is ordered in such a way that we can tell not just that they're different in quality but in quanity as well (how much do they differ). Height, weight, blood pressure, drug dosage, etc.

Ratio scale 
Like interval scale but has a true zero point below which it cant go. Kelvin temperature scale, etc…

Pearson correlation 
All interval data

Chi square 
All nominal data

ttest 
2 groups with interval and nominal data

ANOVA 
more than 2 groups with nominal and interval data

All interval data  which statistical test? 
Pearson correlation

All nominal data  which statistical test? 
Chisquare

Combined interval and nominal data  which statistical test? 
If two groups: ttest; if more than two groups: ANOVA

Meta analysis 
Statistical combination of the results of many studies, yielding a single pvalue that represents the sum of all.

What is the range of correlation analysis values? 
minus 1 to plus 1

What can be infered from a correlation analysis value of 1? 
Strong negative correlation  the variables are inversely proportional. Scatterplot shows bunched up dots with a negative slope.

What can be infered from a correlation analysis value of +1? 
Strong positive correlation  the variables are directly porportional. Scatterplot shows bunched up dots with a positive slope.

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