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Biostats and epidemiology > Biostats and epidemiology > Flashcards

Biostats and epidemiology Flashcards

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1
Q

Definition of type 1 error (alpha error)

A

Rejecting the null hypothesis when it is really true

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2
Q

Definition of a type 2 error (beta error)

A

Not rejecting the null hypothesis when it is really false

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3
Q

Ways to increase statistical power

A
  • Increase the sample size (most common)
  • Increase the expected effect size
  • Increase precision of measurement
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4
Q

Use of the t-test

A

Comparing the means of two groups from a single nominal variable, using means from an Interval variable to see whether the groups are different

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5
Q

Use of one-way ANOVA

A

Compares means of many groups (2 or more) of a single nominal variable using an interval variable

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6
Q

Use of chi squared

A

Tests to see whether two nominal variables (not mean) are independent

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7
Q

Infectious diseases that require mandatory reporting to the local Public Health Department, who will inform the CDC

A

“Salas siente comezón, grita y chilla al horinar”

  • Chlamydia
  • Gonorrhea
  • Chicken pox
  • AIDS
  • Syphilis
  • Salmonella
  • Hepatitis A and B
  • Tuberculosis
  • Lyme disease
  • Other: pertussis, legionnaires, mumps, rubella and measles
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8
Q

Coverage of Medicare

A
  • Ambulance transport
  • Dialysis
  • Speech and occupational therapy
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9
Q

Data analysis test used in cross-sectional studies

A

Chi-squared

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10
Q

Data analysis test used in case-control studies

A

Odds ratio

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11
Q

Data analysis test used in cohort studies

A

Relative risk

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12
Q

Describe the sample size, data evaluated, and duration of phase 1 clinical trials

A
  • Sample size: 20 to 100 (healthy)
  • Data evaluated: safety, toxicity, pharmacokinetics/pharmacodynamics, adverse effects
  • Duration: 1 to 30 days

“Is it SAFE?”

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13
Q

Describe the sample size, data evaluated, and duration of phase 2 clinical trials

A
  • Sample size: 100 to 300 (disease)
  • Data evaluated: dose, tolerability, efficacy, adverse effects
  • Duration: months

“Does it WORK?”

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14
Q

Describe the sample size, data evaluated, and duration of phase 3 clinical trials

A
  • Sample size: 100s to 1000s
  • Data evaluated: compares the new treatment to the current standard of care
  • Duration: months to years

“Any IMPROVEMENT?”

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15
Q

Describe the sample size, data evaluated, and duration of phase 4 clinical trials

A
  • Sample size: 1000s
  • Data evaluated: epidemiology, postmarketing surveillance, common and rare side effects
  • Duration: years

“MARKET”

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16
Q

Define sensitivity

A

Proportion of truly diseased persons in the screened population who are identified as diseased by the screening test (“true positive rate”)

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17
Q

Define specificity

A

Proportion of truly disease-free persons who are identified as non-diseased by the screening test (“true negative rate”)

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18
Q

Define positive predictive value

A

Probability that a person with a positive test is a true positive

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19
Q

Define negative predictive value

A

Probability of no disease in a person with a negative test

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20
Q

Define accuracy

A

Degree to which a measurement, or an estimate based on measurements, represents the true value of the attribute that is being measured

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21
Q

Formula for odds ratio

A

OR = AD/BC

*La probabilidad de que los que estuvieron expuestos desarrollen la enfermedad sobre la probabilidad de que los que no estuvieron expuestos desarrollen la enfermedad ((A/B)/(C/D))

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22
Q

Formula for relative risk

A

RR = (A/A+B)/(C/C+D)

*La incidencia de la enfermedad en los que estuvieron expuestos sobre la incidencia de la enfermedad sobre los que no estuvieron expuestos

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23
Q

Define the attributable risk

A

The difference in risk between exposed and unexposed groups, or the proportion of disease occurrences that are attributable to the exposure

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24
Q

Formula of attributable risk

A

AR = (A/A+B) - (C/C+D)

*La incidencia de la enfermedad en los que estuvieron expuestos menos la incidencia de la enfermedad en los que no estuvieron expuestos

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25
Define a selection bias
When the sample is not representative (nonrandom smapling or treatment allocation of subjects)
26
Examples of selection biases
* Berkson bias * Non-response bias * Healthy worker effect
27
Define the berkson bias
Study population selecten from hospital is less healthy than general population
28
Define the non-response bias
Participating subjects differ from nonrespondents in meaningful ways
29
Define the healthy worker effect
Study population is healthier than the general population
30
Solutions to a selection bias
* Randomization | * Ensure the choice of the right comparison/reference group
31
Define a measurement bias
Information is gathered in a systemically distorted manner
32
Define the Hawthorne effect
Measurement bias in which participants change their behavior in response to their awareness of being observed
33
Solution to a measurement bias
* Using objective, standardized and previously tested methods of data collection * Using control/placebo groups (Hawthorne)
34
Define an expectancy bias
Researcher's beliefs affect outcome (aka Pygmalion effect)
35
Solution to an expectancy bias
Double-blind design
36
Solution to a lead-time bias
Measure "back-end" survival (adjust survival according to the severity of disease at the time of diagnosis)
37
Solution to a recall bias
* Decrease time from exposure to follow-up | * Multiple sources to confirm information
38
Define a confounding bias
Unanticipated factors obscure results (a factor is related to both the exposure and the outcome)
39
Solution to a confounding bias
* Multiple/repeated studies * Crossover studies * Matching * Restriction * Randomization
40
Formula for standard error
SE = SD/square root of the samples
41
Define the nested case-control design
Study starts with cohort study, and those who develop an outcome of interest become cases for a case-control study
42
Black box warnings are added during which phase of the clinical trial
Phase 4
43
Most important characteristic of an ecological study
Unit of analysis is populations, not individuals
44
What is an ecollogical fallacy
Making conclusions regarding individuals within populations
45
Describe a crossover study
Subjects are randomly allocated to a sequence of 2 or more treatments given consecutively
46
Advantage of a crossover study
It allows patients to serve as their own controls
47
Disadvantage of a crossover study
Effects of one treatment may carry over and alter response to a subsequent treatment
48
Solution to the drawback of a crossover study
Have a washout phase
49
Formula for relative risk reduction
RRR = 1 - RR
50
Formula for absolute risk reduction
ARR = (C/C+D) - (A/A+B)
51
Formula for number needed to treat
NNT = 1/ARR
52
Formula for number needed to harm
NNH = 1/AR
53
Define precision
The consistency and reproducibility of a test
54
Methods to increase precision
* Decrease standard deviation | * Increase statistical power
55
Formula of attack rate
Number of individuals who become ill divided by the number of individuals who are at risk of contracting the illness
56
Define an attrition bias
Lose to follow up occurs disproportionately between exposed and unexposed groups
57
Difference between effect modification and confounding bias
Effect modification refers to when the effect of an exposure on an outcome is modified by another variable
58
What should you do to differentiate between effect modification and confounding bias
Stratified analysis (stratification is based on the confounder, if association disappears, it is a confounding bias)
59
Formula for the CI for a population mean
CI = mean +/- Z(SE)
60
Z values for a 95% CI and 99% CI
* 95% = 1.96 | * 99% = 2.58
61
Define a late-look bias
Survey doesn´t uncover patients with severe disease because they die first
62
Define a proficiency bias
When the different skill levels of physicians delivering treatment might affect patient outcomes more than the treatment selection itself
63
Disorders related to a high socioeconomic status (SES)
* Anxiety disorders * Breast cancer * Bipolar disorders
64
Definition of socioeconomic status
Weighted combination of education and occupation status
65
Formula for accuracy
(TP + TN)/Everything
66
Leading causes of death overall in the US (top 3)
1. Heart disease 2. Cancer 3. Unintentional injuries
67
Age group in which suicide is the number 2 cause of death
10 - 34 years old

Biostats and epidemiology (1 decks)

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