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Med Sci 4930 > Midterm > Flashcards

Midterm Flashcards

1
Q

IMRAD

A 
introduction 
materials and methods 
results 
(and)
discussion
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2
Q

other components of a paper

A 
title
abstract
keywords 
references
supplemental
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3
Q

steps of a paper critique

A 
read 
analyze
establish research context 
evaluate 
establish significance of the research
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4
Q

steps of experimental design

A 
background research 
formulate research question
identify variables
generate hypothesis 
determine experimental design
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5
Q

what is optimization of an experiment?

A

set of experiments on its own to optomize variables i.e. cell type, treatment time, concentration of drug

see what works best and then use it

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6
Q

what are controls for? negative? positive?

A

scientific controls minimize the effects of variables other than the independent variable (i.e. control for confounding)

negative - no response expected
positive - effect when there should be effect

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7
Q

when do you use loading controls?

A

western blot - look for a house keeping gene to make sure all lanes have been loaded equally

they are a type of positive control

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8
Q

technical replicates

A

the same sample being used in 3 wells etc

  • control for human error
  • improve accuracy

note: don’t use technical replicates for western blot

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9
Q

biological replicates

A

more than one biological sample i.e. using 2 mice or passaging cell lines and repeating experiment
-account for individuals with differences

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10
Q

descriptive statistics

A

discrete, quantitative analysis of data

summary of one sample of the population

not based on probability

ie demographic data, individual GPA scores etc

summary of your data set, don’t extend to population level

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11
Q

inferential statistics

A

generalized, extended analysis of data

assumes properties of a population from an observed data set

based on probability

ie efficacy/significance of treatment paradigms on general population

generalize to population level

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12
Q

data set

A

recorded raw values of a variable of interest

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13
Q

mean

A

average value of the data set

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14
Q

SD

A

how much the raw values of the data set spread across the mean

  • best used for descriptive statistics
  • describes variability of raw data across mean
  • dependent on n value

(for technical replicates)

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15
Q

SEM

A

how much the sample mean differs from the population mean

  • best used for inferential statistics
  • attempts to find SD of a sampling distribution
  • dependent on n value

(for biological replicates)

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16
Q

why use a large sample?

A

mean of a large sample is likely to be closer to the true population mean than that of aa small sample
i.e. with large sample know the value of the mean with a lot of precision even if the data are very scattered

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17
Q

what is significance?

A

p-value

probability that the changes between two sets of data are true

95% CI/p<0.05 is standard

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18
Q

standard t-test

A

used to compare 2 sets of independent data

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19
Q

paired/repeated measures t-test

A

used to compare 2 sets of related data

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20
Q

One-way ANOVA

A

used in comparing 3+ sets of data (1 variable)

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21
Q

two-way ANOVA

A

used in comparing 3+ sets of data across 2 independent variables

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22
Q

What is ANOVA?

A

basically multiple t-tests performed in sequence but is better because it is more conservative i.e. less chance for type I error (false positive)

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23
Q

What is a post hoc?

A

further analysis of treatment groups after running an ANOVA
reduces probability of discovering a false positive
Turkey’s and Bonferroni are popular ones

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24
Q

Turkey’s post hoc

A

comparison of each mean to every other mean (similar to multiple t-tests)

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25
Q

Bonferroni post hoc

A

corrects the CI (alpha) depending on number on comparisons made (alpha/n)

  • the more comparisons you make the lower your significance
  • overcorrects

test each hypothesis at a lower alpha to reduce chances of making type I errors (false positive) when doing multiple comparisons

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26
Q

additive

A

combined drug effects are consistent with individual drug effect
ie NOT them added together

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27
Q

synergistic

A

combined drug effects produce an effect which is above what is expected from the individual drug effects

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28
Q

independent variable

A

what you are manipulating

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29
Q

dependent variable

A

depends on the independent variable (i.e. what you are measuring)

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30
Q

minimum requirements to conclude that E is a cause of O?

A

correlation/association
E precedes O
replication

don’t need:

  • theory
  • randomization
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31
Q

define epidemiology

A

the study of the occurrence and distribution of health related states or events in specified populations, including the study of the determinants influencing such states, and the application of this knowledge to control the health problems

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32
Q

occurrence

A

incidence and prevalence

33
Q

distribution

A

occurrence by characteristics of person, place and time

34
Q

health related states

A

things that endure over time

ie a chronic disease

35
Q

health related events

A

things discretely dated in time

i.e. heart attack

36
Q

what is a case definition? what should it be?

A

definition of health state/event that is valid and reliable

37
Q

valid

A

measures what is intended

specificity and sensitivity

38
Q

reliable

A

stable, repeated, consistent, over time or by different raters

39
Q

sensitivity

A

probability that a diseased person in the population tested will be identified as diseased by the test

i.e. true positive probability

40
Q

specificity

A

probability that a person without the disease will be correctly identified as non diseased by the test

i.e. true negative probability

41
Q

a, b, c and d

A 
a = true positive 
b = false positive 
c = false negative 
d = true negative
42
Q

sensitivity formula

A

a / (a+c)

43
Q

specificity formula

A

d / (b+d)

44
Q

descriptive epidemiology

A
  • count cases in the population
  • present by characteristics of person, place and time
  • devise testable hypotheses based on unexpected of unusual trends, differences etc
45
Q

analytic epidemiology

A
  • test etiologic hypotheses of risk and protective factors
  • replicate
  • minimize confounding
  • identify protective and risk factors
46
Q

experimental epidemiology

A

randomized clinical trials of interventions i.e. can see if protective factors you identified are as good as you thought they were

47
Q

confounding

A

due to correlation of apparent risk factor and true cause, there is confounded estimate of effect of risk factor on outcome

as a result of confounding can find something that is strong, statistically significant, replaceable and completely wrong

48
Q

what can confounding do?

A
  • create false associations
  • mask true associations
  • change estimated association from risk factor to protective factor or vice versa
49
Q

what are 5 strategies to control confounding?

A

design: randomization, matching, restriction
analysis: stratification, multivariable analysis

50
Q

experimental vs observational study design

A

experimental:

  • use randomization to assign exposure
  • randomization turns confounding into random error (more as n increases)
  • works for known and unknown confounders

observational:

  • exposures selected by self, parent, provider, insurer, happenstance etc
  • use other means (matching, restriction, stratification, multivariable analyses) to minimize confounding
51
Q

what do both prevalence and incidence need to include?

A

the time period i.e. annual incidence or prevalence on december 5th

52
Q

prevalence

A

all population cases/all people in population x100

good for bookkeeping i.e. monitoring trends, comparisons between regions and planning health system

bad for identifying causes

contaminated measure because it is affected by incidence, mortality and recovery

53
Q

incidence

A

all new cases in population/all ppl at risk in population base-10 i.e. per 100 000 etc

essential for studying causes
harder to collect so fewer around
need to determine who is at risk

pure measure, reflects all etiologic forces behind a disease

54
Q

Who is considered at risk when finding incidence?

A

at risk = people who have not been diagnosed or experienced outcome, but could

not at risk = ppl who have already been diagnosed or cannot biologically have it

55
Q

how could decreased prevalence be bad? increased good?

A

decreased could be due to increased fatality

increased could be due reduced mortality

56
Q

what should you critical appraise incidence and prevalence measures?

A

because they are sample estimates of population parameters
so difference by person, place or time can be real or artifact of different methods
ie don’t over interpret small changes in the presence of substantial error

57
Q

case-control studies

A

always control on the basis of OUTCOME

cases have it (i.e. diabetes), controls do not

select your cases and controls and then measure variables of interest

relatively cheap

58
Q

cohort studies

A

always control on the basis of EXPOSURE

exposed are (i.e. obese), unexposed are not

determine exposure of interest and choose ppl that have or don’t, follow up to observe outcomes (i.e. no on has disease at the beginning of the study - see who becomes a case)

very expensive

59
Q

what are case-control studies good for? what kind of bias are they prone to?

A

good for rare diseases (can study all)
long latency periods (if a disease takes a while to develop start with it instead of waiting for it to develop in a cohort study
emergencies (ie find out what is causing food poisoning)

prone to recall bias ie was your pregnancy stressful

60
Q

what are cohort studies good for? what kind of bias are they prone to?

A

good for rare exposures (study all)
frequent outcomes

prone to attrition bias

61
Q

what are the null and range of values for correlation? when do you use correlation?

A

null = 0
goes from -1 to 1

use for 2 continuously distributed variables i.e. dietary Na and BP

62
Q

what are null and range for ration measures of association (RR and OR)?

A

null = 1.0

range - o.ooooo1 to infinity

63
Q

risk

A

prospectively observed probability of outcome

64
Q

risk ratio

A

risk of exposed / risk of unexposed

1.0 is null
ie RR = 2.0 means that outcome is twice as likely in exposed group

used for cohort studies

65
Q

a, b, c and d for ratios

A 
go case (i.e. bad outcome) in first column, exposed on top, not on bottom
non-case on other side, exposed on top and not on bottom
66
Q

risk exposed formula? unexposed?

A

a/ (a+b)

c/(c+d)

67
Q

odds

A

odds = probability of exposure / (1 - probability of exposure)

68
Q

odds ratio

A

ratio of the odds of being exposed in the cases/controls

null = 1.0 
OR = 2.25 would be 2.25 more times likely to have been exposed in cases than controls

used for case-control studies

69
Q

cross product for OR

A

(ad) / (bc)

70
Q

relative risk?

A

often synonymous with RR, but sometimes used for any ratio (i.e. OR)

71
Q

if you match can that variable confound?

A

no i.e. if you age match you are guaranteed that your results are not confounded by age

72
Q

crude vs adjusted RR/OR

A

crude indicates the crude association between exposure and outcome with all confounding present

adjusted cannot be confirmed bc regression has been used to adjust for the effects of known, measured and analyzed confounders

->can see how much confounding is going on by how large change is between crude and adjusted

73
Q

incidence rate ratio

A

ratio of 2 incidence rates
-picks up speed of occurrence

ie RR could be the same, but IRR could be larger because something is happening faster

generally better than RR, especially if time matters

74
Q

incidence rate

A
  • always count the last day*
    measured in events per time unit
    ie 111 events per 1000 person years
75
Q

hazard ratios

A

based on time-to-event
i.e. death

can also be called survival analysis

also measures speed of occurrence

ratio of the two speeds cumulated over the entire period of observation on everyone

HR = 2.0 means that if a patient in one group has not died etc at a certain time point they have twice the probability of having died etc by the next time point compared to someone in the other treatment group

76
Q

what is an etiologic study?

A

what are risk and protective factors for ____ disease

77
Q

what is a prognostic study?

A

what are the risk and protective factors for specific outcomes in people with disease ____

i.e. everyone in study has disease

78
Q

what does prospective mean?

A

measure as they do it

Med Sci 4930 (2 decks)

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