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Spring 2022 > BIOL 437 Week Three > Flashcards

BIOL 437 Week Three Flashcards

1
Q

study design

A
  • program that directs the researcher along the path of systematically collecting, analyzing and intrepreting data
  • allows for descriptive assessment of events and for statistical inference
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2
Q

study types

A

-experimental
-observational
>descriptive
>analytic

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3
Q

descriptive epidemiology

A
  • examining the distribution of disease in a population
  • observing the basic features of it’s distribution
  • when, where, who
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4
Q

analytic epidemiology

A
  • testing a hypothesis about the cause of disease by studying how exposures relate to the disease
  • observational or experimental
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5
Q

analytic epi-studies require info to

A
  • know where to look
  • know what to control for
  • develop viable hypotheses
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6
Q

analytic epi- is built around

A

-the analysis of the relationship between two items
>exposures
>effects (disease)
-looking for determinants or possibe causes of disease
-useful for hypthesis testing

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7
Q

descriptive statistics

A 
-can take on various forms
>tables
>graphs
>numerical summary measures
-application of statistical methods makes it possible to effectively describe the public health problem
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8
Q

helpfulness of descriptive epidemiology

A
  • info about a disease
  • clues to identify a new disease
  • identifies the extent of the public health problem
  • obtains a description of the problem that can be easily communicated
  • identifies the population at greatest risk
  • assists in planning and resource allocation
  • identifies avenues for future research
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9
Q

4 types of descriptive studies

A
  1. Ecologic studies: population level
  2. Case reports
  3. Case series
  4. Cross-sectional surveys
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10
Q

ecological study

A

-involves aggregated data on the population level
-ecological fallacy
>when population level are used for individual level
-advantage: annoymous
-disadvantage: can’t have individual correlation

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11
Q

case report

A

-involves a profile of a single individual

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12
Q

case series

A

-involves a small group of patients with a similar diagnosis
-provide evidence for longer scale studies
>hypotheis gathering

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13
Q

cross-sectional survey

A
  • prevelance survey
  • short period of time
  • no follow-up
  • all variables measured at a point in time
  • no distinction between potential risk factors and outcomes
  • good for examining associations between factors
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14
Q

strengths of cross-sectional

A
  • several associations at once
  • relatively inexpensive
  • short period of time
  • control
  • biases due to observation and loss to follow up does not exist
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15
Q

weakness of cross-sectional

A 
-unable to establish sequence of events
>association NOT causation
-influenced by response bias
-no follow-up
-no incidence or relative risk data
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16
Q

serial surveys

A

-cross-sectional surveys that are routinely conducted

>regular frequencies

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17
Q

cohort study

A

-of persons who have been exposed and are followed over time with selected health outcomes

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18
Q

case-control study

A

-of persons who have been exposed and are followed over time with selected health outcomes

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19
Q

case-control study

A
  • grouping people as cases and controls

- are cases more/less likely than contorls to have had exposures/behaviours

20
Q

2 methods for age-adjusted rates

A
  1. Direct

2. Indirect

21
Q

direct method

A

-assume males and females have same rate

22
Q

indirect method

A

-age-specific rates are unstable
-standardized morbididty ratio (SMR)
>know total counts
>use rate from other gender

23
Q

SMR=1

A

-events observed were same as expected

24
Q

SMR>1

A

-more events observed than expected

25
Q

SMR<1

A

-fewer events observed than expected

26
Q

4 types of data

A
  1. Nominal
  2. Ordinal
  3. Discrete
  4. Continuous
27
Q

nominal

A
  • categorical: unordered
  • dichotomous: 2 levels
  • multichotomous: more than 2 levels
28
Q

ordinal

A

-categorical: ordered, scale data

29
Q

discrete

A

-quantitative
-differ by fixed amounts
Ex. number of sick kids

30
Q

continous

A

-quantitative
-values on a continuum
-interval and ratio
Ex. age, weight

31
Q

interval

A
  • units of equal magnitude without an absolute 0

- ex. temperature

32
Q

ratio

A

-units of equal magnitude, absolute 0
>starting point is 0
Ex. HR, BP, distance

33
Q

attack rate

A
  • cumulative incidence rate

- diseases or events that affect a larger proportion of the population than the conventional incidence rate

34
Q

measures of central tendency

A
  • mean
  • median
  • mode
35
Q

measures of depression

A
  • range
  • interquartile range
  • variance
  • SD
  • coefficient of variation
36
Q

frequency distributions

A
  • values of a variable and records with each value

- rapid visual assessment

37
Q

spread

A
  • variation of dispersion
  • distribution from a central value
    ex. range and SD
38
Q

shape

A
  • symmetrical vs. assymmetrical/skewed

- “tail” of the distribution-skewed

39
Q

positively skewed

A
  • tail to right

- central location is to the left

40
Q

negatively skewed

A
  • tail to left

- central location to right

41
Q

mode

A
  • value that occurs most in data
  • could have more than one
  • used for ‘descriptive’ measure
42
Q

median

A
  • middle value of data set in rank order
  • 50th percentile of the distribution
  • descriptive
43
Q

arithmetic mean

A

-value closest to all the other values in a distribution
-often used in statisical analyses
-best descriptive measure for normally distributed data
>not great for skewed data

44
Q

geometric mean

A

-mean of data on a log scale
>normally distributed
-ex. serial dilutions or environmental sampling

45
Q

standard deviation

A
  • measure of spread
  • often used with the arithmetic mean
  • ‘variability in a data set’
46
Q

standard error (SE) of the mean

A
  • variability expected of repeated samples from the same population
  • quantifies variation in sample means
  • used to determine confidence intervals

Spring 2022 (54 decks)

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