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Biostats > Test 3 > Flashcards

Test 3 Flashcards

1
Q

Goals of ethics review

A

any human research must go through the IRB

  1. Prevent gross ethical violations
  2. Correct for blind spots
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2
Q

History of US regulations

A

1974 National research act protection of biomedical and behavioral human subjects
1979 Belmont report
1991 Common Rule–set of federal regulations

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3
Q

Belmont Principles

A
  1. Respect–informed consent, time, procedures, risks and benefits, voluntary, NO COERCION
  2. Beneficence–research must do good for someone. Medigate risk and justify it. Confidentiality
  3. Justice–risks and rewards. Not equal. researchers reaped benefit from vulnerable people’s risk
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4
Q

IRBs

A

every IRB must be federally registered

  • 2 kinds. A promise to govt and set up our own IRB
  • if only the first, you can use another institution’s IRB
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5
Q

IRB membership

A
  • represent all three divisions. natural, social, humanities

- community members in our case the bishop and the Sanford employee.

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6
Q

IRB duties

A
  • review every proposal. not everything is research.

- only wide generalized knowledge, not classwork, history, stuff for augie.

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7
Q

IRB guidance

A

regulations–who is the primary investigator?
policy
judgement

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8
Q

IRB process

A

-intial proposal (form)
submit all materials like flyers and informed consent forms
-exempt, expedited and full
expedited is minimal risk encountered in daily life
-reviewers
checklists and need some mods
further requests to primary investigators
-final decision–can be denied
-need to follow up. Have to say what happened and report any adverse effects. must submit another request to continue the study

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9
Q

Guidelines for figures

A
  • simplify without falsifying data
  • graph/table
  • brevity and clarity
  • note prior conventions
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10
Q

Tables

A

-need specific data points
-useful for exact comparisons
-stand on its own
-show data and possible manipulations
%s, total, means, averages
-define abbreviations at bottom
-units at top
-limit number of things in the table
-align stuff
-dont go crazy with decimals
-verify all data
-accurate use of symbols

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11
Q

Bar Charts

A
  • less numerically specific
  • examine differences rather than trends
  • compare size, magnitude
  • bars need to be wider than the spaces between them
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12
Q

Line graphs

A

-not numerically specific
-demonstrate movement, changing trends
-generally over time/concentrations
-regular intervals with two axis and units
don’t do line patterns, vary symbols.

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13
Q

Graph parts

A
  • legend or key
  • label at the bottom
  • units on axis
  • use color and symbols to your advantage!
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14
Q

Figures general guidelines

A
  • Is a figure needed?
  • no top title
  • limit 3-5 curves, 6-8 bars
  • label axis with units
  • start scales at 0 otherwise be clear about what youre doing
  • legends and keys
  • should stand alone
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15
Q

Referring

A
  • every table or figure should be referred to in the text

- whats interesting about the figure? not just see results in blah blah

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16
Q

How do we determine experimental unit and the replicate?

A
  • experimental unit stands alone

- replicates are related in some way

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17
Q

experimental unit

A
  • physical entity which can be assigned a treatment at random
  • can receive either the treatment or not
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18
Q

Pseudoreplication

A

-treating multiple measurements on the same units as if they are measurements on independent units

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19
Q

What is replication?

A

the smallest experimental unit to which a treatment is independently applied
treatments must be repeated or independent experimental units in order to avoid pseudoreplication
-how many depends on the natural variation of the data set

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20
Q

MUST HAVE

A
  1. replication
    each treatment tested on more than one experimental unit
  2. randomization
    experimental units are allocated to treatments at random
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21
Q

Example: grass was assigned to 3 different trays and 54 plants were planted

A
  • sample is 3. n = 3.

- randomize by moving around in the growth chamber

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22
Q

Principle component analysis

A
  • data reduction technique
  • only multivariate technique we will look at
  • reduce to see what mainly controls data variation
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23
Q

Data reduction

A
  • sum of data with many p variables by smaller set of k derived (synthetic, composite) variables
  • balancing act between clarity of representation and ease of understanding. If you oversimplify you may lose relevant data
  • Pearson and Hotelling in ecology developed
  • most widely known and used “standard” tests
  • matrix of n objects by p variables which may be correlated
  • m components ranked by variability
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24
Q

Principle components

A
  • ranked by how much variability they explain.
  • NOT related to each other, not linearly correlated
  • composite variables, combo of variables
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25
Q

How principle components are found?

A
  • objects represented by a cloud of n points in multidimensional space
  • centroid is defined by the mean of each variable
  • variance is the average standard dev
  • linear correlation = covariances –> correlation with the same units and scale
26
Q

Geometric Rational

A

-rigidly rotate axes of p dimensional space to new principle axes that:
explain the biggest part of the variance
PC1 highest variance
PC2 second highest variance
-covariance between each pair of PC axes is zero. NOT related
-in practice use more than two variables
-PC1 is direction of max variance and least squares line of best fit

27
Q

Principle Components Analysis Assumptions

A

-MUST be linear
-If non linear the P axis won’t work
used in ecology often

28
Q

Two factor ANOVA with equal and unequal replicates

A

-simultaneous analysis of effect of more than one factor
-Factorial analysis of variance
ADVANTAGES:
-one set of data is enough (multiple one way is too much error)
-economical
-test for interactions that are often biologically signifigant

29
Q

Two Factor ANOVA assumptions

A
  • normal distribution
  • equal variances
  • randomization
  • no heteroscedastic
  • departures from these, if p is close to alpha, then conclusions are suspect
  • try transformations!
30
Q

3 models

A
  • Model I (all factors fixed)
  • Model II (all random factors)
  • Model III (some fixed and some random)
31
Q

For a two factor ANOVA you have six hypotheses

A
  • Ha1 Factor one will have an effect
  • H01 Factor one will not have an effect
  • And same for two more sets
32
Q

Mathy ANOVA things

A 
grand means 
sum of squares total 
sum of squares cells
cs of s - total s of s
-calculate which is more important and causes the most variation
33
Q

How to read a two factor ANOVA correlation graph

A
  • slope = same means no interaction

- height on graph correlates to the effect, symbols for one compare horizontally and vertically

34
Q

Choosing journal

A
  • choose with care

- choosing highest impact factor isn’t always in your best interest

35
Q

Journal writing guidelines

A
  • precision economy, clarity
  • check grammar and spelling
  • see journal for voicing
  • be careful with paraphrasing. Need to have good citations
36
Q

General writing

A
  • start with intro and methods.
  • write design as you go. record data
  • refine methods
  • analyze and interpret results
  • discussion
  • title and abstract
  • self revise 2-3 X before you ask others to read
  • ask for peers you know would be good to read your paper
37
Q

Submission

A
  • cover letter
  • register with journal
  • texts and figures and legends all separate
  • editorial review takes 2 weeks to 2 months
  • specify the reviewers you do or do not want in you cover letter
  • peer review 2-6 more months
38
Q

Submission outcomes

A

-accepted outright
-minor revision
-major revision
additional revision / experiments
-rejected :(

39
Q

rm

A

reset R

40
Q

ls

A

list

41
Q

setwd

A

set working directory

42
Q

getwd

A

get working directory

43
Q

read.csv

A

read in a csv file

44
Q

names

A

retrieve in data names

45
Q

head

A

see begin of data frame

46
Q

dim

A

see dimensions of object

47
Q

str

A

structure of object

48
Q

summary

A

exploring data

49
Q

[]

A

used to call specific points [row,column]

50
Q

subset

A

pull out specific value

51
Q

$

A

use with data frames

52
Q

==

A

comparison

53
Q

&

A

and

54
Q

|

A

or

55
Q

!

A

not

56
Q

!=

A

not equal

57
Q

aggregate

A

split objects and compute summary stats for each

58
Q

tapply

A

apply function over ragged array

59
Q

mean

A

finds mean

60
Q

sd

A

standard deviation

61
Q

median

A

find the median

Biostats (3 decks)

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