P3 Epi Final-Lecture 1,4,5

Front 1

define epidemiology

Back 1

atudy of occurence and distribution of diseases in POPULATION

Front 2

clinical approach vs publich health approach

epi is a metho of which one?

Back 2

clinical- individual level

public health- studies disease at population

epi is method of public health-pop.

Front 3

epi's 3 factors

Back 3

host
agent
environment

Front 4

define environment

Back 4

all other factors inhibit OR promote disease transmiss.

Front 5

agents

Back 5

obvious ones, then u got genetic traits, chemicals, radiation

Front 6

some of the host factors

Back 6

personal behaviors
immunization status
physicologic states(preg) puberty

Front 7

environmental factors

Back 7

physical-weather, geology

biological-food, water, air

social + cultural- crowding, war

Front 8

what did epidimiologist start out focusing on?

what are they focused on now

Back 8

in past- infectous diseases

now-chronic diseases

Front 9

infectous diseases are characterized by

Back 9

single, known identifiable cause

Front 10

pharmaco-epi studies what

Back 10

drug use patterns, drug use effects, and adverse drug effects

Front 11

whats whats diff in adverse event and adverse drug reaction

Back 11

adv event- error while pt is taking med, but doesn't necess. mean it caused event

drug reaction- outcome thats harmful or unpleasent-theres a casual link to drug

Front 12

define side effect

medication error

Back 12

S.E- dose dep effect predicatble + maybe desirable or undesirable

Med Errrr-any preventable event may cause harm to pt

Front 13

FDA uses what to approve new drug

phase1-
2-
3-

Back 13

Clinical trials

1-determines safety
2-small group-for intended pts, tests efficacy
3-thousands of pts, RCT, 1-4 yrs

Front 14

limitations of clinical trials

Back 14

only studies on hundreds/thousnads of ppl-rare SE will show later

can't see long term effex

decay effect
efficacy vs effectivness

Front 15

whats decay effect?

efficacy vs effectivness?

Back 15

decay-overtime effect decre. -trial can't show this

efficacy- intended effect doesn't necessarily have effectivness (out of study ppl will have compliance issues) 15 tabs/day

Front 16

define postmarketing surveillance

Back 16

needed to see long term effects of drugs

means of gathering data about product after approval

Front 17

T or F
some drugs need to do phase 4 clinical trials AFTER approval

Back 17

True

Front 18

examples of pharmacovigilance

Back 18

medwatch-info on serious SE reported by HCP

VAERS- vaccine A.E.

Front 19

define pharmacovigilance

Back 19

science of detecting, assessing, and preventing A.E. of meds

Front 20

whats ultimate goal in establishing relationships between drugs and outcomes

Back 20

establishin a casual relationhip (casuality)

Front 21

whats criteria for causality

Back 21

1) correlation-consist pattern of change
2) temporal ordering
3)rule out alternative explanations

Front 22

whats internal validity

Back 22

approx truth of inferences for cause-effect relationships

Front 23

what kind of variable is independent

Back 23

predictor- its variable being manipulated durr

Front 24

what kind of varibale is dependent

Back 24

outcome -whatever result of changing indep durr again

Front 25

define confounding

Back 25

extraneous varibale that correlates with indep and dep variable (unforseen correlation factor)

need to control for this

Front 26

type 1 error

Back 26

false positive
null is true but reject null

Front 27

type 2

Back 27

false negative
null is false but accept null

Front 28

define sampling

Back 28

selecting units(ppl) from population so you can later generalize results back to population

Front 29

random selection

Back 29

different units are selected by chance

Front 30

parameter vs statistics

Back 30

para-mean if you were able to sample entire POPULATION

statistic- value estimated from sample data

Front 31

obsv vs experiment study

Back 31

obsv-no intervention or exp

exp- manipulation of factor(exposure) and randomization of ppl

Front 32

in regards to data collection
define primary

define secondary

give examples

Back 32

primary-investigator is 1st to collect info
-medical exams, interviews, observations

seondary- data collected by OTHERS of other purposes than study
-medical/employ records, census data

Front 33

define a study design

Back 33

translate conceptual hypothesis into operational one

Front 34

goal of study may be? (3)

Back 34

exploratory- when knowledge is poor, small scale

descriptive-from hypothesis, ex-cross sect, case study

analytical-test hypotheses, ex case control, RCT, cross sect.

Front 35

spectrum for causality

Back 35

estab. cas-----Gen hypothesis
RCT>Cohort>casecontrol>cross sect.

Front 36

observational design-

types

Back 36

alt to exp design, good for drug use patten+ outcome-S.E.

1)case report-clinical exp of 1 pt on drug
2)case series- same but mult. pts

Front 37

case series are of value cuz they

Back 37

study cases of similar disease
and give clinical education, audit and resarch value

Front 38

give an example of case series mentioned in class

Back 38

HIV epidemic
dr's saw pts with kaposi's sarcoma- but not in normal pts(mediaterrian men with cancer)

Front 39

whats an ecologic study

whats missing in this design

Back 39

utilizes aggregate data or combine data with individual data

missin relationship btwn exposure and outcome at individual level(incomplete design)

study must be interpreted carefully to avoid eco fallacy

Front 40

define ecological fallacy

Back 40

taking aggregate data and applying to specific individuals

CAD higher in richer cities

Front 41

cross sectional study

Back 41

disregards length of time (done at 1 pt in time)

provide estimates of prevalence of condition but not incidence

useful in developing new hypothesis not causuality(cause inabillity to determine if exposure was before outcome)

Front 42

case-control study
whens it useful?

Back 42

useful in rare outcomes

study sample of ppl with cancer and group without outcome of interest as control

temporal order of exp + outcome is paramount**

Front 43

whats a common element used in case-control to avoid possible confounding

drawbacks of case-control

Back 43

matching

misclassifaction
selection bias
recall bias

Front 44

longitudinal study

Back 44

2 or MORE observations are collected for every unit in study

offer ability to see changes in study unity and across units

Front 45

2 studies used widely in epidemiology

Back 45

case-control
cohort

Front 46

whats common application of cohort

Back 46

used when its unethical or impossible to intentionally expose pt to drug or intervention

Front 47

cohort drawbacks

viewed as

Back 47

exp+time consuming
not for rare outcomes

as modified approach to RCT-but no random assign

Front 48

TEMPLES neighbor OSMAR isnt trying to find job, TEMPLES thinks all hispanics are lazy (true story)

is this ecological fallacy?

Back 48

NO
individual->group = stereotype

group->individual= ecolog fallacy

Front 49

what are traditional epidemiologic studies

Back 49

observational- no intervention or tx

Front 50

Random assigment?
yes-
no-

Back 50

if yes-experiemntal
if no, is there control grp or multiple measures?

if yes-quasi exper.
no- non-exp(observ.)

Front 51

experimental designs are G.S. in terms of establishing...

Back 51

internal validity

Front 52

random selection vs random assignment

Back 52

rand sel- collecting ppl from population to sample (external validity)

rand assign- 2 grps will preform same, any diff is cuz chance
determines internal validity

Front 53

experimental design classified into

Back 53

signal to noise approach

signal-related to key variable tying to measure
noise-all factors(confounders) make it hard to see signal

want signal to be higher than noise

Front 54

T or F
increasing the signal is better than decreasing the noise

Back 54

F
they both increase the quality of research

Front 55

signal-enhancing designs are called...

whats prototypical factorial design

Back 55

factorial designs


RCT

Front 56

what are 2 major types of noise-reducing designs

Back 56

covariance and blocking designs

Front 57

for assignment to group, what does:
R, N,C stand for

Back 57

R-random
N-nonequal grps
C-cutoff

Front 58

is everyone in a factorial design (2x2) getting same tx?

Back 58

No
some get X11, others get X22

Front 59

for a 2x2..how many factors are there?
for a 2x2x3 how many factors?

Back 59

2 factors(prolly dose + duration)

3 factors

Front 60

how many levels are there in 2x2?
2x2x3?

how many groups?

Back 60

F1-2, F2-2= 4 groups

F1-2, F2-2, F3-3= 12 groups

Front 61

if we are given a pre-intervention test and score avg is 90....what is the score once the pts are broken up into groups

Back 61

about same approx 90

Front 62

what is the null effect

Back 62

its situation where diferent tx combos have NO effect

all scores for tx and setting =90

Front 63

for a factorial design of 3x3x2 how many effects will there be

Back 63

there will be 3 effects,
there is 1 effect for every factor

Front 64

whats a main effect

vs a interaction effect

Back 64

main- you look at 1 factor at time + # in particular lvl is lower than any number(regardless of lvl) it has main interac on outcome variable

if no level with the lowest # the factor has interaction effect on outcome

Front 65

RCT most commonly studies info abou...

Back 65

ADV. DRUG RXS and EFFICACY or EFFECTIVNESS of healthcare services or health technologies(med dev, surgery)

Front 66

randomization ensures equal distibution of

Back 66

confounders

Front 67

main goal of randomized block design

what kind of strategy is this?

Back 67

to reduce noise or variance in data

analytical- it doesnt affect anything with research participants, it groups ppl in data analysis

Front 68

you can't do a block design unless

Back 68

you collected data on potenetial confounders

Front 69

what seperates regression discountinuity (quasi experimentl design) apart from other prepost designs

Back 69

method of participants are assigned to condidtions

assigned based on cutoff score or preprogram measure

Front 70

what are steps in doing a regression discontinuity design

Back 70

1- measure QOL for everyone pre and post w/o txing
2-set cutoff-tx ppl <50QOL, and no tx >50QOL
3- see if results <50QOL line moved up or down compared to the line for >50QOL

Front 71

what is the difference between the cutoff groups regression lines

Back 71

its the discontinuity...its in the regression lines at the cutoff point

Front 72

what is a maturation threat

how to correct

Back 72

something that changes naturally (ex-kids taking math class in school+ tutoring)
is tutoring helping or is it really learning over time form school

add a control group

R---O---X---O (orig study)
------------------------------------
R---O---X---O tutoring grp
R---O--------O non tutoring

Front 73

how do you address an instrumentation threat

Back 73

R---O---X---O

R---O---O---X---O
add another observation

ex-kids learn prof always has B as answer
do new study with both exams being B, then change last exam randomly

Front 74

correlation indicates

its purpose

Back 74

degree of association btwn 2 variables, "r" but not if one caused the other

to make a prediction about 1 variable based on known v.

Front 75

what are 2 correlation directions

Back 75

positive- x incr. y incr

negative xin and y dec.
education + prejudice

Front 76

positive r
negative 1

is a negative # strong or weak?

Back 76

0-1
-1-0

doesn't indicate strength, just look at number (-.8>-.2)

Front 77

does correlation establish causation?

Back 77

No
need temporal order, correlation, and rule out alt

Front 78

pearsons correlation coefficient (r) determines

Back 78

stregnth and direction btwn x and y, measured at INTERVAL LEVLS

Front 79

steps to do a correlation problem

Back 79

1) calc r..make sure denom is done by summing (x-x1)^2 each the multiply total by total y^2

2) calc t value
3) compare t value to table, if > then its statis signif.

Front 80

what are requirements for using pearsons r

Back 80

straight line
interval data
random sample
normally distributed x + y

Front 81

simple regression analysis

Back 81

like correlation, but we are interested in seeing if changes in x CAUSES change in y

Front 82

what is R^2

Back 82

coeffiecent of determination
its % of variance x(dep) accounted by variablitiy in y(indep)

Front 83

what are residuals

Back 83

HINT** its not the left over cum on your moms mouth

diff btwn predicted values and actual value of dependent variable (y)

errors we can't predict

Front 84

error =

what does a + error mean

Back 84

Y(actual value) - y^(predicted)

+ means y^ is under predicting

- is over predict compared to Y

Front 85

if you don't know x
how do you determine error

Back 85

_
Y(actual)- y (avg)

Front 86

if you have x
how do you determine error

Back 86

y(actual)- y^(predicted)

Front 87

how do you determine SStotal

Back 87

you do this when don't know x
its the numerator in r
or _
E(y-y) ^2

Front 88

how do you determine SSerror

Back 88

this is done when you know what x is

E(y-y^ )^2

Front 89

what the equation for SSreg

Back 89

SSreg= SStotal - SSerror

Front 90

whats equation for Proporionate reduction in error or PRE

Back 90

SSreg/ SStotal

Front 91

how do you determine r2
and what does it express

Back 91

well can do r squared
or
find PRE and then put in percentage

it expresses % of variance in y explained by x

Front 92

how do you test statistical sig of regression coeff (r^2)

Back 92

SSreg/dfreedom (1)

--------------------------------
SSerror/ N-2



this is equal to F, compare this umber to Ftable to determine sig or not

Front 93

steps in making a simple regressio formula

Back 93

1)use formula to determine b(slope) SP/SSx and intercept a=y(avg)- bx(avg)

2) make new table-use x to determine y^(predicted)

3)do SStotal equat and SSerror (its y- new predicted)

4) find SStotal

5) calc PRE = r2

6) calc F and compare to table to determine signif.