Prev Med 2

Question 1

sample vs pop + ex

Answer 1

subset of pop (pts w/ high bp managed by fam med dr in Montgomery County; women 21-50yo in NRV Free Clinic in Christiansburg) vs entire ppl (all pts w/ high bp in VA; all adult women)

Question 2

sample types: simple random vs systemic sampling vs stratified vs cluster vs convenience vs quota vs theoretical

Answer 2

q member of pop has equal chance of being selected vs samples = selected over fixed pattern or time interval vs when known diff exists, samples = taken from each category that are proportional to their vol in total pop vs used when pop is known to be relatively unvarying vs selects most readily avail members of pop vs takes %age of pop vs Selecting cases for its potential representation of a
theoretical construct (incidents, slices of life, time
period, or people)

Question 3

why use sample?

Answer 3

time limitations, cost of study

Question 4

representative sample

Answer 4

how sample = selected, how recruitment for participation = done, how ppl = retained and provide clues to whether sample represents pop of interest

Question 5

variables: quantitative vs qualitative

Answer 5

determine relationship b/w IV and DV in pop; can be descriptive (subjects measured once, est associations b/w variables) or experimental (subjects measured before & after tx) vs in-depth understanding of human behavior and reasons governing human behavior, relies on reasons behind various aspects of behavior; used in social sciences

Question 6

random variable

Answer 6

anything capable of being measured; data, observations, measurements, indicators, characteristics

Question 7

data types: categorical vs continuous

Answer 7

nominal (counted data, no scale or rank; ex: blood groups A/B/O/AB or eye color w/ numbered assignment like 1 = blue, 2 = brown, 3 = green); ordinal (order data if 2+ categories, ranked; ex: after tx pt can improve/stay same/become worse, or severity of illness can be minor/moderate/major); dichotomous/binary (counts in whole numbers, no decimals, implies which direction = favorable; ex: nml/abnml, well/sick, living/dead) vs measurements of any value along continuous scale, yes decimals; ex: baby birth wt, length of time to return lab test result, pt height, temp

Question 8

descriptive statistics

Answer 8

simple graphical numerical techniques to summarize info about data; involves pop, sample, statistical inference (how info in sample can draw conclusions about pop), point estimate (single value used to estimate pop parameter), measures of center, measures of dispersion/spread

Question 9

measures of center vs measures of dispersion/spread

Answer 9

represents location of data; mean, median, mode vs represents variability of data ie. spread of distribution; variance (measures variability in sample by mean of squared deviations), standard dev (measures variability away from its mean), range (diff b/w max and min value), interquartile range (diff b/w 25th and 75th percentiles; for skewed distribution)

Question 10

know how to calculate standard dev w/ 68, 95, 99% confidence interval

Answer 10

yep

Question 11

standard error vs confidence level vs margin of error

Answer 11

measure of variation by estimating probable error of sample mean to pop mean; ID sampling error in sampling process; standard dev/sqrt(sample size) vs you’re 95% confident that true mean in a pop is b/w [mean - 2 standard errors] and [mean + 2 standard errors] vs values above and below sample statistic in a confidence interval

Question 12

nml distribution

Answer 12

continuous distribution, unimodal (mean/median/mode = same value), empirical rule (68% of data w/in 1 SD of mean, 95% of data w/in 2 SD of mean, 99% of data w/in 3 SD of mean)

Question 13

know what L and R skewed distributions look like and where mean/median/mode goes

Answer 13

Lecture 10, slide 8&9. also unimodal (b/c one hump)

Question 14

general def of hypothesis testing

Answer 14

sci inquiry into connection b/w cause and effect, sci method to test validity of claim about pop being studied, hypothesis statement specifies characteristics/parameters of process like location or spread

Question 15

goal of hypothesis testing

Answer 15

to see if there is sufficient statistical evidence to reject null hypothesis and accept alt hypothesis

Question 16

5 steps of hypothesis testing

Answer 16

1. develop null and alat hypotheses
2. est appropriate alpha lvl
3. perform test of statistical significance on collected data
4. compare p-value from test w/ alpha
5. conclude result (reject null when p < alpha, fail to reject null when p > alpha)

Question 17

know how to set up null and alt hypotheses

Answer 17

Lecture 10, slide 16-18

Question 18

alpha lvl vs p value

Answer 18

highest risk of making false pos error that PI is willing to accept (if p = 0.05 –> PI accepts 5% risk of being in error when rejecting null) vs probability of obtaining more extreme values than observed test statistic, given null is true; probability of obtaining the result by chance

Question 19

what to think about for statistical vs clinical significance

Answer 19

when p < alpha –> reject null and favor alt; but doesn’t mean it’s worth continuing study or that it’s clinically significant vs does benefit > risk for your pt? is info relevant to your pt? is there practical importance for tx effects?

Question 20

factors affecting sample size

Answer 20

what size effect are you looking for? (takes larger sample size to find small effect than big effect); what’s the significance lvl? (you need smaller lvl for larger sample size); how much variability? (if you expects lots of variation, you need larger sample size); how much power? (if you want more power/statistically significant, you need larger sample size)

Question 21

factors affecting power

Answer 21

effect size (larger effect size –> better chances of finding it using same resources), significance lvl (larger effect size & sample size –> alpha and power inc; power = 1-beta), sample size (larger sample size –> power inc), pop SD (more variable data –> larger sample size needed or else power dec)

Question 22

what does Power answer?

Answer 22

if null = false, what’s the probability that data from experiment will reject null? ie. finding a significant effect when one does exist

Question 23

degrees of freedom

Answer 23

of indep pieces of info that went into calculating estimate; # of values that are free to vary in a data set

Question 24

type I vs II errors

Answer 24

alpha error, false pos (you said there was a sig diff but there actually wasn’t –> you take the “risk” when erroneously rejecting null) vs beta error, false neg

Question 25

what are the 4 questions to determine which stat test to use?

Answer 25

what’s the type of data? are observations indep or dep? what’s the data distribution (any normality)? how many variables are investigated (2 variables => bivariate analysis, >2 variables => multivariable analysis)?

Question 26

IV vs DV

Answer 26

represents quant manipulated in experiment; exposure, risk factor, predictor/regressor/explanatory vs quantity depends on IV that’s being manipulated; outcome, target, criterion

Question 27

ex of parametric vs non parmetric tests

Answer 27

compares means; unpaired/student’s t-test, paired t-test, ANOVA, Pearson coeff vs compares medians; wilcoxon rank sum/MannWhitney U test, wilcoxon signed rank test, Kruskal-Wallis test, Spearman coeff, chi square

Question 28

when to use parametric vs nonparametric test?

Answer 28

when collected data uses ratio or interval scale, nml distribution vs when nothing is known about parameters of variable, small sample size, nominal or ordinal data, doesn’t rely on mean or SD, non nml distribution (bimodal, outliers)

Question 29

student t-test

Answer 29

look at diff in means b/w 2 groups

Question 30

wilcoxon rank sum test

Answer 30

order groups from low to high and rank them, then compare medians to see if one pop = larger than the other

Question 31

paired t-test vs wilcoxon signed ranked test

Answer 31

look at diff in means w/in same group (before/after), when data = nmlly distributed vs look at diff in medians w/in same group, when data = not nmlly distributed

Question 32

ANOVA vs Kruskal Wallis test

Answer 32

look at diff b/w 2+ groups, good for categorical data; but need further testing to see which specific groups were significantly diff vs look at diff b/w 2+ groups when data = not nmlly distributed

Question 33

chi square test vs fisher’s exact test

Answer 33

uses 2x2 table, compares proportions for tx using ratio of actual to expected vs uses 2x2 table for small sample size (<5 observations)

Question 34

pearson coeff vs spearman coeff

Answer 34

looks for corr/similarity b/w 2 variables, for nmlly distributed continuous data vs looks for corr/similarity b/w 2 variables, for non nmlly distributed continuous + ordinal data, for outlier in data

Question 35

r = -1 vs 1 meaning in corr

Answer 35

sign = direction of corr (pos or neg corr), number = magnitude/strength of association (strong or weak)

Question 36

corr coeff values representing weak vs moderate vs strong corr

Answer 36

0-0.3 vs 0.31-0.7 vs >0.7

Question 37

confounding variable

Answer 37

2 variables may be strongly correlated but both can actually be caused by 3rd variable => confounding variable

Question 38

corr vs regression

Answer 38

assume no causal relationship or association vs assumes 1 variable = dep on the other

Question 39

linear regression: y = mx + b

Answer 39

m = slope, regression coeff
x = IV
b = intercept, where line cuts y-axis
y = DV

Question 40

coefficent of determination (r^2)

Answer 40

in corr; how much variability in DV is explained by variability in IV

Question 41

ordinary least squares regression vs logistic regression

Answer 41

used for continuous variables like height; simple (1 variable) and multiple (mult variables) regression vs used for binary variables like pass/fail, work/no work; most common in papers b/c they’re testing if tx works/no works; used for case control studies –> based on log of odds

Question 42

how to talk about controversial topics to pts?

Answer 42

provide patients with risks and benefits and guide them, present info that pt can understand, respect the patient’s choice, don’t talk down to the patient

Question 43

IRB levels of review: exempt vs expedited vs full review

Answer 43

de-ID data, anonymous surveys vs collection of biospecimens from noninvasive, identifiable data vs interventions involving physical and emotional discomfort or sensitive data

Question 44

p value = influenced by? low p value means?

Answer 44

sample size. large effect size –> result has major/clinical/practical importance

Question 45

how to double dip funding?

Answer 45

same or overlapping topic funded by diff agencies, proposing hypotheses already resolved, grant apps shared by other PIs

Question 46

odds ratio = 1 vs >1 vs <1

Answer 46

odds of case being
exposed is same as odds of control being exposed. No relationship between exposure and disease vs odds of case being exposed is greater than odds of control being exposed. Increase in disease with exposure vs odds of case being exposed is less than odds of control being
exposed. Decrease in disease with exposure

Question 47

variance and SD = greatly affected by?

Answer 47

outliers

Question 48

phase I vs II vs III vs IV of clinical trials

Answer 48

safety, drug interaxn, ADME/pharmacokinetics vs short term side effects, efficacy vs short term side effects, efficacy, new formulation, vs ongoing surveillance, new indications

Question 49

ex of conflict of interest in reseaarch

Answer 49

acting as promotional speakers on behalf of companies, accepting gifts, research involving your pts, research limiting right to publish, giving incentives to participate in research, avoiding “bias” when researching your clinical approaches

Question 50

Madsen vs Offit vs Smeeth vs Licciardone

Answer 50

retrospective cohort using Danish registry –> no assoc vs systematic review –> can’t dis/prove but can only educate parents that vax = good vs case control –> no assoc; odds ratio depended on age joining GPRD before or after 1st bday vs dec tx for LBP did no change for 1st 6mo then White ppl improve (significantly but not clinically relevant) LBP than Blacks