6. EBD III Flashcards by J L

Screening using DMFT/DMFS

DMFT/DMFS - the # of decayed, missing, or filled teeth (T) or surfaces (S) in a mouth
● best known of all ____
● deft and defs - primary teeth, “e” is a primary tooth
indicated for ____
● limitations:
○ can be invalid if teeth are lost for ____ reasons (trauma, periodontal disease, ortho)

dental indices
extraction
non-carious

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Diagnosis -
Specific \_\_\_\_
Can involve additional information (complaint of pain, physical sign)
Screening -
Population 
\_\_\_\_

individual

asymptomatic

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Heart attack
10 patients are taking a particular blood pressure drug. 6 of those ten have a heart attack.
   ● \_\_\_\_ = 6/10 = 60%
● \_\_\_\_= 6/4 = 1.5
“probability”
 Difference is in what is in the \_\_\_\_.

risk
odds
denominator

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Rare disease

• Rare disease; odds and risk are\_\_\_\_

similar

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Cohort vs. Case/Control studies

• Cohort (and experimental)
	○ People with \_\_\_\_ and asking if they develop \_\_\_\_
	○ \_\_\_\_ ratio
• Case-control
	○ People with \_\_\_\_; and looking back in time to see if they have exposure to risk factor
	○ \_\_\_\_ ratio

risk factor
outcome/disease
risk
disease
odds

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Cohort

Exposed
Unexposted

Who develops the ____

• Start with \_\_\_\_ and \_\_\_\_

outcome
exposed
unexposed

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Case control

What was the ____?

____ outcome
____ outcome

• Start with the cases and controls

exposure

cases
control

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Event rate - the proportion of patients in a group in whom the event is ____. 40/200 = 20%

observed

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Example 1: EER/CER

• Exposed event rate
• Control event rate
	○ No \_\_\_\_

risk factor

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Relative risk

RR = ____ / ____

RR > 1 a person with exposure is at ____ risk(benefit)
RR < 1 a person with exposure is at ____ risk(benefit)
RR = 1 no effect

Likelihood that someone exposed to a risk factor (or treatment) will develop the disease (or experience a benefit) as compared with someone unexposed.

EER
CER
increased
decreased

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It’s all relative …
RR = 3
3 times more likely to develop condition in the exposed group

* If baseline is 30% > tripling risk will bring to 90%
* If baseline at .1% > would only increase to 0.3%
* Depends on the \_\_\_\_!

baseline risk

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Relative risk = EER/CER = 8/71 = 0.11. The risk of tooth loss is decreased by ____%

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Relative Risk/Risk Ratio

Relative risk = risk of outcome in those with ____/
risk of outcome in those without the ____

____ STUDIES AND ____ STUDIES
RR = 1 no change
RR > 1 increased risk of disease among those with exposure RR < 1 decreased risk of disease among those with exposure

exposure
exposure
cohort
experimental

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Odds Ratio
Odds ratio = odds of ____ in those with the outcome / odds of ____ in those without the outcome

____ STUDIES.
OR = 1 no change
OR > 1 increased frequency of exposure among cases OR < 1 decreased frequency of exposure among cases

exposure
exposure
case control

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7 dental students who went to a bar develop food poisoning. Odds ratio = Odds(cases) = ____ = 2.5 = 8.33
Odds (controls) =____ = 0.3

= 8.33

• Don't need to know everyone who ate fish
• Compare those were affected and those who didn’t, and ask who ate the fish
	○ Odds of those who were food poisoned over those who weren't

a/c

b/d

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Confounding

• Universally, any study that olooks at exposrue of tv and enuropshy outcomes; include things other things that make it more complicated > \_\_\_\_ goes away

association

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Potential confounders

● \_\_\_\_sex
● Body mass index at 14 years
● Parental\_\_\_\_ level
● Parental social class
● \_\_\_\_ at 4 years of age
● Mother’s alcohol consumption during pregnancy
● Mother’s \_\_\_\_ use during pregnancy

gender
education
babysitter
tobacco

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Tooth loss confounding?

• Potential confounders between crown and tooth loss:
	○ \_\_\_\_
• \_\_\_\_ people - adjust for potential confounders; if can't randomize, need to measure \_\_\_\_ status so you can measure the association

money
randomized
socioeconomic

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Effect Modification

Definition: when the magnitude of the ____ of the primary exposure on an outcome (i.e., the association) differs depending on the level of a third ____.

* Third variable - makes relatinoship stronger
* And no varibale - goes away entirely

effect

variable

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• Where ESL - may not be harmful, but can also be beneficial and can ____ them speak more efficiently

help

Effect modification

* No difference bt new \_\_\_\_ and placebo
* Thought there would be a \_\_\_\_
* In this study more \_\_\_\_ than men

drug
relationship
women

Effect modification

• Look at mean HDL between gender
	○ In men, there is a new drug
	○ Statistically \_\_\_\_
• Important in including more women in research studies
• Want to weed out \_\_\_\_; but bc the relationship is different bt two groups you want to see the \_\_\_\_

significant
confounder
difference

Herpes Zoster

• Odds ratio
	○ People with herpes and those without, and back in time looking at exposures
• For RA
	○ No diff bt three age strata
	○ Age is not \_\_\_\_
	○ No diff bt crude odds and adjusted odds ratio
		§ The same with those exposure and not exposed
• For lupus
	○ \_\_\_\_ ratio is diff in youngest than in the two other cohorts
	○ Crude odds ratio is an average of 3 odds ratios
	○ In lupus, age is an \_\_\_\_

effect modifier
odds
effect modifier

Key points:
1. Avoid or adjust for confounding - it ____ true effects
2. Effect modification - look for it and report it
3. A given variable can be a ____, an effect
modifier, both, or ____, depending on the research question …

* Avoid confounding by \_\_\_\_
* Age can be a \_\_\_\_, not always an effect modifier

masks
confounder
neither
randomizing
confounder

Types of Studies 1. Descriptive- used to quantify ____ status in a community (____ and incidence) 2. Analytical - assess the ____ between exposure/disease, cause/effect ``` • Descriptive ○ Improving ____ ○ Not trying to generalize to other clinics • Analytical ○ Make a ____ statement ```

disease prevalence relationship quality

• Inferential | ○ Want to know about the ____

population

Inferential statistics “Is it statistically significant?” ● Based on the ____ and the ____ of effect ● Depending on the ____ of data (categorical, continuous), the statistical tests that are used will vary ... ○ Correlation coefficients - used to compare ____ data ○ T-tests - used to compare ____ data ○ Chi-square tests - used to compare ____ data ○ P values - gives us the confidence to accept or reject the ____ * Large sample size - can be significant if not impressive * Small sample - great result, but not enough to generalize

``` sample size magnitude type continuous continuous categorical null hypothesis ```

• Increase study size > 40 to 80; ____ will go down; but if there is a bias that's a ____ error that is irrespective of increasing the sample

randomized error | systematic

Variable ``` Definition: what is being observed or measured ■ Traits or ____ (age, gender, race) ■ ____ a research is trying to manipulate (DAU attendance) ■ Outcomes that are of ____ (understanding of 4 handed dentistry) ```

characteristics dimensions interest

Types of Data ``` ● Discrete values ○ Daily ____ (yes/no) ○ Sex ○ ____ (male/female/trans/fluid) ● Continuous variables ○ May take any value within a ____ range ○ Age, ____, height ○ ____ of DAU days attended (0% → 100%) ```

``` attendance gender defined weight % ```

Confidence intervals How we go from the sample → population • Normal distribution • Alpha is 0.05 ○ Two tailed, so 0.025 • Value falls in ____ > you'll be confidence

range

Hypothesis testing ○ Ho (null) : there is no difference in the ____ of days of DAU attendance between those who do and do not receive kitty cafe tickets ○ Ha (alternative): Those who receive kitty cafe lottery tickets are more likely to ____ DAU • Continuous variable here

% | attend

P-values The probability of seeing results like ours if, in truth, there is no difference. ● Small p (e.g. 0.03) the observed results would occur ____ if the null hypothesis were ____. ● Large p (e.g. 0.3) the observed results would occur ____ (30% of time) if null hypothesis were ____) ● Based on the p-value, we accept or reject ____. • ____ p ○ Can reject the null hypothesis • ____ p ○ Cannot reject the null hypothesis

``` rarely true often true Ho ``` small large

The “Magical” .05 level Most research studies are said to be statistically significant if p

0.05

Type 1 errors (alpha) | At alpha = 0.05, we accept a ____% chance of rejecting ____ when it is actually ____.

5 Ho true

Type 2 errors (beta) Failing to reject ____ when it is ____.

Ho | false

T-tests ● Assume the dependent variable has a ____ ● ____ test * Same distribution between both examples * If attendance is greater than 45% in those who didn't have lottery attendance, then there is a diff in attendance bt both groups * Test of means

normal distribution | means

Chi-square tests ● The dependent variable does not have a ____ No variable

normal distribution | 5

Fishers Exact tests ● The dependent variable does not have a ____ • If cell has less than ____ > use fishers exact test rather than chi square

normal distribution | 5

Correlation Coefficient ● Between two ____ variables (i.e. GRD grade and DAU attendance)

continuous

Calculating Power Ho (null hypothesis) strength level = 500 Ha (alternative hypothesis) new drug increases strength level Ho: mean = 500 (no effect) Ha: mean > ____ * Don't know the reality * If we think our study is true, and matches reality > we're good * And if no effect in both > we're good * If study says no effect but there is an effect > ____ * If study says there is an effect, but there's no reality > ____

500 type 2 error type 1 error

Calculating Power • Cut off is 533 ○ Greater than this > intervention has an ____ • Okay with 0.05 error ○ ____ for research studies

effect | standard

• Cut point is 533 • More likely to think that test is not true when it is true ○ ____ error

beta

• Power is related to ____ ○ 1 - beta • Alternative hypothesis ○ Think direction will be in a specific way - ____ tailed ○ If can go either way -____ tailed alt hypothesis

beta one two

Calculating Power ``` B = beta ~ ____ E = ____ size A = ____ N = ____size ```

power effect alpha sample

By convention, beta < 20% so Power > ____% * ____ size comes into play * Beta is usually less than 20 and power is greater than 80

80 | sample

B = beta E = effect size (difference between ____) A = alpha N = sample size • Expect big effect size - test pop will ____ from population without intervention a lot > large effect size (more people show up bc of lotto tickets); and vice versa ○ ____ of effect will affect power calculations ○ How well the drug/intervention is designed; should have an idea before going into study about what the ES is

means differ size

• Alpha is usually pre-determined | ○ If beta is 0.2, and alpha is ____

0.05

• All comes down to ____ ○ If expect ES to be largely different; then you don't need a ____sample to be well powered ○ If smaller sample > less ____ > the sample can allow you to have a good ____ regardless of smaller effect size • Sample size is KING for power calculations

sample size large error beta