Brainscape's Knowledge GenomeTM


Top Flashcards (Certified)
All Flashcards

1 USMLE Step 3 Main one > Statistics > Flashcards

Statistics Flashcards

1
Q

Calculate: crude mortality rate

A

Calculated by dividing the number of the deaths by the total population size.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

Calculate: Cause-specific mortality rate

A

Calculated by dividing the number of deaths from a particular disease by the total popluation size

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

Calculate: case fatality rate

A

calculated by dividing the number of deaths from a specific disease by the number of people affected by the disease.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

Calculate: Standardized Mortality Ratio

A

Calcuated by dividing the observed number of deaths by the expect number of deaths. This measure is used sometimes in occupational epidemiology. SMR of 2.0 indicates that the observed mortality in a particular group is twice as high as that in the general population.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

Calculate: Attack rate

A

an incidence measure typically used in infectious disease epidemiology. It is calculated by dividing the number of patients with diease by the total population at risk. For example, attack rate can be calculated for gastroenteritis among people who ate contaminated food.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

Calculate: Maternal mortality rate

A

Calculated by dividing the number of maternal deaths by the number of live births

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

Calculate: Crude birth rate

A

Defined as the number of live births divided by the total population

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

What are the two basic measures of disease occurence?

A

Incidence and prevalence

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

Incidence rate

A

of new cases over a certain period

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

Prevalence

A

of cases over # number of total persons

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

Odds ratio

A

Probability of disease among the exposed/probability of disease among the non exposed (3/4)((a/c)/(b/d)). Described as the odds of disease is # among the exposed when compared to the unexposed. Associated with case control

○ odds of having disease in expose group / odds of having disease in unexposed group
= ad/bc

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

Relative Risk

A

Culmative incidence of disease among the exposed / culmative incidence of disease among the unexposed. (1/2)(a/a+b)/(c/c+d) Described as exposed are # times more likely to develop X when compared to the unexposed.
Associated with cohort studies.

○ probablity of getting disease in exposed group / probability of getting disease in unexposed group
= [a/(a+b)] / [c/(c+d

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

Attributable Risk

A

Decribed ast the number of cases which can be attributed to exposure. Calcuted by Culmative incidence of disease in the exposed minus the culmative indcidence of disease in the unexposed. (1/2)(a/a+b)-(c/c+d)

○ risk in exposed group - risk in unexposed group
= a/(a+b) - c/(c+d)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

PAR

PAR%

A

PAR is # of cases of C among total population attributed to exposure (5-2) and PAR% is the number of cases

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

Lead time bias

A

E early dectection confused with increased survival

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

Late look or Length bias

A

Information gathered at inapproiate time eq survery to study a fatal disease(only those patients still alive will be able to answer

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

Type 1 error (alpha)

A

“find something false” FP, detecting a difference when there is no difference. Reject the null hypothesis when the null hypothesis is true. eq test say man is preggo when he is not. Or FP error. also known as described as alpha which goes up with more power and increase sample size, expected effect size, and precision in measurement

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

alpha equals

A

a=1-B

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

Type 2 error (beta)

A

“miss something” FN, missing a true difference. Failing to reject a false null hypothesis. eq stating that a woman is not pregnant when she actually is. Described as beta. which goes down or decreases with more power and increase sample size, expected effect size, and precision in measurement

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
20
Q

Attributable Risk Reduction%

A

Done after intervention (AR among control)-(AR among intervention group)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
21
Q

NNT

A

NNT=1/ARR

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
22
Q

Relative risk reduction RRR

A

RRR=ARR/RR among non exposed

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
23
Q

Confounding variable

A

Just because umbrella come out during the rain doesnt meant that the rain cause umbrella

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
24
Q

Dose-reponse

A

> increased level of exposure shows an increased relative risk of developing/odds ratio of having a disease

> can be used in OR or RR to support causality

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
25
Sensitivity (SN)
○ % with disease who test positive | = a/(a+c) = TP/(TP+FN)
26
Specificity (SP)
○ % without disease who test negative | = d/(b+d) = TN/(FP+TN)
27
Positive predictive value (PPV) 
○ % positive test results that are true positives | = a/(a+b) = TP/(TP+FP)
28
Negative predictive value (NPV) 
○ % negative test results that are true negatives | = d/(c+d) = TN/(FN+TN)
29
Sensitivity and specificity are intrinsic to the diagnostic test 
○ do not change with prevalence | PPV and NPV do change with prevalence
30
Receiver operating characteristic (ROC) curves are a graphical depiction of a test's performance
○ Y axis: sensitivity ○ X axis: 1-specificity ○ The higher the curve, the better the test This is quantified by the AUC (area under the curve); an AUC of 0.5 states that the test performs no better than chance (bad test!), whereas an AUC of 0.9 suggests a better-performing test
31
Positive Skew
Asymmetrical with tail trailing off to right | Mean > median > model
32
Negative Skew
○Asymmetrical with tail trailing off to left | Mean < median < mode
33
○ Mean very ???to skew  ○ Median somewhat ????to skew Mode very ???to skew
○ Mean very sensitive to skew  ○ Median somewhat resistant to skew Mode very resistant to skew
34
• A certain percentage of all observations will always fall within +/- certain standard deviations of the mean   ○ +/- 1 Standard deviation = ???%  ○ +/- 2 Standard deviations = ???%   +/- 3 Standard deviations = ???%
• A certain percentage of all observations will always fall within +/- certain standard deviations of the mean   ○ +/- 1 Standard deviation = 68%  ○ +/- 2 Standard deviations = 95%   +/- 3 Standard deviations = 99.7%
35
Capacity vs competence  ○ capacity is a ???term competence is a ????term
medical | legal
36
Always do this
• Avoid going to court • Use trained medical interpreters when possible • Committed mentally ill patients retain their rights • Never abandon a patient ○ If a treatment (such as abortion, birth control, etc) is against a physician's personal beliefs - that physician does not have to provide that treatment; however, they are responsible for referring their patient to a provider who is willing and able to provide such care  • Disclose all errors, regardless of harm -If suspected abuse is occurring, physicians are mandated reporters and MUST report to Child Protective Services or Adult Protective Services
37
○ 17-year-old girl whose parents cannot be contacted § ??? ○ 17-year-old girl living on her own § ??? ○ 17-year-old girl who is requests birth control § ?? ○ 16-year-old girl refuses but mother consents § ??? ○ 16-year-old girl consents but mother refuses ?????
○ 17-year-old girl whose parents cannot be contacted § physician may treat a threat to health under in locum parentis ○ 17-year-old girl living on her own § patient can choose whether or not to give consent ○ 17-year-old girl who is requests birth control § provide access even in absence of parental consent ○ 16-year-old girl refuses but mother consents § treat ○ 16-year-old girl consents but mother refuses do not treat
38
Case control
Observational Compares the odds of being exposed between patients with disease and patients without the disease eg Patients with cirrhosis are more likely to have been exposed to heavy alcohol use  OR = (a/b) / (c/d) = ad/bc  
39
Relative Risk (RR) is measure of disease association
Observational ○ asks, "How much more likely are you to get cirrhosis if you drink alcohol?" RR = [a/(a+b)] / [c/(c+d)]
40
Cross-Sectional Study
Observational • Determines disease status and exposure/risk factor status at the same point in time  ○ can show an association but not causality measures disease prevalence 
41
Factorial design study 
Randomizes patients into different interventions with 2 or more variables being studied in each intervention 
42
Crossover Study
• Participants alternate receiving intervention and placebo • Participants act as own controls  ○ improves power and precision of the study all patients receive an intervention
43
Meta-Analysis
• Combines data from multiple studies  ○ better precision than individual studies ○ improves the generalizability of study findings ○ considered to be the highest level of clinical evidence ○ limited by: § quality of individual studies bias in study selection
44
Case series
• Report on observations of patients with known exposure or disease Does not have comparison group so cannot perform hypothesis testing
45
``` 1° (Primary prevention)  ○ ??? § e.g., ??? 2° (Secondary prevention) ○ e??? § e.g., ??? • 3° (Tertiary prevention) ○ re????t e.g., ???? ```
``` 1° (Primary prevention)  ○ prevent disease occurrence § e.g., vaccination 2° (Secondary prevention) ○ early detection of disease to either prevent or decrease morbidity from disease before onset of symptoms § e.g., colonoscopy • 3° (Tertiary prevention) ○ reduce morbidity from disease after symptom onset e.g., medication ```
46
Confounding  -- A third factor is either positively or negatively associated with both the ???and ??? --Confounders are not in the ????? pathway --- if not ????? for can distort true ??? either towards or away from the null hypothesis
Confounding  -- A third factor is either positively or negatively associated with both the exposure and outcome --Confounders are not in the causal pathway --- if not adjusted for can distort true association  either towards or away from the null hypothesis
47
• Selection bias | Nonrandom ????of study participants leads to ????conclusions
• Selection bias | Nonrandom selection of study participants leads to erroneous conclusions
48
• Measurement bias ○ Information is gathered in a way that distorts the information § ?????Effect subjects ?????their ???when they ?????they are being ?????
• Measurement bias ○ Information is gathered in a way that distorts the information § Hawthorne Effect subjects alter their behavior when they know they are being studied
49
• Procedure bias | Different ???not treated the ???
• Procedure bias | Different groups not treated the same
50
Pygmalion effect
nvestigator inadvertently conveys his high expectations to subjects, who then produce the expected result A "self-fulfilling prophecy"
51
Golem Effect
is the opposite pygmalion: study subjects decrease their performance to meet low expectations of investigator.
52
Design bias??
The control group is inappropriately non-comparable to the intervention group
53
Observer bias ???
Investigator's evaluation is impacted by knowledge of exposure statu
54
Ways to Reduce Bias
* Randomization   * Use placebo as control * Blind studies  * Crossover studies
55
Randomization association to ITT
intention-to-treat analysis is used in order to preserve randomization 
56
Examples of Effects that are Not Bias??
* Effect modification * Latent period * Generalizability
57
Crossover studies
> Subject acts as own control | >Limits confounding
58
Level 1 level of evidence
Randomized Controlled Trial (and meta analysis
59
Level 2 level of evidence
Prospective Cohort Study
60
Level 3 level of evidence
Retrospective Cohort Study | Case-Control Study  
61
Level 4 level of evidence
Case Series
62
Level 5 level of evidence
1. Case Report (a report of a single case) 2. Expert Opinion 3. Personal Observation 4. Review
63
• SEM =??? ○ SEM =?? ○ SEM < σ SEM ??as n ??
• SEM = standard error of the mean ○ SEM = σ/√n ○ SEM < σ SEM decreases as n increases
64
``` • T-test ○ compares the means of ??   • ANOVA (analysis of variance) ○ compares the means of???? • χ2 ("chi-squared") ○ tests whether ??? variables are associated  ○ used with 2x2 tables e.g., effect of treatment on disease ```
• T-test ○ compares the means of 2 groups on a continuous variable   • ANOVA (analysis of variance) ○ compares the means of 3 or more groups on a continuous variable • χ2 ("chi-squared") ○ tests whether 2 nominal variables are associated  ○ used with 2x2 tables e.g., effect of treatment on disease
65
AR percent (ARP) is the attributable risk divided by incidence in the exposed (Ie)
ARP = (RR-1)/RR  ○ ARP = 100* (Ie-Iu)/Ie = 100*[a/(a+b) - c/(c+d)]/[a/(a+b)]   note that relative risk (RR) = Ie/Iu = a/(a+b) DIVIDED BY c/(c+d) AR is incidence in the exposed (Ie) - incidence in the unexposed (Iu) = Ie - Iu  ○ Ie = a/(a+b) ○ Iu = c/(c+d) AR = a/(a+b) - c/(c+d)
66
RCA??
○ a problem solving method that focuses on finding the cause (the root) of a medical error in order to identify preventative measures which are subsequently implemented • RCA attempts to answer ○ what happened? ○ why did it occur? ○ how can we prevent this from occuring again?
67
Failure Mode and Effects Analysis (FMEA)???
Definition: a technique used to evaluate risks in order to identify and eliminate known failure, potential failures, systemic, design, process, and service issues. This technique is applied to prevent medical errors from occuring in the first place
68
What should you look for when suspecting selection bias?
Major discrepancy in sample size limiting generalizability. Such as observation group being 105 in one cohort and 35 in the other.
69
Berkson bias
disease studies using only hospial based patient not applicabe to total populaition- selection bias
70
Prevalence bias - Neyman bias
exposure happend long before disease assessmennt which resulted missed diesese in patients that die early or recover. - selection bias
71
Attrition bias
significant loss in of study parciptatnts may cause bias if those lost to follow up differ significantly from remaining subjects.-selection bias
72
Surveillance bias
Risk factor itself causes increased monitoring in exposed group relative to the unexposed group, which increased the probability of identifying disease - observational bias.
73
Observational biases
Inaccurate measurement or classification of disease, exposure, or other variable
74
Selection bias
Inappropiate selection or poor retention of study subjections
75
Hazard Ratio
formula, the hazard ratio, which can be defined as the relative risk of an event happening at time t, is: λ(t) / λ0. A hazard ratio of 3 means that three times the number of events are seen in the treatment group at any point in time.
76
``` Absolute risk reduction ???????? Relative Risk Reduction ??????? Relative Risk ??? Number needed to treat ????? ```
``` Absolute risk reduction ARR: control rate-treatment rate Relative Risk Reduction RRR= ARR/control rate Relative Risk RR= treatment rate/control rate Number needed to treat NNT=1/ARR ```
77
Positive likely hood ratio
LR(+)=sensitivity/1-specificity higher the better
78
Negative Likely hood ratio
LR(-)=1-sensitivity/specificity less than 0.1 and lower is better
79
Confounder effects >>>>>>>and no associated with gradient..confounding can be reduced by ?????? Effect modifier only?????????? and is associated with a ??????, something ????usually revealed by stratification ​
Confounder effects both exposure and outcome and no associated with gradient..confounding can be reduced by randomization Effect modifier only effects outcome and is associated with a gradient, something internal usually revealed by stratification ​

1 USMLE Step 3 Main one (37 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2025 Bold Learning Solutions. Terms and Conditions