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Paper 3 > GPT STUDY GUIDE > Flashcards

GPT STUDY GUIDE Flashcards

(100 cards)

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1
Q

What is a cohort study?

A

A study that follows groups over time to observe outcomes; can be prospective or retrospective.

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2
Q

What are advantages of cohort studies?

A

Can establish temporal relationships, calculate incidence rates.

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3
Q

What are disadvantages of cohort studies?

A

Time-consuming, expensive, potential for loss to follow-up.

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4
Q

What measures are used in cohort studies?

A

Relative risk, incidence rates, attributable risk.

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5
Q

What does a cross-sectional study measure?

A

Prevalence.

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6
Q

What does a case-control study measure?

A

Odds ratios.

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7
Q

What does a cohort study measure?

A

Incidence and relative risk.

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8
Q

What do RCTs measure?

A

Efficacy and effectiveness measures.

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9
Q

What is absolute risk?

A

Actual probability of an event occurring, e.g., 5 in 100.

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10
Q

What is relative risk?

A

Ratio comparing risks between groups.

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11
Q

How is absolute risk calculated?

A

It is equivalent to the incidence rate.

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12
Q

Why might high relative risk not be clinically meaningful?

A

If absolute risk is low, even a high relative risk may have little practical impact.

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13
Q

What is sensitivity?

A

Proportion of true positives correctly identified: TP / (TP + FN).

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14
Q

What is specificity?

A

Proportion of true negatives correctly identified: TN / (TN + FP).

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15
Q

What is positive predictive value (PPV)?

A

TP / (TP + FP).

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16
Q

What is negative predictive value (NPV)?

A

TN / (TN + FN).

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17
Q

How does prevalence affect predictive values?

A

PPV increases with prevalence, NPV decreases.

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18
Q

What is the formula for odds ratio (OR)?

A

(a × d) / (b × c) using a 2×2 table.

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19
Q

What does OR = 1 mean?

A

No association between exposure and outcome.

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20
Q

What does OR > 1 mean?

A

Increased odds of outcome with exposure.

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21
Q

What does OR < 1 mean?

A

Decreased odds of outcome with exposure.

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22
Q

What is a p-value?

A

Probability of observing results if the null hypothesis is true.

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23
Q

What p-value is typically considered statistically significant?

A

p < 0.05.

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24
Q

What are limitations of p-values?

A

They do not indicate effect size or clinical significance.

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25
What is needed when conducting multiple hypothesis tests?
Correction for multiple comparisons to control Type I error.
26
What is a confidence interval (CI)?
A range that expresses uncertainty around an estimate.
27
What does a 95% CI imply?
If study repeated 100 times, 95% of intervals would contain the true value.
28
What does it mean if a 95% CI excludes the null value?
The result is statistically significant (p < 0.05).
29
What type of data does linear regression analyze?
Continuous outcome variables.
30
What type of data does logistic regression analyze?
Binary outcome variables (e.g., yes/no).
31
What is multiple regression used for?
Controlling for confounding variables.
32
What do regression coefficients indicate?
Change in outcome per unit change in predictor.
33
What is R₀ (basic reproduction number)?
Average number of secondary infections from one infected person.
34
What does R₀ > 1 indicate?
An epidemic is likely to occur.
35
What does R₀ = 1 indicate?
The disease is endemic.
36
What does R₀ < 1 indicate?
The disease will eventually die out.
37
What factors influence R₀?
Transmission probability, contact rate, infectious period.
38
What is the formula for herd immunity threshold?
1 - (1 / R₀).
39
What is the herd immunity threshold if R₀ = 4?
0.75
40
What is the formula for case-fatality rate (CFR)?
(Deaths from disease / Total cases) × 100.
41
What is the difference between CFR and mortality rate?
CFR is among cases; mortality rate is among the general population.
42
What is confounding?
A third variable associated with both exposure and outcome.
43
Give an example of confounding.
Age may confound the relationship between exercise and heart disease.
44
How can confounding be controlled?
Randomization, stratification, matching, and regression.
45
What is selection bias?
Systematic differences in participant selection.
46
What is information bias?
Systematic errors in data collection.
47
What is recall bias?
Different accuracy of memory regarding past exposures.
48
What is measurement error?
Random or systematic errors in measurements.
49
What is statistical power?
Probability of detecting a true effect if it exists.
50
What is a Type I error?
False positive — rejecting a true null hypothesis (α, usually 0.05).
51
What is a Type II error?
False negative — failing to reject a false null hypothesis (β).
52
How is power related to Type II error?
Power = 1 - β.
53
What factors affect statistical power?
Effect size, sample size, and variability.
54
What is cost in health economics?
Monetary value of resources used.
55
What is benefit in health economics?
Monetary value of health improvements.
56
What is effectiveness?
Health outcomes achieved in real-world conditions.
57
What is efficacy?
Health outcomes under ideal, controlled conditions.
58
What is utility?
Preference-based measure of health states.
59
What is cost-effectiveness analysis?
Compares cost per unit of health outcome (e.g., cost per life-year saved).
60
What is cost-utility analysis?
Cost per QALY (quality-adjusted life-year).
61
What is cost-benefit analysis?
Both costs and benefits are measured in monetary terms.
62
What is cost-minimization analysis?
Compares costs when outcomes are equivalent.
63
What is a QALY?
Quality-adjusted life year = survival × quality of life.
64
What is a DALY?
Disability-adjusted life year = YLL + YLD.
65
What is YLL?
Years of life lost due to premature mortality.
66
What is YLD?
Years lived with disability.
67
How is YLL calculated?
Life expectancy at age of death - actual age at death.
68
What is HSMR?
(Observed deaths / Expected deaths) × 100; compares hospital mortality rates.
69
Why adjust for case-mix when comparing hospitals?
To account for differences in patient characteristics like age or severity.
70
What methods are used for case-mix adjustment?
Risk adjustment models, stratification.
71
What is accessibility in health care?
Ease of obtaining care.
72
What is acceptability in health care?
Patient satisfaction with the care provided.
73
What is equity in health care?
Fair distribution of care across populations.
74
What is the formula for NNT?
1 / Absolute Risk Reduction.
75
What does NNT = 10 mean?
Treating 10 patients results in 1 additional good outcome.
76
What are criteria for a good screening program?
Important health problem, acceptable treatment, suitable test, known natural history, agreed policy, cost-effective, continuous process.
77
What factors are used to evaluate screening program effectiveness?
Sensitivity, specificity, PPV, and impact of lead time and length time biases.
78
What is lead time bias?
Apparent increase in survival due to earlier detection.
79
What is length time bias?
Overrepresentation of slower-progressing cases in screened population.
80
What is overdiagnosis?
Detection of conditions that would not cause symptoms or death.
81
What are key data governance principles?
Access, storage, use, consent, and anonymization.
82
When is consent required for health data use?
When not using under public interest or anonymized data exemptions.
83
What is ICD-10 used for?
Standardized disease coding for mortality, morbidity, and reimbursement.
84
What is the structure of ICD-10 codes?
Alphanumeric format (e.g., I25.9 for coronary heart disease).
85
What is a systematic review?
A comprehensive, reproducible synthesis of existing studies.
86
What are steps in a systematic review?
Systematic search, quality assessment, data extraction.
87
What are advantages of systematic reviews?
Reduces bias, increases statistical power.
88
What are PRISMA guidelines?
Standards for reporting systematic reviews and meta-analyses.
89
What is meta-analysis?
Statistical pooling of results from multiple studies.
90
What is a forest plot?
Visual representation of individual and pooled study results.
91
What does the diamond in a forest plot represent?
The pooled effect size.
92
What does heterogeneity refer to in meta-analysis?
Variation between study results; measured by I² statistic.
93
What is a fixed effects model?
Assumes one true effect size across all studies.
94
What is a random effects model?
Assumes variation in effect sizes across studies.
95
What are Kaplan-Meier curves used for?
Estimating survival probability over time.
96
What is censoring in survival analysis?
Accounting for incomplete follow-up data.
97
What does a steep Kaplan-Meier curve mean?
Worse survival.
98
What is median survival?
Time at which 50% of participants have experienced the event.
99
What is a hazard ratio (HR)?
Instantaneous risk ratio between groups over time.
100
How does a hazard ratio differ from odds ratio?
HR considers time to event; OR does not.

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