Membership of the Faculty of Public Health (MFPH) Research Methods, Epidemiology and Statistics Flashcards

24 sample cards from the Research Methods, Epidemiology and Statistics deck

Sampled from the end of the deck, so these are different cards from the ones shown on the syllabus page.

1. What is the log-rank test used for in survival analysis?

   To compare the survival distributions (entire Kaplan–Meier curves) of two or more groups, testing the null hypothesis of no difference in survival over the whole follow-up period. It is non-parametric and weights all time points.

2. What four quantities determine the required sample size in a power calculation?

   (1) Significance level $\alpha$ (Type I error, usually 0.05); (2) Power $1-\beta$ (usually 80–90%); (3) the minimum clinically important effect size; and (4) variability of the outcome (SD for continuous; event rates for binary). Larger effect/lower variance → smaller sample needed.

3. How does each of the following change the required sample size: larger effect size, smaller $\alpha$, higher power, greater outcome variance?

   Larger effect size → smaller sample. Smaller $\alpha$ (more stringent) → larger sample. Higher power → larger sample. Greater outcome variance (SD) → larger sample.

4. Define sensitivity and specificity, including their formulas from a 2×2 diagnostic table.

   Sensitivity = proportion of true cases correctly identified $= \frac{TP}{TP+FN}$ (detects disease). Specificity = proportion of true non-cases correctly identified $= \frac{TN}{TN+FP}$ (excludes disease). Both are properties of the test, independent of prevalence.

5. Define positive and negative predictive values with formulas.

   Positive predictive value: probability disease is present given a positive test $= \frac{TP}{TP+FP}$. Negative predictive value: probability disease is absent given a negative test $= \frac{TN}{TN+FN}$. Both depend on disease prevalence.

6. Define the positive and negative likelihood ratios and give their formulas.

   $$LR+ = \frac{\text{sensitivity}}{1-\text{specificity}}, \qquad LR- = \frac{1-\text{sensitivity}}{\text{specificity}}$$ LR+ >10 and LR- <0.1 provide strong evidence to rule in/out disease. Likelihood ratios are prevalence-independent and combine with pre-test odds: post-test odds = pre-test odds × LR.

7. What does an ROC curve plot, and how is the area under the curve (AUC) interpreted?

   An ROC curve plots sensitivity (true positive rate) against $1 - \text{specificity}$ (false positive rate) across all thresholds. AUC = overall discriminatory ability: $0.5$ = no better than chance, $1.0$ = perfect; higher AUC means better discrimination. The optimal cut-off is often nearest the top-left corner.

8. Explain the effect of disease prevalence on predictive values.

   As prevalence rises, PPV increases and NPV decreases (and vice versa). For a fixed-accuracy test, in low-prevalence settings even a positive result yields a low PPV (many false positives), which is why screening rare conditions produces many false positives.

9. Distinguish reliability from validity of a measurement.

   Reliability (precision): consistency/reproducibility of repeated measurements (free of random error). Validity (accuracy): the extent to which a measure reflects the true value/construct (free of systematic error). A measure can be reliable but invalid (consistently wrong), but cannot be valid without being reasonably reliable.

10. Name the main types of reliability and validity assessed in measurement studies.

    Reliability: test–retest, inter-rater, intra-rater, internal consistency (Cronbach's $\alpha$). Validity: face, content, criterion (concurrent and predictive), and construct (convergent and discriminant) validity.

11. What statistic measures inter-rater agreement for categorical data, and how is it interpreted?

    Cohen's kappa ($\kappa$), which corrects observed agreement for agreement expected by chance. Guide: $<0.20$ poor, $0.21$–$0.40$ fair, $0.41$–$0.60$ moderate, $0.61$–$0.80$ good/substantial, $0.81$–$1.00$ very good/almost perfect.

12. List the key steps of a systematic review methodology.

    Define focused question (PICO); write/register a protocol (e.g. PROSPERO); systematic literature search; screen against pre-defined inclusion/exclusion criteria; assess study quality/risk of bias; extract data; synthesise (narrative or meta-analysis); interpret and report (PRISMA).

13. What do PICO and PRISMA stand for in evidence synthesis?

    PICO = Population, Intervention, Comparator, Outcome (framework for structuring a review question). PRISMA = Preferred Reporting Items for Systematic Reviews and Meta-Analyses (reporting guideline, including the flow diagram of study selection).

14. What is meta-analysis, and how do fixed-effect and random-effects models differ?

    Meta-analysis statistically pools quantitative results from multiple studies into a weighted summary estimate. Fixed-effect assumes one true underlying effect (weights by inverse variance). Random-effects assumes effects vary across studies (incorporates between-study variance $\tau^2$), giving wider CIs and used when heterogeneity is present.

15. How is statistical heterogeneity assessed in a meta-analysis ($I^2$ and the forest plot)?

    Cochran's Q (chi-squared) test and the $I^2$ statistic, which estimates the percentage of total variation across studies due to heterogeneity rather than chance: $I^2$ roughly $25\%$ low, $50\%$ moderate, $75\%$ high. A forest plot displays each study's estimate, CI, and the pooled diamond; the $\tau^2$ quantifies between-study variance.

16. What is publication bias, and how is it detected in a meta-analysis?

    Publication bias occurs when studies with positive/significant results are more likely to be published, inflating the pooled estimate. Detected by a funnel plot (asymmetry suggests bias — small negative studies missing) and tests such as Egger's regression test.

17. What are the CASP checklists used for in critical appraisal?

    Critical Appraisal Skills Programme (CASP) checklists are design-specific tools (for RCTs, cohort, case-control, systematic reviews, qualitative studies, etc.) structured around three questions: Are the results valid? What are the results? Will they help locally (applicability)?

18. What is the GRADE framework, and what are its four certainty levels?

    GRADE (Grading of Recommendations, Assessment, Development and Evaluations) rates the certainty of evidence for each outcome and the strength of recommendations. Four levels: High, Moderate, Low, Very low. RCTs start High and observational studies start Low.

19. In GRADE, which factors downgrade and which upgrade the certainty of evidence?

    Downgrade: risk of bias, inconsistency, indirectness, imprecision, and publication bias. Upgrade (for observational evidence): large magnitude of effect, dose–response gradient, and plausible residual confounding that would reduce the observed effect.

20. Outline the traditional hierarchy of evidence from strongest to weakest.

    Systematic reviews/meta-analyses of RCTs > individual RCTs > cohort studies > case-control studies > cross-sectional/ecological studies > case series/case reports > expert opinion. (Higher levels generally have lower risk of bias and confounding.)

21. Define evidence-based public health and the three components informing decisions.

    The conscientious, explicit use of the best available evidence, alongside professional judgement and the local context, to make decisions about populations' health. Three components: best research evidence, practitioner expertise, and population values/preferences and resource/context considerations.

22. Name three common qualitative research methods and the type of question they best address.

    In-depth interviews, focus groups, and (participant) observation/ethnography; documentary analysis is also used. They best address 'why' and 'how' questions — exploring meanings, experiences, beliefs, and processes — rather than measuring frequency or magnitude.

23. What is data saturation in qualitative research, and how is it commonly analysed?

    Data saturation is the point at which collecting further data yields no new themes or insights, guiding sample size. Analysis is commonly via thematic analysis or framework analysis — coding transcripts, grouping codes into themes, and interpreting them (often supported by software such as NVivo).

24. What four criteria (Lincoln and Guba) are used to appraise the trustworthiness/rigour of qualitative research?

    Credibility (internal validity; e.g. triangulation, member checking), Transferability (external validity; thick description), Dependability (reliability; audit trail), and Confirmability (objectivity; reflexivity to acknowledge researcher influence).

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