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Viva Questions & Answers: Critical Appraisal of an RCT

MD Pharmacology Exam Preparation

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SECTION A: Basics & Definitions

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1. Are the results valid? (Internal validity)
2. What are the results? (Magnitude and precision of effect)
3. Are the results applicable to my patients? (External validity / generalizability)

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• CASP (Critical Appraisal Skills Programme) Checklist - 11 questions for RCTs
• CONSORT (Consolidated Standards of Reporting Trials) 2010 Statement - 25-item checklist + flow diagram for reporting RCTs
• JBI Critical Appraisal Checklist for RCTs (Joanna Briggs Institute)
• Cochrane Risk of Bias Tool (RoB 2) - for systematic reviews assessing individual RCT quality

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SECTION B: Internal Validity - Study Design

• Balance known and unknown confounding factors between groups at baseline
• Prevent selection bias in group allocation
• Form the basis for valid statistical inference

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• Performance bias: Unequal care given to participants in different arms due to knowledge of allocation
• Detection bias: Systematic differences in how outcomes are assessed between groups
• Attrition bias (partial): Differential dropout due to awareness of treatment
• Reporting bias: Selective reporting of outcomes based on results

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1. Assessed for eligibility
2. Excluded (with reasons)
3. Randomized
4. Allocated to intervention / control
5. Lost to follow-up / discontinued (with reasons)
6. Analyzed

• Whether the randomized sample is representative
• The extent and reasons for dropouts
• Whether intention-to-treat analysis is feasible

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• Are the groups comparable at baseline on key demographic and clinical variables?
• Were any statistically significant differences present (potential confounders)?
• Was a p-value used to compare baseline characteristics? (Note: this is discouraged by CONSORT; if randomization was done properly, any baseline differences are due to chance and a p-value is not meaningful - use clinical judgment instead)

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SECTION C: Internal Validity - Conduct & Analysis

• Completed the treatment
• Were non-compliant
• Withdrew from the study
• Received the wrong treatment

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• Compare dropout rates and reasons between arms
• Was ITT analysis performed?
• Was missing data handled appropriately (e.g., last observation carried forward, multiple imputation)?
• Was a sensitivity analysis done?

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• Identify and exclude non-compliant participants before randomization
• Wash out prior medications
• Stabilize baseline measurements

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• Primary outcome: The main outcome the trial is designed and powered to detect. Should be pre-specified in the protocol.
• Secondary outcomes: Additional outcomes of interest, but the trial is not powered specifically for these.

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SECTION D: Statistical Concepts

• Does not measure the size of the effect
• Does not tell you the clinical importance of a result
• Sensitive to sample size - a large trial can yield p < 0.05 for a trivially small, clinically meaningless difference
• A non-significant p-value does NOT prove equivalence (it may just mean the study was underpowered)

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• Gives information about both statistical significance AND the size and precision of the effect
• Narrow CI = precise estimate (large sample); Wide CI = imprecise estimate (small sample)
• A CI that crosses the line of no effect (RR=1, OR=1, MD=0) corresponds to p > 0.05

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• Power = 1 - β = probability of detecting a true effect if it exists. Conventionally set at 80% or 90%.
• Type II error (β): Failing to reject the null hypothesis when it is actually false (false negative). Conventionally accepted at β = 0.20 (20%).
• Type I error (α): Rejecting the null hypothesis when it is actually true (false positive). Conventionally α = 0.05.

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1. α (significance level): Usually 0.05
2. Power (1-β): Usually 0.80 or 0.90
3. Expected effect size (clinically meaningful difference)
4. Variability (SD) of the outcome (for continuous outcomes)
5. Expected event rate (for binary outcomes)
6. Allowance for attrition (multiply by 1/(1 - expected dropout rate))

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• ARR = Control Event Rate (CER) - Experimental Event Rate (EER)
• NNT = number of patients who need to be treated for one additional patient to benefit

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• The surrogate may not accurately predict the clinical outcome
• Drugs may improve surrogate endpoints without improving clinical outcomes (e.g., some antiarrhythmics reduced PVCs but increased mortality in CAST trial)
• Regulatory approval based on surrogates may not translate to patient benefit

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SECTION E: External Validity & Applicability

1. Population: Are my patients similar to trial participants (age, sex, comorbidities, ethnicity)?
2. Intervention: Is the intervention feasible in my setting (dose, formulation, delivery method)?
3. Comparator: Was the comparator appropriate? Was placebo used when an effective treatment exists?
4. Outcome: Are the outcomes clinically relevant to my patients?
5. Setting: Is the trial setting (tertiary academic center) different from my practice?
6. Follow-up duration: Was follow-up long enough to capture long-term effects?

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SECTION F: Special Topics (High-Yield for MD Viva)

• Superiority trial: Tests whether the new treatment is BETTER than the comparator (H0: no difference).
• Non-inferiority trial: Tests whether the new treatment is NOT WORSE than the comparator by more than a pre-defined margin (the "delta" or non-inferiority margin).

• The non-inferiority margin must be clinically justified and pre-specified
• ITT analysis is conservative for superiority but anti-conservative for non-inferiority (PP analysis is preferred)
• "Biocreep" - successive non-inferiority trials can progressively dilute efficacy

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• Carryover effect: Effect of first treatment persists into second period (washout period required)
• Period effect: Changes in disease over time confound results
• Not suitable for acute/fatal conditions or treatments that permanently alter the disease

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• Placebo effect
• Natural disease course (regression to the mean)
• Observer bias

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• Overwhelming efficacy (benefit so clear that continuing denies effective treatment to controls)
• Clear harm (safety concern)
• Futility (no realistic chance of finding a significant difference even if the trial is completed)

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• Structured title/abstract identifying it as an RCT
• Pre-specified primary and secondary outcomes
• Sample size calculation details
• Description of randomization sequence generation, allocation concealment, blinding
• CONSORT flow diagram
• Baseline characteristics table
• Primary outcome results with effect size and 95% CI
• Harms reporting
• Trial registration number and funding sources

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SECTION G: Rapid-Fire Questions

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QUICK REFERENCE: Key Formulas

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EXAMINER'S FAVOURITE TRICK QUESTIONS

1. "A drug showed p = 0.001. Is it clinically useful?" - Not necessarily. A very large sample can make a tiny, clinically irrelevant difference statistically significant. Always look at the absolute effect size and NNT.
2. "The trial was randomized, so it must be free of bias." - Wrong. Randomization only controls for allocation bias. Lack of blinding, high attrition, outcome switching, and publication bias can all introduce bias in a properly randomized trial.
3. "The 95% CI crosses 1.0 for RR, so there is definitely no effect." - Wrong. This means the result is not statistically significant at α=0.05. The study may simply be underpowered. Absence of evidence is not evidence of absence.
4. "The trial was double-blind." - Always ask: who exactly was blinded? The patient? The clinician? The outcome assessor? The statistician? CONSORT requires this to be explicit.
5. "The control group received placebo, so the comparison is valid." - Ask: is withholding an effective, available treatment ethical? The Declaration of Helsinki requires placebo-controlled trials to be justified when effective therapy exists.

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Viva Questions & Answers: Critical Appraisal of an Observational Study

MD Pharmacology Exam Preparation

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SECTION A: Study Design Fundamentals

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1. Systematic review/meta-analysis of RCTs
2. RCT
3. Systematic review/meta-analysis of observational studies
4. Prospective cohort study
5. Retrospective cohort study
6. Case-control study
7. Cross-sectional study
8. Case series
9. Case report
10. Expert opinion/editorials

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SECTION B: Cohort Studies

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• Attributable Risk (AR) / Absolute Risk Difference = Incidence in exposed - Incidence in unexposed
• Attributable Risk Percent = AR / Incidence in exposed × 100
• Population Attributable Risk (PAR) - useful for public health decisions

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• < 5% loss to follow-up: minimal concern
• 5-20%: moderate concern; assess whether dropout was differential between groups

• 20%: serious threat to validity

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SECTION C: Case-Control Studies

• Rare diseases (rare outcomes are expensive/impossible to study in cohorts)
• Diseases with long latency periods
• Generating hypotheses about multiple exposures for one outcome
• Outbreaks and pharmacovigilance signal investigation

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• The disease is rare (< 10%) - the rare disease assumption
• Controls are sampled from the source population independently of exposure

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• Hospital controls: convenient but may share exposures with cases (Berkson's bias)
• Population controls: community-based, more representative but harder to recruit
• Nested case-control controls: drawn from within a defined cohort (gold standard)

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• Controls are drawn from the same source population (eliminates Berkson's bias)
• Exposure data was collected prospectively before outcome (eliminates recall bias)
• Efficient - cheaper than analyzing the full cohort
• Temporality is assured
• OR from risk-set sampling directly estimates the RR

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SECTION D: Cross-Sectional Studies

• Estimating disease prevalence
• Identifying associated factors/hypotheses
• Health surveys and needs assessment
• Screening program evaluation

• Cannot establish temporality - you cannot tell whether the exposure preceded the outcome or vice versa
• Cannot calculate incidence or RR
• Prevalent cases only - subjects with a rapidly fatal or rapidly resolving disease are underrepresented (prevalence-incidence bias / Neyman bias)
• Subject to cross-sectional bias: the association found may reflect factors determining disease duration/survival, not disease causation

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SECTION E: Confounding - The Central Issue in Observational Studies

1. Be associated with the exposure
2. Be associated with the outcome (independently)
3. NOT be on the causal pathway between exposure and outcome

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• Restriction: Include only participants within a narrow range of the confounder (e.g., only non-smokers)
• Matching: Match cases to controls (or exposed to unexposed) on key confounders
• Randomization (only in RCTs)

• Stratification (Mantel-Haenszel): Calculate stratum-specific estimates and pool them
• Multivariable regression: Include confounders as covariates in logistic/Cox/linear regression
• Propensity score methods: Matching, stratification, inverse probability weighting (IPW), covariate adjustment using propensity score

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• Unmeasured confounders (variables not collected in the study)
• Imprecisely measured confounders (measurement error in confounder variables)
• Unconsidered confounders

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• New user design: Restricts the study to patients who are initiating the drug (new starts), rather than including prevalent users who have already been on the drug for varying periods. Eliminates depletion of susceptibles bias and immortal time bias.
• Active comparator: Uses patients on an alternative drug for the same indication as the comparison group (instead of non-users). Controls for confounding by indication because both groups have the same condition warranting treatment.

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SECTION F: Specific Biases in Observational Studies

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• Check stratum-specific estimates: if they differ substantially from each other AND from the crude estimate, effect modification may be present
• Confounding is identified when the Mantel-Haenszel pooled estimate differs from the crude estimate

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SECTION G: Causality - Bradford Hill Criteria

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1. Residual confounding can never be fully eliminated - unmeasured variables may explain the association
2. Temporality may be unclear (especially in cross-sectional studies)
3. Multiple biases (recall, selection, observer) can create spurious associations
4. The association may be due to reverse causation (the outcome causes the exposure rather than vice versa)

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SECTION H: Statistical Measures & Interpretation

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• Attributable Risk (AR): The absolute difference in incidence between exposed and unexposed. Tells you how much of the risk in the exposed group is due to the exposure. AR = I_exposed - I_unexposed
• Population Attributable Risk (PAR): The reduction in disease incidence that would occur if the exposure were eliminated from the entire population. Depends on both AR and the prevalence of the exposure in the population.

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• Biological gradient (Bradford Hill criterion #5)
• Causality
• Pharmacological plausibility

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SECTION I: Quality Assessment Tools

1. Selection (4 stars): Representativeness of the exposed cohort; selection of the non-exposed cohort; ascertainment of exposure; demonstration that outcome was not present at start
2. Comparability (2 stars): Comparability of cohorts on the basis of design or analysis (e.g., age, sex controlled)
3. Outcome (3 stars): Assessment of outcome; adequacy of follow-up length; adequacy of follow-up completeness

1. Selection (4 stars): Case definition adequacy; representativeness of cases; selection of controls; definition of controls
2. Comparability (2 stars): Controls for important confounders
3. Exposure (3 stars): Ascertainment of exposure; same method for cases and controls; non-response rate

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• Clear statement of study design in title/abstract
• Study setting, eligibility criteria
• Exposure, outcome, and confounder measurement methods
• Handling of missing data
• Sensitivity analyses
• Limitations (particularly sources of bias and confounding)
• Funding sources

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SECTION J: Pharmacoepidemiology-Specific Concepts

• RCTs are often underpowered for rare adverse events
• RCT populations are highly selected (exclude elderly, pregnant women, children, comorbidities)
• Long-term effects cannot be studied in RCTs
• Post-marketing surveillance (Phase IV) depends on observational methods
• Ethical constraints prevent randomization to potentially harmful exposures

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• Spontaneous reporting systems (Yellow Card, MedWatch, VigiBase) - generate signals
• Disproportionality analysis - Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR)
• Prescription-event monitoring - cohort-based, follow up all patients on a new drug
• Electronic health record (EHR) / claims database studies - large-scale pharmacoepidemiology

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SECTION K: Rapid-Fire Questions

• Incidence = new cases per population at risk per time period (measures risk)
• Prevalence = all existing cases per total population at a point in time (measures burden)
• Prevalence ≈ Incidence × Duration (for stable endemic diseases)

• Case report: Detailed description of one patient with an unusual condition or drug reaction
• Case series: Description of a group of patients with similar presentations, without a comparison group

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EXAMINER'S FAVOURITE TRICK QUESTIONS

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QUICK REFERENCE SUMMARY TABLE

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Viva Questions & Answers: Critical Appraisal of a Systematic Review & Meta-Analysis

MD Pharmacology Exam Preparation

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SECTION A: Definitions & Fundamentals

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• A systematic review may conclude that the included studies are too heterogeneous (in population, intervention, outcome, or study design) to pool statistically
• A systematic review without meta-analysis presents results narratively, summarizing each study's findings
• A meta-analysis without a systematic review is possible (e.g., ad hoc pooling) but is methodologically weak because it lacks a rigorous, bias-free search strategy

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• They synthesize all available evidence on a question, reducing the impact of any single study's random error or bias
• They maximize statistical power by pooling data from many studies
• Their methods are explicit and reproducible, enabling verification
• They can identify consistency or inconsistency across different populations, settings, and contexts
• They reduce the impact of publication bias compared to individual studies (when search is comprehensive)
• They form the basis for clinical practice guidelines and drug regulatory decisions

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SECTION B: Formulating the Question - PICO

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SECTION C: Search Strategy & Study Selection

• Multi-database: MEDLINE (PubMed), EMBASE, Cochrane Central Register of Controlled Trials (CENTRAL), and topic-specific databases (e.g., PsycINFO for mental health)
• Inclusive of grey literature: Conference abstracts, thesis repositories, ClinicalTrials.gov, WHO ICTRP, regulatory agency reports - to reduce publication bias
• Hand-searching: Reference lists of included studies and key journals
• No language or date restrictions (unless scientifically justified)
• Documented with search strings using MeSH terms, free-text terms, Boolean operators (AND, OR, NOT), and truncation/wildcards
• Reproducible: Search date recorded; strategy replicable by an independent researcher

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1. Identification: Total records identified through database searching + additional sources
2. Screening: Records after duplicate removal → records screened by title/abstract → records excluded (with reasons)
3. Eligibility: Full-text articles assessed → full-text articles excluded (with specific reasons)
4. Included: Studies included in systematic review ± included in meta-analysis

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SECTION D: Risk of Bias Assessment

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SECTION E: Meta-Analysis Statistics - The Core of the Viva

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• Horizontal axis: Effect measure (OR, RR, MD). The vertical line of no effect is at 1.0 (for ratio measures) or 0 (for difference measures)
• Each row: One study. The black square = point estimate; its size = weight in the meta-analysis (larger square = larger weight)
• Horizontal line through square: 95% CI. If the line crosses the line of no effect, that study is individually non-significant
• Diamond at the bottom: Pooled estimate. Width of diamond = 95% CI of the pooled estimate
• Heterogeneity statistics: I², Cochran's Q, tau² displayed below or above the diamond

1. Are the individual CIs overlapping? (Visual check for heterogeneity)
2. Does the diamond cross the line of no effect? (Is the pooled result significant?)
3. Is the diamond narrow? (Is the pooled estimate precise?)
4. Are there outlier studies pulling the estimate?

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• Tests the null hypothesis that all studies share a common effect size
• Q = Σ Wᵢ(θᵢ - θ̄)²
• Degrees of freedom = k - 1 (where k = number of studies)
• p < 0.10 (not 0.05) conventionally indicates significant heterogeneity (the test has low power with few studies)
• Limitation: Underpowered when few studies; overpowered when many studies

• I² = [(Q - df) / Q] × 100%
• Measures the proportion of total variability in effect estimates that is due to true heterogeneity (rather than sampling error/chance)

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• τ² = 0: all variation between study estimates is due to within-study sampling error (consistent with fixed-effect model)
• τ² > 0: there is genuine variation in effect sizes across studies

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1. Investigate the source - Is there a clinical or methodological reason? (Different doses, populations, comparators, outcome definitions)
2. Subgroup analysis - Pool only studies that are clinically similar (e.g., by dose group, study design, population)
3. Meta-regression - Statistically explore which study-level characteristics explain the heterogeneity
4. Sensitivity analysis - Remove outlier studies one at a time; does the estimate or heterogeneity change?
5. Consider not pooling - If heterogeneity is unexplained and substantial, a narrative synthesis is more honest than a misleading pooled number
6. Use random-effects model if pooling - But acknowledge the limitations in interpreting the pooled estimate

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• Multiple subgroup analyses inflate the chance of a false positive finding (multiplicity problem)
• Each subgroup has fewer studies, reducing statistical power
• Post-hoc subgroup analyses are hypothesis-generating only and prone to data dredging
• Should be pre-specified in the protocol; post-hoc findings should be clearly labeled as exploratory
• Should use interaction tests (test whether the subgroup effect modifier is significant), not just compare p-values between subgroups

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• Remove high risk-of-bias studies - does the estimate change?
• Remove the largest or smallest study - does the conclusion hold?
• Change the statistical model (fixed → random effects)
• Apply different inclusion criteria
• Re-run the analysis including or excluding unpublished data

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SECTION F: Publication Bias

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• In the absence of publication bias: The plot is symmetrical, shaped like an inverted funnel - large, precise studies cluster near the pooled estimate at the top; smaller, less precise studies scatter more widely at the bottom
• Asymmetry (gap in one bottom corner): Suggests missing small negative/null studies - consistent with publication bias OR small-study effects, methodological heterogeneity, or true heterogeneity

• Funnel plots are only interpretable when there are ≥10 studies (less reliable with fewer)
• Asymmetry does NOT prove publication bias - other explanations include small-study effects (smaller studies may use selected, higher-risk populations), true heterogeneity, and chance

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1. Identifies and removes ("trims") smaller studies causing funnel plot asymmetry
2. Re-estimates the pooled effect
3. Adds ("fills") mirror-image studies on the other side of the funnel to restore symmetry
4. Recalculates the pooled estimate with the added studies

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SECTION G: GRADE - Certainty of Evidence

• RCTs start at High
• Observational studies start at Low

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SECTION H: AMSTAR-2

1. Was the review protocol pre-registered (before the review was conducted)?
2. Was the literature search strategy comprehensive (multiple databases, grey literature)?
3. Were reasons for study exclusion provided?
4. Did reviewers assess risk of bias in individual included studies?
5. Were appropriate meta-analytical methods used?
6. Was risk of bias considered when interpreting/discussing the results?
7. Was publication bias assessed (funnel plot and/or statistical test)?

• One non-critical flaw: Moderate
• More than one non-critical flaw: Low
• One critical flaw (with or without non-critical flaws): Critically Low

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SECTION I: Special Meta-Analysis Methods

• Enables comparisons between treatments that have never been directly compared in a head-to-head RCT
• Produces a ranking of all included treatments (P-scores, SUCRA)
• Maximum use of available evidence

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• Can standardize outcome definitions across studies
• Can perform patient-level subgroup analyses
• Avoids ecological bias
• Can handle time-to-event data properly
• Detects within-study interactions that aggregate data misses
• More accurate, less subject to misinterpretation of published summaries

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SECTION J: Reporting Quality vs. Methodological Quality

• Reporting quality (PRISMA): How completely and transparently is the review reported? A well-reported review may have actually been poorly conducted; the good reporting simply means the authors explained their methods clearly
• Methodological quality (AMSTAR-2): Was the review actually conducted rigorously? Were protocols pre-registered? Were biases minimized?

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SECTION K: Effect Measures in Meta-Analysis

• Cohen's d: < 0.2 = trivial; 0.2-0.5 = small; 0.5-0.8 = medium; > 0.8 = large effect

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SECTION L: Common Pitfalls & Examiner Trick Questions

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• Mandatory pre-registration of protocol in Cochrane's system before the review starts
• Peer-reviewed protocol before data collection
• Standardized RoB 2 / ROBINS-I assessment
• Mandatory GRADE assessment
• Regular updates when new trials emerge
• Open access and transparent

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SECTION M: Rapid-Fire Questions

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QUICK REFERENCE DECISION TREE

SYSTEMATIC REVIEW RECEIVED
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1. Is the PICO focused and pre-specified? (AMSTAR-2 item 2)
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2. Was the search comprehensive (multi-database, grey literature)? (AMSTAR-2 item 4)
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3. Were inclusion/exclusion criteria pre-specified? Were exclusions reasoned? (AMSTAR-2 items 5 & 8)
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4. Was risk of bias assessed in each included study? (RoB 2 / ROBINS-I) (AMSTAR-2 item 9)
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5. Was meta-analysis appropriate? (Homogeneity? Correct model?)
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6. Was heterogeneity assessed and explained? (I², Cochran Q, subgroups)
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7. Was publication bias assessed? (Funnel plot, Egger's test, trim-and-fill)
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8. Was GRADE applied to rate certainty of evidence?
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9. Were conclusions consistent with the evidence (not overclaimed)?
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10. Are results applicable to my patients (PICO match, setting, values)?

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EXAMINER'S FAVOURITE TRICK QUESTIONS

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SUMMARY COMPARISON TABLE: Three Review Types

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Viva Questions & Answers: Critical Appraisal of a Case Report

MD Pharmacology Exam Preparation

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SECTION A: Definitions & Fundamentals

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• No comparison group - there is no control to compare outcomes against
• No randomization - selection bias cannot be excluded
• Single patient - results cannot be generalized to a population
• Susceptible to observer bias, recall bias, and publication bias (positive/dramatic cases get published)
• Cannot establish causation - only raises a hypothesis
• Retrospective in nature - data collected after the fact

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1. Post-marketing surveillance: Pre-marketing RCTs enroll only ~3,000 patients - insufficient to detect rare ADRs (frequency < 1/3,000). Case reports through spontaneous reporting systems fill this gap
2. Rare events that cannot be randomized: It would be unethical to randomize patients to a potentially harmful exposure
3. Novel drug interactions: Two drugs never studied together in an RCT
4. Unusual patient populations: Elderly, pregnant women, children, and patients with rare comorbidities are excluded from most RCTs
5. First signal of a class effect: e.g., thalidomide-related phocomelia was first reported as case reports; COX-2 inhibitor cardiovascular risk was first flagged in case reports before the VIGOR trial confirmed it
6. Unlabeled (off-label) use: Unexpected therapeutic responses or toxicities from off-label prescribing
7. Drug re-purposing signals: Unexpected beneficial effects (e.g., minoxidil for hypertension → case reports of hair regrowth → repurposed as topical alopecia treatment)

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1. Title
2. Key words
3. Abstract (structured)
4. Introduction (What is unique about this case? Why should clinicians know about it?)
5. Patient information (demographics, main complaint, history)
6. Clinical findings (physical examination)
7. Timeline (key dates and events in chronological order)
8. Diagnostic assessment (methods, findings, differential diagnoses)
9. Therapeutic interventions (type, dose, duration, changes)
10. Follow-up and outcomes
11. Discussion (strengths, limitations, literature review, rationale for conclusions)
12. Patient perspective (optional but encouraged)
13. Informed consent

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SECTION B: Structure of a Case Report

• What is the background condition/drug?
• What is novel about this case? (first report? unusual presentation? rare interaction?)
• Why is it relevant to clinicians?

• Patient demographics (age, sex, ethnicity - no identifying information)
• Chief complaint and history of present illness
• Relevant past medical history, medications, allergies, family/social history
• Physical examination findings
• Laboratory, imaging, and other investigations
• Differential diagnosis considered
• Definitive diagnosis made
• Timeline of events (CARE requirement - presented as a table or figure)

• Drug name (generic), dose, route, frequency, duration
• Any dechallenge/rechallenge details
• Other co-medications and their management

• What happened to the patient? (Recovered/resolved/died/improved)
• Time course of resolution
• Any sequelae

• How does this case compare to existing literature?
• Causality assessment (Naranjo, WHO-UMC)
• Proposed mechanism of the ADR/interaction
• Clinical implications and lessons
• Limitations of the case report
• What further research is needed?

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• Temporal relationship is the most critical element in causality assessment. The adverse event MUST appear after drug initiation (temporality = the only essential Bradford Hill criterion)
• Allows calculation of the latency period (time from drug start to ADR onset)
• Documents dechallenge and rechallenge events (crucial for Naranjo scoring)
• Enables peer reviewers and readers to independently assess the plausibility of the drug-event relationship

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SECTION C: Causality Assessment - The Core of MD Pharmacology Viva

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• ≥ 9: Definite ADR
• 5-8: Probable ADR
• 1-4: Possible ADR
• ≤ 0: Doubtful ADR

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• (a) Temporal relationship between drug administration and adverse event
• (b) Alternative causes (other drugs, underlying disease) - are they excluded?
• (c) Dechallenge response - did the event improve on stopping the drug?
• (d) Rechallenge - did the event recur on re-administration?

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• They are rare (missed in clinical trials)
• They are pharmacologically unpredictable (not deducible from the drug's mechanism)
• They require a specific patient genetic or immunological susceptibility

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• Do (Dose-relatedness): Toxic (supertherapeutic dose), Collateral (normal therapeutic dose), Hypersusceptibility (sub-therapeutic dose)
• T (Time-course): Time-independent, Time-dependent (immediate, first-dose, early, intermediate, late, delayed), Withdrawal
• S (Susceptibility): Age, sex, genetic factors, comorbidity, polypharmacy

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SECTION D: Critical Appraisal of a Case Report

1. Were the patient's demographic characteristics clearly described? (Age, sex, relevant background)
2. Was the patient's history clearly described and presented as a timeline? (Temporal relationship assessable)
3. Was the current clinical condition of the patient on presentation clearly described? (Severity, context)
4. Were diagnostic tests or assessment methods and their results clearly described? (Objective data)
5. Was the intervention(s) or treatment procedure(s) clearly described? (Drug name, dose, route, duration)
6. Was the post-intervention clinical condition clearly described? (Outcome documented)
7. Were adverse events (harms) or unanticipated events identified and described? (Safety data)
8. Did the case report provide takeaway lessons? (Educational value)

• Yes/No/Unclear for each item
• No numerical score; overall qualitative judgment of completeness and credibility

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1. Compare to existing literature: How does this case compare to similar previously reported cases? Is this the first report?
2. Apply causality assessment: Explicitly apply Naranjo and/or WHO-UMC criteria and state the result
3. Propose a mechanism: What is the plausible pharmacological/immunological mechanism?
4. Consider differential diagnosis: Why were other causes excluded?
5. State clinical implications: What should clinicians do with this information? (monitor, avoid combination, adjust dose, counsel patients)
6. Acknowledge limitations: Honestly state what is unknown or uncertain
7. Suggest future research: What studies are needed to confirm this signal? (cohort study, nested case-control, pharmacokinetic study)
8. State the learning point: What is the key "take-home message" for practicing clinicians?

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1. Informed consent: Patient (or legal guardian/next of kin) must give explicit written consent for their case to be published. Without consent, the case may only be published in a de-identified manner
2. Anonymization: All identifying information must be removed or altered (exact dates → approximate, specific institution → tertiary care hospital, uncommon demographics → obscured)
3. Institutional review: Many institutions require ethics committee notification or formal exemption even for case reports
4. Patient perspective: CARE 2017 explicitly encourages including the patient's perspective (patient's own words/description of their experience) with their consent
5. Benefit-harm balance: Publishing details that could identify a patient with a stigmatized condition (e.g., HIV, psychiatric illness) requires extra caution
6. Conflict of interest: Authors must declare any financial relationships with drug manufacturers

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SECTION E: Pharmacovigilance & Case Reports

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• PRR (Proportional Reporting Ratio): Ratio of the proportion of reports for Drug X associated with ADR Y vs. the proportion of all other drug reports associated with ADR Y. PRR > 2 with n ≥ 3 reports = signal
• ROR (Reporting Odds Ratio): Analogous to OR in epidemiology; from the 2×2 table of drug × ADR in the database
• IC (Information Component): Bayesian method used by WHO-UMC; IC > 0 = signal

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Case Report (single patient, novel ADR suspected)
        ↓
Case Series (multiple similar reports accumulate)
        ↓
Spontaneous Reporting Database Signal
(Disproportionality analysis: PRR/ROR/IC exceeds threshold)
        ↓
Pharmacoepidemiology Study (nested case-control or cohort)
        ↓
Post-Authorization Safety Study (PASS) mandated by regulator
        ↓
Risk Management Plan: Label update / Contraindication / Market withdrawal

• Thalidomide teratogenicity: Case report by McBride (1961) → withdrawal within months
• Practolol oculomucocutaneous syndrome: Case reports (1974) → withdrawal
• COX-2 inhibitors and cardiovascular risk: Case reports → VIGOR trial signal → rofecoxib withdrawal (2004)

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SECTION F: Specific Pharmacology Concepts in Case Reports

1. Novel ADR: A previously unknown or undescribed adverse drug reaction
2. Known ADR in a new population: e.g., a well-known ADR occurring in a pediatric patient, pregnant woman, or patient with specific organ failure
3. Drug-drug interaction (DDI): Unexpected pharmacokinetic (altered drug levels) or pharmacodynamic (additive, synergistic, antagonistic) interaction between two co-administered drugs
4. Drug-disease interaction: e.g., QT prolongation with a drug in a patient with underlying bradycardia
5. Dose-response relationship for toxicity: Severe toxicity at a dose considered safe in the general population (pharmacogenomic basis)
6. Therapeutic paradox / Unexpected therapeutic effect: e.g., beta-blocker use in thyrotoxicosis, or a drug worsening the condition it is being used to treat
7. Withdrawal syndrome: Unexpected discontinuation reaction
8. Drug overdose / poisoning: Management of an unusual or complex overdose
9. Drug repurposing signal: Beneficial effect in an unintended condition

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1. Temporal relationship: Was the adverse event temporally related to co-administration of both drugs (not either drug alone)?
2. Pharmacokinetic mechanism: Is there a plausible PK mechanism?
   • CYP450 inhibition/induction (e.g., fluconazole inhibits CYP2C9 → increases warfarin levels → bleeding)
   • P-glycoprotein interaction
   • Protein binding displacement
3. Pharmacodynamic mechanism: Is there a plausible PD mechanism?
   • Additive: two QT-prolonging drugs → torsades de pointes
   • Synergistic: two CNS depressants → respiratory depression
   • Antagonistic: non-selective NSAIDs → reduced antihypertensive effect of ACE inhibitors
4. Drug levels: Were plasma drug levels measured? Are they consistent with a DDI?
5. Dechallenge: Did stopping one drug (or both) resolve the adverse event?
6. Literature: Are there prior pharmacokinetic interaction studies supporting the mechanism?

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1. First-ever report of a drug-ADR combination
2. Rare presentation of a known ADR (e.g., unusual severity, atypical timing)
3. Unique patient population (neonates, pregnancy, rare genetic variant)
4. Teaching point relevant to clinical practice (important diagnostic challenge, management insight)
5. Mechanism clarification: Adds to understanding of the pharmacology even if the ADR is known
6. Important public health relevance (widely used drug, potentially underreported ADR)
7. Outcome data for a rare condition (e.g., successful management of a rare poisoning)

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SECTION G: Critical Appraisal - Putting It Together

• Is there a clear timeline?
• Are demographics, history, examination, investigations, intervention, and outcome all described?
• Is informed consent stated?

• Is the diagnosis well-supported? (objective investigations, differential diagnoses considered)
• Is the temporal relationship between drug and event clearly established?
• Is the drug dose, route, and duration clearly stated?
• What is the dechallenge/rechallenge status?

• Apply Naranjo scale (score and category)
• Apply WHO-UMC criteria (category)
• Are alternative causes (other drugs, underlying disease, natural disease course) adequately excluded?

• Is the existing literature appropriately cited and compared?
• Is the proposed mechanism pharmacologically plausible?
• Are limitations honestly acknowledged?
• Is the clinical learning point clearly stated?

• Is patient consent stated?
• Is the case adequately anonymized?

• Is this case novel? Is it pharmacologically or clinically important?
• What is the next step in investigating this signal?

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SECTION H: Rapid-Fire Questions

1. Thalidomide teratogenicity (McBride, Lancet, 1961) → worldwide withdrawal
2. Minoxidil hair growth (case reports of hypertrichosis during oral use) → repurposed as topical alopecia treatment
3. Sildenafil for pulmonary arterial hypertension (originally developed for angina; penile erection as case report finding) → repurposed as PDE-5 inhibitor
4. Aspirin for cardiovascular prevention (antiplatelet effect initially noted as an unexpected finding in arthritis patients)
5. Vigabatrin-associated visual field defects (first detected as case reports before large-scale surveillance)

• Serious (hospitalization, life-threatening, death, disability, congenital anomaly, or medically significant event)
• Unexpected (not listed in the Investigator's Brochure or product label)
• Causally related to the study drug (suspected)

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EXAMINER'S FAVOURITE TRICK QUESTIONS

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QUICK REFERENCE SUMMARY

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Viva Questions & Answers: Research Methodology

MD Pharmacology Exam Preparation

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SECTION A: Fundamentals of Research

1. Basic/fundamental research: Understand mechanisms of disease or drug action (e.g., receptor pharmacology, signal transduction)
2. Applied/translational research: Convert basic science findings into clinical applications ("bench to bedside")
3. Clinical research: Test interventions in human subjects to assess safety, efficacy, and effectiveness
4. Epidemiological/public health research: Understand disease distribution, risk factors, and population-level interventions
5. Health systems research: Evaluate delivery, access, quality, and cost of healthcare
6. Pharmacovigilance research: Monitor drug safety in real-world populations post-marketing

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• Two-tailed (non-directional): Drug A differs from placebo (either increases or decreases BP) - used when direction is not predicted; requires smaller observed difference to reject H₀ but is less powerful
• One-tailed (directional): Drug A reduces BP compared to placebo - used when direction is specified; requires less extreme data to be significant in that direction but cannot detect effects in the other direction

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SECTION B: Study Design

STUDY DESIGNS
├── EXPERIMENTAL (Investigator allocates intervention)
│   ├── Randomized Controlled Trial (RCT)
│   │   ├── Parallel group
│   │   ├── Crossover
│   │   ├── Factorial
│   │   └── Cluster randomized
│   └── Non-randomized / Quasi-experimental
│       ├── Before-after study
│       └── Non-randomized controlled trial
│
└── OBSERVATIONAL (No intervention by investigator)
    ├── Analytical (test hypotheses)
    │   ├── Cohort (prospective / retrospective)
    │   ├── Case-control
    │   └── Cross-sectional
    └── Descriptive (generate hypotheses)
        ├── Case report
        ├── Case series
        └── Ecological / correlational study

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• Diagnosis: Cross-sectional / cohort
• Etiology: Case-control (rare disease) / cohort (rare exposure)
• Therapy: RCT (gold standard)
• Prognosis: Cohort (prospective)
• Prevention: RCT or cohort

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1. Estimate variance/SD of the primary outcome (needed for sample size calculation)
2. Test feasibility of recruitment and data collection
3. Identify problems with inclusion/exclusion criteria
4. Test the questionnaire/data collection tool for clarity and acceptability
5. Estimate dropout/attrition rate for adjustment in final sample size
6. Train research staff
7. Identify unforeseen logistical problems

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SECTION C: Ethics in Research

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1. The interests of the individual research subject must take precedence over the interests of science and society
2. Medical research must conform to generally accepted scientific principles and be based on thorough knowledge of the scientific literature
3. Research involving human subjects must be conducted only by scientifically qualified persons
4. The study must be approved by an independent Ethics Committee (EC) / Institutional Review Board (IRB)
5. Informed consent is mandatory; participants must be voluntary and free to withdraw at any time without penalty
6. Risks must be assessed and be justified by potential benefits
7. Vulnerable populations (children, prisoners, pregnant women, mentally incapacitated) require special protection
8. Placebo-controlled trials are justified only where no proven intervention exists, or where for compelling methodological reasons; but patients must receive best available treatment at study end
9. Results must be published - including negative and inconclusive findings (combating publication bias)
10. The physician's duty to the patient takes precedence over the needs of research

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1. Disclosure: Nature, purpose, duration, procedures, experimental aspects of the study
2. Risks and benefits: Foreseeable risks, discomforts, and anticipated benefits
3. Alternatives: Other available treatments outside the study
4. Confidentiality: How personal data will be protected
5. Voluntariness: Participation is voluntary; refusal or withdrawal will not affect care
6. Contact information: Whom to contact for questions about research or rights
7. Compensation: For participation and for study-related injury
8. Right to withdraw: At any time without penalty
9. Trial registration number
10. Comprehension: Must be ensured; not just signing a form

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1. Consistent with Declaration of Helsinki
2. Foreseeable risks justified by anticipated benefits
3. Participants' rights, safety, and well-being take priority over science/society interests
4. Non-clinical and clinical information adequate to support the proposed trial
5. Protocol scientifically sound and clearly described
6. Medical care and decisions made by qualified physician/dentist
7. Each individual involved in the trial is qualified for their function
8. Freely given informed consent prior to participation
9. All trial information should be recorded, handled, and stored in a way that allows accurate reporting, interpretation, and verification
10. Confidentiality of records protected per applicable regulations
11. Investigational products manufactured, handled, and stored per GMP
12. System with procedures ensuring quality at every step

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• Review unblinded interim data at pre-specified intervals
• Recommend trial continuation, modification, or stopping (for benefit, harm, or futility)
• Ensure the trial does not expose participants to unacceptable risk
• May recommend early stopping rules (O'Brien-Fleming boundaries, Haybittle-Peto rule)

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SECTION D: Sampling

• Population (universe): The entire group of individuals about whom conclusions are to be drawn (e.g., all patients with hypertension in India)
• Sample: A subset of the population selected for study to make inferences about the population
• Sampling: The process of selecting a sample from the population

• Population is too large to study completely (impractical)
• Saves time and cost
• Enables more intensive data collection on each subject
• Destructive tests require sampling (e.g., drug potency testing destroys the tablet)

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SECTION E: Measurement Scales

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SECTION F: Biostatistics

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• Mean = Median = Mode (all equal)
• Symmetrical about the mean
• Described completely by two parameters: mean (μ) and SD (σ)

• Mean ± 1 SD contains ~68% of observations
• Mean ± 2 SD contains ~95% of observations
• Mean ± 3 SD contains ~99.7% of observations

• Histogram (visual) - should be bell-shaped
• Q-Q (quantile-quantile) plot - should be linear
• Shapiro-Wilk test (most powerful; best for small samples, n < 50)
• Kolmogorov-Smirnov test (large samples)
• Skewness and kurtosis values (|skewness| < 2, |kurtosis| < 7 = approximately normal)

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1. α (significance level): Lower α → lower power
2. Effect size (δ): Larger effect → higher power
3. Sample size (n): Larger n → higher power
4. Variability (SD): Lower SD → higher power

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• Zα/2 = Z-value for Type I error (1.96 for α = 0.05, two-tailed)
• Zβ = Z-value for Type II error (0.84 for 80% power; 1.28 for 90% power)
• σ = expected standard deviation of the outcome
• δ = minimum clinically important difference (effect size)

1. α (significance level): Usually 0.05
2. Power (1-β): Usually 80% or 90%
3. Effect size (δ): Minimum clinically meaningful difference - obtained from pilot data or literature
4. Variability (SD): From prior studies or pilot
5. Study design: Parallel, crossover (crossover needs smaller n), matching ratio in case-control
6. Type of outcome: Continuous (t-test formula), binary/dichotomous (proportions formula), time-to-event (log-rank formula)
7. Anticipated dropout/attrition: Inflate sample size by n/(1 - dropout proportion)
8. Number of comparison groups
9. One-tailed vs. two-tailed test
10. Multiple comparison correction (if applicable)

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• Data is ordinal (pain scores, Likert scales)
• Distribution is heavily skewed
• Small sample size (n < 30 and non-normal)
• Presence of outliers that cannot be removed

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SECTION G: Validity and Reliability

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• A test can be reliable without being valid (consistently wrong)
• A test cannot be valid without being reliable (validity requires consistency)
• Reliability is necessary but not sufficient for validity

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SECTION H: Variables and Data

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SECTION I: Research Protocol

1. Title and trial identification (protocol number, version, date)
2. Background and rationale (why this study is needed; gap in evidence)
3. Research question and objectives (primary and secondary objectives)
4. Hypotheses (null and alternative)
5. Study design (type, phase, randomization, blinding)
6. Study population: Inclusion criteria; exclusion criteria; sample size with justification
7. Intervention: Drug name, dose, route, frequency, duration, formulation
8. Comparator: Placebo or active control; justification
9. Randomization and allocation concealment methods
10. Blinding procedures
11. Outcome measures: Primary, secondary; definitions; measurement methods; timing
12. Data collection tools (CRFs, questionnaires)
13. Statistical analysis plan (SAP): Tests to be used; handling of missing data; subgroup analyses
14. Ethical considerations: IEC approval; informed consent procedure; benefits and risks; insurance/indemnity
15. Timeline and milestones
16. Budget and funding sources
17. References

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• All CRF entries must be traceable to source documents (medical records, lab reports)
• Corrections must be made with a single line through the error (not obliteration), with the corrector's initials and date
• No blank fields (use "N/A" or "Not done" as appropriate)
• Electronic CRFs (eCRFs) have built-in validation and audit trails

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SECTION J: Indian Regulatory Context

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1. Quantitative DUS: How much of a drug is used? Uses Defined Daily Dose (DDD) per 1000 inhabitants per day as the unit
2. Qualitative DUS: Is the drug used appropriately? Compare against standard treatment guidelines
3. Drug Prescribing Indicators (WHO): Average number of drugs per prescription; % prescriptions with antibiotics; % prescriptions with injections; % drugs prescribed by generic name; % drugs from Essential Medicines List
4. Prescription Event Monitoring (PEM): Cohort-based; follow-up of all patients prescribed a new drug

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SECTION K: Scales, Tools & Reporting

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SECTION L: Rapid-Fire Questions

• Efficacy: Does the intervention work under ideal, controlled conditions? (Explanatory RCT)
• Effectiveness: Does the intervention work in real-world clinical practice? (Pragmatic RCT, observational study)

• Protocol deviation: Minor departure from the protocol; does not affect participant safety or data integrity
• Protocol violation: Major breach that significantly affects participant rights, safety, or data integrity; must be reported to IEC/sponsor

• Results in death
• Is life-threatening
• Requires inpatient hospitalization or prolongation of hospitalization
• Results in persistent significant disability/incapacity
• Is a congenital anomaly/birth defect
• Is otherwise medically significant

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SECTION M: Examiner's Favourite Trick Questions

• Randomization without blinding: Open-label RCT (e.g., comparing surgery vs. drug therapy)
• Blinding without randomization: Observer-blind observational study
• Both: Double-blind RCT (ideal)
• Neither: Open-label observational study

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QUICK REFERENCE SUMMARY TABLE

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Viva Questions & Answers: Statistical Analysis in Research Methodology

MD Pharmacology Exam Preparation

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SECTION A: Foundations of Statistical Inference

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• For large samples (n > 30): 95% CI = x̄ ± 1.96 × SE
• For small samples (n ≤ 30, normal data): 95% CI = x̄ ± t(df, 0.025) × SE

• Wider CI = less precision (small sample, large variance)
• Narrower CI = more precision (large sample, small variance)

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SECTION B: Choosing the Right Statistical Test

What is the OUTCOME variable?
│
├── CONTINUOUS (normally distributed)
│   ├── Compare 2 groups
│   │   ├── Independent → Independent t-test
│   │   └── Paired/matched → Paired t-test
│   └── Compare ≥3 groups
│       ├── One factor → One-way ANOVA
│       ├── Two factors → Two-way ANOVA
│       └── Repeated measures → Repeated-measures ANOVA
│
├── CONTINUOUS (non-normal or ordinal)
│   ├── Compare 2 independent groups → Mann-Whitney U
│   ├── Compare 2 paired groups → Wilcoxon signed-rank
│   ├── Compare ≥3 independent groups → Kruskal-Wallis
│   └── Compare ≥3 repeated measures → Friedman
│
├── CATEGORICAL (binary/nominal)
│   ├── Two independent groups → Chi-square (or Fisher's exact if expected cell < 5)
│   ├── Two paired groups → McNemar's test
│   └── ≥3 independent groups → Chi-square with multiple categories
│
├── CORRELATION between 2 variables
│   ├── Both continuous + normal → Pearson's r
│   └── One/both ordinal or non-normal → Spearman's ρ
│
├── PREDICTION / REGRESSION
│   ├── Continuous outcome → Linear regression (multiple)
│   ├── Binary outcome → Logistic regression
│   └── Time-to-event → Cox proportional hazards
│
└── TIME-TO-EVENT (survival)
    ├── Describe survival curve → Kaplan-Meier
    └── Compare two survival curves → Log-rank test

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SECTION C: t-Tests in Detail

1. Continuous data (interval or ratio)
2. Both groups approximately normally distributed (or n > 30 by CLT)
3. Independence of observations (no pairing)
4. Homogeneity of variance (Levene's test; if violated, use Welch's t-test which uses separate variances and adjusted df)

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• Before-after measurements on the same subjects (pre-treatment vs. post-treatment BP)
• Matched case-control studies
• Crossover RCTs

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SECTION D: ANOVA

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1. Normal distribution within each group (or n sufficiently large)
2. Homogeneity of variance (Levene's test; if violated, use Welch's ANOVA)
3. Independence of observations
4. If repeated-measures: Sphericity (equal variances of differences between all pairs of time points; tested by Mauchly's test; if violated, apply Greenhouse-Geisser or Huynh-Feldt correction)

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SECTION E: Chi-Square and Fisher's Tests

1. Categorical data
2. Observations are independent
3. Expected frequency in each cell ≥ 5 (if any expected cell < 5, use Fisher's exact test)
4. No more than 20% of cells have expected frequency < 5

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• Any expected cell frequency < 5
• Small total sample size (n < 20-40)
• 2×2 tables specifically (for larger tables, use Monte Carlo chi-square)

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SECTION F: Correlation

• r measures the linear relationship only; it will be 0 for a perfect non-linear (e.g., U-shaped) relationship
• Statistical significance of r depends on both the magnitude AND sample size - a small r can be highly significant in a very large study but clinically meaningless
• Correlation ≠ causation

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• One or both variables are ordinal (pain score, Likert scale)
• Data are non-normally distributed
• There are outliers that disproportionately influence Pearson's r
• The relationship may be monotonic but not strictly linear

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SECTION G: Regression Analysis

• a (intercept): The predicted value of Y when X = 0
• b (regression coefficient / slope): The change in Y for each one-unit increase in X
• ε (error term): Residual unexplained variation

• Linearity: Relationship is linear (check scatter plot)
• Independence: Observations independent
• Normality: Residuals (errors) normally distributed
• Equal variance (Homoscedasticity): Constant variance of residuals across values of X

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• Predicting drug response from multiple patient characteristics
• Adjusting for confounders
• Dose-response modeling

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SECTION H: Survival Analysis

• Death
• Disease relapse
• First hospitalization
• Drug failure
• Adverse drug reaction

• The outcome is the time to an event, not just whether it occurred
• Follow-up periods differ between participants (administrative censoring)
• Some participants have not experienced the event by study end (censored)

• Event: Occurrence of the outcome of interest
• Censoring: Participant has not experienced the event by the time of analysis (either lost to follow-up or study ended; their exact event time is unknown but known to be > last observation time)
• Survival function S(t): Probability that the event has NOT occurred by time t
• Hazard function h(t): Instantaneous rate of event occurrence at time t given survival up to t

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• Y-axis: Survival probability (0 to 1); starts at 1.0
• X-axis: Time (days/months/years)
• Steps (drops): Each step down = one or more events occurring
• Vertical tick marks (|): Censored observations (participants who left the study without experiencing the event)
• Median survival: Time at which survival probability = 0.5 (where the curve crosses the horizontal dashed line at 0.5)
• Two groups: When comparing Drug A vs. Drug B, two KM curves are plotted; wider separation = greater survival difference

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• Only tests whether curves differ overall; does not quantify the magnitude of difference
• Most powerful when the proportional hazards assumption holds (i.e., hazard ratio is constant over time; curves don't cross)
• If curves cross (hazard ratio reverses), log-rank test loses power (consider Wilcoxon-Breslow or stratified analyses)

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• Hazard Ratio (HR): eᵇᵢ = ratio of hazard for a one-unit increase in Xᵢ, adjusting for all other variables
• HR > 1: Higher hazard (worse survival) with increasing X
• HR < 1: Lower hazard (better survival; protective) with increasing X
• HR = 1: No effect
• 95% CI for HR: If it excludes 1.0, statistically significant at p < 0.05

• Log-log (ln[-ln(S(t))]) plot: Should be parallel lines if PH holds
• Schoenfeld residuals test (formal statistical test)

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SECTION I: Diagnostic Test Statistics

                    DISEASE (Gold Standard)
                    Present (+)    Absent (-)    Total
TEST   Positive (+)   TP (a)        FP (c)       a+c
       Negative (-)   FN (b)        TN (d)       b+d
       Total          a+b           c+d          N

• TP (True Positive): Test positive, disease present
• TN (True Negative): Test negative, disease absent
• FP (False Positive): Test positive, disease absent (Type I error in testing)
• FN (False Negative): Test negative, disease present (Type II error in testing)

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1. Compare discriminative ability of two different tests (compare AUCs)
2. Select the optimal cut-off (point closest to the top-left corner = maximizes sensitivity + specificity)
3. Evaluate biomarkers for disease diagnosis or drug response prediction

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• Pre-test probability (prevalence): The probability of disease in the patient BEFORE the test result is known
• Post-test probability: The probability of disease AFTER accounting for the test result

1. Convert pre-test probability to pre-test odds: Odds = P / (1 - P)
2. Multiply by Likelihood Ratio: Post-test odds = Pre-test odds × LR
3. Convert back: Post-test probability = Post-test odds / (Post-test odds + 1)

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SECTION J: Agreement and Reliability Statistics

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• X-axis: Mean of the two measurements (for each subject)
• Y-axis: Difference between the two measurements (Method 1 - Method 2)
• Draw horizontal lines at: Mean difference (bias), Mean difference ± 1.96 × SD of differences (= 95% Limits of Agreement)

• If the mean difference is near zero: Little systematic bias between methods
• If most differences lie within the limits of agreement (±1.96 SD): Methods agree clinically
• Limits of agreement should be clinically acceptable for the methods to be interchangeable
• Fan-shaped pattern: Proportional bias (differences increase as the mean increases)

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SECTION K: Data Transformation & Distribution Concepts

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• Pharmacokinetic parameters (AUC, Cmax, t½) which are log-normally distributed
• Bioequivalence studies: the 90% CI of the geometric mean ratio (Test/Reference) must be 80%-125% for equivalence
• Antibody titers, dilutions, microbiological counts

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SECTION L: Multiple Testing and Corrections

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• Tests are not independent (correlated outcomes)
• Number of comparisons is large
• It may cause clinically important effects to be dismissed as non-significant

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SECTION M: Effect Size Measures

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SECTION N: Clinical Significance vs. Statistical Significance

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• Pain VAS: 13 mm (out of 100mm) = MCID
• NRS pain: 1.5 points (out of 10)
• HRQOL (SF-36): 5-10 points depending on domain
• Forced Expiratory Volume (FEV₁): 200 mL or 12% change
• HbA1c: 0.5% absolute change

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SECTION O: Rapid-Fire Questions

• Z-test: Used when population SD is known OR sample is large (n > 30); uses Z distribution (standard normal)
• t-test: Used when population SD is unknown (estimated from sample) and/or n ≤ 30; uses t-distribution (heavier tails for small df; approaches Z as n → ∞)

• One-way: Tests effect of ONE categorical independent variable (factor) with ≥3 levels on a continuous outcome
• Two-way: Tests effects of TWO categorical factors simultaneously, including their interaction (whether the effect of Factor A depends on the level of Factor B)

• SPSS (IBM): Most used in Indian medical research; menu-driven; good for basic to moderate analyses
• R (free, open-source): Most flexible; can perform virtually any analysis; used in academic/research settings
• SAS: Standard in pharmaceutical industry and regulatory submissions
• STATA: Epidemiology-focused; excellent for survival analysis
• GraphPad Prism: Easy for basic statistics and graph generation; popular in pharmacology labs
• Epi Info (CDC): Free; good for epidemiological surveys; used in India

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SECTION P: Examiner's Favourite Trick Questions

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MASTER REFERENCE TABLE: Statistical Tests

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