GC070 Is This The Best Drug For Me
Pharmacogenomics is the study of how an individual's genetic makeup influences their response to drugs, enabling personalized medication selection to optimize efficacy and minimize adverse effects.
Is This the Best Drug for Me? — Clinical Pharmacology of Drug Evaluation
This lecture, delivered by Professor Bernard M.Y. Cheung (Division of Clinical Pharmacology, HKU), is fundamentally about how we decide whether a drug deserves to be prescribed. It is NOT a pharmacology lecture about specific drug mechanisms. Instead, it teaches you the critical appraisal framework: how drugs are evaluated through clinical trials, how to interpret trial data, and how to weigh efficacy, safety, tolerability, and cost-effectiveness when choosing the "best" drug for a patient.
Why this matters clinically and for exams: Every prescribing decision you make as a doctor should be justified by evidence. Examiners love to test whether you understand the difference between efficacy and effectiveness, can calculate NNT, know what a P-value actually means, and appreciate the limitations of the evidence base. This lecture is the foundational "evidence-based medicine meets prescribing" session for General Clerkship.
Learning Objectives (directly from slides)
Choose the best drug based on its efficacy, safety and cost-effectiveness. [1]
Specifically, you should be able to:
- Understand strengths and limitations of clinical evidence
- Distinguish efficacy from effectiveness
- Understand absolute risk reduction (ARR), relative risk reduction (RRR), and odds ratios
- Grasp the difference between statistical significance and clinical significance
- Appreciate that individuals/subgroups may have different benefit-risk profiles (personalised medicine)
- Interpret clinical trial, pharmacoepidemiological, and pharmacoeconomic data
- Recognise that prescription quality improves when efficacy, safety, and cost-effectiveness are considered
Core Concepts and Mechanisms (from First Principles)
The most important properties of a drug are: Efficacy, Quality & Safety, Tolerability, Cost-effectiveness. [1]
| Property | What it means | Why it matters |
|---|---|---|
| Efficacy | Does the drug actually work under ideal conditions (i.e., in a controlled trial)? | A drug that doesn't work is useless regardless of how safe it is |
| Quality & Safety | Is the drug manufactured consistently? Does it cause harm? | A drug that works but kills patients is worse than no drug |
| Tolerability | Can patients actually take it without intolerable side effects? | A drug patients can't tolerate won't be taken → no benefit |
| Cost-effectiveness | Is the benefit worth the financial cost compared to alternatives? | Healthcare budgets are finite; a marginally better drug at 100× the cost may not be justified |
Think of it this way: Efficacy tells you the drug CAN work. Safety tells you it WON'T harm. Tolerability tells you patients WILL take it. Cost-effectiveness tells you society CAN afford it.
New conditions, new pathogens, new understanding of disease mechanisms (e.g. new receptor, enzyme or pathway identified), new mutations. [1]
More choices, to differ from generics, resistance, more specificity. [1]
Better efficacy, better safety, better tolerability, better compliance. [1]
The lecture groups reasons into three categories:
| Category | Examples |
|---|---|
| Unmet medical need | SARS-CoV-2 (new pathogen), new cancer mutations requiring targeted therapy |
| Market and scientific reasons | Antimicrobial resistance requires new antibiotics; patent expiry means generics flood market so companies develop new molecules; new receptor discovery opens novel pathways |
| Improvements over existing drugs | Better efficacy (more potent), better safety profile (fewer ADRs), better tolerability (fewer subjective side effects), better compliance (once-daily vs. four-times-daily dosing) |
Why 'better compliance' matters
Compliance (adherence) is a bridge between efficacy and effectiveness. A drug that is dosed once weekly (e.g., semaglutide) will have better real-world effectiveness than a drug dosed three times daily, even if their efficacy in trials is identical, simply because patients actually take it.
The Evaluation of Drugs: Clinical Trials
Assessment of a method of treatment in a planned experiment involving patients. [1]
This distinguishes a clinical trial from: (a) laboratory studies (not involving patients), (b) anecdotes or case reports (not planned experiments), (c) clinical experience (not controlled).
Traditional recipe used by generations, anecdotes, clinical experience of a lifetime, expert opinion — "the man in the white coat recommends." [1]
Before the era of evidence-based medicine, treatments were based on tradition, authority, and uncontrolled observation. The problem: humans are subject to confirmation bias, placebo effects, regression to the mean, and confounding. Clinical trials exist to overcome these biases.
Phase I: 20-80 people, evaluate safety, determine safe dosage range, identify side effects. [1] Phase II: 100-300 people, evaluate effectiveness and further safety. [1] Phase III: 1,000-3,000 people, confirm effectiveness, monitor side effects, compare to standard treatment. [1] Phase IV: post-marketing studies, additional information on risks, benefits, and optimal use. [1]
| Phase | n | Primary Goal | Key Points |
|---|---|---|---|
| I | 20–80 | Safety, dosing | Usually healthy volunteers (except oncology); first-in-human; dose-escalation designs |
| II | 100–300 | Efficacy signal + safety | First time tested in patients with the disease; proof-of-concept |
| III | 1,000–3,000 | Confirmatory efficacy | Compared to standard care or placebo; basis for regulatory approval; large enough to detect common ADRs |
| IV | Variable | Post-marketing surveillance | Detect rare/long-term ADRs; study in broader populations (elderly, children, comorbidities) |
Exam Pearl — Phase I trials
Phase I trials test safety, NOT efficacy. The exception is oncology, where Phase I trials are sometimes done in cancer patients (because you can't ethically give cytotoxic drugs to healthy volunteers).
6. Trial Design: The Gold Standard
Best trials are: Prospective, Controlled, Randomised, Blinded. [1]
Need to know how good the new treatment is compared to no treatment (placebo) and current standard or best treatment. [1]
Without a control group, you cannot attribute improvement to the drug — it could be natural disease remission, placebo effect, or regression to the mean. Controls can be:
- Placebo (no active treatment) — used when no proven treatment exists, or when adding to standard care
- Active comparator (current best treatment) — ethically required when withholding proven treatment would cause harm
To even out the biases (a process at any stage of inference tending to produce results that depart systematically from true values). It facilitates blinding. It allows the null hypothesis that any difference between treatment groups is due to chance. [1]
Randomisation ensures that known and unknown confounders are evenly distributed between groups. If you let doctors choose who gets the new drug, they might preferentially give it to sicker or healthier patients, introducing selection bias.
Knowledge of assignment influences the subjects' response and the investigator's conduct and evaluation. [1]
| Level of Blinding | Who is blinded | Purpose |
|---|---|---|
| Single-blind | Patient only | Reduces placebo/nocebo effect in patient |
| Double-blind | Patient + investigator | Also prevents investigator from biasing assessment |
| Triple-blind | Patient + investigator + analyst | Prevents statistical analysis bias |
The lecture uses the analogy of blind tasting (wine) and blind audition (orchestra) to illustrate how removing knowledge of identity leads to more objective assessment.
Subject characteristics are important. Defined by inclusion criteria and exclusion criteria. Affect generalisability of the conclusions. [1]
This is a crucial concept. Clinical trials deliberately select patients — they exclude the very old, very young, pregnant, those with multiple comorbidities, those on many medications. This means:
- Internal validity (can we trust the result?) is HIGH in trials
- External validity / generalisability (does this apply to MY patient?) may be LIMITED
High Yield — Generalisability
A common exam trap: a trial shows Drug X works in 50-year-old men without comorbidities. Can you confidently prescribe it to an 85-year-old woman with CKD and heart failure? The answer is: not necessarily. The trial's inclusion/exclusion criteria limit its generalisability. This connects directly to the concept of personalised medicine — individuals or subgroups may experience a different benefit-risk profile.
Usually calculated by a statistician, who needs to know: the P value required (usually < 0.05), the anticipated difference between treatments relative to the SD (effect size), drop out rate. Usually at least a power of 80% to detect a difference between treatments at the 5% level of significance is expected. [1]
Power = 1 − β (where β = probability of Type 2 error). Power of 80% means there is an 80% chance of detecting a true difference if one exists.
Why this matters: An underpowered study (too few patients) may miss a real treatment effect (false negative / Type 2 error). The slide showing N=16 vs N=500 illustrates that larger samples give narrower 95% confidence intervals — more precision.
Essential Statistical Concepts
Range, median, Mean, SD, SE, 95%CI, Null hypothesis, P value, T-tests, chi-square test, Correlation coefficient, Odds ratio, Relative risk, relative risk reduction, Absolute risk reduction, NNT. [1]
Why this matters: The 95% CI tells you the range within which the true population mean lies with 95% confidence. The SE shrinks as N increases (hence larger studies = more precise estimates). If the 95% CI for a treatment effect crosses zero (or crosses 1.0 for a ratio), the result is NOT statistically significant.
Null hypothesis: any difference observed is due to chance. [1] P values: the probability of obtaining a test statistic at least as extreme as the one observed. [1] Type 1 error: null hypothesis wrongly rejected when it is true (false positive). [1] Type 2 error: null hypothesis not rejected despite being false (false negative). [1]
| Error Type | Meaning | Analogy | Controlled by |
|---|---|---|---|
| Type 1 (α) | You say the drug works when it doesn't | Convicting an innocent person | Setting α (usually 0.05) |
| Type 2 (β) | You say the drug doesn't work when it does | Acquitting a guilty person | Increasing sample size / power |
P-value ≠ clinical significance
A P-value < 0.05 means the result is statistically significant — it is unlikely to be due to chance alone. But it does NOT mean the result is clinically meaningful. A drug that lowers blood pressure by 0.5 mmHg more than placebo could achieve P < 0.001 in a massive trial, but 0.5 mmHg is clinically irrelevant. Always ask: is the EFFECT SIZE meaningful?
Continuous data → T-test (means) or Mann-Whitney (rank order). Categorical data → Chi-square (observed vs expected frequencies). Time-to-event ("survival data") → Log-rank (observed vs expected events). [1]
If outcome depends on several factors (multivariate): Continuous → Multiple regression. Categorical → Logistic regression. Time-to-event → Cox proportional hazard. [1]
| Data Type | Summary Statistic | Univariate Test | Multivariate Method |
|---|---|---|---|
| Continuous | Mean, SD, SE, Median | T-test, Mann-Whitney | Multiple regression |
| Categorical | Frequencies | Chi-square | Logistic regression |
| Time-to-event | Median survival | Log-rank | Cox proportional hazard |
Why multivariate methods? In real clinical research, outcomes depend on multiple factors (age, sex, comorbidities, baseline severity). Multivariate methods allow you to adjust for confounders and isolate the independent effect of the treatment.
13. The Worked Example
Efficacy analysis (per protocol): Active drug mortality 10/50 = 20%, Control 50/100 = 50%. [1] Effectiveness analysis (intention-to-treat): Active drug mortality 35/100 = 35%, Control 50/100 = 50%. [1]
The lecture gives a brilliant numerical example:
- 100 patients randomised to active drug, 100 to control
- Of the 100 on active drug, only 50 continue taking it as prescribed (50 stop due to intolerance/non-compliance)
- After 1 year: 10 of 50 compliant patients die, and 25 of 50 non-compliant patients die → total 35/100 die in the drug group
- In the control group: 50/100 die
| Analysis | Drug Group Mortality | Control Mortality | Interpretation |
|---|---|---|---|
| Efficacy (per-protocol) | 10/50 = 20% | 50/100 = 50% | How well the drug works when actually taken as prescribed |
| Effectiveness (intention-to-treat) | 35/100 = 35% | 50/100 = 50% | How well the drug works in the real world (including non-compliance) |
High Yield — Efficacy vs Effectiveness
Efficacy = how well a drug works under ideal conditions (per-protocol analysis). Effectiveness = how well a drug works in the real world (intention-to-treat analysis). Intention-to-treat is the primary analysis in most trials because it preserves randomisation and reflects real-world outcomes. Per-protocol analysis can overestimate treatment benefit because it only includes "good" patients.
14. Number Needed to Treat (NNT) — The Core Calculation
Absolute risk of stroke in drug-treated group = 10%. Absolute risk of stroke in placebo control group = 20%. Absolute risk reduction = 10%. Relative risk reduction = ARR/absolute risk without treatment = 50%. NNT = 1/ARR = 10. [1]
| Measure | Formula | Value in Example | Meaning |
|---|---|---|---|
| Absolute Risk Reduction (ARR) | Control event rate − Drug event rate | 20% − 10% = 10% | The actual reduction in risk for an individual patient |
| Relative Risk Reduction (RRR) | ARR / Control event rate | 10%/20% = 50% | The proportional reduction — sounds impressive but doesn't tell you absolute magnitude |
| Number Needed to Treat (NNT) | 1 / ARR | 1/0.10 = 10 | You need to treat 10 patients to prevent 1 stroke |
Exam Trap — RRR vs ARR
Drug companies love to advertise RRR because it sounds bigger. "Our drug reduces stroke risk by 50%!" sounds amazing. But if the baseline risk was only 0.2%, the ARR is 0.1%, and the NNT is 1000 — meaning you treat 1000 people to prevent 1 stroke. Always ask for the ARR, not just the RRR. This is a classic exam question stem.
Worked calculation from the 2×2 table in slides:
| Stroke | No Stroke | Total | |
|---|---|---|---|
| Drug | 10 | 90 | 100 |
| Placebo | 20 | 80 | 100 |
| Total | 30 | 170 | 200 |
- Event rate (drug) = 10/100 = 10%
- Event rate (placebo) = 20/100 = 20%
- ARR = 20% − 10% = 10%
- RRR = 10%/20% = 50%
- NNT = 1/10% = 10
NNT = 10 means that you prevent one stroke for every 10 persons treated. [1]
Reliability of Study Designs
Case report (−), Case-control study (+), Cohort study (++), RCT (+++), Meta-analysis (++++), N=1 trial (+++++). [1]
| Study Design | Direction | Reliability | Key Feature |
|---|---|---|---|
| Case report | Usually retrospective | − | Hypothesis-generating only |
| Case-control | Usually retrospective | + | Compares cases with disease to controls without; good for rare diseases |
| Cohort | Usually prospective | ++ | Follows groups over time; can establish temporal sequence |
| RCT | Usually prospective | +++ | Gold standard for testing interventions; minimises bias |
| Meta-analysis | Usually retrospective (of prospective trials) | ++++ | Combines multiple trials; increases statistical power |
| N=1 trial | Usually prospective | +++++ | The drug is tested in the individual patient themselves |
N=1 Trial — The Most Reliable?
The lecture provocatively ranks the N=1 trial as the MOST reliable. Why? Because it answers the question "Is this the best drug for ME?" — which is the lecture title. In an N=1 trial, the patient alternates between active treatment and placebo in a blinded fashion. The result applies directly to that specific patient. While meta-analyses provide population-level evidence, an N=1 trial provides personalised evidence.
There may be a number of trials addressing the same research question. The results may vary. There is a need to summarise the results. [1]
Meta-analysis: a statistical way of combining the results of different studies. Usually the results of a trial is given a certain statistical weight (a large trial is given more weight). [1]
The lecture shows a forest plot of statin trials for major coronary events [1]. Key features of reading a forest plot:
| Element | Meaning |
|---|---|
| Horizontal line per study | 95% CI for that study's effect estimate |
| Box on the line | Point estimate; box SIZE = statistical weight |
| Vertical line at 1.0 | Line of no effect (for ratios) |
| Diamond at bottom | Combined (pooled) estimate with its CI |
| If CI crosses 1.0 | Result not statistically significant for that study |
The combined estimate for statins: OR 0.73 (95% CI 0.70–0.77) — a 27% relative reduction in major coronary events, which is highly significant [1].
Patient selection (inclusion/exclusion criteria, selection biases). Protocol restrictions (e.g. fewer drug interactions). Good medical care, frequent follow up. Good compliance. Limited duration. Very expensive. Address a limited number of hypotheses. [1]
This is why real-world studies (pharmacoepidemiology) complement clinical trials:
| Feature | Clinical Trial | Real-World Study |
|---|---|---|
| Patients | Highly selected | Representative of real world |
| Compliance | Monitored/encouraged | Imperfect |
| Follow-up | Frequent, standardised | Variable |
| Drug interactions | Often excluded | Present |
| Comorbidities | Often excluded | Present |
| Duration | Usually years at most | Can be decades |
| Rare ADRs | May miss | Can detect |
| Biases | Minimised by design | Potential biases (confounding) |
| Cost | Very expensive | Cheaper (electronic records) |
Real world studies: Advantages — readily extracted from electronic records, large numbers possible, representative of real world, can study interactions and comorbidities, long-term outcomes. Disadvantages — missing information, imperfect compliance, potential biases. [1]
Post-Marketing Surveillance (Phase IV)
A drug that has been approved and marketed may still need to be under surveillance. Rare side effects may not be revealed in clinical trials (if the incidence is 1 in 10,000, then the chance of missing this is 37% when 10,000 patients have been exposed). [1]
Clinical trials may not detect long term adverse effects like cancer. Side effects, if unexpected, may be missed (e.g. cough and ACEIs). Standard assessment of adverse effects may miss side effects (valvulopathy after dexfenfluramine). [1]
Why 37%? The probability of NOT seeing an event in n exposures when the true rate is p is (1−p)^n. For p = 1/10,000 and n = 10,000: (1−0.0001)^10,000 ≈ 0.37 = 37%. This is a beautifully simple calculation that illustrates why post-marketing surveillance is essential.
Definition: any untoward event (does not need to be related to treatment). All adverse events must be documented. Management (no action, observation, treatment, hospitalisation, surgery). Outcome (fully or partially recovered, persistent, death). [1]
Key distinction: An adverse EVENT is anything bad that happens during the trial. An adverse DRUG REACTION is an adverse event judged to be causally related to the drug. All events must be documented regardless of suspected causation.
Voluntary in HK to Drug Office, Dept of Health. Yellow card system in the UK. In USA, reports to the FDA Adverse Event Reporting System. [1]
Hong Kong ADR Reporting
In Hong Kong, adverse drug reaction reporting is VOLUNTARY, submitted to the Drug Office, Department of Health. [1] This is important to know for exams. The UK uses a "Yellow Card" system. The US uses FAERS (FDA Adverse Event Reporting System). Voluntary reporting leads to underreporting — a significant limitation.
From the related GC lecture on clinical pharmacology [2]:
| Type | Name | Characteristics | Example |
|---|---|---|---|
| A | Augmented | Dose-dependent, predictable, extension of pharmacological effect | Hypotension from antihypertensives |
| B | Bizarre | Dose-independent, unpredictable, idiosyncratic or immunological | Anaphylaxis to penicillin |
| C | Chronic | Related to long-term/cumulative use | Tardive dyskinesia from antipsychotics |
| D | Delayed | Appears after drug discontinuation or delayed onset | Carcinogenesis |
| E | End of use | Withdrawal reactions | Rebound hypertension after stopping clonidine |
The DoTS classification is an alternative three-dimensional model: Dose, Timing, Susceptibility — considered superior because it doesn't force ADRs into rigid categories [2].
The lecture objectives explicitly mention:
Appreciating that individuals or subgroups may experience a different benefit-risk profile (personalised medicine). [1]
This connects to several exam-relevant concepts from supporting lectures:
| Concept | Example | Source |
|---|---|---|
| Pharmacogenomics | TPMT/NUDT15 testing before azathioprine; HLA-B*5801 before allopurinol | [2][3] |
| CYP450 polymorphisms | CYP2D6 poor metabolisers → altered drug metabolism | [2] |
| Renal impairment | Dose adjustment or avoidance of renally-cleared drugs (e.g. bisphosphonates CI if eGFR < 30) | [4] |
| Age | Beers criteria for potentially inappropriate medications in older adults | [5] |
| Pregnancy | ACEI contraindicated (crosses placenta → oligohydramnios) | [6] |
| Comorbidities | Beta-blockers in asthma → bronchoconstriction; cardioselective beta-blockers (bisoprolol) may be safer | [7] |
Clinical Approach: How to Choose the "Best Drug"
Based on the lecture framework, here is the systematic approach:
- Look for RCT evidence (Phase III trials)
- Check effect size (ARR, NNT), not just P-values
- Is the endpoint clinically meaningful (hard endpoints like mortality/MI, not just surrogate markers)?
- Review Phase III trial ADR data
- Check Phase IV / post-marketing data for rare ADRs
- Consider the specific patient's risk factors for ADRs
- Side effect profile relevant to THIS patient
- Dosing convenience (once daily > multiple daily; oral > injection for most)
- Drug interactions with current medications
- Compare NNT with drug cost
- Generic alternatives available?
- Does incremental benefit justify incremental cost?
- Was the patient's demographic represented in the trials?
- Pharmacogenomic considerations?
- Comorbidities that alter benefit-risk balance?
Integration with Related Material
GC 029 focuses on the practical prescribing process (correct drug, dose, route, duration, avoiding errors). GC 070 is the evidence evaluation that precedes prescribing — you must first determine the BEST drug before worrying about prescribing it correctly.
The Beers Criteria and STOPP/START criteria [5] operationalise the concept from GC 070 that subgroups may have different benefit-risk profiles. Drugs that are appropriate for younger adults may be potentially inappropriate in older adults due to altered pharmacokinetics, increased susceptibility to ADRs, and polypharmacy interactions.
GC 035 explains the pharmacokinetic parameters (absorption, distribution, metabolism, excretion) that influence both efficacy and safety. Understanding CYP450 enzymes (especially 3A4, 2D6, 2C9) [2] helps explain why some patients respond differently — directly relevant to the personalised medicine theme of GC 070.
The statin meta-analysis shown in the forest plot [1] is a practical example of how meta-analyses guide prescribing decisions. Similarly, the evidence base for antiplatelet therapy in ACS comes from large RCTs with NNT calculations.
Likely Exam Questions
1. A new antihypertensive drug reduces stroke incidence from 4% to 3% over 5 years compared to placebo. What is the NNT?
- ARR = 4% − 3% = 1% = 0.01
- NNT = 1/0.01 = 100
- Trap: The RRR is 25% (1%/4%), which sounds impressive, but you need to treat 100 people for 5 years to prevent 1 stroke.
2. In a clinical trial, the per-protocol analysis shows a significant benefit but the intention-to-treat analysis does not. What is the most likely explanation?
- Non-compliance/dropouts in the treatment group diluted the effect. The ITT analysis is more conservative and reflects real-world effectiveness.
3. Which phase of clinical trials is primarily designed to evaluate safety in a small number of subjects?
- Phase I (20–80 subjects)
4. A drug trial reports P = 0.03 for a reduction in LDL cholesterol of 0.1 mmol/L. Comment on this result.
- Statistically significant but NOT clinically significant. The effect size (0.1 mmol/L) is too small to produce meaningful cardiovascular benefit.
5. Why is randomisation important in a clinical trial?
- To even out known and unknown confounders between groups, facilitate blinding, and allow the null hypothesis that differences are due to chance.
6. Distinguish between efficacy and effectiveness. Give an example of why they may differ. (4 marks)
- Efficacy = drug performance under ideal conditions (per-protocol); Effectiveness = drug performance in real world (ITT)
- They differ because of non-compliance, dropouts, and real-world comorbidities/interactions
- Example: a drug with 60% efficacy may have only 30% effectiveness if half of patients cannot tolerate it
7. A pharmaceutical company advertises that their drug reduces MI risk by 50%. The baseline MI rate in the control group was 2%. Calculate the ARR and NNT. Comment on the clinical significance. (4 marks)
- Drug MI rate = 2% × 0.5 = 1%; ARR = 2% − 1% = 1%; NNT = 100
- 50% RRR sounds impressive but the ARR is only 1%
- NNT of 100 means treating 100 patients to prevent 1 MI — cost-effectiveness must be considered
8. List 4 limitations of clinical trials that may affect their applicability to clinical practice. (4 marks)
- Strict inclusion/exclusion criteria limit generalisability
- Protocol restrictions (fewer drug interactions)
- Good compliance not reflective of real world
- Limited duration may miss long-term effects (e.g., carcinogenesis)
From reviewing past summative papers [8][9][10], pharmacology and evidence-based medicine questions commonly test:
- Calculation of NNT from a 2×2 table
- Distinguishing statistical vs clinical significance
- Identifying appropriate study design for a clinical question
- Phase I–IV trial classification
- Efficacy vs effectiveness distinction
- ADR classification and reporting
- Pharmacogenomics (TPMT, HLA-B*5801)
High Yield Summary
Core exam concepts from this lecture:
- Four key drug properties: Efficacy, Safety/Quality, Tolerability, Cost-effectiveness
- Phase I = Safety (small numbers), Phase II = Efficacy signal, Phase III = Confirmatory (large), Phase IV = Post-marketing surveillance
- Best trial design: Prospective, Controlled, Randomised, Blinded
- Efficacy (per-protocol) vs Effectiveness (intention-to-treat): ITT is the primary analysis in most trials
- ARR = Control event rate − Treatment event rate; RRR = ARR / Control event rate; NNT = 1 / ARR
- Statistical significance ≠ clinical significance — always consider effect size
- Type 1 error = false positive (α); Type 2 error = false negative (β); Power = 1 − β
- SE = SD / √N; 95% CI = Mean ± 1.96 × SE
- Post-marketing surveillance is needed because trials may miss rare ADRs (1 in 10,000 → 37% chance of missing after 10,000 exposures)
- ADR reporting in HK is voluntary to Drug Office, Department of Health
- N=1 trial is ranked as most reliable for an individual patient
- Personalised medicine: individuals/subgroups may have different benefit-risk profiles due to age, genetics, comorbidities
Active Recall - Is This the Best Drug for Me?
[1] Lecture slides: GC 070. Is this the best drug for me.pdf (all pages) [2] Lecture slides: Block A - Introduction to Clinical pharmacology (II) (Drug Interactions, adverse drug reactions).pdf [3] Senior notes: Block A - Chronic diarrhoea_ irritable bowel syndrome and inflammatory bowel disease.pdf (azathioprine/TPMT section) [4] Senior notes: Block A - Back pain in an elderly woman_ osteoporosis and related fractures.pdf (bisphosphonate contraindications) [5] Lecture slides: GC 079 (supp-2)STOPP-START-V3.pdf [6] Lecture slides: Block C - I am pregnant_ medical problems complicating pregnancy.pdf [7] Senior notes: Block A - Clinical Pharmacology of anti-HT and anti-HF medications.pdf [8] Past papers: 2023 Fourth Summative MCQ.pdf [9] Past papers: 2024 Fourth Summative MCQ.pdf [10] Past papers: 2025 Fourth Summative MCQ.pdf
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