Biostatistics 80/20 - Introduction to Medicine

1. The 2x2 Contingency Table - Master This First

2. Diagnostic Test Characteristics

Sensitivity and Specificity

Source: Harrison's 22e, Table 4-1 - "The sensitivity or true-positive rate reflects how well the new test identifies patients with disease... The specificity, or true-negative rate, reflects how well the new test correctly identifies patients without disease."

Predictive Values

• Goldman-Cecil Medicine, Chapter 8

Likelihood Ratios (LRs)

• LR > 10 or LR < 0.1 = large, often conclusive change in probability
• LR 2-5 / 0.2-0.5 = moderate shift
• LR 1-2 / 0.5-1 = small, rarely important shift

3. Bayes' Theorem in Practice

Post-test odds = Pre-test odds × Likelihood Ratio

• Harrison's 22e, "Measures of Disease Probability and Bayes' Rule"

4. Measures of Disease Frequency

5. Measures of Effect (Treatment and Risk)

• Goldman-Cecil Medicine, "From These Data, One Can Also Calculate"

6. Study Design Hierarchy

1. Systematic Review / Meta-Analysis - Combines data from multiple studies; highest level when well-done
2. Randomized Controlled Trial (RCT) - Gold standard for therapeutic questions; eliminates confounding
3. Cohort Study - Prospective (follow exposed/unexposed forward); measures incidence and RR; good for rare exposures
4. Case-Control Study - Retrospective (identify cases and controls, look backward); measures OR; good for rare diseases, fast and cheap
5. Cross-Sectional Study - Snapshot; measures prevalence; cannot determine causality
6. Case Reports / Case Series - Hypothesis generating only

• Only RCTs can control for unmeasured confounding
• Cohort studies can calculate incidence and RR; case-control studies can only calculate OR
• Cross-sectional studies show association, not causation
• Pediatric Board Review (9780323876988), Table 29.2

7. Bias - The Big Three

• Recall bias - Awareness of disease alters subject's memory of prior exposure (especially in case-control studies)
• Lead-time bias - Early detection of disease is mistaken for increased survival
• Loss-to-follow-up bias - Patients who drop out are not randomly distributed (e.g., sicker patients drop out more)
• Hawthorne effect - Participants change behavior because they know they are being studied
• Observer-expectancy bias - Researcher's belief in treatment efficacy affects their assessment

8. Statistical Significance vs. Clinical Significance

• Pediatric Board Review, "Alpha and Beta"

9. Evidence-Based Medicine (EBM) Steps

1. Formulate the clinical question (PICO: Patient, Intervention, Comparison, Outcome)
2. Search for evidence (PubMed, Cochrane, UpToDate, guidelines)
3. Critically appraise for internal validity (was the study conducted without bias?) and external validity (are results generalizable to your patient?)
4. Apply evidence to the individual patient, incorporating patient preferences

• Was allocation truly random?
• Were patients and assessors blinded?
• Was analysis by intention-to-treat (ITT)? (This is almost always the correct approach - analyze by original group assignment regardless of actual treatment received)
• Was the study adequately powered?
• Were outcomes clinically meaningful, not just surrogate endpoints?
• Harrison's 22e; Pediatric Board Review

10. Quick-Reference Mnemonics

Summary Visual: What Each Study Design Measures

Cross-sectional   → Prevalence, association (not causation)
Case-control      → OR (rare diseases, fast, retrospective)
Cohort            → RR, Incidence (rare exposures, prospective)
RCT               → Causation, ARR, NNT (gold standard)
Systematic Review → Pooled effect estimates (highest level)

BIOSTATISTICS STUDY QUESTIONS

1st & 2nd Year Medical School | 40 Questions | MCQ + SAQ

SECTION A: DATA TYPES & DESCRIPTIVE STATISTICS (Questions 1-6)

• A) Nominal, ordinal, continuous, ordinal
• B) Ordinal, nominal, continuous, ordinal
• C) Nominal, continuous, ordinal, nominal
• D) Nominal, ordinal, ordinal, continuous
• E) Ordinal, ordinal, continuous, nominal

• Blood type = nominal (no meaningful order; A, B, AB, O have no ranking)
• Pain score = ordinal (ordered 0-10, but the intervals between scores are not equal - a jump from 1 to 2 may not equal a jump from 7 to 8)
• Serum sodium = continuous (can take any value within a range, with meaningful equal intervals and a true zero)
• Cancer stage = ordinal (I < II < III < IV in severity, but the difference between Stage I and II may not equal the difference between III and IV)

• A) Mean = Median = Mode
• B) Mean < Median < Mode
• C) Mean > Median > Mode
• D) Median > Mean > Mode
• E) Mode > Mean > Median

• A) The drug is statistically and clinically significant
• B) The drug is neither statistically nor clinically significant
• C) The drug is statistically significant but may not be clinically significant
• D) The drug is clinically significant but not statistically significant
• E) The confidence interval indicates the result is not significant

• A) 0.3%
• B) 1%
• C) 2.5%
• D) 5%
• E) 10%

SECTION B: STUDY DESIGN (Questions 7-15)

• A) Cross-sectional survey of blood pressure and stroke prevalence
• B) Case-control study matching stroke patients to controls
• C) Prospective cohort study following treated and untreated patients
• D) Randomized double-blind placebo-controlled trial
• E) Systematic review of existing observational studies

• A) Relative risk
• B) Attributable risk
• C) Odds ratio
• D) Hazard ratio
• E) Incidence rate ratio

• Directly calculate incidence of colorectal cancer in each group
• Calculate Relative Risk (RR) directly, which is more intuitive than OR
• Study multiple outcomes from a single exposure (e.g., aspirin's effect on MI, GI bleeding, and colorectal cancer simultaneously)
• Avoid recall bias since exposure data are in objective prescription records (not self-report)

• A) Per-protocol analysis
• B) As-treated analysis
• C) Intention-to-treat (ITT) analysis
• D) Efficacy analysis
• E) Sensitivity analysis

• A) Alcohol consumption causes elevated liver enzymes
• B) Elevated liver enzymes cause increased alcohol consumption
• C) An association between alcohol intake and elevated liver enzymes exists in this population at this point in time
• D) Alcohol exposure precedes liver enzyme elevation
• E) Randomized trials are now unnecessary to confirm this finding

• The high I² (68%) raises concerns about whether the pooled result applies uniformly
• Are the studies representative of your patient population? (external validity)
• Was there publication bias (tendency to only publish positive results)?
• What is the NNT? Is the benefit clinically meaningful?
• Are there safety signals in the individual studies?
• Do existing guidelines support this change?

• A) The randomization failed and the study is invalid
• B) High uric acid is likely a side effect of the new drug
• C) This finding is likely a Type I error due to multiple comparisons
• D) Uric acid is a confounder and must be adjusted for
• E) The alpha level should be reduced for future trials

• A) Prospective cohort study
• B) Randomized controlled trial
• C) Case-control study
• D) Cross-sectional study
• E) Ecological study

SECTION C: MEASURES OF EFFECT (Questions 16-21)

• A) 1.5
• B) 2.67
• C) 4.0
• D) 6.0
• E) 8.0

• Risk in smokers = 80/400 = 0.20 (20%)
• Risk in non-smokers = 30/600 = 0.05 (5%)
• RR = 0.20 / 0.05 = 4.0

• A) 4
• B) 8
• C) 12
• D) 25
• E) 33

• ARR = 12% - 8% = 4% = 0.04
• NNT = 1/ARR = 1/0.04 = 25

• A) 40% absolute reduction (down to 18%)
• B) 2% absolute reduction (down to 28%)
• C) 12% absolute reduction (down to 18%)
• D) 50% absolute reduction (down to 15%)
• E) The NNT cannot be applied to this patient

• Stroke rate in treatment group: 1.5%/year
• Stroke rate in control group: 2.5%/year
• Major bleeding rate in treatment group: 3.0%/year
• Major bleeding rate in control group: 2.0%/year

• ARR = 2.5% - 1.5% = 1.0%/year
• NNT = 1/0.01 = 100 patients treated per year to prevent 1 stroke

• ARI (absolute risk increase) = 3.0% - 2.0% = 1.0%/year
• NNH = 1/0.01 = 100 patients treated per year to cause 1 major bleed

• A) The study population is very large
• B) The disease under study is rare (< 5% prevalence)
• C) The control group is matched to the case group
• D) The exposure is rare in the population
• E) The study has a long follow-up period

SECTION D: DIAGNOSTIC TESTS (Questions 22-30)

• A) 9
• B) 10
• C) 85
• D) 99
• E) 108

• Disease present = 10 patients; Disease absent = 990 patients
• TP = 85% of 10 = 8.5 ≈ 9
• FP = (1 - specificity) × 990 = 10% × 990 = 99

• TP = 9 (0.85 × 10), FN = 1, FP = 99 (0.10 × 990), TN = 891

• Disease present = 200; Disease absent = 800
• TP = 170 (0.85 × 200), FN = 30, FP = 80 (0.10 × 800), TN = 720

• A) High specificity
• B) High sensitivity
• C) High positive predictive value
• D) High negative predictive value
• E) Low likelihood ratio positive

• A) 30%
• B) 46%
• C) 62%
• D) 80%
• E) 95%

• LR+ = Sensitivity / (1 - Specificity) = 0.80 / 0.05 = 16
• Pre-test odds = 0.05 / (1 - 0.05) = 0.05/0.95 = 0.0526
• Post-test odds = 0.0526 × 16 = 0.842
• Post-test probability = 0.842 / (1 + 0.842) = 0.842/1.842 = 45.7% ≈ 46%

• Deciding to initiate broad-spectrum antibiotics in a patient where resistance risk is high
• Confirming HIV before disclosing a positive result to a patient
• Deciding to proceed with a high-risk surgical procedure
• Confirming cancer before starting chemotherapy In these cases, you want near-certainty before acting (SpPin - high specificity, positive result rules in disease).

• A) It increases to > 90%
• B) It remains at 85%
• C) It drops to approximately 30%
• D) It drops to approximately 17%
• E) It drops to near zero

• LR- = 0.035
• Pre-test odds = 0.85 / 0.15 = 5.67
• Post-test odds = 5.67 × 0.035 = 0.198
• Post-test probability = 0.198 / 1.198 = 16.5% ≈ 17%

• A) Sensitivity increases, specificity decreases
• B) Sensitivity decreases, specificity increases
• C) Both sensitivity and specificity increase
• D) Both sensitivity and specificity decrease
• E) PPV decreases, NPV decreases

• A) The nocebo effect
• B) Berkson's bias
• C) Bayes' theorem
• D) The Bradford Hill criteria
• E) The Hawthorne effect

• LR+ = Sensitivity / (1 - Specificity) - how much a positive result increases the probability of disease
• LR- = (1 - Sensitivity) / Specificity - how much a negative result decreases the probability of disease

1. Estimate your patient's pre-test probability from clinical context
2. Apply the LR to convert pre-test → post-test probability

SECTION E: HYPOTHESIS TESTING, BIAS & CONFOUNDING (Questions 31-40)

• A) The larger trial committed a Type I error
• B) The smaller trial was likely underpowered (Type II error)
• C) The p-value of 0.12 proves no difference exists
• D) The larger trial's result is clinically more significant
• E) Both trials have the same statistical power

• A) Drug A is significantly better than Drug B
• B) Drug B is significantly better than Drug A
• C) The difference between drugs is not statistically significant
• D) The study was overpowered
• E) The result is clinically significant

1. Smoking is associated with the exposure (coffee consumption) - smokers tend to drink more coffee
2. Smoking is independently associated with the outcome (Parkinson's disease) - epidemiological studies show smoking appears to reduce Parkinson's risk
3. Smoking is not on the causal pathway between coffee and Parkinson's - it acts as a separate variable, not a mediator

• Restriction: Analyze only non-smokers and assess if the coffee-Parkinson's association persists
• Matching: Match coffee drinkers and non-drinkers on smoking status at study entry
• Stratified analysis: Calculate RR separately for smokers and non-smokers, then compare
• Multivariable (logistic/Cox) regression: Include smoking as a covariate to estimate the coffee effect adjusted for smoking
• Propensity score methods: Create a propensity score for coffee drinking that balances smoking status

• Perform stratified analysis: Calculate the RR for coffee and Parkinson's separately in smokers and non-smokers
• If the RR is the same in both strata → smoking is a confounder (report a single adjusted RR)
• If the RR differs meaningfully between strata → smoking is an effect modifier (the effect of coffee on Parkinson's is different in smokers vs. non-smokers; report stratified results separately)
• Effect modification is a biological reality to be reported; confounding is a bias to be controlled.

• A) Confounding
• B) Lead-time bias
• C) Recall bias
• D) Selection bias
• E) Observer-expectancy bias

• A) Recall bias and selection bias
• B) Lead-time bias and length bias
• C) Confounding and information bias
• D) Volunteer bias and observer bias
• E) Hawthorne effect and nocebo effect

• A) Recall bias
• B) Placebo effect
• C) Confounding
• D) Effect modification
• E) Berkson's bias

• A) Recall bias
• B) Lead-time bias
• C) Loss-to-follow-up bias (attrition bias)
• D) Length bias
• E) Hawthorne effect

1. Temporality (required): Children must have been exposed to power lines/EMF before developing leukemia, not after diagnosis. Longitudinal study data showing exposure preceded disease onset is essential.
2. Strength of association: The relative risk or odds ratio should be large and robust. A weak association (e.g., RR = 1.1) is more easily explained by confounding or bias. A strong association (e.g., RR > 3) is harder to explain by chance.
3. Dose-response relationship: Children living closer to power lines, or with higher measured EMF exposure, should have proportionally higher leukemia rates. A clear gradient strengthens causal arguments.
4. Consistency: The association should be replicated across multiple independent studies in different countries, populations, and research groups. A finding unique to one study is less convincing.
5. Biological plausibility: There should be a known or proposed biological mechanism by which EMF could damage DNA or disrupt normal cell cycling to promote leukemia. Without a plausible mechanism, the association remains unexplained.
6. Specificity: EMF exposure should be specifically linked to leukemia (or at least a defined spectrum of malignancies), not associated with every disease simultaneously.
7. Experiment: Animal studies or controlled experiments exposing cells/animals to similar EMF levels should produce leukemia-promoting effects.

• A) A case-control study is preferred because mortality is a common outcome
• B) A cross-sectional study would be adequate because it measures both exposure and outcome
• C) A double-blind RCT is ideal but an observational cohort could be used if an RCT is impractical
• D) A meta-analysis should be performed first before any primary research is conducted
• E) A case series of Metformin-treated patients would provide sufficient evidence

ANSWER KEY SUMMARY