Notes
Biostatistics
Biostats & Evidence-Based Medicine
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Biostatistics
Biostats & Evidence-Based Medicine
Sensitivity, specificity, PPV, NPV, study types, biases.
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Diagnostic test statistics
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Sensitivity = TP/(TP+FN) — rules OUT disease when negative (SnNOUT)
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Specificity = TN/(TN+FP) — rules IN disease when positive (SpPIN)
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PPV = TP/(TP+FP) — depends on prevalence
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NPV = TN/(TN+FN) — depends on prevalence
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LR+ = sens/(1-spec); LR- = (1-sens)/spec
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Pre-test probability × LR = post-test odds
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Study designs (strongest to weakest)
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Meta-analysis > systematic review > RCT > cohort > case-control > cross-sectional > case series/report
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RCT: gold standard for causation; randomization eliminates confounding
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Cohort: prospective; can calculate INCIDENCE + RELATIVE RISK
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Case-control: retrospective; calculates ODDS RATIO; good for rare diseases
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Cross-sectional: prevalence; snapshot in time
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Biases
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Selection bias: non-random selection (Berkson, healthy worker)
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Recall bias: case-control studies (cases remember exposure better)
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Lead-time bias: screening makes disease appear longer just because diagnosed earlier
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Length bias: slowly progressing disease over-represented in screening
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Confounding: third variable associated with both exposure and outcome
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Effect modification: relationship varies across subgroups (NOT a bias)
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Hawthorne effect: subjects change behavior because being observed
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Pygmalion effect: researcher's expectations affect outcome
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Hypothesis testing
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Type I error (α): false positive — reject true null
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Type II error (β): false negative — fail to reject false null; Power = 1-β
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Increasing sample size ↑ power
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p-value <0.05 = statistically significant by convention
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Confidence interval: if it includes 1 (for ratios) or 0 (for differences), result is not significant
High-yield pearls
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Use sensitive test for SCREENING (rule out); specific test for CONFIRMATION (rule in)
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Number needed to treat (NNT) = 1/absolute risk reduction (ARR)
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Number needed to harm (NNH) = 1/absolute risk increase
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Odds ratio approximates relative risk when disease is rare
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