Clinical epidemiology: biology helps understand disease. However treatments developed from
biological mechanisms are just hypotheses. There is so much we do not know about disease
mechanisms, and hypotheses need to be tested. In the old days doctors used simple
observation, which was not reliable. To turn observation into sound scientific principles, we use
clinical epidemiology methods. Clinical epidemiology is also used to assess health care cost, so
the limited funding can be used more effectively to help the population, not an individual.
Basic principles:
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Questions: individual - sick or well; how often does a disease occur (30 yo man in ED,
unresponsive, turning blue, shallow breathing, 30 years ago-alcohol, diabetes, heart
attack/stroke vs now); what are the risk factors; how accurate are tests used to
diagnose; prognosis; treatment options; population - prevention, early detection and
treatment, caused of the illness (mechanism), and cost
Outcomes of diseases: 5”D”s, death, disease, discomfort, disability and dissatisfaction
Numbers and probability: quantitative measure provides the strongest support for clinical
science; clinical predictions are always uncertain, but can be expressed as probabilities
Population and samples:
Bias: a process or factor that can produce results departing from the true value; reasons
why double blind, placebo controlled, randomized studies are the gold standard
○ Selection bias: age, gender, ethnicity, education, socioeconomic status, general
health conditions, etc
○ Information bias: self report can be subjective depends on the motivations, e.g.,
mothers with birth defect kids may answer more eagerly about second hand
smoking, or minimize own their drinking habits; not avoidable
○ Measurement bias: questionnaires, medical record review
○ Confounding bias: during analysis, certain associated factors may have
associations, but not causal relationships: an apple a day keeps the doctor away
Example: studying exercise and heart attack in a large factory, an exercise
program was provided; volunteers were compared to those who did not
volunteer, volunteers received routine checkups, including EKG; 1) selection
bias: volunteer, 2) measure bias: volunteers received more routine check up,
received treatment early, 3) confounding bias: volunteers may have less
smokers, or drinkers, etc
○ Statistics: to estimate random chances; proper study design can minimize biases,
but can not eliminate random chances P11 F1.2
■ Internal validity - whether the study results reflect the samples being
studied; depends on study design, data collection and analysis, and is
affected by all the biases and random variations
■ External validation - the degree the results hold true in other situations
(generalizability). To improve generalizability, avoid using unusual
groups.
■ Sampling bias: clinical research often takes place in medical centers
which can over-represent the serious end of the disease spectrum.
Measurement: qualitative vs quantitative
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Nominal data: no inherent order; categorical (eye color, ethnicity); dichotomous
data (dead/alive, yes/no, present/absent, etc)
○ Ordinal data: has some inherent order, but difficult to specify, grades I-IV edema,
heart murmurs or muscle strength, cancer grades
○ Interval data: there is inherent order, can be continuous or (temp, IQ test, BP,
serum chemistry, weight…), or discrete, expressed as counts (No of pregnancy,
number of seizures…). One specific type is ratio data, such as height, zero is the
lowest, can use math to add, subtract, multiply…
Validity/accuracy: the degree to which the data measure what we intended to measure
(how close to the true value); valid measurement means low systemic error
○ Data that can be directly measures by physical means (weight, serum chemistry;
measuring instruments are subject to validity check with certain standards)
○ Data can not be measured physically, such as pain, emotions, etc; we use
scales. These measurements are often discounted by physicians, as they can be
subjective, however they often are important to patients
Reliability/precision: can be reproduced; lacks random variation: hospital vitals are often
reliable, but may not be very accurate depends on the measurement methods
Variations: measurement variations, biological variations (diurnal, hormonal etc):
Distributions:
○ Central tendency: mean, median, mode
○ Dispersion: range, standard deviation, percentile (quantile, decile)
○ Normal distribution (Gaussian distribution): derived from mathematical theory;
reflects only random variations, ⅔ within 1 SD, 95% within 2 SD; it’s often
assured clinical measurements are “normally” distributed
Disease vs tests: true/false positive; true/false negative
○ Sensitivity: true positive/diseased; highly sensitive tests are used for screening; if
the result is negative, rules out a disease; parallel tests increase sensitivity
○ Specificity: true negative/not diseased; highly specific tests are used for
confirmation; when positive, rules in a disease; serial tests increase specificity
○ Positive vs negative predictive value of a test: probability of disease, is affected
by prevalence, specificity and sensitivity; highly sensitive tests will have better
negative predictive value, and highly specific test will have a better positive
predictive value
○ Likelihood ratios (LR):
■ Positive LR = sensitivity/(1-specificity); >10 indicates highly specific test
■ Negative LR = (1-sensitivity)/specificity: <0.1 indicates highly sensitive t