Biostatistics Flashcards
Cross-sectional study
assess frequency of disease (and related risk factors) at a PARTICULAR POINT IN TIME. Measures disease PREVALENCE. Cannot establish causality
Case-control study (retrospective)
“known disease” compares a group of people with disease to a group without disease. Looks for prior exposure or risk factor Measures OR
Cohort Study (prospective or retrospective)
“known exposure” compares a group with a given exposure or risk factor to a group without such experience. Looks to see if exposure increases the likelihood of disease. Measures RR
Sensitivity
TP/(TP+FN) Highly SeNsitive when Negative rules disease OUT “SNNOUT” Screening test
Specificity
TN/(TN+FP) Highly SPecific when Positive test rules disease IN “SPPIN” Confirmatory testing
Positive Predictive Value (PPV)
proportion of positive test results that are true positive TP/(TP+FP) Varies directly with prevalence/pre-test probability
Negative Predictive Value (NPV)
proportion of negative test results that are true negative TN/(TN+FN) Varies inversely with prevalence/pre-test probability
Incidence
looks at new cases incidence rate = # of new cases / # of people at risk
Prevalence
looks at all current cases = # of existing case / # of people at risk ~pretest probability
Odds Ratio
used for case control studies
Relative Risk
used in cohort studies
Attributable Risk
Relative Risk Reduction (RRR)
RRR = 1 - RR
proportion of risk reduction attributable to intervention
e.g. if 2% of patients who receive a flu shot develop the flu, while 8% of unvaccinated patient develop the flu, then:
RR = 2/8 = 0.25 and RRR = 0.75
Absolute Relative Risk (ARR)
Difference in Risk attributable to the intervention as compared to a control
e.g. if 8% of people who receive a placebo vaccine develop the flu vs. 2% of people who receive a flu vaccine, then:
ARR = 8% - 2% = 6% = 0.06
Number Needed to Treat (NNT)
NNT = 1 / ARR
(for the benefit of one patient)
Number Needed to Harm (NNH)
NNH = 1 / AR
who need to be exposed for a risk factor for 1 patient to be harmed
Selection Bias
Error in assigning subjects to a study group reulting in an unrepresentative sample.
Reduce bias: Randomization, correct comparison group
Recall Bias
Awareness of disorder alters recall by subjects; common in retrospective studies
Reduce Bias: decrease time from exposure to follow-up
Measurement Bias
Information is gathered in a way that distorts it
e.g. miscalibrated scale consistently overstates weight of subjects
reduce bias: use standardized method of data collection
Procedure Bias
Subjects in different groups are not treated the same
reduce bias: blinding and use of placebo
Observer-expectancy bias
Researcher’s belief in the efficacy of a treatment changes the outcome of that treatment (‘self-fulfilling prophecy’)
reduce bias: blinding and use of placebo
Confounding bias
When a factor is related to both the exposure and outcome, but not on the causal pathway –> factor distorts of confuses effect of exposure on outcome
e.g. pulmonary disease more common in coal workers than general population; people who work in coal mines also smoke more frequently.
reduce bias: multiple/repeated studies; crossover studies (subjects act as own controls); matching (patients with similar characteristics in both treatment and control goup)
Lead-time bias
Early detection is confused with increased survival
reduce bias: measure “back-end” survival (adjust survival according to the severity of disease at time of diagnosis)
Mean
average
most affected by ouliers
