** Key Chapter 12 Review Questions Key **
�12.1 Introduction
- Systematic error and random
error.
- Parameters are
error-free quantifications. Estimates are error-prone
statistics.
- Estimators have hats.
Parameters are �hatless.�
- Bias
- Imprecision.
- Valid
- Imprecise
- Random error is balanced
(equal number of overestimates and underestimates), is sample size
dependent (less random error in large samples), and can be dealt with via
laws of probability.
�12.2 Random Error
- False. Probability models
only address random error.
- False. Both subjective and
objective probabilities are based on an underlying concept of expected
frequencies. In addition, they obey the same mathematical laws.
- Confidence intervals and
hypothesis tests
- Random error would be
eliminated. Increasing the sample size has no effect on systematic error.
- True. Random error causes our
estimates to be imprecise.
�12.3 Systematic Error
- Selection, information,
confounding
- False. Nondifferential
misclassification biases measures of effect toward the null, or in
some instances, not at all.
- False. Bias away from the null overstates risk.
- Confounding is a distortion
in a measure of effect brought about by extraneous ("lurking")
factors.
- (a) Associated with the
exposure (b) Independent risk factor for disease (c) not intermediate in
the causal pathway
- Confundere
= to mix-up
- Berkson's
bias, also know as "Hospital admission rate"
- No. It will tend to have the
same amount of bias. (It will have less random error, however.)
- Recall bias
- Information bias
- When it is not associated
with the exposure
- This question from Rothman is
thought provoking. Clearly, smoking causes lung cancer in both men and
women. Given the source of the question, one can hypothesize that a
response should address causal mechanisms and causal complements in the populations.
It the populations have similar causal mechanisms, then
we can generalize a study to a population whose characteristics differ
from those in the study population. Otherwise, we cannot.
- It could be considered
confounding if the medical indication or the severity of the condition can
be measured and adjusted for in the analysis, or it might be considered
uncontrolled confounding or selection bias because it acts like a
confounder but is a consequence of being "selected" to get the
drug.