HIM 3200 Midterm Review

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HIM 3200Midterm Review

• Dr. Burton

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Mid-term review

• Types of data
• Normal distribution
• Variance
• Standard deviation and z scores
• 2 X 2 table
• Hypothesis testing H0 HA
• t-test
• Pearson r/Linear regression
• Chi square

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Measurements

• Frequency
• Incidence
• The frequency of new occurrences of disease,
  injury, or death in the study population during
  the time being examined.
• Prevalence
• The number of persons in defined population that
  had a specified disease or condition
• Point prevalence (at a particular point in time.)
• Period prevalence (the sum of the point
  prevalence at the beginning of the interval plus
  the incidence during the interval.)

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Measurements

• Frequency
• Incidence
• Prevalence
• Risk
• The proportion of persons who are unaffected at
  the beginning of a study period but who undergo
  the risk event during the study period.

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• Risk event
• Death
• Disease
• Injury
• Cohort
• Persons at risk for the event .

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Measurements

• Frequency
• Incidence
• Prevalence
• Risk
• The proportion of persons who are uneffected at
  the beginning of a study period but who undergo
  the risk event during the study period.
• Rates
• The frequency of events that occur in a defined
  time period, divided by the average population at
  risk.

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Rates
Numerator
Rate ------------------- x Constant
multiplier
Denominator

• The constant multiplier is usually 100, 1000,
  10,000 or 100,000.
• Types of rates
• Incidence rates (i.e. Per 1000)
• Prevalence rates (Proportional i.e. 20)
• Incidence density (frequency of new events per
  person time)

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• Equations for the most commonly used population
  data.
• (Mortality) Table 1 10 p.18 Osborn text
• (Morbidity) Table 1 11 p. 21 Osborn text

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Differential and nondifferential error

• Bias is a differential error
• A nonrandom, systematic, or consistent error in
  which the values tend to be inaccurate in a
  particular direction.
• Nondifferential are random errors

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Bias

• Three most problematic forms of bias in medicine
• 1. Selection (Sampling) Bias
  The following are biases that
  distort results because of the selection process
  
• Admission rate (Berksons) bias
• Distortions in risk ratios occur as a result of
  different hospital admission rate among cases
  with the risk factor, cases without the risk
  factor, and controls with the risk factor
  causing greatly different risk-factor
  probabilities to interfere with the outcome of
  interest.
• Nonresponse bias
• i.e. noncompliance of people who have scheduled
  interviews in their home.
• Lead time bias
• A time differential between diagnosis and
  treatment among sample subjects may result in
  erroneous attribution of higher survival rates to
  superior treatment rather than early detection.

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Bias

• Three most problematic forms of bias in medicine
• 1. Selection (Sampling) Bias
• Admission rate (Berksons) bias
• Nonresponse bias
• Lead time bias
• 2. Information (misclassification) Bias
• Recall bias
• Differentials in memory capabilities of sample
  subjects
• Interview bias
• blinding of interviewers to diseased and control
  subjects is often difficult.
• Unacceptability bias
• Patients reply with desirable answers

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Bias

• Three most problematic forms of bias in medicine
• 1. Selection (Sampling) Bias
• Admission rate (Berksons) bias
• Nonresponse bias
• Lead time bias
• 2. Information (misclassification) Bias
• Recall bias
• Interview bias
• Unacceptability bias
• 3. Confounding
• A confounding variable has a relationship with
  both the dependent and independent variables that
  masks or potentiates the effect of the variable
  on the study.

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Neyman bias

• late look bias if it results in selecting fewer
  individuals with severe disease because they died
  before detection.
• length bias in screening programs which tend to
  select less aggressive cases for treatment.

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2 X 2 Tablecomparing the test results of two
observers
Observer No. 1
Positive
Negative
Total
a
b
a b
Positive
Observer No. 2
d
c
c d
Negative
a c
b d
abcd
Total
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_ A B
A B - C
D C D
A C B D

• Sensitivity A/(A C)
• Specificity D/(B D)
• False- positive rate B/(B D)
• False-negative rate C/(A C)
• Positive predictive value A/(A B)
• Negative predictive value D/ (D C)
• Accuracy (A D) / (A B C D)

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Types of Variation

• Nominal variables
• Dichotomous (Binary) variables
• Ordinal (Ranked) variables
• Continuous (Dimensional) variables
• Ratio variables
• Risks and Proportions as variables

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Nominal
A
Social Security Number
O
123 45 6789 312 65 8432 555 44 7777
Blood Type
B
AB
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Types of Variation

• Nominal variables
• Dichotomous (Binary) variables
• Ordinal (Ranked) variables
• Continuous (Dimensional) variables
• Ratio variables
• Risks and Proportions as variables

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Dichotomous (Binary) variables
WNL Not WNL
Normal Abnormal
Accept Reject
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Types of Variation

• Nominal variables
• Dichotomous (Binary) variables
• Ordinal (Ranked) variables
• Continuous (Dimensional) variables
• Ratio variables
• Risks and Proportions as variables

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Ordinal (Ranked) variables
Strongly agree, agree, neutral, disagree,
strongly disagree
a b c d e
1 2 3 4 5
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Types of Variation

• Nominal variables
• Dichotomous (Binary) variables
• Discrete variables
• Ordinal (Ranked) variables
• Continuous (Dimensional) variables
• Ratio variables
• Risks and Proportions as variables

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Continuous (Dimensional) variables
Temperature 32 F
Height Blood Pressure Weight
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Types of Variation

• Nominal variables
• Dichotomous (Binary) variables
• Discrete variables
• Ordinal (Ranked) variables
• Continuous (Dimensional) variables
• Ratio variables
• Risks and Proportions as variables

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Ratio variables

• A continuous scale that has a true zero point

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Measures of Central Tendency

• Mode the value with the highest number of
  observations in a data set.
• Median the middle observation when data have
  been arranged from highest to lowest.
• Mean (arithmetic) the average value of all
  observed values.

? (xi)
Mean x
Ni
Sum ? Observed values xi Total number of
observations Ni
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Raw data and results of Cholesterol levels in
26 subjects p.115

• Number of observations or N 26
• Initial HDL values 31, 41, 44, 46, 47, 47, 48,
  49, 52, 53, 54, 57, 58, 58, 60, 60, 62, 63,
  64, 67, 69, 70,
• 77, 78, 81, 90 mg/dl
• Highest values 90 mg/dl
• Lowest value 31 mg/dl
• Mode 47, 48, 58, 60 mg/dl
• Median (57 58)/2 57.5 mg/dl
• Sum of the values ? (xi) 1496 mg/dl
• Means, x 1496/26 57.5 mg/dl

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Percentiles (quantiles)

• The median is the 50
• The 75th percentile is the point where 75 of
  observations lie below and 25 are above. (3rd
  quartile, Q3)
• The 25th percentile is the point where 25 of
  observations lie below and 75 are above. (1st
  quartile, Q1)
• Interquartile range (Q3 Q1)

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Raw data and results of Cholesterol levels in
26 subjects p.115

• Number of observations or N 26
• Initial HDL values 31, 41, 44, 46, 47, 47, 48,
  48, 49, 52, 53, 54, 57, 58, 58, 60, 60, 62,
  63, 64, 67, 69, 70,
• 77, 78, 81, 90 mg/dl
• Highest values 90 mg/dl
• Lowest value 31 mg/dl
• Mode 47, 48, 58, 60 mg/dl
• Median (57 58)/2 57.5 mg/dl
• Sum of the values ? (xi) 1496 mg/dl
• Means, x 1496/26 57.5 mg/dl
• Interquartile range 64 48 16 mg/dl

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Measures of dispersion based on the Mean.

• Mean deviation
• Variance
• Standard deviation s

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Raw data and results of Cholesterol levels in
26 subjects p.115

• Number of observations or N 26
• Initial HDL values 31, 41, 44, 46, 47, 47, 48,
  48, 49, 52, 53, 54, 57, 58, 58, 60, 60, 62,
  63, 64, 67, 69, 70,
• 77, 78, 81, 90 mg/dl
• Highest values 90 mg/dl
• Lowest value 31 mg/dl
• Mode 47, 48, 58, 60 mg/dl
• Median (57 58)/2 57.5 mg/dl
• Sum of the values ? (xi) 1496 mg/dl
• Means, x 1496/26 57.5 mg/dl
• Interquartile range 64 48 16 mg/dl
• Sum of squares (TSS) 4,298.46 mg/dl
• Variance, s squared 171.94 mg/dl
• Standard Deviation, s 171.94 mg/dl 13.1
  mg/dl

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Theoretical normal (gaussian) distribution

• ? stands for the mean in a theoretical
  distribution
• ? stands for the standard deviation in a
  theoretical population.

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Theoretical normal distribution with standard
deviations
-3?
-2?
-?
?
?
2?
3?
-3
-2
-1
1
2
3
0
Z scores
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Three Common Areas Under the Curve

• Three Normal distributions with different areas

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Process of Testing Hypotheses

• Test are designed to determine the probability
  that a finding represents the true deviation from
  what is expected.
• This chapter focuses on the justification for and
  interpretation of the p value designed to
  minimized type I error.
• Science is based of the following principles
• Previous experience serves as the basis for
  developing hypotheses
• Hypotheses serve as the basis for developing
  predictions
• Predictions must be subjected to experimental or
  observational testing.

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Hypothesis testing
Truth
H0 True
H0 False
a
b
Type II Error
Correct
Accept H0
Decision
d
c
Correct
Type I Error
Reject H0
Alpha error rejecting the null H0 when it is
true
Beta error accepting the null H0 when it is
false
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The power of a test

• (probability that a test detects differences that
  actually exist) can be determined by using the
  formula 1 beta (1 - ?)
• 80 is usually acceptable

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Hypothesis Testing

• 1. State question in terms of
• H0 no difference or relationship (null)
• Ha is difference or relationship (alternative)
• 2. Decide on appropriate research design and
  statistic
• Select significance (alpha) level and N
• Collect data
• Analyze and perform calculation to get P-value
• Draw and state conclusions by comparing alpha
  with P-value

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Theoretical normal distribution with standard
deviations
-3?
-2?
-?
?
?
2?
3?
Z scores
-3
-2
-1
1
2
3
0
Probability
Upper tail .1587 .02288
.0013 Two-tailed .3173 .0455 .0027
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When is a specific test used?

• Students t test to compare the means of two
  small (n lt 30) independent samples.
• Paired t-test to compare the means of two
  paired samples (e.g. before and after)
• F test to compare means of three or more
  samples or groups.
• Chi-Square test comparing two or more
  independent proportions.
• Correlation coefficient measures the strength
  of the association between two variables.
• Regression analysis Provides an equation that
  estimates the change in a dependent variable (y)
  per unit change in an independent variable (x).