Biostatistics in Practice
description
Transcript of Biostatistics in Practice
Biostatistics in Practice
Peter D. ChristensonBiostatistician
http://gcrc.LABioMed.org/Biostat
Session 2: Summarization of Quantitative
Information
Readings for Session 2from StatisticalPractice.com
Units: E.g., Mouse or litter, Not e.g., mg/ml.
RAW data, preferably.
Summary: Particular method depends on structure in the raw data.
Bell curve: often “natural”.
Want ranges (for what?).
• Units of Analysis“Experimental units”
• Look at the data
• Summary statistics• Typical values and their variability• Correlation
• Normal distribution
• Confidence intervals
Units of Analysis
Go over this entire reading at StatisticalPractice.com.
The author states that some students are “more similar” to each other than are other students, or some students are “independent”. What does this mean?
“Independent” really refers to the measurement that is made, not the units such as students or classes or schools.
If knowledge of the value for a student does not change the likelihood of another student’s value, given class means, then the students are independent for this measurement.
Would students from the same class likely be independent on height? How about on knowing some academic fact, such as what a case-control study is?
Example: Units and Independence
Ten mice receive treatment A, each is bled, and blood samples are each divided into 3 aliquots. The same is done for 10 mice on treatment B.
1. A serum hormone is measured in the 60 aliquots and compared between A and B. The unit is a mouse, their means from 3 aliquots each are independent, N=10+10, and aliquots for a mouse are not independent.
2. One of the 30 A aliquots is further divided into 25 parts and 5 different in vitro challenges each made to 5 of the parts. The same is done for a single B aliquot. For the challenge experiment, each part is a unit, their values are independent, and N=25+25. For comparing A and B, there are only N=1+1 units, the two mice.
Look at the Data
Statistical methods depend on the “form” of a set of data, which can be assessed with some common useful graphics:
Graph Name Y-axis X-axis
Histogram Count or % Category
Scatterplot Continuous Continuous
Dot Plot Continuous Category
Box Plot Percentiles Category
Line Plot Mean or value Category
Examples on following slides are from StatisticalPractice.com
Data Graphical Displays
Histogram Scatter plot
Raw DataSummarized*
*Raw data version is a stem-leaf plot. We will see one later.
Data Graphical Displays
Dot Plot Box Plot
Raw Data Summarized*
Data Graphical Displays
Line or Profile Plot
Summarized - “antennae” can represent various ranges
Week
Look at the Data, Continued
What do we look for?
Histograms: Ideal: Symmetric, bell-shaped.Potential Problems:• Skewness.• Multiple peaks.• Many values at, say, 0, and bell-shaped otherwise• Outliers.
Scatter plots: Ideal: Football-shaped; ellipse.Potential Problems:• Outliers.• Funnel-shaped.• Gap with no values for one or both variables.
Example Histogram: OK for Default* Analyses
• Symmetric.• One peak.• Roughly bell-shaped.• No outliers.
*software default, typical mean, SD, confidence intervals.
876543210
150
100
50
0
Intensity
Freq
uenc
yHistograms: Not OK for Default Analyses
Skewed
Need to transform intensity to another
scale, e.g. Log(intensity)
1207020
20
10
0
Tumor Volume
Freq
uenc
y
Multi-Peak
Need to summarize with percentiles, not mean.
Histograms: Not OK for Default Analyses
Truncated Values
Need to use percentiles for most analyses.
Outliers
Need to use median, not mean, and percentiles.
1050
60
50
40
30
20
10
0
Assay Result
Freq
uenc
y
LLOQ
Undetectable in 28 samples (<LLOQ)
840
100
50
0
Expression LogRatio
Freq
uenc
y
Example Scatter Plot: OK for Typical Analyses
Scatter Plot: Not OK for Typical Analyses
Gap and Outlier
Consider analyzing subgroups.
Funnel-Shaped
Could transform y-value to another scale, e.g.
logarithm.
0 100 200 300 400
0
50
100
150
EPO
nRB
C C
ount
All Subjects:r = 0.54 (95% CI: 0.27 to 0.73)p = 0.0004
EPO < 150:r = 0.23 (95% CI: -0.11 to 0.52)p = 0.17
EPO > 300:r = -0.04 (95% CI: -0.96 to 0.96)p = 0.96
Ott, Amer J Obstet Gyn 2005;192:1803-9.Ferber et al, Amer J Obstet Gyn 2004;190:1473-5.
Summary Statistics: I
Typical Values (“Location”):• Mean for symmetric data.• Median for skewed data.• Geometric mean for some skewed data (see later slide).
Variation in Values (“Spread”. Standard deviation=SD):• Standard, convention, non-intuitive values.• SD=~ Avg. deviation of values from their mean.• SD of what? E.g., SD of individuals, or of group means.• Fundamental, critical measure for most statistical methods.
See graphs in reading for how mean and SD change if units of measurement change, e.g., nmoles to mg:
• Mean (a + b*X) = a + b*Mean(X)• SD (a + b*X) = b*SD(X)
Examples: Mean and SD
Mean = 60.6 min.
Note that the entire range of data in A is about 6SDs wide, and is the source of the “Six Sigma” process used in business quality control.
95857565554535
25
20
15
10
5
0
Time
Freq
uenc
y
SD = 9.6 min.
201510
15
10
5
0
OD
Freq
uenc
y
Mean = 15.1 min. SD = 2.8 min.
A B
876543210
150
100
50
0
Intensity
Freq
uenc
yExamples: Mean and SD
Skewed
1207020
20
10
0
Tumor Volume
Freq
uenc
y
Multi-Peak
Mean = 1.0 min.SD = 1.1 min. Mean = 70.3 min.
SD = 22.3 min.
Summary Statistics: IIRule of Thumb:
For bell-shaped distributions of data (“normally” distributed):
• ~ 68% of values are within mean ±1 SD• ~ 95% of values are within mean ±2 SD• ~ 99.7% of values are within mean ±3 SD
Geometric means (see next slide):Used for some skewed data.1. Take logs of individual values.2. Find, say, mean ±2 SD → mean (low, up) of the
logged values.3. Find antilogs of mean, low, up. Call them GM, low2,
up2 (back on original scale).4. GM is the “geometric mean”. The interval (low2,up2)
is skewed about GM (corresponds to graph).
Geometric MeansThese are flipped histograms rotated 90º, and box plots.
Any base for the log transformation gives a symmetric distribution. [Ln used here; log10 gives same GM and bounds.]
=~ 909.6
=~ 11.6
GM = exp(4.633) = 102.8
low2 = exp(4.633-2*1.09) = 11.6
upp2 = exp(4.633+2*1.09) = 909.6
=~ 102.8
Summary Statistics: III (Correlation)
We will examine calculation details later.
• With 2 continuous measures, always look at scatterplot.• See graphs in readings for values ranging from -1 (perfectly
inverse relation) to +1 (perfectly direct). Zero=no relation.• Measures linear association.• Very sensitive to outliers.• Specific to the ranges of the two variables.• Typically, cannot extrapolate to populations with other
ranges.• Subgroups may not have the same correlation; in fact, they
could have the opposite association (ecological fallacy).• Special correlations are used for non-symmetric data.• Measures association, not causation.
Correlation Depends on Ranges of X and Y
Graph B contains only the graph A points in the ellipse.
Correlation is reduced in graph B.
Thus: correlations for the same quantities X and Y may be quite different in different study populations.
BA
Correlation and Measurement Precision
A lack of correlation for the subpopulation with 5<x<6 may be due to inability to measure x and y well.
Again, lack of evidence is not evidence of “lack” (of association in this setting).
BA
r=0 for s
B
overall
5 6
12
10
Confidence Intervals: I• See beginning of reading for the goal of confidence intervals.
• CIs are not about individuals, but rather about populations, i.e., groups of individuals.
• A mean from a sample estimates the mean of the entire population.
• 95% CI for the mean is a range of values we're 95% sure contains the unknown mean.
• Reading example: N=40 non-smokers. Vitamin C mean±2SD is 90±2*35 = 20 to 160 = “normal range”. Our estimate of the unknown mean for all non-smokers is 90, but how confident are we about that estimate? Need a ±range for it that we are 95% confident contains the unknown mean.
Confidence Intervals: II• Can calculate a CI for any unknown parameter.
• Typical 95% CI for a mean is roughly: mean ± 2SD/√N.• Larger SD → wider CI.• Larger N → narrower CI.• More confidence → wider CI.• For reading example, CI=~ 90 ± 2*35/√40 = 78 to 102.
• I am being sloppy with terminology. The underlined mean above is the always-to-be-unknown mean for the population (everyone). The other mean, before ±, is the mean that is calculated from the sample of N, denoted X-bar, and it estimates the unknown mean, denoted μ.
• Note explicit use of N; correct unit of analysis is critical. What if we measured vitamin C on 10 days for each subject?
Confidence vs. Prediction Intervals
• Typical 95% CI for a mean is roughly: mean ± 2SD/√N.• Recall that this CI is the range of values we're 95% sure
contains the unknown mean for “everyone”.
• What about (normal) ranges for individuals?• This is often called a prediction interval (PI) = “normal”
range = reference range.• 95% of individuals fall in a 95% PI.• 95% chance that an individual falls in a 95% PI.• Typical 95% PI for an individual is roughly: mean ± 2SD.
• With large N (how large? often N>30 is used), do not need bell-shaped data distribution for the CI, but that shape IS needed for the PI, regardless of N. Otherwise, we use percentiles for normal ranges.
CI and PI for the Antibody Example
=~ 909.6
=~ 11.6
GM = exp(4.633) = 102.8
low2 = exp(4.633-2*1.09) = 11.6
upp2 = exp(4.633+2*1.09) = 909.6
=~ 102.8
So, there is 95% assurance that an individual is between 11.6 and 909.6, the PI.
So, there is 95% assurance that the pop- ulation mean is between 92.1 and 114.8, the CI.
GM = exp(4.633) = 102.8
lower = exp(4.633-2*1.09/√394) = 92.1
upper = exp(4.633+2*1.09/√394) = 114.8