lecture 2 basic bayes and two stage normal … bayes and two stage normal normal model… diagnostic...
TRANSCRIPT
![Page 1: Lecture 2 Basic Bayes and two stage normal … Bayes and two stage normal normal model… Diagnostic Testing Diagnostic Testing Ask Marilyn ® BY MARILYN VOS SAVANT A particularly](https://reader030.vdocuments.net/reader030/viewer/2022021718/5b88aba17f8b9abe1e8bcbee/html5/thumbnails/1.jpg)
Lecture 2Basic Bayes and two stage
normal normal model…
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Diagnostic Testing
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Diagnostic Testing
Ask
Marilyn®
BY MARILYN VOS SAVANT
A particularly interesting and important question today is that of testing for drugs. Suppose it is assumed that about 5% of the general population uses
drugs. You employ a test that is 95% accurate, which we’ll say means that if the individual is a user, the test will be positive 95% of the time, and if the individual is a nonuser, the test will be negative 95% of the time. A person is
selected at random and is given the test. It’s positive. What does such a result suggest? Would you conclude that the individual is a drug user?
What is the probability that the person is a drug user?
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a
d
True positives
True negatives
Disease StatusTest Outcome
Diagnostic Testing
False positives
False negativesc
b
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Diagnostic Testing
• “The workhorse of Epi”: The 2 × 2 table
a + b + c + db + da + cTotal
c + d dcTest -
a + bbaTest +
TotalDisease -Disease +
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Diagnostic Testing
• “The workhorse of Epi”: The 2 × 2 table
a + b + c + db + da + cTotal
c + d dcTest -
a + bbaTest +
TotalDisease -Disease +
ca
aDPSens
+=+= )|(
db
dDPSpec
+=−= )|(
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Diagnostic Testing
• “The workhorse of Epi”: The 2 × 2 table
a + b + c + db + da + cTotal
c + d dcTest -
a + bbaTest +
TotalDisease -Disease +
ca
aDPSens
+=+= )|(
db
dDPSpec
+=−= )|(
ba
aDPPPV
+=+= )|(
dc
dDPNPV
+=−= )|(
positive
predicted value
negative
predicted value
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Diagnostic Testing
• Marilyn’s Example
100095050Total
905 9032Test -
954748Test +
TotalDisease -Disease +
PPV = 51%
NPV = 99%
Sens = 0.95
Spec = 0.95
P(D) = 0.05
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Diagnostic Testing
• Marilyn’s Example
1000800200Total
770 76010Test -
23040190Test +
TotalDisease -Disease +
PPV = 83%
NPV = 99%
Sens = 0.95 = 190/200
Spec = 0.95 = 760/800
P(D) = 0.20
Point: PPV depends on prior probability of disease in the population
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Diagnostic Testing & Bayes Theorem
•P(D): prior distribution, that is, the prevalence of disease in the population•P(+|D): likelihood function, that is, the probability ofpositive test given that the person has the disease
(specificity)•P(D|+): positive predicted value, that is, the probabilitythat, given that the test is positive, the person has the disease (posterior probability)
P(D | +) =P(+ | D)P(D)
P(+)
P(+) = P(+ | D)P(D) + P(+ | D )P(D )
0.95 0.20
0.23
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Bayes & MLMs…
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A Two-stage normal normal model
y ij = θ j + ε ij
i = 1,..., n j , j = 1,.., J
ε ij ~ N (0,σ 2)
θ j ~ N (θ ,τ 2)
unit within the cluster
cluster
cluster-specific random effect
within cluster variance
between clusters varianceof the “true” cluster specific
means (heterogeneity parameter)
overall mean
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Terminology
• Two stage normal normal model
• Variance component model• Two-way random effects ANOVA• Hierarchical model with a random intercept and no covariates
Are all the same thing!
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Testing in Schools
• Goldstein and Spiegelhalter JRSS (1996)
• Goal: differentiate between `good' and `bad‘ schools
• Outcome: Standardized Test Scores
• Sample: 1978 students from 38 schools
– MLM: students (obs) within schools (cluster)
• Possible Analyses:
1. Calculate each school’s observed average score (approach A)
2. Calculate an overall average for all schools (approach B)
3. Borrow strength across schools to improve individual school
estimates (Approach C)
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Shrinkage estimation
• Goal: estimate the school-specific average
score
• Two simple approaches:
– A) No shrinkage
– B) Total shrinkage
θ j
y j =1
n j
y ij
i=1
n j
∑
y =
n j
σ 2y j
j=1
J
∑
n j
σ 2
j=1
J
∑
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ANOVA and the F test
• To decide which estimate to use, a traditional
approach is to perform a classic F test for
differences among means
• if the group-means appear significant variable
then use A
• If the variance between groups is not
significant greater that what could be
explained by individual variability within
groups, then use B
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Shrinkage Estimation: Approach C
• We are not forced to choose between A and B
• An alternative is to use the a weighted combination between A and B
ˆ θ j = λ j y j + (1− λ j )y
λ j =τ 2
τ 2 + σ j
2;σ j
2 = σ 2/n j
Empirical Bayes estimate
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Shrinkage estimation
• Approach C reduces to approach A (no
pooling) when the shrinkage factor is equal to
1, that is, when the variance between groups
is very large
• Approach C reduces to approach B,
(complete pooling) when the shrinkage factor
is equal to 0, that is, when the variance
between group is close to be zero
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A Case study: Testing in Schools
• Why borrow across schools?
• Median # of students per school: 48, Range: 1-
198
• Suppose small school (N=3) has: 90, 90,10
(avg=63)
• Difficult to say, small N ⇒ highly variable
estimates
• For larger schools we have good estimates, for
smaller schools we may be able to borrow
information from other schools to obtain more
accurate estimates
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0 1 0 2 0 3 0 4 0
02
04
06
08
01
00
s cho o l
sco
reTesting in Schools
Model:
Mean Scores & C.I.s for Individual Schools
µ
bj
E(Yij ) = θ j = µ + b j
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Fixed and Random Effects
• Standard regression models: εij ~ N(0,σ2)
Yij = µ + εij E(Yij)=µ (overall average)
Yij = µ + b*j + εij E(Yij)=θj (observed school avgs)
• A random effects model:
Yij | bj = µ + bj + εij, where: bj ~ N(0,τ2)
Fixed Effects
Random Effects:
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Testing in Schools: Shrinkage Plot
0 1 0 2 0 3 0 4 0
02
04
06
08
01
00
s cho o l
sco
re
D i re c t S a m p le E s tsB a ye s S hrunk E s ts
µ
b*j
bj
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Some Bayes Concepts
• Frequentist: Parameters are “the truth”
• Bayesian: Parameters have a distribution
• “Borrow Strength” from other observations
• “Shrink Estimates” towards overall averages
• Compromise between model & data
• Incorporate prior/other information in estimates
• Account for other sources of uncertainty
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Relative Risks for Six Largest Cities
.2025
.1600
.3025
.0169
.0625
.0169
Statistical
Variance
0.451.0San Diego
0.400.45Houston
0.550.25Dallas/Ft Worth
0.130.60Chicago
0.251.4New York
0.130.25Los Angeles
Statistical
Standard Error
RR Estimate (%
per 10
micrograms/ml
City
Approximate values read from graph in Daniels, et al. 2000. AJE
y j σ j σ j
2
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Point estimates (MLE) and 95% CI of the airPollution effects in the six cities
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Two-stage normal normal model
y j = θ j + ε j
ε j ~ N(0,σ j
2)
θ j ~ N(θ,τ 2)
RR estimate in city j True RR in city j
Within city statisticalUncertainty (known)
Heterogeneity acrosscities in the true RR
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Two sources of variance
y j = θ j + ε j
θ j = µ + b j
y j = µ + b j + ε j
V (y j ) = V (b j ) + V (ε j ) = τ 2 + σ j
2
λ j =τ 2
τ 2 + σ j
2shrinkage factor
Totalvariance
Variancebetween
Variancewithin
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Estimating Overall Mean
• Idea: give more weight to more precise values
• Specifically, weight estimates inversely proportional to their variances
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Estimating the overall mean
(Der Simonian and Laird, Controlled Clinical Trial 1986)
ˆ τ 2 =1
J − 1( y j − y ) 2 −
1
Jσ j
2
j
∑j
∑
h j =1
σ j
2 + τ 2; w j = h j / h j
j
∑
ˆ µ =
w j y j
j
∑
w j
j
∑;V ( ˆ µ ) =
1
w j
j
∑
“crude” estimate of
the heterogeneity
parameter
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Calculations for Empirical Bayes Estimates (redo this using the “meta”
function in stata..)
1.0037.30.65Over-
all
.09
.11
.07
.27
.18
.27
wj
3.5
4.1
2.6
10.1
6.9
10.1
1/TV
.285
,243
.385
.0994
.145
.0994
Total
Var
(TV)
.2025
.160
.3025
.0169
.0625
.0169
Stat Var
1.0SD
0.45Hou
0.25Dal
0.60Chi
1.4NYC
0.25LA
RRCity
overall = .27* 0.25 + .18*1.4 + .27*0.60 + .07*0.25 + .11*0.45 + 0.9*1.0 =
0.65
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Software in R
yj <-c(0.25,1.4,0.60,0.25,0.45,1.0)
sigmaj <- c(0.13,0.25,0.13,0.55,0.40,0.45)
tausq <- var(yj) - mean(sigmaj^2)
TV <- sigmaj^2 + tausq
tmp<- 1/TV
ww <- tmp/sum(tmp)
v.muhat <- sum(ww)^{-1}
muhat <- v.muhat*sum(yj*ww)
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Two Extremes
• Natural variance >> Statistical variance
– Weights wj approximately constant
– Use ordinary mean of estimates regardless
of their relative precision
• Statistical variance >> Natural variance
– Weight each estimator inversely
proportional to its statistical variance
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Empirical Bayes Estimation
ˆ θ j = λ j y j + (1− λ j ) ˆ µ
λ j =τ 2
τ 2 + σ j
2
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Calculations for Empirical Bayes Estimates
0.160.651.0037.31/37.3=
0.027
0.65Over-
all
0.24
0.23
0.25
0.12
0.19
0.12
0.75
0.58
0.56
0.61
1.1
0.32
.29
.34
.21
.83
.57
.83
.09
.11
.07
.27
.18
.27
3.5
4.1
2.6
10.1
6.9
10.1
1/TV
.285
,243
.385
.0994
.145
.0994
Total
Var
.2025
.160
.3025
.0169
.0625
.0169
Stat Var
1.0SD
0.45Hou
0.25Dal
0.60Chi
1.4NYC
0.25LA
RRCityλ j
ˆ θ j se( ˆ θ j )w j
0.83x0.25 + (1-0.83)x0.65
se ( ˆ θ j ) =1
σ j
2+
1
τ 2
−1
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* *** * *
0 .0 0 .5 1 .0 1 .5 2 .0
S h r in k a g e o f E m p ir ic a l B a y e s E s tim a te s
P e rc e nt Inc re a s e in M o rta li ty
o oooo o
Maximum likelihood estimates
Empirical Bayes estimates
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0 .0 0 .2 0 .4 0 .6 0 .8 1 .0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
S e n s itiv ity o f E m p ir ic a l B a y e s E s tim a te s
N a tura l V a ria nc e
Re
lative
Ris
k
ˆ µ
y j
τ 2
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Key Ideas
• Better to use data for all cities to estimate the relative risk for a particular city
– Reduce variance by adding some bias
– Smooth compromise between city specific estimates and overall mean
• Empirical-Bayes estimates depend on measure of natural variation
– Assess sensitivity to estimate of NV (heterogeneity parameter )τ 2
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Caveats
• Used simplistic methods to illustrate the key ideas:
– Treated natural variance and overall estimate as known when calculating uncertainty in EB estimates
– Assumed normal distribution or true relative risks
• Can do better using Markov Chain Monte Carlo methods – more to come
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In Stata (see 1.4 and 1.6, also Lab 1)
• xtreg with the mle option
• xtmixed: preferred for continuous
outcomes
• gllamm