presentation_6-9-2014-12-23-30

Report
Comparison of Bayesian and
Frequentist Meta-Analytical
Approaches for Analyzing Time to
Event Data
By Brenda Crowe
(Joint work with Monica Bennett, Karen Price, James Stamey
and John Seaman Jr)
Midwest Biopharmaceutical Statistics Workshop 2014
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Outline
• Background/motivation
• Overview of results for rare events (mainly for
binary data)
• Simulation study (time to event data)
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Methods, parameters
Software
Results
Discussion, recommendations
• References
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Definition
Meta-analysis (MA) refers to the combining of
evidence from relevant studies using appropriate
statistical methods to allow inferences to be made
to the population of interest.
(Definition from FDA’s 2013 white paper on meta-analysis
http://www.fda.gov/downloads/Drugs/NewsEvents/UCM372069.pdf)
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Background/Motivation
• Lots of literature comparing MA methods for
binary data
• E.g., Sweeting et al. (2004, 2006), Bradburn et
al. (2007)
• Not much for time-to-event data, though
anticipate problems similar to binary data
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Background/Motivation
• December 2008 FDA issued a guidance for
assessing cardiovascular (CV) risk in diabetes
drugs.
• The guidance requires that the upper limit of the
2-sided 95% confidence interval for the risk ratio
be less than 1.8 prior to submission and less
than 1.3 after submission.
• This can be shown by performing a metaanalysis of phase 2 and 3 clinical trials and if
these are insufficient, a large safety trial must be
conducted.
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Background/Motivation: Our
Research
• Used simulation study to compare the
performance of several meta-analytic
approaches in the survival analysis context.
• Considered two frequentist approaches and a
Bayesian approach with and without informative
prior.
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Overview of statistical challenges and
considerations for analysis of rare adverse events
Mostly for Binary Data
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Statistical Issues with Meta-analysis of
Rare/Sparse Adverse Event Data
• Standard inferences for meta-analysis rely on large
sample approximations. They may not be accurate
and reliable when number of events is low.
• Zero events observed in one or both treatment arms
for some studies
• Low power to detect heterogeneity (especially when
the number of studies is modest)
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Metrics and Methods
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Metric Choices
Binary outcomes
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risk difference (RD),
arcsin risk difference*
risk ratio (RR),
odds ratio (OR)
Time to event: hazard ratio, …
* The arcsin link is not often used in clinical trials, but it is the
asymptotically variance-stabilizing transformation for the binomial
distribution. This link is studied by, for example, Rücker et al.
(2009).
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Behavior When Zero Events Occur in
One or Both Arms of a Study
Difference Metrics
• Risk difference is defined but its variance = 0 for total zero
studies
• Arcsin difference and its variance are defined
Relative Metrics
• Log odds ratio and log risk ratio are undefined (as are their
variances)
• Hazard ratio and its variance are undefined
Note: methods for combining studies have different
properties for handling zero events
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Common Binary Meta-analysis
Methods
Important: Stratify by study
Fixed Effect*
• Inverse variance (IV)
• Mantel Haenszel (MH)
• Peto
• Logistic Regression
• Exact stratified odds ratio
• Exact stratified risk
difference (Tian et al.,
2009)
Random Effects*
• Inverse variance: Use a
method to estimate the
among-study variance
such as method of
moments (DerSimonian
Laird [DSL]), anova
(Hedges and Olkin)
• Mixed effects logistic
regression
*There are Bayesian versions of many of these methods. See, for example, Higgins and
Spiegelhalter (2002). The paper includes some WinBugs code.
See Deeks and Higgins (2010) for a concise overview of most of the non-Bayesian methods.
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What do People do with Zero Cells
• Use a method that can handle zeros (e.g., MH
risk difference, arcsin difference, Bayesian)
• Exclude studies with zero cells (more likely for
relative metrics)
• Collapse ‘small’ studies with a similar
randomization allocation ratio (Nissen, 2010)
• Use a continuity correction
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Continuity Corrections (3 Ways)
1. Constant
• Add 0.5 (or some other fixed constant) to each cell
2. Treatment arm continuity correction
• Choose a proportionality constant k
• Add k / (Sample size for Opposite Treatment Arm) to
each cell
• May be less biased in presence of severe imbalance
3. Empirical continuity correction
• Based on an empirical estimate of the pooled effect
size using the non-zero event studies
Sweeting et al, Stat in Med, 2004 and 2006
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Which Method is Best?
It depends on . . .
• Number of studies
• Sample size per trial and arm
• Event rate
• Amount of heterogeneity
Note: None of the 2 major simulation studies
(Bradburn et al., Sweeting et al.) assessed risk
ratio.
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Comparison of Binary Methods
(Excludes RR Methods) for Sparse Data
• Alternative CCs perform better than the constant
• IV (OR and RD) and DSL (OR and RD) perform v. poorly
• Peto method performed well for very low event rates, but
bias increases with greater group imbalance and larger
treatment effect
• MH OR with no CC or alternative CC, logistic regression,
exact stratified and Bayesian fixed effects perform fairly
well (event rates >= 0.5% to 1% of the sample sizes
studied in Sweeting and Bradburn)
• MH RD has low bias and low power for very sparse data
See Sweeting, 2004 & 2006 and Bradburn, 2007 for further info
Simulation Study
Meta-analytical approaches for analyzing time to event data
Overview of Methods
1. Standard Cox proportional hazards (CPH)
2. CPH with Firth correction term (penalized
likelihood)
3. Bayesian CPH (with and without informative
prior)
• All methods model two treatment arms and
stratify by study
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Cox Proportional Hazards
• The proportional hazards survival model for patient i
in study j is
H ij t   0 j (t ) exp( βxij )
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i = 1, … , ns
j = 1, … , s
λ0j(t) is the baseline hazard for study j
xij = 1 if patient i in study j is on treatment and xij = 0
otherwise
• β is the log hazard ratio.
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CPH with Firth Correction
• When events are rare the problem of monotone
likelihood can be encountered.
• Estimates may not be available due to lack of
convergence.
• Estimates may be imprecise and have large standard
errors.
• Firth (1993) developed a penalization method used to
reduce bias in maximum likelihood parameter estimates.
• Heinze and Schemper (2001) adapted the Firth method
to be used with the Cox model.
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Bayesian CPH
• Basic model assumes constant baseline hazard
over time and specifies prior distributions for λ
and β.
Hij t   0 j exp(βxij )
 ~ Normal(, )
2
0 j ~ Gamma(a, b)
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Study Designs for Simulation
• 3 phase 2 studies:
• n0 = 50, n1 = 150, duration = 90 days
• 3 phase 3 studies:
• n0 = 250, n1 = 500, duration = 1 year
• 1 outcome study:
Included in the 1st
meta-analysis
study grouping
Included in the
2nd study
grouping
• n0 = 3500, n1 = 3500, duration = 2 years
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Simulation Design/Parameters
• For each meta-analysis study grouping the
following factorial design is used.
• 1000 data sets are generated for each of the
scenarios.
• Three hazard ratios and three baseline event rates were
used
• HR = 1.0, 1.3, 1.8
• λ0 = 0.01, 0.02; 0.05 (events/person year)
• 10% uniform dropout rate
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Simulation Design/Parameters
• Two study groupings
1. All phase 2 and 3 (with 4 analysis methods)
2. All 7 studies (with 3 analysis methods)
• Exponential distribution for data generation
(constant hazard over time).
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Bayesian Parameters
1.
Diffuse priors
• Lambda0j ~gamma(0.01, 0.01)
• Beta~normal(0,1000)
2. More informative priors
• Used shape parameter for gamma prior = 0.01, 0.02 and 0.05
for corresponding event rates
• Rate parameter = 1
• For log hazard ratio, for exp(beta) = 1.0, prior mean = 0
• For exp(beta) = 1.3, prior mean was 0.25 and for exp(beta)=1.5,
prior mean was 0.5
Used prior variance of 2 for each. Informative priors were only
used for first study grouping.
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SAS 9.2
• PROC PHREG
proc phreg data=meta.gendata;
strata=study;
*use FIRTH option to perform Firth correction;
model time*event(0) = treatment / firth;
*use BAYES statement for Bayesian analysis;
bayes seed=1 initial = NBI= NMC= coeffprior=
run;
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R
• coxph{survival}
• coxph(Surv(time,event)~ treatment + strata(study),
data=gendata)
• coxphf{coxphf}
• coxphf(Surv(time,event)~ treatment + strata(study),
data=gendata)
• For the Bayesian methods WinBUGS or OpenBUGS can be
used.
• The models for the Bayesian methods are based on the
model in the “Leuk: survival analysis using Cox regression”
example in WinBUGS.
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Simulation Results
Standardized Bias Plots: Meta-analysis
of Phase 2 and 3 Trials.
Firth gives best results (closest to zero bias line) in all situations.
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95% CI Coverage Plots : Metaanalysis of Phase 2 and 3 Trials
Bayes with informative prior has overly high coverage in all scenarios (as do CPH and Firth,
but they have less bad).
Bayes with diffuse prior has lower coverage than desired, with exception of one scenario
(lambda = 0.05), which may be because of the bias seen on previous slide
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Proportion of Upper Bounds Less Than 1.8:
Meta-analysis of All Phase 2 and 3 Studies
For true log HR = 0 and 0.262 (HR = 1, 1.3), higher proportions are better.
For true HR = 1.8, lower are better.
Firth does well/best in all situations.
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Standardized Bias Plots : Meta-analysis
of all Studies
All methods have std. bias close to zero, with exception of Bayesian method,
where drops to -0.1for HR = 1.8.
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95% CI Coverage Plots : Meta-analysis
of all Studies
Coverage in most scenarios is between 0.94 and 0.96.
Exceptions are when true log HR = 0. E.g., Bayes and CPH have coverage = 0.935
when baseline event rate is 0.01.
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Proportion of Upper Bounds less than 1.8:
Meta-analysis of All Studies.
All methods perform well.
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Concluding Remarks: Time to Event
Data
• Based on the scenarios we studied, the Firth correction
to the CPH is a good option for analyzing time-to-event
data when the baseline event rate is low.
• For Bayesian method, informative prior reduces the bias
of the estimated log HR.
• However a misspecified prior makes the situation worse
(results not shown)
• With larger number of events there is not a big difference
between the methods.
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Concluding Remarks: Binary Data
• IV and DSL poor choices for rare events
• If need continuity correction, adding a constant to each
cell is not the best choice
• Would Firth correction be good for binary data?
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References
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Bennett, M. M., Crowe, B. J., Price, K. L., Stamey, J. D., & Seaman, J. W., Jr. (2013). Comparison of bayesian
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References
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References
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Sweeting, M. J., Sutton, A. J., & Lambert, P. C. (2004). What to add to nothing? Use and avoidance of continuity
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The End
May 2014
Duffy21 Lake,
BC
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