statistical methods in clinical trials
DESCRIPTION
Statistical Methods in Clinical Trials. Continuous Data. Types of Data. Continuous Blood pressure Time to event. Categorical sex. quantitative. qualitative. Discrete No of relapses. Ordered Categorical Pain level. Types of data analysis (Inference). Parametric Vs Non parametric. - PowerPoint PPT PresentationTRANSCRIPT
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Statistical Methods in Clinical Trials
Continuous Data
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Types of Data
ContinuousBlood pressureTime to event
Ordered CategoricalPain level
DiscreteNo of relapses
Categoricalsex
quantitative qualitative
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Types of data analysis (Inference)
ParametricVs
Non parametric
FrequentistVs
Bayesian
Model basedVs
Data driven
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Continuous Data
Part I Classical InferencePart II Bayesian Inference
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Part IClassical Inference
1. One sample2. Paired data3. Two samples4. Many samples
» One way anova» Two way anova» Ancova
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One sample
From a population of patients we draw a sample of size n of patients and measure some response on each of these. We want to test whether the average response in this population is equal to (or is larger or smaller than) some predefined value μ0 (e.g. the corresponding value in the healthy population).
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Estimation• Assume is a sample (i.i.d) of
size n from some distribution F with mean μ and standard deviation σ. Then
and
• Are (i) unbiased and (ii) consistent estimators of μ and σ² i.e.
(i)
(ii) (LLN)
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Estimation in general
• The maximum likelihood method• The moment method• Uniformly Minimum Variance Unbiased
estimators• Least squares • etc
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Hypothesis testing
We can e.g. test the null hypothesis
using the test statistic
when n is large, T follows, under the null hypothesis, the standard normal distribution (CLT).
against
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Hypothesis testing
For moderate values of n assuming
follow a normal distribution, then
follows, under the null hypothesis, the t (student) distribution with (n-1) degrees of freedom.
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The true distribution among the patients(exponential with mean 0.5.)
The distribution of the average whenn is large
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Confidence intervals• A (1-α)100% confidence interval for μ has
the form
(i)
(ii)
when n is large
when n moderate but sample normally distributed.
A hypothesis (or a confidence interval) can be one sided or two sided
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95% confidence interval
1-α=0.95
The normaldistribution
The tdistribution
1-α=0.95
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Correspondence TheoremHypothesis testing and confidence intervals
are two sides of the same coin!
Parameter value according to null hypothesis not included in
confidence interval (α)
Reject null hypothesis (α)
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Paired data
• Sometimes we measure a response at baseline and at the end of the treatment. A suitable analysis can be to consider the endpoint changes from baseline.
• and to use the (one sample) test statistic
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Two independent samples From two populations/distributions we draw two
samples of sizes n1 and n2 of patients and measure some response on each of these. We want to test whether the average responses, μ1 and μ2, in these two populations are equal. The population variances are denoted by σ1² and σ2² respectively.
H (null)0 H (alternative)A
1 2 1 2
1 2
1 2
1 2
1 2
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Two samples (continued)• When both sample sizes are large we can use
the central limit theorem according to which the test statistic below follows the standard normal distribution
• When the sample sizes are moderate but normal with equal variances we can use the t-statistic (having n1+n2-2 d.f.) and the pooled variance.
• The remaining case can be handled using a method known as Satterwaite’s confidence intervals.
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Many samplesOne way analysis of variance
• Here we want to compare several (k) groups (treatments) with respect to the average response in each group. The following model is assumed
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Anova1
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Anova1 continued
• The deviation of an individual observation from the overall mean can be described by
• The null hypothesis and the alternative hypothesis can be formulated as
SST/SST SSA/SSB SSE/SSW
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Anova 1 continued
• Manipulating these gives:
MSA
• When the null hypothesis is false we expect the ratio MSA/MSE to be large. To calculate exact significance levels we use the fact that it F-distributed with (k-1,N-k) d.f.
MSE MSA
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Anova tableSource of variation
Sum of squares
d.f. Mean squares
F/p-value
Treatment SSA K-1 MSA F0=MSA/MSE /p
Error SSE N-k MSE
Total SST N-1
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• Example: 3 groups with the same mean 20 and the same standard deviation 2. We reject for large values of F0.
Source of variation
Sum of squares
d.f. Mean squares
F0/p-value
Treatment 9.56 2 4.78 1.22/ 0.29
Error 4671.1 1197 3.9
Total 4680.65 1199
F is F-distributed and F0 the value of MSA/MSE
We cannot reject
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• Example: 3 groups with means 20, 20 and 20.5 and the same standard deviation 3.
Source of variation
Sum of squares
d.f. Mean squares
F/p-value
Treatment
59.4 2 29.7 3.25/ 0.039
Error 10942.8 1197 9.14
Total 11002.3 1199We can reject
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We can reject
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Multiple comparisons• When we reject the null hypothesis we only know that
the groups are not equal but some of them might still be. To find out more we have to consider all the pairwise comparisons between the groups. With k groups this gives m=k(k-1)/2 such comparisons. How do we do this and still have a reasonable overall significance level? The simplest way to deal with this is using Bonferroni’s inequality. This implies that when performing m tests if each test is at the 1-α/m level then the tests taken simultaneously will be on the 1- α level. We will deal with problem in detail later on.
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Two way anova• Here we assume that individuals can differ
with respect to two factors (two drugs, treatment and centre). The following model is usually suitable for this situation
• As before the deviation of an individual observation from the overall average can be decomposed into terms related to the effects of treatment, center, treatment by center as well as to a pure random component.
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Anova2
• We use a similar notation as before
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• Tests of treatment effect, center effect or treatment by center interaction can be performed by using the appropriate ratio between mean square values.
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Anova2
• As an example assume we want to test if there is a treatment effect:
• This can be tested using the test statistic
• Which has under the null hypothesis an F distribution with (a-1) and ab(n-1) degrees of freedom.
• The various tests are summarized in the following table
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Two-way anova
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Analysis of covariance
• Here we assume that individuals can differ with respect baseline values. It is sometimes desirable to adjust the model for the endpoint measures Y so baseline values X are taken into account. The following model can then be useful
The analysis uses an F distribution based onMean sums of squares (cf. p.319)
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Various forms of models and relation between them
LM: Assumptions:
1. independence,
2. normality,
3. constant parameters
GLM: assumption 2) Exponential family
LMM: Assumptions 1) and 3) are modified
GLMM: Assumption 2) Exponential family and assumptions 1) and 3) are modified
Repeated measures: Assumptions 1) and 3) are modified
Longitudinal dataMaximum likelihood
Classical statistics (Observations are random, parameters are unknown constants)
Bayesian statistics, parameters are random
LM - Linear model
GLM - Generalised linear model
LMM - Linear mixed model
GLMM - Generalised linear mixed model
Non-linear models
Mixed models, both random and constant parameters
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The Linear Mixed-effects Model
• The linear mixed effects model is quite flexible and does not need balance, independence etc. Usually some version of maximum l likelihood is used for the inference
Average evolution Subject specific
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Why should we use multilevel models?
Sometimes:single levelmodels can beseriouslymisleading!
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Intake (S5)
Exit(S7)
Source: Snijders & Bosker (1999: 28)
Conclusion based on student-level data only: Schools have adverse effect on students
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Intake (S5)
Exit (S7)
Source: Snijders & Bosker (1999: 28)
S1S2
S3S4
S5S6
S1S2
S3S4
S5S6
Mean of school 1
Using school-level data only: schools have adverse effect on students
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Intake (S5)
Exit (S7)
Source: Snijders & Bosker (1999: 28)
S1S2
S3S4
S5S6
S1S2
S3S4
S5S6
Using both student-level & school-level data: Stronger students at entry score higher at exit
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Part IIBayesian Inference
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What is a probability?
1. the limit of a relative frequencyProblem: Not all events all repeatable!
2. the degree of plausibility Or of beliefProblem: subjective?
P=1/6P=?
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Bayes in brief
• Bayes’ conclusions were accepted by Laplace in 1781
• Rediscovered by Condorcet, and remained unchallenged until
• Boole questioned them. • Since then Bayes' techniques have
been subject to controversy.
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Is Classical Inference Really Classical?• Only in the sense that it started
a realtively long time ago with Fisher around 1920.
• Bayesian Statistics is even more classical since it was dominating until then.
• Classical inference was introduced as a way to introduce objectivity in the scientific process.
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Simplified scientific Process
1. A theory or a hypothesis needs to be tested
2. We perform an experience and obtain some data
3. Are our data in agreement with our theory?
4. If the answer to the above question is no, we reject the theory. Otherwise we cannot reject it!
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Falsifiability (or refutability or testability) is the logical possibility that an assertion can be shown false by an observation or a physical experiment.
For example, "all men are mortal" is unfalsifiable, since no amount of observation could ever demonstrate its falsehood. "All men are immortal," by contrast, is falsifiable, by the presentation of just one dead man
A white mute swan, common to Eurasia and North America. Two black swans, native to Australia
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Classical Paradigmvs
The Bayesian paradigm
• The classical paradigm is based on the consideration of
P[ Data | Theory ] (1)• How likely is the data if the theory was
to be true?
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Classical vs Bayesian (cont’d)
• The Bayesian paradigm is based on the consideration of
P[ Theory | Data](2)• How much support or belief is there in the
theory given the data?
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Bayes’ Formula
Where T = Theory
and D = Data
Simple formula with many interesting implications.
D] P[] T P[ T] | D P[ D] | T P[
States that
(3)
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Implications of Bayes’ Formula
1. (1) and (2) are not equivalent.
2. To work out (2) we have to estimate P[ T ] i.e. we need to put a probability on our belief in the theory we are testing.
3. We cannot make P[ T ] disappear. (Similar to the uncertainty principle in quantum mechanics?)
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• Some toxin is associated with certain symptoms. denotes the toxin level in patients having the symptoms and 0 the toxin level in healthy individuals. We want to test if there is a difference. A test can be based on a sample of size n through
Example: p-values
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Under the null hypothesis H0: = 0,
z is an observation from a t-distribution with n-1 degrees of freedom.
If, moreover, the p-value is less than 0.05 it is then customary to consider the result as significant, i.e. we reject the null hypothesis.
We return to this in moment!
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Preliminaries
• Consider two Hypothesis H0 and H1
D] P[] H P[ ]H | D P[ D] | H P[ 00
0
D] P[] H P[ ]H | D P[ D] | H P[ 11
1
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• We consider the odds ratio between the two
]H | D P[]H | D P[
] H P[] H P[
D] | H P[D] | H P[
1
0
1
0
1
0
Odds ratio = prior odds x likelihood ratio
New old Bayes factor
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Result
1
0
00 BF] H P[
] H P[-11 D] | H P[
]H | P[D]H | P[D BF
1
0
where
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Back to the t-test
P. M. Lee Bayesian Statistics: An Introduction 2nd Ed. London: Arnold, p. 131 (1997)
shows that in the case of the t-est and under quite general conditions:
2
2
BFz
e
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Consequences
• Assume that there is no agreement on the effect of the toxin so we take P(H0)=0.5. Then:
1
2z-
1
2z-0
2
2 e1e
11 D] | P[H
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• Assume further that the value of z in the experiment turns out to be 2. Since this leads to a p-value of 0.044 we conclude that the result is significant at the 0.05 level. Setting z=2 in the formula above leads to:
12.0e1 D] | P[H1
22-
0
2
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Z=2P[
H0|
D]
P[ H0]
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P[ H
0| D
]
P[ H0]
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Conclusions
• By introducing the element of degree of belief about a theory, we arrive at conclusions that do not agree with those obtained using the frequentist approach, i.e. prior knowledge matters
• Prior knowledge is part of the Bayesian approach.
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Backup slidesBayesian Analysis in Clinical Trials
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• The Advantages:– Formal system for incorporating existing information– Natural approach to inference– Generally more efficient– Well suited for decision making
• The Challenges:– Determining appropriate prior probabilities– Computational complexity– Lack of familiarity– Lack of software tools
Bayesian Framework
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What is the difference?
Approach Traditional Bayesian
Question How likely are the trial results given there really is no difference among treatments?
How likely is it that there is a true difference among treatments given the trial data?
Approval based on Pivotal trial (Hypothesis testing)
Weight of evidence (revising beliefs in light of new evidence)
Design Single stage Adaptive
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In what settings have Bayesian desings been used?
• Pharmacokinetics• Phase I (CRM)• Phase II (Thall/Simon)• Phase III (Spiegelhalter, Parmar, others)• Meta-analysis• Medical device clinical trials (guidelines
from 2006)
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Summary of applications
• Methods for incomplete data• Adaptive designs (including adaptive
sample size)• Confirmatory trials• Evaluating a modified version of an
approaved product• Synthesising data in post market
surveillance
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1
0
00
1
1
00
0
11
0
00
0
11
0
1
00
1
0
1
0
1
0
BF] H P[] H P[-11D] | H P[
]H | P[D]H | P[D] H P[
] H P[-11
C11
CC1
C1CD] | H P[
CD] | H P[CD] | H P[D] | H P[-1C
D] | H P[]H | P[D]H | P[D
] H P[] H P[D] | H P[
]H | P[D]H | P[D
] H P[] H P[
D] | H P[D] | H P[
Proof