Transcript of Giuseppe Biondi-Zoccai, MD Sapienza University of Rome, Latina, Italy...
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- Giuseppe Biondi-Zoccai, MD Sapienza University of Rome, Latina,
Italy giuseppe.biondizoccai@uniroma1.itgbiondizoccai@gmail.com
Medical statistics for cardiovascular disease Part 1
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- Learning milestones Key concepts Key concepts Bivariate
analysis Bivariate analysis Complex bivariate analysis Complex
bivariate analysis Multivariable analysis Multivariable analysis
Specific advanced methods Specific advanced methods
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- Why do you need to know statistics? CLINICIAN RESEARCHER
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- A collection of methods
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- The EBM 3-step approach How an article should be appraised, in
3 steps: Step 1 Are the results of the study (internally) valid?
Step 2 What are the results? Step 3 How can I apply these results
to patient care? Guyatt and Rennie, Users guide to the medical
literature, 2002
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- The Cochrane Collaboration Risk of Bias Tool
http://www.cochrane.org
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- The ultimate goal of any clinical or scientific observation is
the appraisal of causality
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- Force:* precisely defined (p
- Superiority RCT Possibly greatest medical invention ever
Randomization of adequate number of subjects ensures prognostically
similar groups at study beginning If thorough blinding is enforced,
even later on groups maintain similar prognosis (except for effect
of experiment) Sloppiness/cross-over makes arm more similar - >
traditional treatment is not discarded Per-protocol analysis almost
always misleading
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- Equivalence/non-inferiority RCT Completely different paradigm
Goal is to conclude new treatment is not meaningfully worse than
comparator Requires a subjective margin Sloppiness/cross-over makes
arm more similar -> traditional treatment is more likely to be
discarded Per-protocol analysis possibly useful to analyze safety,
but bulk of analysis still based on intention-to-treat
principle
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- Superiority, equivalence or non- inferiority? Vassiliades et
al, JACC 2005
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- Possible outcomes in a non-inferiority trial (observed
difference & 95% CI) New Treatment Better New Treatment
Worse
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- Typical non-inferiority design Hiro et al, JACC 2009
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- Cumulative meta-analysis Antman et al, JAMA 1992
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- Meta-analysis of intervention studies De Luca et al, EHJ
2009
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- Funnel plot
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- Indirect and network meta-analyses Indirect Direct plus
indirect (i.e. network) Jansen et al, ISPOR 2008
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- Resampling Resampling refers to the use of the observed data or
of a data generating mechanism (such as a die or computer-based
simulation) to produce new hypothetical samples, the results of
which can then be analyzed. The term computer-intensive methods
also is frequently used to refer to techniques such as these
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- Bootstrap The bootstrap is a modern, computer-intensive,
general purpose approach to statistical inference, falling within a
broader class of resampling methods. Bootstrapping is the practice
of estimating properties of an estimator (such as its variance) by
measuring those properties when sampling from an approximating
distribution. One standard choice for an approximating distribution
is the empirical distribution of the observed data.
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- Jacknife Jacknifing is a resampling method based on the
creation of several subsamples by excluding a single case at the
time. Thus, the are only N jacknife samples for any given original
sample with N cases. After the systematic recomputation of the
statistic estimate of choice is completed, an point estimate and an
estimate for the variance of the statistic can be calculated.
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- The Bayes theorem
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- The main feature of Bayesian statistics is that it takes into
account prior knowledge of the hypothesis
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- Bayes theorem P (D | H) * P (H) P (H | D) P (D | H) * P (H)
_____________ _____________ P (D) P (D) P (H | D) = Likelihood of
hypothesis (or conditional probability of B) Prior (or marginal)
probability of hypothesis Posterior (or conditional) probability of
hypothesis H Probability of the data (prior or marginal probability
of B: normalizing constant) Thus it relates the conditional and
marginal probabilities of two random events and it is often used to
compute posterior probabilities given observations.
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- Frequentists vs Bayesians
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- Classical statistical inference vs Bayesians inference
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- A Bayesian is who, vaguely expecting a horse, and catching a
glimpse of a donkey, strongly believes he has seen a mule Before
the next module, a question for you: who is a Bayesian?
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- A Bayesian is who, vaguely expecting a horse, and catching a
glimpse of a donkey, strongly believes he has seen a mule Before
the next module, a question for you: who is a Bayesian?
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- A Bayesian is who, vaguely expecting a horse, and catching a
glimpse of a donkey, strongly believes he has seen a mule Before
the next module, a question for you: who is a Bayesian?
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- JMP Statistical Discovery Software JMP is a software package
that was first developed by John Sall, co-founder of SAS, to
perform simple and complex statistical analyses. It dynamically
links statistics with graphics to interactively explore,
understand, and visualize data. This allows you to click on any
point in a graph, and see the corresponding data point highlighted
in the data table, and other graphs. JMP provides a comprehensive
set of statistical tools as well as design of experiments and
statistical quality control in a single package. JMP allows for
custom programming and script development via JSL, originally know
as "John's Scripting Language. An add-on JMP Genomics comes with
over 100 analytic procedures to facilitate the treatment of data
involving genetics, microarrays or proteomics. Pros: very
intuitive, lean package for design and analysis in research Cons:
less complete and less flexible than the complete SAS system Price:
.
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- R R is a programming language and software environment for
statistical computing and graphics, and it is an implementation of
the S programming language with lexical scoping semantics. R is
widely used for statistical software development and data analysis.
Its source code is freely available under the GNU General Public
License, and pre-compiled binary versions are provided for various
operating systems. R uses a command line interface, though several
graphical user interfaces are available. Pro: flexibility and
programming capabilities (eg for bootstrap), sophisticated
graphical capabilities. Cons: complex and user-unfriendly
interface. Price: free.
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- S and S-Plus S-PLUS is a commercial package sold by TIBCO
Software Inc. with a focus on exploratory data analysis, graphics
and statistical modeling It is an implementation of the S
programming language. It features object-oriented programming
capabilities and advanced analytical algorithms (eg for robust
regression, repeated measurements, ) Pros: flexibility and
programming capabilities (eg for bootstrap), user-friendly
graphical user interface Cons: complex matrix programming
environment Price: -.
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- SAS SAS (originally Statistical Analysis System, 1968) is an
integrated suite of platform independent software modules provided
by SAS Institute (1976, Jim Goodnight and Co). The functionality of
the system is very complete and built around four major tasks: data
access, data management, data analysis and data presentation.
Applications of the SAS system include: statistical analysis, data
mining, forecasting; report writing and graphics; operations
research and quality improvement; applications development; data
warehousing (extract, transform, load). Pros: very complete tool
for data analysis, flexibility and programming capabilities (eg for
Bayesian, bootstrap, conditional, or meta-analyses), large volumes
of data Cons: complex programming environment, labyrinth of modules
and interfaces, very expensive Price: -
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- Statistica STATISTICA is a powerful statistics and analytics
software package developed by StatSoft, Inc. Provides a wide
selection of data analysis, data management, data mining, and data
visualization procedures. Features of the software include basic
and multivariate statistical analysis, quality control modules and
a collection of data mining techniques. Pros: extensive range of
methods, user-friendly graphical interface, has been called the
king of graphics Cons: limited flexibility and programming
capabilities, labyrinth Price: .
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- SPSS SPSS (originally, Statistical Package for the Social
Sciences) is a computer program used for statistical analysis
released in its first version in 1968 and now distributed by IBM.
SPSS is among the most widely used programs for statistical
analysis in social science. It is used by market researchers,
health researchers, survey companies, government, education
researchers, marketing organizations and others. Pros: extensive
range of tests and procedures, user-friendly graphical interface.
Cons: limited flexibility and programming capabilities. Price:
.
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- Stata Stata (name formed by blending "statistics" and "data) is
a general-purpose statistical software package created in 1985 by
StataCorp. Stata's full range of capabilities includes: data
management, statistical analysis, graphics generation, simulations,
custom programming. Most meta-analyses tools were first developed
for Stata, and thus this package offers one of the most extensive
library of statistical tools for systematic reviewers Pros:
flexibility and programming capabilities (eg for bootstrap, or
meta-analyses), sophisticated graphical capabilities Cons:
relatively complex interface Price: -
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- WinBUGS and OpenBUGS WinBUGS (Windows-based Bayesian inference
Using Gibbs Sampling) is a statistical software for the Bayesian
analysis of complex statistical models using Markov chain Monte
Carlo (MCMC) methods, developed by the MRC Biostatistics Unit, at
the University of Cambridge, UK. It is based on the BUGS (Bayesian
inference Using Gibbs Sampling) project started in 1989. OpenBUGS
is the open source variant of WinBUGS. Pros: flexibility and
programming capabilities Cons: complex interface Price: free
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- Take home messages
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- Advanced statistical methods are best seen as a set of modular
tools which can be applied and tailored to the specific task of
interest. The concept of generalized linear model highlights how
most statistical methods can be considered part of a broader family
of methods, depending on the specific framework or link
function.
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- Many thanks for your attention! For any query:
giuseppe.biondizoccai@uniroma1.it gbiondizoccai@gmail.com For these
slides and similar slides:
http://www.metcardio.org/slides.html