giuseppe biondi-zoccai, md sapienza university of rome, latina, italy...

Giuseppe Biondi-Zoccai, MD Sapienza University of Rome, Latina, Italy [email protected]@gmail.com Medical statistics for cardiovascular disease Part 1

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

Why do you need to know statistics? CLINICIAN RESEARCHER

A collection of methods

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

The Cochrane Collaboration Risk of Bias Tool http://www.cochrane.org

The ultimate goal of any clinical or scientific observation is the appraisal of causality

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

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

Superiority, equivalence or non- inferiority? Vassiliades et al, JACC 2005

Possible outcomes in a non-inferiority trial (observed difference & 95% CI) New Treatment Better New Treatment Worse

Typical non-inferiority design Hiro et al, JACC 2009

Cumulative meta-analysis Antman et al, JAMA 1992

Meta-analysis of intervention studies De Luca et al, EHJ 2009

Funnel plot

Indirect and network meta-analyses Indirect Direct plus indirect (i.e. network) Jansen et al, ISPOR 2008

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

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.

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.

The Bayes theorem

The main feature of Bayesian statistics is that it takes into account prior knowledge of the hypothesis

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.

Frequentists vs Bayesians

Classical statistical inference vs Bayesians inference

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?

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: .

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.

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: -.

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: -

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: .

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: .

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: -

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

Take home messages

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.

Many thanks for your attention! For any query: [email protected] [email protected] For these slides and similar slides: http://www.metcardio.org/slides.html

giuseppe biondi-zoccai, md sapienza university of rome, latina, italy...

Documents

misleading slide

principle slide

new treatment worse

funnel plot slide

appraisal of causality

clinician researcher

protocol analysis

bulk of analysis