Explore
For Enterprise
Join for Free
Log In

Artes e Humanas
Negócios
Ciência da Computação
Ciência de Dados
Tecnologia da informação
da saúdesaúde
Matemática e Lógica
Desenvolvimento Pessoal
Ciência e Engenharia Física
Ciências Sociais
Línguas
Diplomas e certificados

Explore todo o Coursera

Navegar
Pesquisar
For Enterprise
Entrar
Inscreva-se gratuitamente

Evidence-based Data Analysis (part 5)

Loading...

Pesquisa Reprodutível

Universidade Johns Hopkins

4.5 (3,216 classificações) | 51K alunos inscritos

Curso 1 de 10 no Ciência de Dados Programa de cursos integrados

Visualizar o programa do curso

This course focuses on the concepts and tools behind reporting modern data analyses in a reproducible manner. Reproducible research is the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them. The need for reproducibility is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations. Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary. In addition, reproducibility makes an analysis more useful to others because the data and code that actually conducted the analysis are available. This course will focus on literate statistical analysis tools which allow one to publish data analyses in a single document that allows others to easily execute the same analysis to obtain the same results.

Habilidades que você aprenderá

Knitr, Data Analysis, R Programming, Markup Language

Avaliações

4.5 (3,216 classificações)

5 stars
2,119 ratings
4 stars
795 ratings
3 stars
207 ratings
2 stars
58 ratings
1 star
37 ratings

JT

Jul 23, 2017

should be included much more in course 1. it would have been great to know up front how easy it is to mix text and code, not from a reproducibility standpoint, but just to take notes.

CT

Apr 22, 2019

This course is very helpful in terms of not only doing the analysis but also getting to know the finer nuances of making a structured markdown document for future reproducible.

Na lição

Week 3: Reproducible Research Checklist & Evidence-based Data Analysis

This week covers what one could call a basic check list for ensuring that a data analysis is reproducible. While it's not absolutely sufficient to follow the check list, it provides a necessary minimum standard that would be applicable to almost any area of analysis.

Communicating Results6:54

Reproducible Research Checklist (part 1)8:21

Reproducible Research Checklist (part 2) 10:20

Reproducible Research Checklist (part 3) 6:54

Evidence-based Data Analysis (part 1)3:51

Evidence-based Data Analysis (part 2) 3:34

Evidence-based Data Analysis (part 3) 4:25

Evidence-based Data Analysis (part 4) 4:47

Evidence-based Data Analysis (part 5) 7:56

Ministrado por

Roger D. Peng, PhD
Associate Professor, Biostatistics
Jeff Leek, PhD
Associate Professor, Biostatistics
Brian Caffo, PhD
Professor, Biostatistics

So the, my only thinking is that we should

just take this principle and apply it everywhere, right?

As much as possible to create analytic pipelines from evidence-based components.

So and to standardize it.

And so jeff has a, another contribution to this.

Is, he calls this the deterministic statistical machine.

The idea that you have a algorithm or pipeline that produces

a deterministic result given a set of data and a minimal,

minimal set of inputs.

And so once this evidence based pipeline

is established, you don't mess with it, alright?

So I call this, kind of, transparent box analysis, right?

So it's a box, so you don't mess with

it, it's like Al Gore's lock box, but it's transparent

because you know exactly what's happening in there, alright, so

it's not a black box, you can see what's happening.

But the point is, you don't mess with it.

So, one of the goals of this is

to reduce the so-called researcher degrees of freedom,

where people can tinker with every single tuning parameter

in the whole pipeline, which is, kind of, growing exponentially.

And to kind of get whatever they want out of the analysis.

So I think a lot, this is kind of analogous to

what something ideal with what it

is [INAUDIBLE] pre-specifies clinical trial protocol.

So when you run a clinical trail, you have to say

everything you're going to do, including how you're going to analyze the data.

And wouldn't it be nice if someone had just written this for you?

Well, if you use a

standardized kind of pipeline someone has written it for you.

And you just, you can pre-specify exactly what

you can do for him for a given situation.

So, the basic structure of this the cartoon of course will depend

on what you are actually, what question you're actually trying to ask.

But you have some data set some minimal input metadata perhaps

and then there's a series of modules that it goes through.

Which can be quite long in the reprocessing and sort of statistical

methods involved in all these pieces here.

and, and you can imagine that there'd be like a benchmark set of

data sets or data set that is used to evaluate various types of Statistical

methods and then you can produce some sort of report which perhaps can

be cut and pasted into a methods sections or posted in some public depository.

I just wanted to do a quick case study for what I mean by this is one of the

things I do a lot is estimate acute effects of avian air pollution exposure.

So so these are cor-relational types of studies a lot of this

stuff kind of originated from the 70's and 80's so it's not.

We start doing this yesterday, we have a long history of this kind of research.

The basic question, are short-term changes in pollution

associated with short-term changes in a population health outcome?

These are usually conducted at the level of the community.

And we have a lot, and there's a lot

of statistical research that has gone, some of it done

by myself, that has gone into this type of analysis.

So this is what the data typically look like.

We have, death counts, this is in New York City.

We're going for a couple years here.

And then you have particulate matter levels down here at the bottom.

Now you can see there's not as much particulate matter data

as there is mortality data, because there's a lot of missing data.

So, essentially you just want to say, is

this top thing correlated with the bottom thing.

So question is, can we encode

everything that we found in the statistical

and epidemiological research into a single package?

The answer is yes.

Time series studies like this don't have a huge range of variation.

And so they typically involve similar types of data.

You know, it might be hospitalization instead of

mortality or what not, but it's often very similar.

And so can we create kind of

a deterministic statistical machine for this area?

So the basic

kind of pipeline that looks, it's a very simple pipeline.

This is not a very complicated analysis for the most part.

You want to check the data, see if there

are any outliers, high leverage types of points.

Pollution data is often skewed, so you want to check for that.

Look for overdispersion.

Do you want to fill in the missing data? The answer is absolutely no.

There's been a lot of work on that.

It doesn't, it doesn't turn out well.

The big question really here is model selection, so one

of the things that we have to worry about is

called unmeasured confounding in these types of time series studies.

And you don't measure a lot of things that vary over time.

So I guess this is like your batch effects.

And so you have to, there are various approaches

to estimating of how you adjust these unmeasured confounders.

We use semiparametric progression methods to do this, so.

Estimating the degrees of freedom, this is, has the most profound effect

on any kind of association that you might estimate, so this is critical.

So, but, you know, there is lots

of research on how to do this.

There's been a couple of papers One is mine.

One of the various approaches to e, estimating

this number of degrees of freedom and there, and

you can settle on a, you know, one or

two approaches that are That are better than others.

So we can just implement that.

Other aspects of the model tend not to be that important.

Again, whether you adjust for temperature and weather and other

things it kind of doesn't really matter how you do that.

There's other things that you're typically interested

in, multiple lag analysis and sensitivity analysis.

So you want to see, you know, you can

select a model here, but if you want to see

if you can move the model back and forth

a little bit Does your association change dramatically, right?

So those are the typical things that you want to see, in this kind of analysis.

And when I review, you know, one paper a month, of, of

a time series study these are the things that I always ask for.

So. >> Roger.

>> Yeah.

>> Is there a 15 second response to why.

Computation is so bad in the setting? >> Oh, because the data

are missing systematically so the, the, the pollution data is very difficult to

collect, and so they typically only measure it only once every six days.

So there's five days missing for every six to eight days.

One observation for every six days.

So, you can try to imp-to inpute it but you just

add a lot of noise for a little bit of savings advice.

So my thinking, I, I feel like this is, you know, you can't have one obviously.

There's lots of various data analysis.

Different problems require different expertise.

I can't handle everything, obviously.

So the idea is you can create a curated library of these machines.

providing, kind of, the standardized, state

of the art, analysis type, well, just

state of the art in can have different meanings in different areas, obviously.

Time series analysis solutions are a very well developed

area, there's not a lot of research, statistical research,

going on, a lot of the questions have been settled.

So, we could have a, kind of, archive of these kinds of data analysis.

An archive for data analysis.

What analogy is this kind of Cochrane Collaboration which, I don't know if

any of you are familiar with but

it's basically an archive of evidence-based medicine.

So you can go to the Cochrane

Collaboration search for things like, you can just

ask, you can search for, you know, can you use vitamin C to treat asthma.

And so here, in the

summary it says. You know, we reviewed evidence.

So, they, they just basically did meta-analyses.

They reviewed evidence from nine clinical trials of Vitamin C.

[COUGH] In general, the evidence, the, the trails were very small.

It's not possible to recommend either the use or the avoidance of Vitamin C.

In asthma. Okay so.

So, we don't have to worry about vitamin C.

That's not going to help us, it's not going to hurt us.

So, something like, so this, this is

the basis of a lot of evidence-based interventions,

evidence-based treatments.

So something like this could be done for data analysis I think.

So, we can have a packages that encode the analyses

pipelines Curated by experts, you know, knowledgeable in the field.

These don't have to be perfect solutions but it the idea is that, they'd

be, they'd be pretty good solutions to kind of cover most of the bases.

And then so, that's kind of all

I wanted to say.

I, I think, so, I, I, I kind of did a bait and switch.

I didn't really talk about reproducible research.

But I do think it's important.

I, except for the fact I don't think

it Really fundamentally addresses the utmost important question.

Whether you can trust the analysis.

And so I think, so the I think, so

what I call this evidence based data analysis which

some people don't like that phrase but I just

come up with organized standardized best practices for given

scientific areas and questions.

And it's not that I don't think that they, I don't think they exist.

Especially it's really professional kind of

organizing and getting together and honest.

I think this is something that could give reviewers a

powerful tool and without dramatically increasing the burden on them.

Like you know, requiring them to reproduce the analysis would I think that we should

put more effort into kind of addressing

the up stream aspects of, of scientific research.

Explore nosso catálogo

Registre-se gratuitamente e obtenha recomendações, atualizações e ofertas personalizadas.

O Coursera proporciona acesso universal à melhor educação do mundo fazendo parcerias com as melhores universidades e organizações para oferecer cursos on-line.

© 2019 Coursera Inc. Todos os direitos reservados.

Baixar na App Store

Baixar no Google Play

Coursera
Sobre
Liderança
Carreiras
Lista de cursos
Certificados
Certificados MasterTrack™
Notas
For Enterprise
For Government

Comunidade
Learners
Partners
Developers
Beta Testers
Translators

Conectar
Blog
Facebook
LinkedIn
Twitter
Instagram
YouTube
Tech Blog

Mais
Termos
Privacidade
Ajuda
Acessibilidade
Imprensa
Contato
Diretório
Afiliados