Byron Wallace, Phd

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來自 Johns Hopkins University 的課程

系统综述及元分析入门

1053 個評分

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Johns Hopkins University

系统综述及元分析入门

1053 個評分

我们将介绍对临床试验进行系统综述和元分析的方法，课程内容包括如何构建一个能够回答的研究问题，确定收入和排除条件，寻找证据，提取数据，评价临床试验中偏倚的风险，并进行元分析。顺利完成课程后，同学们将能够： -描述进行系统综述的步骤 -采用“受试者，干预措施，比较方法，结果”(PICO)框架来提出能够回答的问题 -描述从临床试验报告中收集和提取数据的过程 -描述辩证地分析临床试验偏倚风险的方法 -描述和解释元分析的结果

從本節課中

Searching Principles and Bias Assessment

In this module we will look at finding the evidence, as well as key sources, search strategy, and assessing the risk of bias.

Byron Wallace, Phd2:00

與講師見面

Tianjing Li, MD, MHS, PHD
Assistant Professor, Epidemiology
Bloomberg School of Public Health
Kay Dickersin, PhD
Professor, Epidemiology
Bloomberg School of Public Health

I was at Tufts University and I happened to get connected

with Thomas Trikalinos and Joseph Lau, and Chris Schmid.

And at the time I was working on machine learning and

natural language processing stuff.

And it occurred to all of us jointly, along with my advisor, Carla Brodley,

that perhaps we could expedite some of the processes that were being done manually

in evidence synthesis using machine learning and natural image processing.

So ever since then I've been working on that.

The aspect that's received the most attention is probably on

the screening side.

So this is where you're identifying the literature,

the evidence to be included in the systematic review that you're performing.

And there's been a lot of work that's looked at using automation techniques, and

in particular, automatic classification techniques to expedite that process.

And I think those technologies are actually pretty well established.

And I think the general consensus is that you can reduce the labor by about half

without sacrificing sensitivity or recall to the relevant evidence,

which is really important.

So these tools, we've developed some of them and other people as well,

including for example James Thomas and his group.

Our stuff you can find at Abstrackr, so it's free to use and it's open source.

But again, that's just one one instance,

and I think generally these kinds of technologies have been helpful.

The other bit that's kind of more emerging, I think,

are methods for automating the actual extraction of structured information

from publications describing the results of trials and other findings.

And this I think is still in the research phases, but it's certainly something that

I'm working on and a bunch of other people are as well.

But from my own plug I would say we have this thing called RobotReviewer that we've

been working on that does a lot of this and

tries to automate things like risk bias appraisal.

Julian Higgins has also done similar things with one of his students.

So I think there's a lot of interest in this space generally.

So it'll be exciting to see where it all goes.

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