Evidence map›Paper›PMID 32033614›Full record

ArticleTrials2020

Online randomized controlled experiments at scale: lessons and extensions to medicine.

Ron Kohavi, Diane Tang, Ya Xu, Lars G Hemkens, John P A Ioannidis

Open access · goldAbstract read
In one paragraph

Article in Trials, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
4.1field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 33 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Online multiple hypothesis testing.Statistical science : a review journal of the Institute of Mathematical Statistics · 2023
    Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. [Benefit assessment of digital health applications-challenges and opportunities].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2021
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 5 institutions in 2 countries.

Ron KohaviAnalysis & Experimentation, Microsoft, One Microsoft way, Redmond, WA, 98052, USA.
Diane TangGoogle, 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA.
Ya XuLinkedIn, 950 W Maude Ave, Sunnyvale, CA, 94085, USA.
Lars G HemkensBasel Institute for Clinical Epidemiology and Biostatistics, Department of Clinical Research, University Hospital Basel, University of Basel, 4031, Basel, Switzerland.
John P A IoannidisStanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Medical School Office Building, Room X306, 1265 Welch Rd, Stanford, CA, 94305, USA. jioannid@stanford.edu.
Google (United States) · USLinkedIn (United States) · USMicrosoft (United States) · USStanford University · USUniversity Hospital of Basel · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMany technology companies, including Airbnb, Amazon, Booking.com, eBay, Facebook, Google, LinkedIn, Lyft, Microsoft, Netflix, Twitter, Uber, and Yahoo!/Oath, run online randomized controlled experiments at scale, namely hundreds of concurrent controlled experiments on millions of users each, commonly referred to as A/B tests. Originally derived from the same statistical roots, randomized controlled trials (RCTs) in medicine are now criticized for being expensive and difficult, while in technology, the marginal cost of such experiments is approaching zero and the value for data-driven decision-making is broadly recognized. METHODS AND

resultsThis is an overview of key scaling lessons learned in the technology field. They include (1) a focus on metrics, an overall evaluation criterion and thousands of metrics for insights and debugging, automatically computed for every experiment; (2) quick release cycles with automated ramp-up and shut-down that afford agile and safe experimentation, leading to consistent incremental progress over time; and (3) a culture of 'test everything' because most ideas fail and tiny changes sometimes show surprising outcomes worth millions of dollars annually. Technological advances, online interactions, and the availability of large-scale data allowed technology companies to take the science of RCTs and use them as online randomized controlled experiments at large scale with hundreds of such concurrent experiments running on any given day on a wide range of software products, be they web sites, mobile applications, or desktop applications. Rather than hindering innovation, these experiments enabled accelerated innovation with clear improvements to key metrics, including user experience and revenue. As healthcare increases interactions with patients utilizing these modern channels of web sites and digital health applications, many of the lessons apply. The most innovative technological field has recognized that systematic series of randomized trials with numerous failures of the most promising ideas leads to sustainable improvement.

conclusionWhile there are many differences between technology and medicine, it is worth considering whether and how similar designs can be applied via simple RCTs that focus on healthcare decision-making or service delivery. Changes - small and large - should undergo continuous and repeated evaluations in randomized trials and learning from their results will enable accelerated healthcare improvements.

Indexed as

Decision Making, OrganizationalInternet-Based InterventionRandomized Controlled Trials as TopicResearch DesignHumansMobile ApplicationsA/B testsHealthcare decision-makingOnline experimentsRandomizationTrials

Identifiers

PMID32033614
PMCPMC7007661
OpenAlexW3005139125

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.