Evidence map›Paper›PMID 34092894›Full record

ArticleJournal of official statistics2020

Comparing the Ability of Regression Modeling and Bayesian Additive Regression Trees to Predict Costs in a Responsive Survey Design Context.

James Wagner, Brady T West, Michael R Elliott, Stephanie Coffey

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of official statistics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
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

4 authors.

James WagnerUniversity of Michigan, 4053 ISR, 426 Thompson St., Ann Arbor, MI 48104, U.S.A.
Brady T WestUniversity of Michigan, 4053 ISR, 426 Thompson St., Ann Arbor, MI 48104, U.S.A.
Michael R ElliottUniversity of Michigan, 4053 ISR, 426 Thompson St., Ann Arbor, MI 48104, U.S.A.
Stephanie CoffeyU.S. Census Bureau, 4600 Silver Hill Road, Suitland, MD 20746, U.S.A.

Funding

Advancing the Science of Responsive Design Using Bayesian MethodologyR01AG058599 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI WAGNER, JAMES R · 2018 to 2020
$684k
NIA NIH HHS R01 AG058599
6 · The paper itself

Abstract

Responsive survey designs rely upon incoming data from the field data collection to optimize cost and quality tradeoffs. In order to make these decisions in real-time, survey managers rely upon monitoring tools that generate proxy indicators for cost and quality. There is a developing literature on proxy indicators for the risk of nonresponse bias. However, there is very little research on proxy indicators for costs and almost none aimed at predicting costs under alternative design strategies. Predictions of survey costs and proxy error indicators can be used to optimize survey designs in real time. Using data from the National Survey of Family Growth, we evaluate alternative modeling strategies aimed at predicting survey costs (specifically, interviewer hours). The models include multilevel regression (with random interviewer effects) and Bayesian Additive Regression Trees (BART).

Indexed as

machine learningSurvey cost models

Identifiers

PMID34092894
PMCPMC8174792

What OpenQuestion holds

Textmetadata
LicenceTDM
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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.