Evidence map›Paper›PMID 29104450›Full record

ArticleHealth services & outcomes research methodology2017

Optimizing Variance-Bias Trade-off in the TWANG Package for Estimation of Propensity Scores.

Layla Parast, Daniel F McCaffrey, Lane F Burgette, Fernando Hoces de la Guardia, Daniela Golinelli, Jeremy N V Miles, Beth Ann Griffin

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Health services & outcomes research methodology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
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  6. Comparative Safety and Effectiveness of Biologic Therapy for Crohn's Disease: A CA-IBD Cohort Study.Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association · 2023
    Article
  7. Article
  8. Article
  9. Article
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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

7 authors.

Layla ParastRAND Corporation, 1776 Main Street, Santa Monica, CA, 90403 USA.
Daniel F McCaffreyEducational Testing Service, 660 Rosedale Road Princeton, NJ 08541 USA.
Lane F BurgetteRAND Corporation, 1200 South Hayes Street Arlington, Virginia 22202 USA.
Fernando Hoces de la GuardiaRAND Corporation, 1776 Main Street, Santa Monica, CA, 90403 USA.
Daniela GolinelliMathematica Policy Research, 1100 1st Street, NE, Washington, DC 20002 USA.
Jeremy N V MilesRAND Corporation, 1776 Main Street, Santa Monica, CA, 90403 USA.
Beth Ann GriffinRAND Corporation, 1200 South Hayes Street Arlington, Virginia 22202 USA.

Funding

Improving TWANG as a Research Tool for Addiction ResearchersR01DA034065 · NIDA · RAND CORPORATION · PI GRIFFIN, BETH ANN, MCCAFFREY, DANIEL F · 2013 to 2015
$953k
Estimating the Causal Effect of Interacting with Pet Dogs and CatsR01HD066591 · NICHD · RAND CORPORATION · PI MILES, JEREMY NOWELL VILLETTE · 2010 to 2011
$429k
NICHD NIH HHS R01 HD066591NIDA NIH HHS R01 DA034065
6 · The paper itself

Abstract

While propensity score weighting has been shown to reduce bias in treatment effect estimation when selection bias is present, it has also been shown that such weighting can perform poorly if the estimated propensity score weights are highly variable. Various approaches have been proposed which can reduce the variability of the weights and the risk of poor performance, particularly those based on machine learning methods. In this study, we closely examine approaches to fine-tune one machine learning technique (generalized boosted models [GBM]) to select propensity scores that seek to optimize the variance-bias trade-off that is inherent in most propensity score analyses. Specifically, we propose and evaluate three approaches for selecting the optimal number of trees for the GBM in the twang package in R. Normally, the twang package in R iteratively selects the optimal number of trees as that which maximizes balance between the treatment groups being considered. Because the selected number of trees may lead to highly variable propensity score weights, we examine alternative ways to tune the number of trees used in the estimation of propensity score weights such that we sacrifice some balance on the pre-treatment covariates in exchange for less variable weights. We use simulation studies to illustrate these methods and to describe the potential advantages and disadvantages of each method. We apply these methods to two case studies: one examining the effect of dog ownership on the owner's general health using data from a large, population-based survey in California, and a second investigating the relationship between abstinence and a long-term economic outcome among a sample of high-risk youth.

Indexed as

causal inferencemachine learningpropensity score

Identifiers

PMID29104450
PMCPMC5667923

What OpenQuestion holds

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Registered trials

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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.