A comparison of 12 algorithms for matching on the propensity score. (7th October 2013)
- Record Type:
- Journal Article
- Title:
- A comparison of 12 algorithms for matching on the propensity score. (7th October 2013)
- Main Title:
- A comparison of 12 algorithms for matching on the propensity score
- Authors:
- Austin, Peter C.
- Abstract:
- <abstract abstract-type="main" id="sim6004-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <p id="sim6004-para-0001">Propensity‐score matching is increasingly being used to reduce the confounding that can occur in observational studies examining the effects of treatments or interventions on outcomes. We used Monte Carlo simulations to examine the following algorithms for forming matched pairs of treated and untreated subjects: optimal matching, greedy nearest neighbor matching without replacement, and greedy nearest neighbor matching without replacement within specified caliper widths. For each of the latter two algorithms, we examined four different sub‐algorithms defined by the order in which treated subjects were selected for matching to an untreated subject: lowest to highest propensity score, highest to lowest propensity score, best match first, and random order. We also examined matching with replacement. We found that (i) nearest neighbor matching induced the same balance in baseline covariates as did optimal matching; (ii) when at least some of the covariates were continuous, caliper matching tended to induce balance on baseline covariates that was at least as good as the other algorithms; (iii) caliper matching tended to result in estimates of treatment effect with less bias compared with optimal and nearest neighbor matching; (iv) optimal and nearest neighbor matching resulted in estimates of treatment effect with negligibly less variability than<abstract abstract-type="main" id="sim6004-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <p id="sim6004-para-0001">Propensity‐score matching is increasingly being used to reduce the confounding that can occur in observational studies examining the effects of treatments or interventions on outcomes. We used Monte Carlo simulations to examine the following algorithms for forming matched pairs of treated and untreated subjects: optimal matching, greedy nearest neighbor matching without replacement, and greedy nearest neighbor matching without replacement within specified caliper widths. For each of the latter two algorithms, we examined four different sub‐algorithms defined by the order in which treated subjects were selected for matching to an untreated subject: lowest to highest propensity score, highest to lowest propensity score, best match first, and random order. We also examined matching with replacement. We found that (i) nearest neighbor matching induced the same balance in baseline covariates as did optimal matching; (ii) when at least some of the covariates were continuous, caliper matching tended to induce balance on baseline covariates that was at least as good as the other algorithms; (iii) caliper matching tended to result in estimates of treatment effect with less bias compared with optimal and nearest neighbor matching; (iv) optimal and nearest neighbor matching resulted in estimates of treatment effect with negligibly less variability than did caliper matching; (v) caliper matching had amongst the best performance when assessed using mean squared error; (vi) the order in which treated subjects were selected for matching had at most a modest effect on estimation; and (vii) matching with replacement did not have superior performance compared with caliper matching without replacement. © 2013 The Authors. <italic>Statistics in Medicine</italic> published by John Wiley &amp; Sons, Ltd.</p> </abstract> … (more)
- Is Part Of:
- Statistics in medicine. Volume 33:Number 6(2014)
- Journal:
- Statistics in medicine
- Issue:
- Volume 33:Number 6(2014)
- Issue Display:
- Volume 33, Issue 6 (2014)
- Year:
- 2014
- Volume:
- 33
- Issue:
- 6
- Issue Sort Value:
- 2014-0033-0006-0000
- Page Start:
- 1057
- Page End:
- 1069
- Publication Date:
- 2013-10-07
- Subjects:
- Medical statistics -- Periodicals
Statistique médicale -- Périodiques
Statistiques médicales -- Périodiques
610.727 - Journal URLs:
- http://onlinelibrary.wiley.com/ ↗
- DOI:
- 10.1002/sim.6004 ↗
- Languages:
- English
- ISSNs:
- 0277-6715
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 8453.576000
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 3446.xml