Using Mixed Integer Programming for Matching in an Observational Study of Kidney Failure After Surgery

Zubizarreta, José R.

doi:10.1080/01621459.2012.703874

Cited by 190 publications

(182 citation statements)

References 30 publications

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“…Yang et al adapted the network for fine balance to handle nearfine balance, provided the user determines the permitted upper and lower bounds on imbalance per category. Zubizarreta (2012) took fine balance and near-fine balance a step further to seek fine or nearfine balance for multiple covariates that are not necessarily nominal. For continuous covariates, a difference of means or a (coarsened) Kolmogorov-Smirnov balance score can be used to either constrain balance or penalize imbalance, as long as the balance measure can be modeled as a linear function.…”

Section: Fine Balancementioning

confidence: 99%

Complexity and Approximation Results for the Balance Optimization Subset Selection Model for Causal Inference in Observational Studies

Sauppe

Jacobson²,

Sewell³

2014

INFORMS Journal on Computing

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Matching is widely used in the estimation of treatment effects in observational studies. However, the matching paradigm may be too restrictive in many cases because exact matches often do not exist in the available data. One mechanism for overcoming this issue is to relax the requirement of exact matching on some or all of the covariates (attributes that may affect the response to treatment) to a requirement of balance on the covariate distributions for the treatment and control groups. The balance optimization subset selection (BOSS) model can be used to identify a control group featuring optimal covariate balance. This paper explores the relationship between the matching and BOSS models and shows how BOSS subsumes matching. Complexity and approximation results are presented for the resulting models. Computational results demonstrate some of the important trade-offs between matching and BOSS. Data, as supplemental material, are available at http://dx.doi.org/10.1287/ijoc.2013.0583 . There is a video associated with this paper. Click here to view the Video Overview . To save the file, right click and choose “Save Link As” from the menu.

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Section: Fine Balancementioning

confidence: 99%

Complexity and Approximation Results for the Balance Optimization Subset Selection Model for Causal Inference in Observational Studies

Sauppe

Jacobson²,

Sewell³

2014

INFORMS Journal on Computing

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“…Los métodos de matching o pareamiento permiten construir dos muestras lo más parecidas posible en las covariables observadas, a semejanza de la estructura de un experimento aleatorizado. 13 Un valor P menor de 0,05 fue considerado como indicador de significación estadística. Se utilizaron los programas Statistica 9,0 (Statsoft, Tulsa, OK, USA), Stata 12 (Collage Station, Texas, USA) y EPIDAT 2,0 (OPS/OMS y Xunta de Galicia).…”

Section: Pareamiento (Matching) Según Uso Prenatal De Corticoidesunclassified

Factores perinatales asociados a la mortalidad neonatal en recién nacidos de muy bajo peso: estudio multicéntrico

Grandi¹,

Gonzalez²,

Zubizarreta

et al. 2016

Arch Argent Pediat

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“…Global optimization for integer problems has application in various areas such as resource application in production planning, redundancy optimization in reliability network, chemical process design, medical and biological systems applications, and multi-agents systems among others [15,17,7,50,44,38,10,9,34,53,12,55,2,45,6,42,24,30,16]. A variety of approaches for solving such problems were earlier developed, including the cutting plane algorithm proposed by Gomory [13] and the branchand-bound algorithm by Land and Doig [21].…”

Section: Introductionmentioning

confidence: 99%

A Two-Phase Combined Gradient-Tunneling Based Algorithm for Constrained Integer Programming Problems

Srivastava

Spinello

2015

Journal of Information and Optimization Sciences

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We propose a gradient-based optimization procedure consisting of two distinct phases for locating the global optimum of integer problems with a linear or non-linear objective function subject to linear or non-linear constraints. In the first phase a local minimum of the function is found using the gradient approach coupled with hemstitching moves when a constraint is violated in order to return the search to a feasible region. In the second phase a tunneling function is constructed at this local minimum in order to find another point on the other side of the barrier where the function value is approximately the same. In the next cycle the search for the global optimum commences again using this new found point as the starting state. The search continues until no improvement in function the result is found. Results from the proposed optimization method are presented and compared with those from the gradient method involving 3 n enumeration proposed earlier, as well as with other published results.

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Using Mixed Integer Programming for Matching in an Observational Study of Kidney Failure After Surgery

Cited by 190 publications

References 30 publications

Complexity and Approximation Results for the Balance Optimization Subset Selection Model for Causal Inference in Observational Studies

Complexity and Approximation Results for the Balance Optimization Subset Selection Model for Causal Inference in Observational Studies

Factores perinatales asociados a la mortalidad neonatal en recién nacidos de muy bajo peso: estudio multicéntrico

A Two-Phase Combined Gradient-Tunneling Based Algorithm for Constrained Integer Programming Problems

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