Literature DB >> 30411349

Comparison group selection in the presence of rolling entry for health services research: Rolling entry matching.

Allison Witman1, Christopher Beadles2, Yiyan Liu3, Ann Larsen3, Nilay Kafali4, Sabina Gandhi5, Peter Amico6, Thomas Hoerger2.   

Abstract

OBJECTIVE: To demonstrate rolling entry matching (REM), a new statistical method, for comparison group selection in the context of staggered nonuniform participant entry in nonrandomized interventions. STUDY
SETTING: Four Health Care Innovation Award (HCIA) interventions between 2012 and 2016. STUDY
DESIGN: Center for Medicare and Medicaid Innovation HCIA participants entering these interventions over time were matched with nonparticipants who exhibited a similar pattern of health care use and expenditures during each participant's baseline period. DATA EXTRACTION
METHODS: Medicare fee-for-service claims data were used to identify nonparticipating, fee-for-service beneficiaries as a potential comparison group and conduct REM. PRINCIPAL
FINDINGS: Rolling entry matching achieved conventionally-accepted levels of balance on observed characteristics between participants and nonparticipants. The method overcame difficulties associated with a small number of intervention entrants.
CONCLUSIONS: In nonrandomized interventions, valid inference regarding intervention effects relies on the suitability of the comparison group to act as the counterfactual case for the intervention group. When participants enter over time, comparison group selection is complicated. Rolling entry matching is a possible solution for comparison group selection in rolling entry interventions that is particularly useful with small sample sizes and merits further investigation in a variety of contexts. © Health Research and Educational Trust.

Entities:  

Keywords:  causal inference; comparison groups; matching; methods; propensity scores

Mesh:

Year:  2018        PMID: 30411349      PMCID: PMC6407360          DOI: 10.1111/1475-6773.13086

Source DB:  PubMed          Journal:  Health Serv Res        ISSN: 0017-9124            Impact factor:   3.402


  10 in total

1.  Matching methods for causal inference: A review and a look forward.

Authors:  Elizabeth A Stuart
Journal:  Stat Sci       Date:  2010-02-01       Impact factor: 2.901

2.  An application of propensity score matching using claims data.

Authors:  John D Seeger; Paige L Williams; Alexander M Walker
Journal:  Pharmacoepidemiol Drug Saf       Date:  2005-07       Impact factor: 2.890

3.  A simulation study of the number of events per variable in logistic regression analysis.

Authors:  P Peduzzi; J Concato; E Kemper; T R Holford; A R Feinstein
Journal:  J Clin Epidemiol       Date:  1996-12       Impact factor: 6.437

4.  Comparison group selection in the presence of rolling entry for health services research: Rolling entry matching.

Authors:  Allison Witman; Christopher Beadles; Yiyan Liu; Ann Larsen; Nilay Kafali; Sabina Gandhi; Peter Amico; Thomas Hoerger
Journal:  Health Serv Res       Date:  2018-11-09       Impact factor: 3.402

5.  Importance of events per independent variable in proportional hazards regression analysis. II. Accuracy and precision of regression estimates.

Authors:  P Peduzzi; J Concato; A R Feinstein; T R Holford
Journal:  J Clin Epidemiol       Date:  1995-12       Impact factor: 6.437

6.  Importance of events per independent variable in proportional hazards analysis. I. Background, goals, and general strategy.

Authors:  J Concato; P Peduzzi; T R Holford; A R Feinstein
Journal:  J Clin Epidemiol       Date:  1995-12       Impact factor: 6.437

7.  Assessing the comparative effectiveness of newly marketed medications: methodological challenges and implications for drug development.

Authors:  S Schneeweiss; J J Gagne; R J Glynn; M Ruhl; J A Rassen
Journal:  Clin Pharmacol Ther       Date:  2011-11-02       Impact factor: 6.875

8.  An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies.

Authors:  Peter C Austin
Journal:  Multivariate Behav Res       Date:  2011-06-08       Impact factor: 5.923

9.  Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples.

Authors:  Peter C Austin
Journal:  Stat Med       Date:  2009-11-10       Impact factor: 2.373

10.  Active safety monitoring of newly marketed medications in a distributed data network: application of a semi-automated monitoring system.

Authors:  J J Gagne; R J Glynn; J A Rassen; A M Walker; G W Daniel; G Sridhar; S Schneeweiss
Journal:  Clin Pharmacol Ther       Date:  2012-05-16       Impact factor: 6.875

  10 in total
  5 in total

1.  Comparison group selection in the presence of rolling entry for health services research: Rolling entry matching.

Authors:  Allison Witman; Christopher Beadles; Yiyan Liu; Ann Larsen; Nilay Kafali; Sabina Gandhi; Peter Amico; Thomas Hoerger
Journal:  Health Serv Res       Date:  2018-11-09       Impact factor: 3.402

2.  A Propensity-Matched Cohort Study of Tocilizumab in Patients With Coronavirus Disease 2019.

Authors:  Tyler C Lewis; Samrachana Adhikari; Vasishta Tatapudi; Meredith Holub; Dennis Kunichoff; Andrea B Troxel; Robert A Montgomery; Daniel H Sterman
Journal:  Crit Care Explor       Date:  2020-11-16

3.  Effectiveness of BBIBP-CorV vaccine against severe outcomes of COVID-19 in Abu Dhabi, United Arab Emirates.

Authors:  Nawal Al Kaabi; Abderrahim Oulhaj; Subhashini Ganesan; Farida Ismail Al Hosani; Omer Najim; Halah Ibrahim; Juan Acuna; Ahmed R Alsuwaidi; Ashraf M Kamour; Ashraf Alzaabi; Badreyya Ahmed Al Shehhi; Habiba Al Safar; Salah Eldin Hussein; Jehad Saleh Abdalla; Dalal Saeed Naser Al Mansoori; Ahmed Abdul Kareem Al Hammadi; Mohammed A Amari; Ahmed Khamis Al Romaithi; Stefan Weber; Santosh Elavalli; Islam Eltantawy; Noura Khamis Alghaithi; Jumana Nafiz Al Azazi; Stephen Geoffrey Holt; Mohamed Mostafa; Rabih Halwani; Hanif Khalak; Wael Elamin; Rami Beiram; Walid Zaher
Journal:  Nat Commun       Date:  2022-06-09       Impact factor: 17.694

4.  Minimising the biases in the observational study of resuscitative endovascular balloon occlusion of the aorta: a research protocol for a prospective study analysed with propensity score matching with time-varying covariates.

Authors:  Yosuke Matsumura; Atsushi Shiraishi; Shigeki Kushimoto
Journal:  BMJ Open       Date:  2022-04-01       Impact factor: 2.692

Review 5.  Matching with time-dependent treatments: A review and look forward.

Authors:  Laine E Thomas; Siyun Yang; Daniel Wojdyla; Douglas E Schaubel
Journal:  Stat Med       Date:  2020-04-03       Impact factor: 2.373

  5 in total

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