PMID- 32587893 OWN - NLM STAT- PubMed-not-MEDLINE LR - 20240330 IS - 2381-4683 (Electronic) IS - 2381-4683 (Linking) VI - 5 IP - 1 DP - 2020 Jan-Jun TI - Developing and Validating Metamodels of a Microsimulation Model of Infant HIV Testing and Screening Strategies Used in a Decision Support Tool for Health Policy Makers. PG - 2381468320932894 LID - 10.1177/2381468320932894 [doi] LID - 2381468320932894 AB - Background. Metamodels can simplify complex health policy models and yield instantaneous results to inform policy decisions. We investigated the predictive validity of linear regression metamodels used to support a real-time decision-making tool that compares infant HIV testing/screening strategies. Methods. We developed linear regression metamodels of the Cost-Effectiveness of Preventing AIDS Complications Pediatric (CEPAC-P) microsimulation model used to predict life expectancy and lifetime HIV-related costs/person of two infant HIV testing/screening programs in South Africa. Metamodel performance was assessed with cross-validation and Bland-Altman plots, showing between-method differences in predicted outcomes against their means. Predictive validity was determined by the percentage of simulations in which the metamodels accurately predicted the strategy with the greatest net health benefit (NHB) as projected by the CEPAC-P model. We introduced a zone of indifference and investigated the width needed to produce between-method agreement in 95% of the simulations. We also calculated NHB losses from "wrong" decisions by the metamodel. Results. In cross-validation, linear regression metamodels accurately approximated CEPAC-P-projected outcomes. For life expectancy, Bland-Altman plots showed good agreement between CEPAC-P and the metamodel (within 1.1 life-months difference). For costs, 95% of between-method differences were within $65/person. The metamodels predicted the same optimal strategy as the CEPAC-P model in 87.7% of simulations, increasing to 95% with a zone of indifference of 0.24 life-months ( approximately 7 days). The losses in health benefits due to "wrong" choices by the metamodel were modest (range: 0.0002-1.1 life-months). Conclusions. For this policy question, linear regression metamodels offered sufficient predictive validity for the optimal testing strategy as compared with the CEPAC-P model. Metamodels can simulate different scenarios in real time, based on sets of input parameters that can be depicted in a widely accessible decision-support tool. CI - (c) The Author(s) 2020. FAU - Soeteman, Djora I AU - Soeteman DI AUID- ORCID: 0000-0001-8743-2604 AD - Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, Massachusetts. FAU - Resch, Stephen C AU - Resch SC AD - Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, Massachusetts. FAU - Jalal, Hawre AU - Jalal H AUID- ORCID: 0000-0002-8224-6834 AD - Department of Health Policy and Management, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania. FAU - Dugdale, Caitlin M AU - Dugdale CM AD - Medical Practice Evaluation Center, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts. FAU - Penazzato, Martina AU - Penazzato M AD - HIV and Hepatitis Department, World Health Organization, Geneva, Switzerland. FAU - Weinstein, Milton C AU - Weinstein MC AD - Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, Massachusetts. FAU - Phillips, Andrew AU - Phillips A AD - Institute for Global Health, University College, London, UK. FAU - Hou, Taige AU - Hou T AD - Medical Practice Evaluation Center, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts. FAU - Abrams, Elaine J AU - Abrams EJ AD - ICAP at Columbia University, Mailman School of Public Health, Columbia University, New York, New York. FAU - Dunning, Lorna AU - Dunning L AD - Medical Practice Evaluation Center, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts. FAU - Newell, Marie-Louise AU - Newell ML AD - Institute for Development Studies, Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK. FAU - Pei, Pamela P AU - Pei PP AD - Medical Practice Evaluation Center, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts. FAU - Freedberg, Kenneth A AU - Freedberg KA AD - Medical Practice Evaluation Center, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts. FAU - Walensky, Rochelle P AU - Walensky RP AD - Medical Practice Evaluation Center, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts. FAU - Ciaranello, Andrea L AU - Ciaranello AL AD - Medical Practice Evaluation Center, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts. LA - eng GR - R01 HD079214/HD/NICHD NIH HHS/United States GR - R37 AI093269/AI/NIAID NIH HHS/United States GR - R01 AI093269/AI/NIAID NIH HHS/United States GR - T32 AI007433/AI/NIAID NIH HHS/United States GR - R37 AI058736/AI/NIAID NIH HHS/United States PT - Journal Article DEP - 20200612 PL - United States TA - MDM Policy Pract JT - MDM policy & practice JID - 101707716 PMC - PMC7294506 OTO - NOTNLM OT - Bland-Altman plots OT - decision-making tool OT - infant HIV screening OT - metamodeling COIS- The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. EDAT- 2020/06/27 06:00 MHDA- 2020/06/27 06:01 PMCR- 2020/06/12 CRDT- 2020/06/27 06:00 PHST- 2020/03/19 00:00 [received] PHST- 2020/05/08 00:00 [accepted] PHST- 2020/06/27 06:00 [entrez] PHST- 2020/06/27 06:00 [pubmed] PHST- 2020/06/27 06:01 [medline] PHST- 2020/06/12 00:00 [pmc-release] AID - 10.1177_2381468320932894 [pii] AID - 10.1177/2381468320932894 [doi] PST - epublish SO - MDM Policy Pract. 2020 Jun 12;5(1):2381468320932894. doi: 10.1177/2381468320932894. eCollection 2020 Jan-Jun.