Design, analysis, power, and sample size calculation for three‐phase interrupted time series analysis in evaluation of health policy interventions.
Objective: To discuss the study design and data analysis for three‐phase interrupted time series (ITS) studies to evaluate the impact of health policy, systems, or environmental interventions. Simulation methods are used to conduct power and sample size calculation for these studies. Methods: We con...
| Publicado en: | Journal of Evaluation in Clinical Practice Vol. 26; no. 3; pp. 826 - 842 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Jun2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=143357736&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143357736 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13561294 EV1 jtl: Journal of Evaluation in Clinical Practice issn: 13561294 maglogo: Y pubinfo: dt: Jun2020 vid: 26 iid: 3 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 143357736 143357736 145878254 143357736 10.1111/jep.13266 143357736 ppf: 826 ppct: 16 formats: tig: atl: Design, analysis, power, and sample size calculation for three‐phase interrupted time series analysis in evaluation of health policy interventions. aug: au: Zhang, Bo Liu, Wei Lemon, Stephenie C. Barton, Bruce A. Fischer, Melissa A. Lawrence, Colleen Rahn, Elizabeth J. Danila, Maria I. Saag, Kenneth G. Harris, Paul A. Allison, Jeroan J. affil: Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester Massachusetts sug: subj: Interrupted Time Series Analysis Data Analysis, Statistical Sample Size Health Policy Evaluation Human Study Design Simulations Software Effect Size Quasi-Experimental Studies Regression ab: Objective: To discuss the study design and data analysis for three‐phase interrupted time series (ITS) studies to evaluate the impact of health policy, systems, or environmental interventions. Simulation methods are used to conduct power and sample size calculation for these studies. Methods: We consider the design and analysis of three‐phase ITS studies using a study funded by National Institutes of Health as an exemplar. The design and analysis of both one‐arm and two‐arm three‐phase ITS studies are introduced. Results: A simulation‐based approach, with ready‐to‐use computer programs, was developed to determine the power for two types of three‐phase ITS studies. Simulations were conducted to estimate the power of segmented autoregressive (AR) error models when autocorrelation ranged from −0.9 to 0.9 with various effect sizes. The power increased as the sample size or the effect size increased. The power to detect the same effect sizes varied largely, depending on testing level change, trend changes, or both. Conclusion: This article provides a convenient tool for investigators to generate sample sizes to ensure sufficient statistical power when three‐phase ITS study design is implemented. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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