UMMS Affiliation

Department of Medicine, Division of Internal Medicine; Meyers Primary Care Institute; Department of Population and Quantitative Health Sciences; Division of Preventive and Behavioral Medicine; UMass Worcester Prevention Research Center

Publication Date

2020-03-01

Document Type

Article

Disciplines

Biostatistics | Clinical Epidemiology | Epidemiology | Health Policy | Health Services Administration | Health Services Research | Investigative Techniques

Abstract

Objective: The purpose of this study was to present the design, model, and data analysis of an interrupted time series (ITS) model applied to evaluate the impact of health policy, systems, or environmental interventions using count outcomes. Simulation methods were used to conduct power and sample size calculations for these studies.

Methods: We proposed the models and analyses of ITS designs for count outcomes using the Strengthening Translational Research in Diverse Enrollment (STRIDE) study as an example. The models we used were observation-driven models, which bundle a lagged term on the conditional mean of the outcome for a time series of count outcomes.

Results: A simulation-based approach with ready-to-use computer programs was developed to calculate the sample size and power of two types of ITS models, Poisson and negative binomial, for count outcomes. 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 to detect the same magnitude of parameters varied largely, depending on the testing level change, the trend change, or both. The relationships between power and sample size and the values of the parameters were different between the two models.

Conclusion: This article provides a convenient tool to allow investigators to generate sample sizes that will ensure sufficient statistical power when the ITS study design of count outcomes is implemented.

Keywords

Count outcomes, Interrupted time series, Policy evaluation, Power, Quasi-experimental design, Sample size calculation, Segmented regression

Rights and Permissions

© 2019 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

DOI of Published Version

10.1016/j.conctc.2019.100474

Source

Contemp Clin Trials Commun. 2019 Oct 16;17:100474. doi: 10.1016/j.conctc.2019.100474. eCollection 2020 Mar. Link to article on publisher's site

Journal/Book/Conference Title

Contemporary clinical trials communications

PubMed ID

31886433

Related Resources

Link to Article in PubMed

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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