A method to reduce imbalance for site-level randomized stepped wedge implementation trial designs

Robert A Lew, Christopher J Miller, Bo Kim, Hongsheng Wu, Kelly Stolzmann, Mark S Bauer, Robert A Lew, Christopher J Miller, Bo Kim, Hongsheng Wu, Kelly Stolzmann, Mark S Bauer

Abstract

Background: Controlled implementation trials often randomize the intervention at the site level, enrolling relatively few sites (e.g., 6-20) compared to trials that randomize by subject. Trials with few sites carry a substantial risk of an imbalance between intervened (cases) and non-intervened (control) sites in important site characteristics, thereby threatening the internal validity of the primary comparison. A stepped wedge design (SWD) staggers the intervention at sites over a sequence of times or time waves until all sites eventually receive the intervention. We propose a new randomization method, sequential balance, to control time trend in site allocation by minimizing sequential imbalance across multiple characteristics. We illustrate the new method by applying it to a SWD implementation trial.

Methods: The trial investigated the impact of blended internal-external facilitation on the establishment of evidence-based teams in general mental health clinics in nine US Department of Veterans Affairs medical centers. Prior to randomization to start time, an expert panel of implementation researchers and health system program leaders identified by consensus a series of eight facility-level characteristics judged relevant to the success of implementation. We characterized each of the nine sites according to these consensus features. Using a weighted sum of these characteristics, we calculated imbalance scores for each of 1680 possible site assignments to identify the most sequentially balanced assignment schemes.

Results: From 1680 possible site assignments, we identified 34 assignments with minimal imbalance scores, and then randomly selected one assignment by which to randomize start time. Initially, the mean imbalance score was 3.10, but restricted to the 34 assignments, it declined to 0.99.

Conclusions: Sequential balancing of site characteristics across groups of sites in the time waves of a SWD strengthens the internal validity of study conclusions by minimizing potential confounding.

Trial registration: Registered at ClinicalTrials.gov as clinical trials # NCT02543840 ; entered 9/4/2015.

Keywords: Imbalance; Nested control trails; Stepped wedge.

Conflict of interest statement

Authors’ information

N/A.

Ethics approval and consent to participate

This protocol was approved by the VA Central Institutional Review Board as a mixed quality improvement/research protocol. This aspect of the protocol was quality improvement, using widely available administrative data only; thus, consent was not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

    1. Shadish WR, Cook TD, Campbell DT. Experimental and quasi-experimental designs for generalized causal inference. Boston: Houghton Mifflin; 2002.
    1. Fisher RA. An examination of the different possible solutions of a problem in incomplete blocks. Ann Eugenics. 1940;10:52–75. doi: 10.1111/j.1469-1809.1940.tb02237.x.
    1. Fisher RA. The design of experiments. 9. London: Macmillan; 1971.
    1. Hughes JP, Granston TS, Heagerty PJ. Current issues in the design and analysis of stepped wedge trial. Contemp Clin Trials. 2015;45(Part A):55–60. doi: 10.1016/j.cct.2015.07.006.
    1. Hemming K, Lilford R, Girling AJ. Stepped-wedge cluster randomised controlled trials: a generic framework including parallel and multiple-level designs. Stat Med. 2015;34:181–196. doi: 10.1002/sim.6325.
    1. Bauer MS, Miller C, Kim B, Lew R, Stolzmann K, Sullivan J, Riendeau R, Pitcock J, Williamson A, Connolly S, Elwy AR, Weaver K. Effectiveness of implementing a collaborative chronic care model on mental health clinician teams and the mental health of patients receiving care: a randomized trial. JAMA Netw Open. 2019;2(3):e190230. doi: 10.1001/Jamanetworkopen.2019.0230.
    1. Kirchner JE, Ritchie MJ, Pitcock JA, Parker LE, Curran GM, Fortney JC. Outcomes of a partnered facilitation strategy to implement primary care-mental health. J Gen Intern Med. 2014;29(Suppl 4):904–912. doi: 10.1007/s11606-014-3027-2.
    1. Woltmann E, Grogan-Kaylor A, Perron B, Georges H, Kilbourne AM, Bauer MS. Comparative effectiveness of collaborative chronic care models for mental health conditions across primary, specialty, and behavioral health care settings: systematic review and meta-analysis. Am J Psychiatry. 2012;169:790–804. doi: 10.1176/appi.ajp.2012.11111616.
    1. Miller CJ, Grogan-Kaylor A, Perron BE, Kilbourne AM, Woltmann E, Bauer MS. Collaborative chronic care models for mental health conditions: cumulative meta-analysis and metaregression to guide future research and implementation. Med Care. 2013;51:922–930. doi: 10.1097/MLR.0b013e3182a3e4c4.
    1. Rosenbaum PR, Ross RN, Silber JH. Minimum distance matched sampling with fine balance in an observational study of treatment for ovarian cancer. J Am Stat Assoc. 2007;102:75–83. doi: 10.1198/016214506000001059.
    1. Silber JH, Rosenbaum PR, Ross RN, Ludwig JM, Wang W, Niknam BA, Mukherjee N, Saynisch PA, Even-Shoshan O, Kelz RR, Fleisher LA. Template matching for auditing hospital cost and quality health. Health Serv Res. 2014;49:1446–1474. doi: 10.1111/1475-6773.12156.
    1. Pimentel SD, Kelz RR, Silber JH, Rosenbaum PR. Indirect standardization matching: assessing specific advantage and risk synergy. J Am Stat Assoc. 2015;110:515–527. doi: 10.1080/01621459.2014.997879.
    1. Silber JH, Rosenbaum PR, Ross RN, Ludwig JM, Wang W, Niknam BA, Hill AS, Even-Shoshan O, Kelz RR, Fleisher LA. Indirect standardization matching: assessing specific advantage and risk synergy. Health Serv Res. 2016;51:2330–2357. doi: 10.1111/1475-6773.12470.
    1. Zubizarreta JR. Stable weights that balance covariates for estimation with incomplete outcome data. J Amer Stat Assoc. 2015;110:910–922. doi: 10.1080/01621459.2015.1023805.
    1. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70:41–55. doi: 10.1093/biomet/70.1.41.
    1. Li F, Turner EL, Heagerty PJ, Murray DM, Vollmer WM, DeLong ER. The method of randomization for cluster-randomized trials: challenges of including patients with multiple chronic conditions. Int J Stat Med Res. 2016;5:2–7. doi: 10.6000/1929-6029.2016.05.01.1.
    1. Li F, Lokhnygina Y, Murray DM, Heagerty PJ, DeLong ER. An evaluation of constrained randomization for the design and analysis of group-randomized trials. Stat Med. 2016;35:1565–1579. doi: 10.1002/sim.6813.
    1. Carter BR, Hood K. Balance algorithm for cluster randomized trials. BMC Med Res Methodol. 2008;8:1–8. doi: 10.1186/1471-2288-8-65.
    1. Boyd S, Vandenberghe L. Convex optimization, with corrections 2008. Cambridge: Cambridge U Press; 2008.

Source: PubMed

3
Suscribir