A data-driven examination of which patients follow trial protocol

Maren K Olsen; Karen M Stechuchak; Anna Hung; Eugene Z Oddone; Laura J Damschroder; David Edelman; Matthew L Maciejewski

doi:10.1016/j.conctc.2020.100631

A data-driven examination of which patients follow trial protocol

Contemp Clin Trials Commun. 2020 Aug 13:19:100631. doi: 10.1016/j.conctc.2020.100631. eCollection 2020 Sep.

Authors

Maren K Olsen^{1

2}, Karen M Stechuchak¹, Anna Hung^{1

3}, Eugene Z Oddone^{1

4}, Laura J Damschroder^{5

6}, David Edelman^{1

4}, Matthew L Maciejewski^{1

4

7}

Affiliations

¹ Durham Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, NC, USA.
² Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.
³ DCRI, Duke University, Durham, NC, USA.
⁴ Division of General Internal Medicine, Department of Medicine, Duke University, Durham, NC, USA.
⁵ Ann Arbor VA HSR&D Center for Clinical Management Research, Ann Arbor, MI, USA.
⁶ VA PROVE QUERI, Ann Arbor, MI, USA.
⁷ Department of Population Health Sciences, Duke University, Durham, NC, USA.

Abstract

Protocol adherence in behavioral intervention clinical trials is critical to trial success. There is increasing interest in understanding which patients are more likely to adhere to trial protocols. The objective of this study was to demonstrate the use of a data-driven approach to explore patient characteristics associated with the lowest and highest rates of adherence in three trials assessing interventions targeting behaviors related to lifestyle and risk for cardiovascular disease. Each trial included a common set of baseline variables. Model-based recursive partitioning (MoB) was applied in each trial to identify participant characteristics of subgroups characterized by these baseline variables with differences in protocol adherence. Bootstrap resampling was conducted to provide optimism-corrected c-statistics of the final solutions. In the three trials, rates of protocol adherence varied from 56.9% to 87.5%. Evaluation of heterogeneity of protocol adherence via MoB in each trial resulted in trees with 2-4 subgroups based on splits of 1-3 variables. In two of the three trials, the first split was based on pain in the past week, and those reporting lower pain were less likely to be adherent. In one of these trials, the second and third splits were based on education and employment, where those with lower education levels and who were employed were less likely to be adherent. In the third trial, the two splits were based on smoking status and then marriage status, where smokers who were married were least likely to be adherent. Optimism-corrected c-statistics ranged from 0.54 to 0.63. Model-based recursive partitioning can be a useful approach to explore heterogeneity in protocol adherence in behavioral intervention trials. An important next step would be to assess whether patterns hold in other similar studies and samples. Identifying subgroups who are less likely to be adherent to an intervention can help inform modifications to the intervention to help tailor the intervention to these subgroups and increase future uptake and impact.

Trial registration: ClinicalTrials.gov identifiers: NCT01828567, NCT02360293, and NCT01838226.

Keywords: Model-based recursive partitioning; Protocol adherence; Subgroup; Veterans.

Associated data

ClinicalTrials.gov/NCT02360293
ClinicalTrials.gov/NCT01828567
ClinicalTrials.gov/NCT01838226

Abstract

Associated data

Grants and funding