A data-driven examination of which patients follow trial protocol

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.

Fig. 1

ACTIVATE trial: MoB final solution. We used the default value of statistical significance for the fluctuation tests (alpha = 0.05). Instead of specifying a Bonferroni correction (which would have altered the statistical significance to 0.05/15), we chose to post prune by Akaike's Information Criteria fit index and set the minimum node sample size as 30. Finally, we specified maxLM-type test as the fluctuation test for ordered factor variables. All other control parameters were kept at their default values. The final sample size was 207 due to missing data on candidate baseline characteristics.

Fig. 2

GPS trial: MoB final solution. We used the default value of statistical significance for the fluctuation tests (alpha = 0.05). Instead of specifying a Bonferroni correction (which would have altered the statistical significance to 0.05/15), we chose to post prune by Akaike's Information Criteria fit index and set the minimum node sample size as 30. Finally, we specified maxLM-type test as the fluctuation test for ordered factor variables. All other control parameters were kept at their default values. The final sample size was 192 due to missing data on candidate baseline characteristics.

Fig. 3

Stay Strong trial: MoB final solution. We used the default value of statistical significance for the fluctuation tests (alpha = 0.05). Instead of specifying a Bonferroni correction (which would have altered the statistical significance to 0.05/15), we chose to post prune by Akaike's Information Criteria fit index and set the minimum node sample size as 30. Finally, we specified maxLM-type test as the fluctuation test for ordered factor variables. All other control parameters were kept at their default values. The final sample size was 173 due to missing data on candidate baseline characteristics.