Assessment of a Precision Medicine Analysis of a Behavioral Counseling Strategy to Improve Adherence to Diabetes Self-management Among Youth: A Post Hoc Analysis of the FLEX Trial

Anna R Kahkoska, Michael T Lawson, Jamie Crandell, Kimberly A Driscoll, Jessica C Kichler, Michael Seid, David M Maahs, Michael R Kosorok, Elizabeth J Mayer-Davis, Anna R Kahkoska, Michael T Lawson, Jamie Crandell, Kimberly A Driscoll, Jessica C Kichler, Michael Seid, David M Maahs, Michael R Kosorok, Elizabeth J Mayer-Davis

Abstract

Importance: The Flexible Lifestyles Empowering Change (FLEX) trial, an 18-month randomized clinical trial testing an adaptive behavioral intervention in adolescents with type 1 diabetes, showed no overall treatment effect for its primary outcome, change in hemoglobin A1c (HbA1c) percentage of total hemoglobin, but demonstrated benefit for quality of life (QoL) as a prespecified secondary outcome.

Objective: To apply a novel statistical method for post hoc analysis that derives an individualized treatment rule (ITR) to identify FLEX participants who may benefit from intervention based on changes in HbA1c percentage (primary outcome), QoL, and body mass index z score (BMIz) (secondary outcomes) during 18 months.

Design, setting, and participants: This multisite clinical trial enrolled 258 adolescents aged 13 to 16 years with type 1 diabetes for 1 or more years, who had literacy in English, HbA1c percentage of total hemoglobin from 8.0% to 13.0%, a participating caregiver, and no other serious medical conditions. From January 5, 2014, to April 4, 2016, 258 adolescents were recruited. The post hoc analysis excluded adolescents missing outcome measures at 18 months (2 participants [0.8%]) or continuous glucose monitoring data at baseline (40 participants [15.5%]). Data were analyzed from April to December 2018.

Interventions: The FLEX intervention included a behavioral counseling strategy that integrated motivational interviewing and problem-solving skills training to increase adherence to diabetes self-management. The control condition entailed usual diabetes care.

Main outcomes and measures: Subgroups of FLEX participants were derived from an ITR estimating which participants would benefit from intervention, which would benefit from control conditions, and which would be indifferent. Multiple imputation by chained equations and reinforcement learning trees were used to estimate the ITR. Subgroups based on ITR pertaining to changes during 18 months in 3 univariate outcomes (ie, HbA1c percentage, QoL, and BMIz) and a composite outcome were compared by baseline demographic, clinical, and psychosocial characteristics.

Results: Data from 216 adolescents in the FLEX trial were reanalyzed (166 [76.9%] non-Hispanic white; 108 teenaged girls [50.0%]; mean [SD] age, 14.9 [1.1] years; mean [SD] diabetes duration, 6.3 [3.7] years). For the univariate outcomes, a large proportion of FLEX participants had equivalent predicted outcomes under intervention vs usual care settings, regardless of randomization, and were assigned to the muted group (HbA1c: 105 participants [48.6%]; QoL: 63 participants [29.2%]; BMIz: 136 participants [63.0%]). Regarding the BMIz univariate outcome, mean baseline BMIz of participants assigned to the muted group was lower than that of those assigned to the intervention and control groups (muted vs intervention: mean difference, 0.48; 95% CI, 0.21 to 0.75; P = .002; muted vs control: mean difference, 0.86; 95% CI, 0.61 to 1.11; P < .001); this group also had a higher proportion of individuals with underweight or normal weight using weight status cutoffs (95 [69.9%] in muted group vs 24 [54.6%] in intervention group and 11 [30.6%] in control group; χ24 = 24.67; P < .001). The approach identified subgroups estimated to benefit based on HbA1c percentage (54 participants [25.0%]), QoL (89 participants [41.2%]), and BMIz (44 participants [20.4%]). Regarding the HbA1c percentage outcome, participants expected to benefit from the intervention did not have significantly higher baseline HbA1c percentages than those expected to benefit from usual care (9.4% vs 9.2%; difference, 0.2%; 95% CI, -0.16% to 0.56%; P = .44). However, participants in the muted group had higher mean HbA1c percentages at baseline than those assigned to the intervention or control groups (muted vs intervention: 9.9% vs 9.4%; difference, 0.5%; 95% CI, 0.13% to 0.89%; P = .02; muted vs control; 9.9% vs 9.2%; difference, 0.7%; 95% CI, 0.34% to 1.08%; P = .001). No significant differences were found between subgroups estimated to benefit in terms of the composite outcome from the FLEX intervention (91 participants [42.1%]) vs usual care (125 participants [57.9%]).

Conclusions and relevance: The precision medicine approach represents a conceptually and analytically novel approach to post hoc subgroup identification. More work is needed to understand markers of positive response to the FLEX intervention.

Trial registration: ClinicalTrial.gov identifier: NCT01286350.

Conflict of interest statement

Conflict of Interest Disclosures: Dr Kichler reported receiving grants from the National Institute of Diabetes and Digestive and Kidney Diseases during the conduct of the study. Dr Seid reported receiving grants from the National Institutes of Health during the conduct of the study and grants from the Helmsley Charitable Trust outside the submitted work. Dr Maahs reported receiving personal fees from Abbott, Sonofi, and Novo Nordisk and grants from Dexcom outside the submitted work. No other disclosures were reported.

Figures

Figure 1.. CONSORT Diagram for the Flexible…
Figure 1.. CONSORT Diagram for the Flexible Lifestyles Empowering Change (FLEX) Intervention Randomized Clinical Trial and Post Hoc Analysis
Figure 2.. Overview of Post Hoc Analyses…
Figure 2.. Overview of Post Hoc Analyses of Differential Response in Randomized Trial Data
Effect modification analysis of intervention effect is when the observed effect of an intervention is examined across levels of a third, prespecified effect-modifier variable. Precision medicine analysis of the intervention effect is when an individualized treatment rule (ITR) is applied to the entire data set to estimate subgroups expected to benefit from the intervention, expected to benefit from usual care, and expected to be indifferent to treatment group. Δ indicates change.

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Source: PubMed

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