Individual-centric N-of-1 trials: a case study assessing the effect of alcohol abstinence on mood levels

Popularized in the 1980s, N-of-1 trials have emerged as a useful study design to assess the effects of interventions in single individuals. This study design consists of observing outcomes over time for the same individual under periods of exposure to an intervention and a comparator. Despite the simple idea, N-of-1 trials can require strong assumptions in the analysis phase to identify and estimate causal effects. As an illustrative example, we present an N-of-1 trial aiming at assessing the effect of alcohol abstinence on mood.

The N-of-1 trial participant decided to join a month-long nationwide alcohol abstinence campaign and was interested in the effects of alcohol abstinence on his mood. Every eight hours, the participant collected data about his own mood levels, number of alcohol units consumed, and social interactions, before, during, and after the alcohol abstinence period. Mood levels were measured using a 5-point Likert scale ranging from -2 to 2. To analyze the N-of-1 trial data, we relied on an explicit causal framework and made precise assumptions about the data generating process. We used a g-computation algorithm to estimate, for each time point, the individual-specific difference between the expected mood outcomes under the "always abstain from alcohol" intervention and "always drinking as usual" comparator.

Overall, 264 time points were recorded, 171 under no intervention, and 93 during the intervention (alcohol abstinence) period. After adjusting for the other time-varying causes of mood, no statistically significant effect of alcohol units on mood level was found for measurements at the same time point; however, the number of alcohol units reported had a statistically significant negative effect on mood levels at the subsequent time point. The mean of the individual-specific average treatment effects across the entire study period was 0.05 (95%CI: -0.06, 0.15).

N-of-1 trials can be truly individual-centric studies, tailored to the needs and preferences of the participants. Analyzing data from N-of-1 trials can be complex, and the use of a causal framework can help inform the analyses.