Using marginal structural models to analyze randomized clinical trials with non-adherence and lost to follow up - Lancet, Elizabeth A.; Borrell, Luisa N.; Holbrook, Janet; Morabia, Alfredo (Annals of epidemiology, 2021)
•Unlike the Intention-to-Treat approach, Marginal Structural Models can account for the effects of time-varying treatments in the presence of censoring and time-dependent confounding influenced by prior treatment.•A randomized clinical trial on asthma treatment was used as working model to determine whether the use of Marginal Structural Models to adjust for post-randomization bias would change the results of the original Intention-to-Treat analysis.•Despite the occurrence of non-adherence and censorship, the use of Marginal Structural Models did not change the findings of the original Intention-to-Treat analysis. Our findings are comparable to that of other trials with similar proportions of non-adherence and censorship comparing Marginal Structural Models to Intention-to-Treat.•Because no adherence or censorship thresholds currently exist to assist researchers to determine when Marginal Structural Models may be superior to Intention-to-Treat, Marginal Structural Models are recommended as a sensitivity analysis to the Intention-to-Treat approach in clinical trials with non-adherence and censorship. In the presence of non-adherence and lost to follow up, results of an Intention to Treat (ITT) analysis may be biased as it is measuring the effect of assignment rather than the effect of treatment. Given that Marginal Structural Models (MSMs) adjust for such issues, this study examines the use of MSMs to assess the validity of ITT analyses in the presence of non-adherence and lost to follow up in an existing randomized clinical trial on asthma treatment. Inverse probability weights were obtained from a pooled logistic regression assessing the probability of staying on assigned treatment (adherence) and of remaining uncensored (censored) for subjects at each visit by treatment arm. Weights were then pooled into a MSM analysis using a Poisson generalized estimating equation with an independent correlation matrix. Out of 488 participants, 174 (36%) did not adhere to the baseline assignment and 85 (17%) were lost to follow up by the end of the study. The adjusted relative risks (RR), and 95% confidence intervals (CI), obtained from the MSMs (theophylline vs. montelukast; RR=1.24; 95% CI: 0.83,1.84; theophylline vs. placebo: RR=1.01; 95% CI: 0.70,1.48; and montelukast vs. placebo: RR=0.83; 95% CI: 0.57,1.19) were nearly identical to that of the ITT analysis (theophylline vs. montelukast: RR=1.22; 95% CI: 0.82,1.86; theophylline vs. placebo: RR=0.99; 95% CI: 0.67,1.50; and montelukast vs. placebo: RR=0.82; 95% CI: 0.55,1.21). Concordance between the results of ITT and MSMs indicate adherence and censoring may not invalidate ITT analysis. However, no adherence or censorship thresholds currently exist to assist researchers in determining when MSMs may be superior to ITT in the analysis of clinical trials with non-adherence or censorship issues, and therefore, MSMs should be conducted as a sensitivity analysis to the ITT approach in clinical trials.