Background: Premature discontinuation and other forms of noncompliance with treatment assignment can complicate\ncausal inference of treatment effects in randomized trials. The intent-to-treat analysis gives unbiased estimates for causal\neffects of treatment assignment on outcome, but may understate potential benefit or harm of actual treatment. The corresponding\nupper confidence limit can also be underestimated.\nPurpose: To compare estimates of the hazard ratio and upper bound of the two-sided 95% confidence interval from\ncausal inference methods that account for noncompliance with those from the intent-to-treat analysis.\nMethods: We used simulations with parameters chosen to reflect cardiovascular safety trials of diabetes drugs, with a\nfocus on upper bound estimates relative to 1.3, based on regulatory guidelines. A total of 1000 simulations were run\nunder each parameter combination for a hypothetical trial of 10,000 total subjects randomly assigned to active treatment\nor control at 1:1 ratio. Noncompliance was considered in the form of treatment discontinuation and cross-over at specified\nproportions, with an assumed true hazard ratio of 0.9, 1, and 1.3, respectively. Various levels of risk associated with\nbeing a non-complier (independent of treatment status) were evaluated. Hazard ratio and upper bound estimates from\ncausal survival analysis and intent-to-treat were obtained from each simulation and summarized under each parameter\nsetting.\nResults: Causal analysis estimated the true hazard ratio with little bias in almost all settings examined. Intent-to-treat\nwas unbiased only when the true hazard ratio = 1; otherwise it underestimated both benefit and harm. When upper\nbound estimates from intent-to-treat were 1.3, corresponding estimates from causal analysis were also 1.3 in almost\n100% of the simulations, regardless of the true hazard ratio. When upper bound estimates from intent-to-treat were\n\\1.3 and the true hazard ratio = 1, corresponding upper bound estimates from causal analysis were 1.3 in up to 66%\nof the simulations under some settings.\nLimitations: Simulations cannot cover all scenarios for noncompliance in real randomized trials.\nConclusion: Causal survival analysis was superior to intent-to-treat in estimating the true hazard ratio with respect to\nbias in the presence of noncompliance. However, its large variance should be considered for safety upper bound exclusion\nespecially when the true hazard ratio = 1. Our simulations provided a broad reference for practical considerations\nof biasââ?¬â??variance trade-off in dealing with noncompliance in cardiovascular safety trials of diabetes drugs. Further\nresearch is warranted for the development and application of causal inference methods in the evaluation of safety upper\nbounds.
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