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Table 1 Summary. The table identifies some problems that arise in randomised trials with long-term follow-up, whether the problem affects acute, intermittent or sustained interventions; the estimand of primary interest; the primary methods used to obtain those estimands; and whether the analysis is applicable to cross-sectional analyses or longitudinal analyses

From: Analysis of randomised trials with long-term follow-up

Problem Does the problem affect acute, intermittent or sustained interventions? What causal effects can be estimated? Methods Methods suitable for cross-sectional or longitudinal analyses?
Non-compliance most commonly affects sustained or intermittent interventions, but can also affect acute interventions the complier average causal effect (i.e., the average effect in people who would adhere to whichever treatment they were assigned to) instrumental variable regression, propensity score methods when there is a summary measure of compliance across the entire treatment period, both methods can be applied to cross-sectional or longitudinal analyses
Treatment switching affects intermittent or sustained interventions the average effect in people who do not switch treatments (or the average effect of some other defined treatment pattern) marginal structural models, g-estimation can be applied to cross-sectional analyses, but are most valuable when analyses are longitudinal
Loss to follow-up this is not a treatment-level issue: loss-to-follow-up can occur with any type of intervention the average treatment effect mixed longitudinal models, multiple imputation mixed longitudinal models are applicable to longitudinal analyses, multiple imputation can be applied to cross-sectional or longitudinal analyses
Truncation by death and other events this is not a treatment-level issue: truncation can occur with any type of intervention the survivor average causal effect (i.e., the average effect in people who would have survived no matter which treatment they were allocated to) sensitivity methods, propensity score-based methods methods are most well-developed for cross-sectional analyses, extensions of propensity score-based methods are available for longitudinal analyses