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The timings of visits in observational longitudinal data may depend on the study outcome, and this can result in bias if ignored. Assessing the extent of visit irregularity is important because it can help determine whether visits can be treated as repeated measures or as irregular data. We propose plotting the mean proportions of individuals with 0 visits per bin against the mean proportions of individuals with >1 visit per bin as bin width is varied and using the area under the curve (AUC) to assess the extent of irregularity. The AUC is a single score which can be used to quantify the extent of irregularity and assess how closely visits resemble repeated measures. Simulation results confirm that the AUC increases with increasing irregularity while being invariant to sample size and the number of scheduled measurement occasions. A demonstration of the AUC was performed on the TARGet Kids! study which enrolls healthy children aged 0–5 years with the aim of investigating the relationship between early life exposures and later health problems. The quality of statistical analyses can be improved by using the AUC as a guide to select the appropriate analytic outcome approach and minimize the potential for biased results.

Background Follow-up frequency is an important design parameter in longitudinal studies. We quantified the impact of reducing follow-up frequency on the precision of estimated regression parameters, and investigated the impact of incorrectly assuming an exchangeable correlation structure on estimates of the loss of precision resulting from reduced follow-up.Methods We estimated the loss in precision on deleting every second observation from three longitudinal cohorts: patients with Childhood Systemic Lupus Erythematosus (cSLE), the Canadian Haemophilia Prophylaxis Study (CHPS), and patients with Juvenile Dermatomyositis (JDM). We compared these results with those from a theoretical formula assuming an exchangeable correlation structure.Results The increase in sample size needed to compensate for halving follow-up frequency was 9%, 6% and 28% for the cSLE, CHPS and JDM cohorts respectively. Under the assumption of an exchangeable correlation, the estimated increases in sample size were 22%, 11% and 10% respectively.Conclusions Reducing follow-up frequency can result in minimal loss of information, as seen in the CHPS cohort. While using a theoretical formula based on an exchangeable correlation structure is convenient, it can be inaccurate when the true correlation structure is not exchangeable.

Clinic-based cohort studies enroll patients on first being admitted to the clinic, and follow them as part of usual care, with interest being in the marginal mean of the outcome process. As the required frequency of follow-up varies among patients, these studies often feature irregular visit times, with no two patients sharing a visit time. Inverse-intensity weighting has been developed to handle this, however it requires that the visit process be conditionally independent of the outcome given the observed history. When patients schedule visits in response to changes in their health (for example a disease flare), the conditional independence assumption is no longer plausible, leading to biased results. We suggest additional information that can be collected to ensure that conditional independence holds, and examine how this might be used in the analysis. This allows clinic-based cohort studies to be used to determine longitudinal outcomes without incurring bias due to irregular follow-up.

When cost data are collected in a clinical study, interest centers on the between-treatment difference in mean cost. When censoring is present, the resulting loss of information can be limited by collecting cost data for several pre-specified time intervals, leading to censored longitudinal cost data. Most models for marginal costs stratify by time interval. However, in few other areas of biostatistics would we stratify by default. We argue that there are benefits to considering more general models: for example, in some settings, pooling regression coefficients across intervals can improve the precision of the estimated between-treatment difference in mean cost. Previous work has used inverse-probability-weighted GEEs coupled with an independent working variance to estimate parameters from these more general models. We show that the greatest precision benefits of non-stratified models are achieved by using more sophisticated working variance matrices.