Analysis of Randomised Trials Including Multiple Births When Birth Size Is Informative

BACKGROUND: Informative birth size occurs when the average outcome depends on the number of infants per birth. Although analysis methods have been proposed for handling informative birth size, their performance is not well understood. Our aim was to evaluate the performance of these methods and to provide recommendations for their application in randomised trials including infants from single and multiple births. METHODS: Three generalised estimating equation (GEE) approaches were considered for estimating the effect of treatment on a continuous or binary outcome: cluster weighted GEEs, which produce treatment effects with a mother-level interpretation when birth size is informative; standard GEEs with an independence working correlation structure, which produce treatment effects with an infant-level interpretation when birth size is informative; and standard GEEs with an exchangeable working correlation structure, which do not account for informative birth size. The methods were compared through simulation and analysis of an example dataset. RESULTS: Treatment effect estimates were affected by informative birth size in the simulation study when the effect of treatment in singletons differed from that in multiples (i.e. in the presence of a treatment group by multiple birth interaction). The strength of evidence supporting the effectiveness of treatment varied between methods in the example dataset. CONCLUSIONS: Informative birth size is always a possibility in randomised trials including infants from both single and multiple births, and analysis methods should be pre-specified with this in mind. We recommend estimating treatment effects using standard GEEs with an independence working correlation structure to give an infant-level interpretation

A re-randomisation design for clinical trials

Background: Recruitment to clinical trials is often problematic, with many trials failing to recruit to their target sample size. As a result, patient care may be based on suboptimal evidence from underpowered trials or non-randomised studies. Methods: For many conditions patients will require treatment on several occasions, for example, to treat symptoms of an underlying chronic condition (such as migraines, where treatment is required each time a new episode occurs), or until they achieve treatment success (such as fertility, where patients undergo treatment on multiple occasions until they become pregnant). We describe a re-randomisation design for these scenarios, which allows each patient to be independently randomised on multiple occasions. We discuss the circumstances in which this design can be used. Results: The re-randomisation design will give asymptotically unbiased estimates of treatment effect and correct type I error rates under the following conditions: (a) patients are only re-randomised after the follow-up period from their previous randomisation is complete; (b) randomisations for the same patient are performed independently; and (c) the treatment effect is constant across all randomisations. Provided the analysis accounts for correlation between observations from the same patient, this design will typically have higher power than a parallel group trial with an equivalent number of observations. Conclusions: If used appropriately, the re-randomisation design can increase the recruitment rate for clinical trials while still providing an unbiased estimate of treatment effect and correct type I error rates. In many situations, it can increase the power compared to a parallel group design with an equivalent number of observations

Prevalence and reporting of recruitment, randomisation and treatment errors in clinical trials: a systematic review

Background/Aims: In clinical trials, it is not unusual for errors to occur during the process of recruiting, randomising and providing treatment to participants. For example, an ineligible participant may inadvertently be randomised, a participant may be randomised in the incorrect stratum, a participant may be randomised multiple times when only a single randomisation is permitted or the incorrect treatment may inadvertently be issued to a participant at randomisation. Such errors have the potential to introduce bias into treatment effect estimates and affect the validity of the trial, yet there is little motivation for researchers to report these errors and it is unclear how often they occur. The aim of this study is to assess the prevalence of recruitment, randomisation and treatment errors and review current approaches for reporting these errors in trials published in leading medical journals. Methods:We conducted a systematic review of individually randomised, phase III, randomised controlled trials published in New England Journal of Medicine, Lancet, Journal of the American Medical Association, Annals of Internal Medicine and British Medical Journal from January to March 2015. The number and type of recruitment, randomisation and treatment errors that were reported and how they were handled were recorded. The corresponding authors were contacted for a random sample of trials included in the review and asked to provide details on unreported errors that occurred during their trial. Results: We identified 241 potentially eligible articles, of which 82 met the inclusion criteria and were included in the review. These trials involved a median of 24 centres and 650 participants, and 87% involved two treatment arms. Recruitment, randomisation or treatment errors were reported in 32 in 82 trials (39%) that had a median of eight errors. The most commonly reported error was ineligible participants inadvertently being randomised. No mention of recruitment, randomisation or treatment errors was found in the remaining 50 of 82 trials (61%). Based on responses from 9 of the 15 corresponding authors who were contacted regarding recruitment, randomisation and treatment errors, between 1% and 100% of the errors that occurred in their trials were reported in the trial publications. Conclusions: Recruitment, randomisation and treatment errors are common in individually randomised, phase III trials published in leading medical journals, but reporting practices are inadequate and reporting standards are needed. We recommend researchers report all such errors that occurred during the trial and describe how they were handled in trial publications to improve transparency in reporting of clinical trials

The effectiveness of ω-3 polyunsaturated fatty acid interventions during pregnancy on obesity measures in the offspring: an up-to-date systematic review and meta-analysis.

BACKGROUND: The potential role of ω-3 long chain polyunsaturated fatty acid (LCPUFA) supplementation during pregnancy on subsequent risk of obesity outcomes in the offspring is not clear and there is a need to synthesise this evidence. OBJECTIVE: A systematic review and meta-analysis of randomised controlled trials (RCTs), including the most recent studies, was conducted to assess the effectiveness of ω-3 LCPUFA interventions during pregnancy on obesity measures, e.g. BMI, body weight, fat mass in offspring. METHODS: Included RCTs had a minimum of 1-month follow-up post-partum. The search included CENTRAL, MEDLINE, SCOPUS, WHO's International Clinical Trials Reg., E-theses and Web of Science databases. Study quality was evaluated using the Cochrane Collaboration's risk of bias tool. RESULTS: Eleven RCTs, from ten unique trials, (3644 children) examined the effectiveness of ω-3 LCPUFA maternal supplementation during pregnancy on the development of obesity outcomes in offspring. There were heterogeneities between the trials in terms of their sample, type and duration of intervention and follow-up. Pooled estimates did not show an association between prenatal intake of fatty acids and obesity measures in offspring. CONCLUSION: These results indicate that maternal supplementation with ω-3 LCPUFA during pregnancy does not have a beneficial effect on obesity risk. Due to the high heterogeneity between studies along with small sample sizes and high rates of attrition, the effects of ω-3 LCPUFA supplementation during pregnancy for prevention of childhood obesity in the long-term remains unclear. Large high-quality RCTs are needed that are designed specifically to examine the effect of prenatal intake of fatty acids for prevention of childhood obesity. There is also a need to determine specific sub-groups in the population that might get a greater benefit and whether different ω-3 LCPUFA, i.e. eicosapentaenoic (EPA) vs. docosahexanoic (DHA) acids might potentially have different effects