Understanding the cluster randomised crossover design::a graphical illustraton of the components of variation and a sample size tutorial

Abstract Background In a cluster randomised crossover (CRXO) design, a sequence of interventions is assigned to a group, or ‘cluster’ of individuals. Each cluster receives each intervention in a separate period of time, forming ‘cluster-periods’. Sample size calculations for CRXO trials need to account for both the cluster randomisation and crossover aspects of the design. Formulae are available for the two-period, two-intervention, cross-sectional CRXO design, however implementation of these formulae is known to be suboptimal. The aims of this tutorial are to illustrate the intuition behind the design; and provide guidance on performing sample size calculations. Methods Graphical illustrations are used to describe the effect of the cluster randomisation and crossover aspects of the design on the correlation between individual responses in a CRXO trial. Sample size calculations for binary and continuous outcomes are illustrated using parameters estimated from the Australia and New Zealand Intensive Care Society – Adult Patient Database (ANZICS-APD) for patient mortality and length(s) of stay (LOS). Results The similarity between individual responses in a CRXO trial can be understood in terms of three components of variation: variation in cluster mean response; variation in the cluster-period mean response; and variation between individual responses within a cluster-period; or equivalently in terms of the correlation between individual responses in the same cluster-period (within-cluster within-period correlation, WPC), and between individual responses in the same cluster, but in different periods (within-cluster between-period correlation, BPC). The BPC lies between zero and the WPC. When the WPC and BPC are equal the precision gained by crossover aspect of the CRXO design equals the precision lost by cluster randomisation. When the BPC is zero there is no advantage in a CRXO over a parallel-group cluster randomised trial. Sample size calculations illustrate that small changes in the specification of the WPC or BPC can increase the required number of clusters. Conclusions By illustrating how the parameters required for sample size calculations arise from the CRXO design and by providing guidance on both how to choose values for the parameters and perform the sample size calculations, the implementation of the sample size formulae for CRXO trials may improve

Shifting identities: social and cultural factors that shape decision making around sustaining breastfeeding

In the UK, women’s beliefs, attitudes and behaviours around breastfeeding are shaped by myriad influences and by changing social and structural factors and cultural mores. Whilst public health discourse equates breastfeeding with ‘good mothering’ and health professionals emphasise ‘breast as best’, these normative values compete with other standards or criteria of ‘good mothering’ held by others within women’s social networks that exert influence on them. Moreover, cultural and structural factors affecting the pattern of women’s labour market participation, specifically public policy emphasis on return to paid work aligned with policies directed at reconciling work and family act as constraints on sustaining optimal breastfeeding i.e. exclusive breastfeeding for six months as advised by the World Health Organisation (2003). For women in this study, initiating and sustaining breastfeeding was subject of a complex process that contributed to multiple valued outcomes: nurturing thriving and healthy babies, experiencing themselves as ‘competent’ mothers, successfully managing shifting identities and negotiating competing pressures in the real life context of their daily lives and relationships with ‘significant others’. Even as women struggled to present and see themselves as ‘good mothers’, they were active agents and not just acted upon. They sought to reconcile the value they placed on breastfeeding with seeing themselves and being seen by others as ‘good mothers’. Thus, they sought out situations where breastfeeding was highly valued (such as support groups), and developed strategies to counter or avoid threats to their sense of themselves as nurturing and competent mothers that was related to, but not synonymous with sustaining breastfeeding. Midwives and health visitors in this study encouraged women to breastfeed but not in the way that this is generally portrayed in much of the current literature. Analysis of observed interactions between women who had chosen to breastfeed and midwives and health visitors suggests more of a negotiated encounter in which these health professional considered the whole situation of the woman and her struggle to be a ‘good mother’

Enrollment challenges in multicenter, international studies: the example of the GAS trial

Introduction: Randomized trials are important for generating high‐quality evidence, but are perceived as difficult to perform in the pediatric population. Thus far there has been poor characterization of the barriers to conducting trials involving children, and the variation in these barriers between countries remains undescribed. The General Anesthesia compared to Spinal anesthesia (GAS) trial, conducted in seven countries between 2007 and 2013, provides an opportunity to explore these issues. Methods: We undertook a descriptive analysis to evaluate the reasons for variation in enrollment between countries in the GAS trial, looking specifically at the number of potential subjects screened, and the subsequent application of four exclusion criteria that were applied in a hierarchical order. Results: A total of 4023 patients were screened by 28 centers in seven countries. Australia and the USA screened the most subjects, accounting for 84% of all potential trial participants. The percentage of subjects eliminated from the screened pool by each exclusion criterion varied between countries. Exclusion due to a predefined condition (H1) eliminated only 5% of potential subjects in Italy and the UK, but 37% in Canada. Exclusions due to a contraindication or a physician's refusal most impacted enrollment in Australia and the USA. The patient being “too large for spinal anesthesia” was the most commonly cited by anesthetists who refused to enroll a patient (64% of anesthetist refusals). The majority of surgeon refusals came from the USA, where surgeons preferred the patient to receive a general anesthetic. The percentage of approached parents refusing to consent ranged from a low of 3% in Italy to a high of 70% in the USA and Netherlands. The most frequently cited reason for parent refusal in all countries was a preference for general anesthesia (median: 43%, range: 32%‐67%). However, a sizeable proportion of parents in all countries had a contrasting preference for spinal anesthesia (median: 25%, range: 13%‐31%), and 23% of U.S. parents expressed concern about randomization. Conclusion: The GAS trial highlights enrollment challenges that can occur when conducting multicenter, international, pediatric studies. Investigators planning future trials should be aware of potential differences in screening processes across countries, and that exclusions by anesthetists and surgeons may vary in reason, in frequency, and by country. Furthermore, investigators should be aware that the U.S. centers encountered particularly high surgeon and parental refusal rates and that U.S. parents were uniquely concerned about randomization. Planning trials that address these difficulties should increase the likelihood of successfully recruiting subjects in pediatric trials