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Umbrella trials are an innovative trial design where different treatments are matched with subtypes of a disease, with the matching typically based on a set of biomarkers. Consequently, when patients can be positive for more than one biomarker, they may be eligible for multiple treatment arms. In practice, different approaches could be applied to allocate patients who are positive for multiple biomarkers to treatments. However, to date there has been little exploration of how these approaches compare statistically. We conduct a simulation study to compare five approaches to handling treatment allocation in the presence of multiple biomarkers - equal randomisation; randomisation with fixed probability of allocation to control; Bayesian adaptive randomisation (BAR); constrained randomisation; and hierarchy of biomarkers. We evaluate these approaches under different scenarios in the context of a hypothetical phase II biomarker-guided umbrella trial. We define the pairings representing the pre-trial expectations on efficacy as linked pairs, and the other biomarker-treatment pairings as unlinked. The hierarchy and BAR approaches have the highest power to detect a treatment-biomarker linked interaction. However, the hierarchy procedure performs poorly if the pre-specified treatment-biomarker pairings are incorrect. The BAR method allocates a higher proportion of patients who are positive for multiple biomarkers to promising treatments when an unlinked interaction is present. In most scenarios, the constrained randomisation approach best balances allocation to all treatment arms. Pre-specification of an approach to deal with treatment allocation in the presence of multiple biomarkers is important, especially when overlapping subgroups are likely.

BACKGROUND: The symptom of heavy menstrual bleeding (HMB) diminishes quality-of-life for many mid-age women and imposes substantial societal burden. We investigated our hypothesis that HMB reflects impaired endometrial vasoconstriction due to endometrial glucocorticoid deficiency. Does reversing this deficiency, by short-term luteal-phase treatment with exogenous glucocorticoid (dexamethasone), ameliorate HMB? METHODS: In our Bayesian response-adaptive parallel-group placebo-controlled randomised trial, five pre-planned interim analyses used primary outcome data to adjust randomisation probabilities to favour doses providing most dose-response information. Participants with HMB, recruited from Lothian (Scotland) NHS clinics and via community invitations/advertisements, were aged over 18 years; reported regular 21-42 day menstrual cycles; and had measured menstrual blood loss (MBL) averaging ≥ 50 mL over two screening periods. Identically encapsulated placebo, or one of six Dexamethasone doses (0·2 mg, 0·4 mg, 0·5 mg, 0·6 mg, 0·75 mg, 0·9 mg), were taken orally twice-daily over five days in the mid-luteal phase of three menstrual cycles. Participants, investigators, and those measuring outcomes were masked to group assignment. Primary outcome, change in average MBL from screening to 'treatment', was analysed by allocated treatment, for all with data. TRIAL REGISTRATION: ClinicalTrials.gov NCT01769820; EudractCT 2012-003,405-98 FINDINGS: Recruitment lasted 29/01/2014 to 25/09/2017; 176 were screened, 107 randomised and 97 provided primary outcome data (n = 24,5,9,21,8,14,16 in the seven arms, placebo to 1·8 mg total daily active dose). In Bayesian normal dynamic linear modelling, 1·8 mg dexamethasone daily showed a 25 mL greater reduction in MBL from screening, than placebo (95% credible interval 1 to 49 mL), and probability 0·98 of benefit over placebo. Adverse events were reported by 75% (58/77) receiving dexamethasone, 58% (15/26) taking placebo. Three serious adverse events occurred, two during screening, one in a placebo participant. No woman withdrew due to adverse effects. INTERPRETATION: Our adaptive trial in HMB showed that dexamethasone 1·8 mg daily reduced menstrual blood loss. The role of dexamethasone in HMB management deserves further investigation. FUNDING: UK MRC DCS/DPFS grant MR/J003611/1.

BACKGROUND: Response to treatments is highly heterogeneous in cancer. Increased availability of biomarkers and targeted treatments has led to the need for trial designs that efficiently test new treatments in biomarker-stratified patient subgroups. METHODS: We propose a novel Bayesian adaptive randomisation (BAR) design for use in multi-arm phase II trials where biomarkers exist that are potentially predictive of a linked treatment's effect. The design is motivated in part by two phase II trials that are currently in development. The design starts by randomising patients to the control treatment or to experimental treatments that the biomarker profile suggests should be active. At interim analyses, data from treated patients are used to update the allocation probabilities. If the linked treatments are effective, the allocation remains high; if ineffective, the allocation changes over the course of the trial to unlinked treatments that are more effective. RESULTS: Our proposed design has high power to detect treatment effects if the pairings of treatment with biomarker are correct, but also performs well when alternative pairings are true. The design is consistently more powerful than parallel-groups stratified trials. CONCLUSIONS: This BAR design is a powerful approach to use when there are pairings of biomarkers with treatments available for testing simultaneously.