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Phase II/III clinical trials are efficient two-stage designs that test multiple experimental treatments. In stage 1, patients are allocated to the control and all experimental treatments, with the data collected from them used to select experimental treatments to continue to stage 2. Patients recruited in stage 2 are allocated to the selected treatments and the control. Combined data of stage 1 and stage 2 are used for a confirmatory phase III analysis. Appropriate analysis needs to adjust for selection bias of the stage 1 data. Point estimators exist for normally distributed outcome data. Extending these estimators to time to event data is not straightforward because treatment selection is based on correlated treatment effects and stage 1 patients who do not get events in stage 1 are followed-up in stage 2. We have derived an approximately uniformly minimum variance conditional unbiased estimator (UMVCUE) and compared its biases and mean squared errors to existing bias adjusted estimators. In simulations, one existing bias adjusted estimator has similar properties as the practically unbiased UMVCUE while the others can have noticeable biases but they are less variable than the UMVCUE. For confirmatory phase II/III clinical trials where unbiased estimators are desired, we recommend the UMVCUE or the existing estimator with which it has similar properties.

Advances in medical technology have led to a better understanding of heterogeneity of diseases and patients, and to the development of targeted medicines. This development is beneficial to society but can come at an increased cost to pharmaceutical manufacturers due to the costs associated with developing and manufacturing a diagnostic test. For such medicines, the conventional pricing structure, where a therapy is approved if it is deemed cost-effective, may not appropriately incentivise targeted drug development. We model the decision-making processes for both the healthcare provider and the pharmaceutical manufacturer, capturing their main priorities, and populate it with information from a recent appraisal by the National Institute of Health and Care Excellence. Healthcare providers prefer a stratified drug to be developed for a subgroup of the population when the drug is on average effective in the subgroup but with a detrimental effect in the complement. Whilst pharmaceutical manufacturers’ preferences are similar, regions of disagreement exist. We show how preferences can be aligned by either penalising the development of a non-stratified drug or rewarding the development of a stratified drug. The cost and position of alignment depends on the true value of health to the healthcare provider, among other parameters.

Aims There are reports of a marked increase in perioperative mortality in patients admitted to hospital with a fractured hip during the COVID-19 pandemic in the UK, USA, Spain, and Italy. Our study aims to describe the risk of mortality among patients with a fractured neck of femur in England during the early stages of the COVID-19 pandemic. Methods We completed a multicentre cohort study across ten hospitals in England. Data were collected from 1 March 2020 to 6 April 2020, during which period the World Health Organization (WHO) declared COVID-19 to be a pandemic. Patients ≥ 60 years of age admitted with hip fracture and a minimum follow-up of 30 days were included for analysis. Primary outcome of interest was mortality at 30 days post-surgery or postadmission in nonoperative patients. Secondary outcomes included length of hospital stay and discharge destination. Results In total, 404 patients were included for final analysis with a COVID-19 diagnosis being made in 114 (28.2%) patients. Overall, 30-day mortality stood at 14.4% (n = 58). The COVID-19 cohort experienced a mortality rate of 32.5% (37/114) compared to 7.2% (21/290) in the non-COVID cohort (p < 0.001). In adjusted analysis, 30-day mortality was greatest in patients who were confirmed to have COVID-19 (odds ratio (OR) 5.64, 95% confidence interval (CI) 2.95 to 10.80; p < 0.001) with an adjusted excess risk of 20%, male sex (OR 2.69, 95% CI 1.37 to 5.29; p = 0.004) and in patients with ≥ two comorbidities (OR 4.68, CI 1.5 to 14.61; p = 0.008). Length of stay was also extended in the COVID-19 cohort, on average spending 17.6 days as an inpatient versus 12.04 days in the non-COVID-19 group (p < 0.001). Conclusion This study demonstrates that patients who sustain a neck of femur fracture in combination with COVID-19 diagnosis have a significantly higher risk of mortality than would be normally expected. Cite this article: Bone Joint Open 2020;1-11:697–705.

Recently, several study designs incorporating treatment effect assessment in biomarker-based subpopulations have been proposed. Most statistical methodologies for such designs focus on the control of type I error rate and power. In this paper, we have developed point estimators for clinical trials that use the two-stage adaptive enrichment threshold design. The design consists of two stages, where in stage 1, patients are recruited in the full population. Stage 1 outcome data are then used to perform interim analysis to decide whether the trial continues to stage 2 with the full population or a subpopulation. The subpopulation is defined based on one of the candidate threshold values of a numerical predictive biomarker. To estimate treatment effect in the selected subpopulation, we have derived unbiased estimators, shrinkage estimators, and estimators that estimate bias and subtract it from the naive estimate. We have recommended one of the unbiased estimators. However, since none of the estimators dominated in all simulation scenarios based on both bias and mean squared error, an alternative strategy would be to use a hybrid estimator where the estimator used depends on the subpopulation selected. This would require a simulation study of plausible scenarios before the trial.

Background\ud \ud Young people (age 16-25 years) with long term health conditions tend to disengage from health services resulting in poor health outcomes. They are prolific users of digital communications. Innovative UK National Health Service (NHS) clinicians use digital communication with these young people. The NHS plans to use digital communication with patients more widely.\ud \ud Objectives\ud \ud To explore how health care engagement can be improved using digital clinical communication; understand effects, impacts, costs and necessary safeguards; provide critical analysis of its use, monitoring and evaluation.\ud \ud Design\ud \ud Observational mixed methods case studies; systematic scoping literature reviews; assessment of patient reported outcome measures; public and patient involvement (PPI); consensus development through focus groups.\ud \ud Setting\ud \ud Twenty NHS specialist clinical teams from across England and Wales, providing care for 13 different long term physical or mental health conditions. \ud \ud Participants\ud \ud 165 young people aged 16-25 years living with a long term health condition; 13 parents; 173 clinical team members; 16 Information Governance Specialists.\ud \ud Interventions\ud \ud Clinical teams and young people variously used: mobile phone calls, text messages, email, Voice over Internet Protocol. \ud \ud Main outcome measures\ud \ud Empirical work: \ud \ud Thematic and ethical analysis of qualitative data; annual direct costs; Did Not Attend, Accident and Emergency Attendance and Hospital Admissions rates plus clinic specific clinical outcomes. Scoping reviews: patient, health professional and service delivery outcomes and technical problems. Patient reported outcome measures: scale validity, relevance and credibility.\ud \ud Data sources\ud \ud Observation, interview, structured survey, routinely collected data, focus groups, peer reviewed publications.\ud \ud Results\ud \ud Digital communication enables access for young people to the right clinician when it makes a difference for managing their health condition. This is valued as additional to traditional clinic appointments. This access challenges the nature and boundaries of therapeutic relationships but can improve them, increase patient empowerment and enhance activation. Risks include increased dependence on clinicians, inadvertent disclosure of confidential information and communication failures, but clinicians and young people mitigate these risks. Workload increases, and staff time is the main cost. Clinical teams had not evaluated impact of their intervention, and analysis of routinely collected data did not identify any impact. There are no currently used generic outcome measures but the Patient Activation Measure and the Physician Humanistic Behaviour Questionnaire are promising. Scoping reviews suggest digital clinical communication is acceptable to young people but with no clear evidence of benefit except for mental health.\ud \ud Limitations \ud \ud Qualitative data was mostly from clinician enthusiasts. No interviews were achieved with young people who do not attend clinics. Clinicians struggled to estimate workload. Only eight full sets of routine data were available.\ud \ud Conclusions \ud \ud Timely digital clinical communication is perceived as making a difference to health care and health outcome for young people with long term conditions but this is not supported by evidence that measures health outcome. Such communication is challenging and costly to provide but valued by young people.\ud \ud

Background \ud The ‘classical’ technique of rapid sequence induction (RSI) was described in 1970. With the introduction of new drugs and equipment in recent years, a wide variation in this technique has been used. The role of cricoid pressure is controversial due to the lack of scientific evidence. Moreover, gentle mask ventilation has been recommended in situations such as obesity and critically ill patients, to prevent hypoxaemia during the apnoeic period. In identifying multiple techniques, we conducted a national postal survey to establish the current practice of RSI in the UK.\ud \ud Methods \ud A survey consisting of 17 questions was created and posted to 255 National Health Service (NHS) hospitals in the UK. We included two copies of the questionnaire in each envelope; one to be completed by the airway lead and the other by a trainee in the same department. The difference in responses from consultants and trainees were assessed using the chi-squared test and the Fisher's exact test.\ud \ud Results \ud In total we received 272 responses (response rate 53%) of which 266 (58% from consultants and 42% from trainees) were analysed. A majority of the respondents (67.7%) pre-oxygenated by monitoring end-tidal oxygen concentration and 76.3% of the respondents use 20–25° head up tilt for all RSIs. Propofol is the most commonly used induction agent (63.9% of all respondents). Opioid has been used by 80.5% of respondents and only 17.7% of respondents use suxamethonium for all cases and others choose rocuronium or suxamethonium based on clinical situation. Although 92% of anaesthetists use cricoid pressure, 83% of them never objectively measure the force used. During apnoeic period 17% of the respondents use gentle mask ventilation.\ud \ud Conclusions \ud Our survey demonstrated a persistent variation in the practice of RSI amongst the anaesthetists in the UK. The ‘classical’ technique of RSI is now seldom used. Increasing awareness of optimising oxygenation during apnoeic period and wider use of newer drugs such as propofol, rocuronium, and opioids has driven the current practice of RSI.

Objective: \ud To evaluate the effect of implementing real-time audiovisual feedback with and without postevent debriefing on survival and quality of cardiopulmonary resuscitation quality at in-hospital cardiac arrest.\ud \ud Design: \ud A two-phase, multicentre prospective cohort study.\ud \ud Setting: \ud Three UK hospitals, all part of one National Health Service Acute Trust.\ud \ud Patients: \ud One thousand three hundred and ninety-five adult patients who sustained an in-hospital cardiac arrest at the study hospitals and were treated by hospital emergency teams between November 2009 and May 2013.\ud \ud Interventions: \ud During phase 1, quality of cardiopulmonary resuscitation and patient outcomes were measured with no intervention implemented. During phase 2, staff at hospital 1 received real-time audiovisual feedback, whereas staff at hospital 2 received real-time audiovisual feedback supplemented by postevent debriefing. No intervention was implemented at hospital 3 during phase 2.\ud \ud Measurements and Main Results: \ud The primary outcome was return of spontaneous circulation. Secondary endpoints included other patient-focused outcomes, such as survival to hospital discharge, and process-focused outcomes, such as chest compression depth. Random-effect logistic and linear regression models, adjusted for baseline patient characteristics, were used to analyze the effect of the interventions on study outcomes. In comparison with no intervention, neither real-time audiovisual feedback (adjusted odds ratio, 0.62; 95% CI, 0.31–1.22; p = 0.17) nor real-time audiovisual feedback supplemented by postevent debriefing (adjusted odds ratio, 0.65; 95% CI, 0.35–1.21; p = 0.17) was associated with a statistically significant improvement in return of spontaneous circulation or any process-focused outcome. Despite this, there was evidence of a system-wide improvement in phase 2, leading to improvements in return of spontaneous circulation (adjusted odds ratio, 1.87; 95% CI, 1.06–3.30; p = 0.03) and process-focused outcomes.\ud \ud Conclusions: \ud Implementation of real-time audiovisual feedback with or without postevent debriefing did not lead to a measured improvement in patient or process-focused outcomes at individual hospital sites. However, there was an unexplained system-wide improvement in return of spontaneous circulation and process-focused outcomes during the second phase of the study.

During the development of new therapies, it is not uncommon to test whether a new treatment works better than the existing treatment for all patients who suffer from a condition (full population) or for a subset of the full population (subpopulation). One approach that may be used for this objective is to have two separate trials, where in the first trial, data are collected to determine if the new treatment benefits the full population or the subpopulation. The second trial is a confirmatory trial to test the new treatment in the population selected in the first trial. In this paper, we consider the more efficient two-stage adaptive seamless designs (ASDs), where in stage 1, data are collected to select the population to test in stage 2. In stage 2, additional data are collected to perform confirmatory analysis for the selected population. Unlike the approach that uses two separate trials, for ASDs, stage 1 data are also used in the confirmatory analysis. Although ASDs are efficient, using stage 1 data both for selection and confirmatory analysis introduces selection bias and consequently statistical challenges in making inference. We will focus on point estimation for such trials. In this paper, we describe the extent of bias for estimators that ignore multiple hypotheses and selecting the population that is most likely to give positive trial results based on observed stage 1 data. We then derive conditionally unbiased estimators and examine their mean squared errors for different scenarios. Copyright © 2015 John Wiley & Sons, Ltd.\ud