Bayesian adaptive designs for multi-arm trials: an orthopaedic case study

This is the final version. Available on open access from BMC via the DOI in this recordAvailability of data and materials: The data used in this study were generated as part of the CAST study. Requests to share individual, de-identified participant data, aggregated data, data dictionaries, and other study documents from this study should be sent to the CAST Chief Investigator (SEL). Data sharing requests will be assessed on their individual merits. The FACTS files used to simulate the Bayesian adaptive designs are publicly available at https://github.com/egryan90/Bayesian-adaptive-designs-for-CAST-study-Ryan-et-al.-2019Background: Bayesian adaptive designs can be more efficient than traditional methods for multi‐arm randomised controlled trials. The aim of this work was to demonstrate how Bayesian adaptive designs can be constructed for multi‐arm phase III clinical trials and assess potential benefits that these designs offer. Methods: We constructed several alternative Bayesian adaptive designs for the Collaborative Ankle Support Trial (CAST), which was a randomised controlled trial that compared four treatments for severe ankle sprain. These incorporated response adaptive randomisation, arm dropping, and early stopping for efficacy or futility. We studied the Bayesian designs’ operating characteristics via simulation. We then virtually re‐executed the trial by implementing the Bayesian adaptive designs using patient data sampled from the CAST study to demonstrate the practical applicability of the designs. Results: We constructed five Bayesian adaptive designs, each of which had high power and recruited fewer patients on average than the original design’s target sample size. The virtual executions 2 showed that most of the Bayesian designs would have led to trials that declared superiority of one of the interventions over the control. Bayesian adaptive designs with RAR or arm dropping were more likely to allocate patients to better performing arms at each interim analysis. Similar estimates and conclusions were obtained from the Bayesian adaptive designs as from the original trial. Conclusions: Using CAST as an example, this case study showed how Bayesian adaptive designs can be constructed for phase III multi‐arm trials using clinically relevant decision criteria. These designs demonstrated that they can potentially generate earlier results and allocate more patients to betterperforming arms. We recommend the wider use of Bayesian adaptive approaches in phase III clinical trials.Medical Research Council (MRC)National Co‐ordinating Centre for Health Technology AssessmentNational Institute of Health Researc

Bayesian adaptive designs for multi-arm trials: an orthopaedic case study

This is the final version. Available on open access from BMC via the DOI in this recordAvailability of data and materials: The data used in this study were generated as part of the CAST study. Requests to share individual, de-identified participant data, aggregated data, data dictionaries, and other study documents from this study should be sent to the CAST Chief Investigator (SEL). Data sharing requests will be assessed on their individual merits. The FACTS files used to simulate the Bayesian adaptive designs are publicly available at https://github.com/egryan90/Bayesian-adaptive-designs-for-CAST-study-Ryan-et-al.-2019Background: Bayesian adaptive designs can be more efficient than traditional methods for multi‐arm randomised controlled trials. The aim of this work was to demonstrate how Bayesian adaptive designs can be constructed for multi‐arm phase III clinical trials and assess potential benefits that these designs offer. Methods: We constructed several alternative Bayesian adaptive designs for the Collaborative Ankle Support Trial (CAST), which was a randomised controlled trial that compared four treatments for severe ankle sprain. These incorporated response adaptive randomisation, arm dropping, and early stopping for efficacy or futility. We studied the Bayesian designs’ operating characteristics via simulation. We then virtually re‐executed the trial by implementing the Bayesian adaptive designs using patient data sampled from the CAST study to demonstrate the practical applicability of the designs. Results: We constructed five Bayesian adaptive designs, each of which had high power and recruited fewer patients on average than the original design’s target sample size. The virtual executions 2 showed that most of the Bayesian designs would have led to trials that declared superiority of one of the interventions over the control. Bayesian adaptive designs with RAR or arm dropping were more likely to allocate patients to better performing arms at each interim analysis. Similar estimates and conclusions were obtained from the Bayesian adaptive designs as from the original trial. Conclusions: Using CAST as an example, this case study showed how Bayesian adaptive designs can be constructed for phase III multi‐arm trials using clinically relevant decision criteria. These designs demonstrated that they can potentially generate earlier results and allocate more patients to betterperforming arms. We recommend the wider use of Bayesian adaptive approaches in phase III clinical trials.Medical Research Council (MRC)National Co‐ordinating Centre for Health Technology AssessmentNational Institute of Health Researc

"Sometimes, it just stops me from doing anything": A qualitative exploration of epilepsy management in people with intellectual disabilities and their carers

$\textit{Purpose}$: Epilepsy affects 1 in 5 people with an intellectual disability (ID), but little is known about their experiences of living with epilepsy. A qualitative study was conducted to investigate the impact and management of epilepsy in people with ID. $\textit{Materials and methods:}$ People with epilepsy and ID and their carers were invited to take part in semi-structured interviews. Eleven participants with ID and their carers were interviewed together, one participant with ID and their carer were interviewed separately, two interviews took place with the participant with ID only, and one interview took place with the carer only. The interviews were transcribed verbatim, coded, and analyzed thematically (dual independent coding for 30% of the transcripts). $\textit{Results:}$ Three themes emerged (participant characteristics, living with epilepsy, epilepsy management and information needs) which indicated the following: 1) diversity regarding health profiles, communication abilities, severity of epilepsy, perceived control of epilepsy, and support needs; 2) a reduction in severity and frequency of seizures for a sizeable proportion of participants through antiepileptic drugs; 3) the lifelong impact of epilepsy and related seizures on participants' activities and quality of life; 4) the perceived burden of epilepsy and difficulty managing the condition for a large proportion of participants; 5) high levels of satisfaction with epilepsy-related services and care; and 6) an overall lack of written accessible information about epilepsy. $\textit{Conclusions:}$ This study has highlighted a significant impact of epilepsy and related seizures on the daily lives and quality of life of people with ID. Although a sizeable proportion of participants and their carers considered their epilepsy to be well controlled, the majority reported difficulties managing epilepsy and minimizing its impact on their wellbeing. Excluding care staff and the support provided by epilepsy clinics, the participants had not accessed any adapted self-management or information resources about epilepsy

Probing empirical contact networks by simulation of spreading dynamics

Disease, opinions, ideas, gossip, etc. all spread on social networks. How these networks are connected (the network structure) influences the dynamics of the spreading processes. By investigating these relationships one gains understanding both of the spreading itself and the structure and function of the contact network. In this chapter, we will summarize the recent literature using simulation of spreading processes on top of empirical contact data. We will mostly focus on disease simulations on temporal proximity networks -- networks recording who is close to whom, at what time -- but also cover other types of networks and spreading processes. We analyze 29 empirical networks to illustrate the methods

Understanding the links between hearing impairment and dementia : development and validation of the social and emotional impact of hearing impairment (SEI-HI) questionnaire

Background The links between hearing impairment (HI) and dementia have been well documented, but factors mediating this relationship remain unknown. Major consequences of HI are social and emotional dysfunction, and as the risk of dementia increases linearly with the severity of HI, it is plausible that socio-emotional difficulties may play a role in this association. Objective The aim of this study was to develop and validate a tool to analyse levels of hearing-related disability, to investigate ultimately whether subjective disability contributes to risk of cognitive impairment compared with hearing thresholds alone. Methods Development and validation of the questionnaire, the Social and Emotional Impact of Hearing Impairment (SEI-HI), was conducted in four phases: (1) content; (2) scoring and outcomes; (3) validation; (4) feasibility in a sample of people with cognitive impairment. Results Considerable evidence was found for the internal and external reliability of the tool with high construct validity, concurrent validity and test-retest values of the SEI-HI questionnaire. A feasibility check on 31 patients with mild cognitive impairment or dementia showed the SEI-HI questionnaire was easy to administer and well-received. Conclusion The SEI-HI questionnaire is a relevant instrument to assess hearing-related disability which can be used in people with cognitive decline to assess further impact on risk of developing dementia

Adult granulosa cell tumor associated with endometrial carcinoma: a case report

<p>Abstract</p> <p>Introduction</p> <p>If strict criteria for the diagnosis of carcinoma are used and all patients with granulosa cell tumors are considered, the best estimate of the incidence of associated endometrial carcinomas is under 5%. In patients with granulosa cell tumors, estrogen-dependent endometrial cancers are rarely found, and most of these endometrial cancers are well-differentiated endometrioid adenocarcinomas that carry a good prognosis when detected early.</p> <p>Case presentation</p> <p>We report the case of a 65-year-old post-menopausal Nigerian woman of the Igbo tribe with an adult granulosa cell tumor that was initially treated as endometrial carcinoma. She underwent a total abdominal hysterectomy and a bilateral salpingo-oophorectomy after histopathologic confirmation of a well-differentiated granulosa cell tumor of the ovary and a nuclear grade 1 adenocarcinoma of the endometrium (International Federation of Obstetricians and Gynecologists stage 1B). She had a good post-operative recovery and was discharged 10 days after treatment.</p> <p>Conclusion</p> <p>The association between adult granulosa cell tumors of the ovary and endometrial carcinomas is rare. A high index of suspicion as well as good imaging and histopathologic analyses are important in making this diagnosis.</p

The effectiveness of interventions to change six health behaviours: a review of reviews

Background: Several World Health Organisation reports over recent years have highlighted the high incidence of chronic diseases such as diabetes, coronary heart disease and cancer. Contributory factors include unhealthy diets, alcohol and tobacco use and sedentary lifestyles. This paper reports the findings of a review of reviews of behavioural change interventions to reduce unhealthy behaviours or promote healthy behaviours. We included six different health-related behaviours in the review: healthy eating, physical exercise, smoking, alcohol misuse, sexual risk taking (in young people) and illicit drug use. We excluded reviews which focussed on pharmacological treatments or those which required intensive treatments (e. g. for drug or alcohol dependency). Methods: The Cochrane Library, Database of Abstracts of Reviews of Effectiveness (DARE) and several Ovid databases were searched for systematic reviews of interventions for the six behaviours (updated search 2008). Two reviewers applied the inclusion criteria, extracted data and assessed the quality of the reviews. The results were discussed in a narrative synthesis. Results: We included 103 reviews published between 1995 and 2008. The focus of interventions varied, but those targeting specific individuals were generally designed to change an existing behaviour (e. g. cigarette smoking, alcohol misuse), whilst those aimed at the general population or groups such as school children were designed to promote positive behaviours (e. g. healthy eating). Almost 50% (n = 48) of the reviews focussed on smoking (either prevention or cessation). Interventions that were most effective across a range of health behaviours included physician advice or individual counselling, and workplace- and school-based activities. Mass media campaigns and legislative interventions also showed small to moderate effects in changing health behaviours. Generally, the evidence related to short-term effects rather than sustained/longer-term impact and there was a relative lack of evidence on how best to address inequalities. Conclusions: Despite limitations of the review of reviews approach, it is encouraging that there are interventions that are effective in achieving behavioural change. Further emphasis in both primary studies and secondary analysis (e.g. systematic reviews) should be placed on assessing the differential effectiveness of interventions across different population subgroups to ensure that health inequalities are addressed.</p

Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups

<p>Abstract</p> <p>Background</p> <p>Hospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification models from a large data set of geriatric in-patients' assessment data and to evaluate their predictive performance (aim#1), and to identify high-risk subgroups from the data (aim#2).</p> <p>Methods</p> <p>A data set of n = 5,176 single in-patient episodes covering 1.5 years of admissions to a geriatric hospital were extracted from the hospital's data base and matched with fall incident reports (n = 493). A classification tree model was induced using the C4.5 algorithm as well as a logistic regression model, and their predictive performance was evaluated. Furthermore, high-risk subgroups were identified from extracted classification rules with a support of more than 100 instances.</p> <p>Results</p> <p>The classification tree model showed an overall classification accuracy of 66%, with a sensitivity of 55.4%, a specificity of 67.1%, positive and negative predictive values of 15% resp. 93.5%. Five high-risk groups were identified, defined by high age, low Barthel index, cognitive impairment, multi-medication and co-morbidity.</p> <p>Conclusions</p> <p>Our results show that a little more than half of the fallers may be identified correctly by our model, but the positive predictive value is too low to be applicable. Non-fallers, on the other hand, may be sorted out with the model quite well. The high-risk subgroups and the risk factors identified (age, low ADL score, cognitive impairment, institutionalization, polypharmacy and co-morbidity) reflect domain knowledge and may be used to screen certain subgroups of patients with a high risk of falling. Classification models derived from a large data set using data mining methods can compete with current dedicated fall risk screening tools, yet lack diagnostic precision. High-risk subgroups may be identified automatically from existing geriatric assessment data, especially when combined with domain knowledge in a hybrid classification model. Further work is necessary to validate our approach in a controlled prospective setting.</p

Structural basis of nucleosome assembly by the Abo1 AAA+ ATPase histone chaperone

The fundamental unit of chromatin, the nucleosome, is an intricate structure that requires histone chaperones for assembly. ATAD2 AAA+???ATPases are a family of histone chaperones that regulate nucleosome density and chromatin dynamics. Here, we demonstrate that the fission yeast ATAD2 homolog, Abo1, deposits histone H3???H4 onto DNA in an ATP-hydrolysis-dependent manner by in vitro reconstitution and single-tethered DNA curtain assays. We present cryo-EM structures of an ATAD2 family ATPase to atomic resolution in three different nucleotide states, revealing unique structural features required for histone loading on DNA, and directly visualize the transitions of Abo1 from an asymmetric spiral (ATP-state) to a symmetric ring (ADP- and apo-states) using high-speed atomic force microscopy (HS-AFM). Furthermore, we find that the acidic pore of ATP-Abo1 binds a peptide substrate which is suggestive of a histone tail. Based on these results, we propose a model whereby Abo1 facilitates H3???H4 loading by utilizing ATP

Do Humans Optimally Exploit Redundancy to Control Step Variability in Walking?

It is widely accepted that humans and animals minimize energetic cost while walking. While such principles predict average behavior, they do not explain the variability observed in walking. For robust performance, walking movements must adapt at each step, not just on average. Here, we propose an analytical framework that reconciles issues of optimality, redundancy, and stochasticity. For human treadmill walking, we defined a goal function to formulate a precise mathematical definition of one possible control strategy: maintain constant speed at each stride. We recorded stride times and stride lengths from healthy subjects walking at five speeds. The specified goal function yielded a decomposition of stride-to-stride variations into new gait variables explicitly related to achieving the hypothesized strategy. Subjects exhibited greatly decreased variability for goal-relevant gait fluctuations directly related to achieving this strategy, but far greater variability for goal-irrelevant fluctuations. More importantly, humans immediately corrected goal-relevant deviations at each successive stride, while allowing goal-irrelevant deviations to persist across multiple strides. To demonstrate that this was not the only strategy people could have used to successfully accomplish the task, we created three surrogate data sets. Each tested a specific alternative hypothesis that subjects used a different strategy that made no reference to the hypothesized goal function. Humans did not adopt any of these viable alternative strategies. Finally, we developed a sequence of stochastic control models of stride-to-stride variability for walking, based on the Minimum Intervention Principle. We demonstrate that healthy humans are not precisely “optimal,” but instead consistently slightly over-correct small deviations in walking speed at each stride. Our results reveal a new governing principle for regulating stride-to-stride fluctuations in human walking that acts independently of, but in parallel with, minimizing energetic cost. Thus, humans exploit task redundancies to achieve robust control while minimizing effort and allowing potentially beneficial motor variability