Search

Background The prevalence of genital chlamydia and gonorrhoea is higher in the 16–24 years age group than those in other age group. With users, we developed the theory-based safetxt intervention to reduce sexually transmitted infections. Objectives To establish the effect of the safetxt intervention on the incidence of chlamydia/gonorrhoea infection at 1 year. Design A parallel-group, individual-level, randomised superiority trial in which care providers and outcome assessors were blinded to allocation. Setting Recruitment was from 92 UK sexual health clinics. Participants Inclusion criteria were a positive chlamydia or gonorrhoea test result, diagnosis of non-specific urethritis or treatment started for chlamydia/gonorrhoea/non-specific urethritis in the last 2 weeks; owning a personal mobile phone; and being aged 16–24 years. Allocation Remote computer-based randomisation with an automated link to the messaging system delivering intervention or control group messages. Intervention The safetxt intervention was designed to reduce sexually transmitted infection by increasing partner notification, condom use and sexually transmitted infection testing before sex with new partners. It employed educational, enabling and incentivising content delivered by 42–79 text messages over 1 year, tailored according to type of infection, gender and sexuality. Comparator A monthly message regarding trial participation. Main outcomes The primary outcome was the incidence of chlamydia and gonorrhoea infection at 12 months, assessed using nucleic acid amplification tests. Secondary outcomes at 1 and 12 months included self-reported partner notification, condom use and sexually transmitted infection testing prior to sex with new partner(s). Results Between 1 April 2016 and 23 November 2018, we assessed 20,476 people for eligibility and consented and randomised 6248 participants, allocating 3123 to the safetxt intervention and 3125 to the control. Primary outcome data were available for 4675 (74.8%) participants. The incidence of chlamydia/gonorrhoea infection was 22.2% (693/3123) in the intervention group and 20.3% (633/3125) in the control group (odds ratio 1.13, 95% confidence interval 0.98 to 1.31). There was no evidence of heterogeneity in any of the prespecified subgroups. Partner notification was 85.6% in the intervention group and 84.0% in the control group (odds ratio 1.14, 95% confidence interval 0.99 to 1.33). At 12 months, condom use at last sex was 33.8% in the intervention group and 31.2% in the control group (odds ratio 1.14, 95% confidence interval 1.01 to 1.28) and condom use at first sex with most recent new partner was 54.4% in the intervention group and 48.7% in the control group (odds ratio 1.27, 95% confidence interval 1.11 to 1.45). Testing before sex with a new partner was 39.5% in the intervention group and 40.9% in the control group (odds ratio 0.95, 95% confidence interval 0.82 to 1.10). Having two or more partners since joining the trial was 56.9% in the intervention group and 54.8% in the control group (odds ratio 1.11, 95% confidence interval 1.00 to 1.24) and having sex with someone new since joining the trial was 69.7% in the intervention group and 67.4% in the control group (odds ratio 1.13, 95% confidence interval 1.00 to 1.28). There were no differences in safety outcomes. Additional sensitivity and per-protocol analyses showed similar results. Limitations Our understanding of the mechanism of action for the unanticipated effects is limited. Conclusions The safetxt intervention did not reduce chlamydia and gonorrhoea infections, with slightly more infections in the intervention group. The intervention increased condom use but also increased the number of partners and new partners. Randomised controlled trials are essential for evaluating health communication interventions, which can have unanticipated effects. Future work Randomised controlled trials evaluating novel interventions in this complex area are needed. Trial registration This trial is registered as ISRCTN64390461. Funding This project was funded by the National Institute for Health and Care Research (NIHR) Public Health Research programme and will be published in full in Public Health Research; Vol. 11, No. 1. See the NIHR Journals Library website for further project information.

Background Non-random selection of analytic subsamples could introduce selection bias in observational studies. We explored the potential presence and impact of selection in studies of SARS-CoV-2 infection and COVID-19 prognosis. Methods We tested the association of a broad range of characteristics with selection into COVID-19 analytic subsamples in the Avon Longitudinal Study of Parents and Children (ALSPAC) and UK Biobank (UKB). We then conducted empirical analyses and simulations to explore the potential presence, direction and magnitude of bias due to this selection (relative to our defined UK-based adult target populations) when estimating the association of body mass index (BMI) with SARS-CoV-2 infection and death-with-COVID-19. Results In both cohorts, a broad range of characteristics was related to selection, sometimes in opposite directions (e.g. more-educated people were more likely to have data on SARS-CoV-2 infection in ALSPAC, but less likely in UKB). Higher BMI was associated with higher odds of SARS-CoV-2 infection and death-with-COVID-19. We found non-negligible bias in many simulated scenarios. Conclusions Analyses using COVID-19 self-reported or national registry data may be biased due to selection. The magnitude and direction of this bias depend on the outcome definition, the true effect of the risk factor and the assumed selection mechanism; these are likely to differ between studies with different target populations. Bias due to sample selection is a key concern in COVID-19 research based on national registry data, especially as countries end free mass testing. The framework we have used can be applied by other researchers assessing the extent to which their results may be biased for their research question of interest.

Factorial trials offer an efficient method to evaluate multiple interventions in a single trial, however the use of additional treatments can obscure research objectives, leading to inappropriate analytical methods and interpretation of results. We define a set of estimands for factorial trials, and describe a framework for applying these estimands, with the aim of clarifying trial objectives and ensuring appropriate primary and sensitivity analyses are chosen. This framework is intended for use in factorial trials where the intent is to conduct "two-trials-in-one" (ie, to separately evaluate the effects of treatments A and B), and is comprised of four steps: (i) specifying how additional treatment(s) (eg, treatment B) will be handled in the estimand, and how intercurrent events affecting the additional treatment(s) will be handled; (ii) designating the appropriate factorial estimator as the primary analysis strategy; (iii) evaluating the interaction to assess the plausibility of the assumptions underpinning the factorial estimator; and (iv) performing a sensitivity analysis using an appropriate multiarm estimator to evaluate to what extent departures from the underlying assumption of no interaction may affect results. We show that adjustment for other factors is necessary for noncollapsible effect measures (such as odds ratio), and through a trial re-analysis we find that failure to consider the estimand could lead to inappropriate interpretation of results. We conclude that careful use of the estimands framework clarifies research objectives and reduces the risk of misinterpretation of trial results, and should become a standard part of both the protocol and reporting of factorial trials.

We describe a new command, artcat, that calculates sample size or power for a randomized controlled trial or similar experiment with an ordered categorical outcome, where analysis is by the proportional-odds model. artcat implements the method of Whitehead (1993, Statistics in Medicine 12: 2257–2271). We also propose and implement a new method that 1) allows the user to specify a treatment effect that does not obey the proportional-odds assumption, 2) offers greater accuracy for large treatment effects, and 3) allows for noninferiority trials. We illustrate the command and explore the value of an ordered categorical outcome over a binary outcome in various settings. We show by simulation that the methods perform well and that the new method is more accurate than Whitehead’s method.

Previous research has established an impact of acute exercise on cognitive performance, which has inspired investigations into neurobiological mechanisms that may underlie the observed benefits. Pupillary responses have been posited to reflect activation of such underlying neurobiological mechanisms. The current study recruited healthy young adults to investigate the effects of a single bout of moderate-to-vigorous intensity aerobic exercise on subsequent performance and pupillary responses during an inhibitory control task. Results showed that an acute bout of exercise was related to shorter reaction times and increased tonic pupil dilation during an inhibitory control task. Although pupillary responses did not mediate the acute exercise effect on inhibitory control, higher cardiorespiratory fitness was associated with greater phasic pupil dilation following exercise relative to seated rest. The current study supported the plausibility of the pupillary response as a marker of LC-NE system activation that is sensitive to acute exercise. Whether pupillary responses could account for transient benefits of acute exercise on brain and cognition remains unclear.

Simulation studies are a powerful tool in epidemiology and biostatistics, but they can be hard to conduct successfully. Sometimes unexpected results are obtained. We offer advice on how to check a simulation study when this occurs, and how to design and conduct the study to give results that are easier to check. Simulation studies should be designed to include some settings where answers are already known. They should be coded sequentially, with data generating mechanisms checked before simulated data are analysed. Results should be explored carefully, with scatterplots of standard error estimates against point estimates a powerful tool. Failed estimation and outlying estimates should be identified and avoided by changing data generating mechanisms or coding realistic hybrid analysis procedures. Finally, surprising results should be investigated by methods including considering whether sources of variation are correctly included. Following our advice may help to prevent errors and to improve the quality of published simulation studies.

The quantitative analysis of research data is a core element of empirical research. The performance of statistical methods that are used for analyzing empirical data can be evaluated and compared using computer simulations. A single simulation study can influence the analyses of thousands of empirical studies to follow. With great power comes great responsibility. Here, we argue that this responsibility includes replication of simulation studies to ensure a sound foundation for data analytical decisions. Furthermore, being designed, run, and reported by humans, simulation studies face challenges similar to other experimental empirical research and hence should not be exempt from replication attempts. We highlight that the potential replicability of simulation studies is an opportunity quantitative methodology as a field should pay more attention to.

Objective Missing data can produce biased estimates in interrupted time series (ITS) analyses. We reviewed recent ITS investigations on health topics for determining 1) the data management strategies and statistical analysis performed, 2) how often missing data were considered and, if so, how they were evaluated, reported and handled. Study design and setting This was a scoping review following standard recommendations from the PRISMA Extension for Scoping Reviews. We included a random sample of all ITS studies that assessed any intervention relevant to health care (eg, policies or programmes) with individual-level data, published in 2019, with abstracts indexed on MEDLINE. Results From 732 studies identified, we finally reviewed 60. Reporting of missing data was rare. Data aggregation, statistical tools for modelling population-level data and complete case analyses were preferred, but these can lead to bias when data are missing at random. Seasonality and other time-dependent confounders were rarely accounted for and, when they were, missing data implications were typically ignored. Very few studies reflected on the consequences of missing data. Conclusion Handling and reporting of missing data in recent ITS studies performed for health research have many shortcomings compared with best practice.

Background In the interrupted time series (ITS) approach, it is common to average the outcome of interest at each time point and then perform a segmented regression (SR) analysis. In this study, we illustrate that such 'aggregate-level' analysis is biased when data are missing at random (MAR) and provide alternative analysis methods. Methods Using electronic health records from the UK, we evaluated weight change over time induced by the initiation of antipsychotic treatment. We contrasted estimates from aggregate-level SR analysis against estimates from mixed models with and without multiple imputation of missing covariates, using individual-level data. Then, we conducted a simulation study for insight about the different results in a controlled environment. Results Aggregate-level SR analysis suggested a substantial weight gain after initiation of treatment (average short-term weight change: 0.799kg/week) compared to mixed models (0.412kg/week). Simulation studies confirmed that aggregate-level SR analysis was biased when data were MAR. In simulations, mixed models gave less biased estimates than SR analysis and, in combination with multilevel multiple imputation, provided unbiased estimates. Mixed models with multiple imputation can be used with other types of ITS outcomes (eg, proportions). Other standard methods applied in ITS do not help to correct this bias problem. Conclusion Aggregate-level SR analysis can bias the ITS estimates when individual-level data are MAR, because taking averages of individual-level data before SR means that data at the cluster level are missing not at random. Avoiding the averaging-step and using mixed models with or without multilevel multiple imputation of covariates is recommended.

In this paper we study approaches for dealing with treatment when developing a clinical prediction model. Analogous to the estimand framework recently proposed by the European Medicines Agency for clinical trials, we propose a `predictimand' framework of different questions that may be of interest when predicting risk in relation to treatment started after baseline. We provide a formal definition of the estimands matching these questions, give examples of settings in which each is useful and discuss appropriate estimators including their assumptions. We illustrate the impact of the predictimand choice in a dataset of patients with end-stage kidney disease. We argue that clearly defining the estimand is equally important in prediction research as in causal inference., Comment: under review

ObjectiveTo improve communication of harm in publications of randomised controlled trials via the development of recommendations for visually presenting harm outcomes.DesignConsensus study.Setting15 clinical trials units registered with the UK Clinical Research Collaboration, an academic population health department, Roche Products, andTheBMJ.ParticipantsExperts in clinical trials: 20 academic statisticians, one industry statistician, one academic health economist, one data graphics designer, and two clinicians.Main outcomemeasuresA methodological review of statistical methods identified visualisations along with those recommended by consensus group members. Consensus on visual recommendations was achieved (at least 60% of the available votes) over a series of three meetings with participants. The participants reviewed and critically appraised candidate visualisations against an agreed framework and voted on whether to endorse each visualisation. Scores marginally below this threshold (50-60%) were revisited for further discussions and votes retaken until consensus was reached.Results28 visualisations were considered, of which 10 are recommended for researchers to consider in publications of main research findings. The choice of visualisations to present will depend on outcome type (eg, binary, count, time-to-event, or continuous), and the scenario (eg, summarising multiple emerging events or one event of interest). A decision tree is presented to assist trialists in deciding which visualisations to use. Examples are provided of each endorsed visualisation, along with an example interpretation, potential limitations, and signposting to code for implementation across a range of standard statistical software. Clinician feedback was incorporated into the explanatory information provided in the recommendations to aid understanding and interpretation.ConclusionsVisualisations provide a powerful tool to communicate harms in clinical trials, offering an alternative perspective to the traditional frequency tables. Increasing the use of visualisations for harm outcomes in clinical trial manuscripts and reports will provide clearer presentation of information and enable more informative interpretations. The limitations of each visualisation are discussed and examples of where their use would be inappropriate are given. Although the decision tree aids the choice of visualisation, the statistician and clinical trial team must ultimately decide the most appropriate visualisations for their data and objectives. Trialists should continue to examine crude numbers alongside visualisations to fully understand harm profiles.

Simulation studies allow us to explore the properties of statistical methods.They provide a powerful tool with a multiplicity of aims; among others: evaluating and comparing new or existing statistical methods, assessing violations of modelling assumptions, helping with the understanding of statistical concepts, and supporting the design of clinical trials.The increased availability of powerful computational tools and usable software has contributed to the rise of simulation studies in the current literature.However, simulation studies involve increasingly complex designs, making it difficult to provide all relevant results clearly.Dissemination of results plays a focal role in simulation studies: it can drive applied analysts to use methods that have been shown to perform well in their settings, guide researchers to develop new methods in a promising direction, and provide insights into less established methods.It is crucial that we can digest relevant results of simulation studies.Therefore, we developed INTEREST: an INteractive Tool for Exploring REsults from Simulation sTudies.The tool has been developed using the Shiny framework in R and is available as a web app or as a standalone package.It requires uploading a tidy format dataset with the results of a simulation study in R, Stata, SAS, SPSS, or comma-separated format.A variety of performance measures are estimated automatically along with Monte Carlo standard errors; results and performance summaries are displayed both in tabular and graphical fashion, with a wide variety of available plots.Consequently, the reader can focus on simulation parameters and estimands of most interest.In conclusion, INTEREST can facilitate the investigation of results from simulation studies and supplement the reporting of results, allowing researchers to share detailed results from their simulations and readers to explore them freely.

BackgroundNon-random selection into analytic subsamples could introduce selection bias in observational studies of SARS-CoV-2 infection and COVID-19 severity (e.g. including only those have had a COVID-19 PCR test). We explored the potential presence and impact of selection in such studies using data from self-report questionnaires and national registries.MethodsUsing pre-pandemic data from the Avon Longitudinal Study of Parents and Children (ALSPAC) (mean age=27.6 (standard deviation [SD]=0.5); 49% female) and UK Biobank (UKB) (mean age=56 (SD=8.1); 55% female) with data on SARS-CoV-2 infection and death-with-COVID-19 (UKB only), we investigated predictors of selection into COVID-19 analytic subsamples. We then conducted empirical analyses and simulations to explore the potential presence, direction, and magnitude of bias due to selection when estimating the association of body mass index (BMI) with SARS-CoV-2 infection and death-with-COVID-19.ResultsIn both ALSPAC and UKB a broad range of characteristics related to selection, sometimes in opposite directions. For example, more educated participants were more likely to have data on SARS-CoV-2 infection in ALSPAC, but less likely in UKB. We found bias in many simulated scenarios. For example, in one scenario based on UKB, we observed an expected odds ratio of 2.56 compared to a simulated true odds ratio of 3, per standard deviation higher BMI.ConclusionAnalyses using COVID-19 self-reported or national registry data may be biased due to selection. The magnitude and direction of this bias depends on the outcome definition, the true effect of the risk factor, and the assumed selection mechanism.Key messagesObservational studies assessing the association of risk factors with SARS-CoV-2 infection and COVID-19 severity may be biased due to non-random selection into the analytic sample.Researchers should carefully consider the extent that their results may be biased due to selection, and conduct sensitivity analyses and simulations to explore the robustness of their results. We provide code for these analyses that is applicable beyond COVID-19 research.

Single bouts of aerobic exercise can modulate cortical excitability and executive cognitive function, but less is known about the effect of light intensity exercise, an intensity of exercise more achievable for certain clinical populations. Fourteen healthy adults (aged 22 to 30) completed the following study procedures twice (≥7 days apart) before and after 30-minutes of either light aerobic exercise (cycling) or seated rest: neurocognitive battery (multitasking performance, inhibitory control and spatial working memory), paired pulse TMS-measures of cortical excitability. Significant improvements in response times during multitasking performance and increases in intracortical facilitation (ICF) were seen following light aerobic exercise. Light aerobic exercise can modulate cortical excitability and some executive function tasks. Populations with deficits in multitasking ability may benefit from this intervention.

To assess motor cortex neurophysiology, including the mechanisms of neuroplasticity, transcranial magnetic stimulation (TMS) is typically applied to the motor "hotspot"- the optimal site for inducing a twitch in a given target muscle. It is known that the effects of suprathreshold repetitive TMS (rTMS) spread along functional connections beyond the specific cortical stimulation target, and yet, it is unknown whether the aftereffects of subthreshold intermittent theta-burst stimulation (iTBS), an ultra-high frequency patterned rTMS protocol, extend beyond the targeted muscle. We investigated whether and to what extent iTBS induces changes in the cortical output to other intrinsic hand muscles with adjacent cortical representation to the target. Sixteen healthy adults underwent neuronavigated TMS-iTBS targeting the first dorsal interosseus (FDI) hotspot. Proportion of motor evoked potentials (MEPs) at the resting motor threshold (RMT), baseline MEP amplitude, and iTBS-induced changes in MEP amplitude were compared between FDI, abductor pollicis brevis (APB) and abductor digiti minimi (ADM) muscles. MEP amplitudes recorded from the three muscles at RMT and suprathreshold intensities indicated the chosen hotspots were relatively selective for FDI. Nevertheless, iTBS induced significant facilitation of MEPs recorded from both FDI and APB, but not ADM. Surprisingly, the MEP modulation was greater in APB, even when controlling for the baseline MEP amplitude. These results indicate that iTBS modulation of cortico-spinal excitability extends beyond the representation of the targeted muscle. Results have implications both for how iTBS may be used in clinical treatment and for the safety guidelines for the application of iTBS.

Background The long-term health consequences of participation in American style football (ASF) are not well understood. Methods We conducted a retrospective cohort study of men who had played in the NFL after 1960. Participants were studied using a standardized self-administered questionnaire designed to determine both the exposure history to ASF and the prevalence of chronic pain, sleep apnea, cardiometabolic disease, and neurocognitive impairment. Logistic regression and negative binomial regression models were used to assess associations between age, ethnicity, body-mass index during professional football career, field position, and football career duration with individual and multiple afflictions. Results In this cohort of former NFL players (n = 3745), approximately one quarter of the eligible former players (27%) reported two or more medical afflictions (chronic pain, cardiometabolic disease, sleep apnea, or neurocognitive impairment). Career duration was significantly associated with an increase in the number of comorbidities. Age, race, and body-mass index were associated with all affliction categories, other than neurocognitive impairment, which was similarly prevalent in middle-aged players and older players. Earlier age when first playing the sport was protective against cardiometabolic affliction. Conclusions Former NFL players report significant combinations of cross-system afflictions. Future work will be required to determine mechanistic underpinnings. However, attention to the whole player, rather than specific organ systems seems critical to improve long-term health outcomes in former ASF professional athletes.

Objective To quantify the effects of a series of text messages (safetxt) delivered in the community on incidence of chlamydia and gonorrhoea reinfection at one year in people aged 16-24 years. Design Parallel group randomised controlled trial. Setting 92 sexual health clinics in the United Kingdom. Participants People aged 16-24 years with a diagnosis of, or treatment for, chlamydia, gonorrhoea, or non-specific urethritis in the past two weeks who owned a mobile phone. Interventions 3123 participants assigned to the safetxt intervention received a series of text messages to improve sex behaviours: four texts daily for days 1-3, one or two daily for days 4-28, two or three weekly for month 2, and 2-5 monthly for months 3-12. 3125 control participants received a monthly text message for one year asking for any change to postal or email address. It was hypothesised that safetxt would reduce the risk of chlamydia and gonorrhoea reinfection at one year by improving three key safer sex behaviours: partner notification at one month, condom use, and sexually transmitted infection testing before unprotected sex with a new partner. Care providers and outcome assessors were blind to allocation. Main outcome measures The primary outcome was the cumulative incidence of chlamydia or gonorrhoea reinfection at one year, assessed by nucleic acid amplification tests. Safety outcomes were self-reported road traffic incidents and partner violence. All analyses were by intention to treat. Results 6248 of 20 476 people assessed for eligibility between 1 April 2016 and 23 November 2018 were randomised. Primary outcome data were available for 4675/6248 (74.8%). At one year, the cumulative incidence of chlamydia or gonorrhoea reinfection was 22.2% (693/3123) in the safetxt arm versus 20.3% (633/3125) in the control arm (odds ratio 1.13, 95% confidence interval 0.98 to 1.31). The number needed to harm was 64 (95% confidence interval number needed to benefit 334 to ∞ to number needed to harm 24) The risk of road traffic incidents and partner violence was similar between the groups. Conclusions The safetxt intervention did not reduce chlamydia and gonorrhoea reinfections at one year in people aged 16-24 years. More reinfections occurred in the safetxt group. The results highlight the need for rigorous evaluation of health communication interventions. Trial registration ISRCTN registry ISRCTN64390461 .

Individualized and precision medicine approaches to exercise for cognitive and brain health in aging have the potential to improve intervention efficacy. Predicting adherence to an exercise intervention in older adults prior to its commencement will allow for adaptive and optimized approaches that could save time (no need to demonstrate failure before changing course) and money (cost of de-implementing approaches that do not work for certain individuals) which ultimately could improve health outcomes (e.g., preventative medicine approaches prior to the onset of symptoms). Individual differences in brain structure and function in older adults are potential proxies of brain and brain reserve or maintenance and may provide strong predictions of adherence. We hypothesized that brain-based measures would predict adherence to a six-month randomized controlled trial of exercise in older adults, alone and in combination with psychosocial, cognitive and health measures. In 131 older adults (aged 65.79 ± 4.65 years, 63% female) we found, using regularized elastic net regression within a nested cross-validation framework, that brain structure (cortical thickness and cortical surface area) in somatosensory, inferior temporal, and inferior frontal regions and functional connectivity (degree count) in primary information processing (somatosensory, visual), executive control, default, and attentional networks, predicted exercise adherence (R2 = 0.15, p < 0.001). Traditional survey and clinical measures such as gait and walking self-efficacy, biological sex and perceived stress also predicted adherence (R2 = 0.06, p = 0.001) but a combined multimodal model achieved the highest predictive strength (R2 = 0.22, p

Composite endpoints are commonly used to define primary outcomes in randomized controlled trials. A participant may be classified as meeting the endpoint if they experience an event in one or several components (eg, a favorable outcome based on a composite of being alive and attaining negative culture results in trials assessing tuberculosis treatments). Partially observed components that are not missing simultaneously complicate the analysis of the composite endpoint. An intuitive strategy frequently used in practice for handling missing values in the components is to derive the values of the composite endpoint from observed components when possible, and exclude from analysis participants whose composite endpoint cannot be derived. Alternatively, complete record analysis (CRA) (excluding participants with any missing components) or multiple imputation (MI) can be used. We compare a set of methods for analyzing a composite endpoint with partially observed components mathematically and by simulation, and apply these methods in a reanalysis of a published trial (TOPPS). We show that the derived composite endpoint can be missing not at random even when the components are missing completely at random. Consequently, the treatment effect estimated from the derived endpoint is biased while CRA results without the derived endpoint are valid. Missing at random mechanisms require MI of the components. We conclude that, although superficially attractive, deriving the composite endpoint from observed components should generally be avoided. Despite the potential risk of imputation model mis-specification, MI of missing components is the preferred approach in this study setting.

Background An estimand is a precise description of the treatment effect to be estimated from a trial (the question) and is distinct from the methods of statistical analysis (how the question is to be answered). The potential use of estimands to improve trial research and reporting has been underpinned by the recent publication of the ICH E9(R1) Addendum on the use of estimands in clinical trials in 2019. We set out to assess how well estimands are described in published trial protocols. Methods We reviewed 50 trial protocols published in October 2020 in Trials and BMJ Open. For each protocol, we determined whether the estimand for the primary outcome was explicitly stated, not stated but inferable (i.e. could be constructed from the information given), or not inferable. Results None of the 50 trials explicitly described the estimand for the primary outcome, and in 74% of trials, it was impossible to infer the estimand from the information included in the protocol. The population attribute of the estimand could not be inferred in 36% of trials, the treatment condition attribute in 20%, the population-level summary measure in 34%, and the handling of intercurrent events in 60% (the strategy for handling non-adherence was not inferable in 32% of protocols, and the strategy for handling mortality was not inferable in 80% of the protocols for which it was applicable). Conversely, the outcome attribute was stated for all trials. In 28% of trials, three or more of the five estimand attributes could not be inferred. Conclusions The description of estimands in published trial protocols is poor, and in most trials, it is impossible to understand exactly what treatment effect is being estimated. Given the utility of estimands to improve clinical research and reporting, this urgently needs to change.

Sedentary behavior increases the risk for multiple chronic diseases, early mortality, and accelerated cognitive decline in older adults. Interventions to reduce sedentary behavior among older adults are needed to improve health outcomes and reduce the burden on healthcare systems. We designed a randomized controlled trial that uses a self-affirmation manipulation and gain-framed health messaging to effectively reduce sedentary behavior in older adults. This message-based intervention lasts 6 weeks, recruiting 80 healthy but sedentary older adults from the community, between the ages of 60 and 95 years. Participants are randomly assigned to one of two groups: 1) an intervention group, which receives self-affirmation followed by gain-framed health messages daily or 2) a control group, which receives daily loss-framed health messages only. Objective physical activity engagement is measured by accelerometers. Accelerometers are deployed a week before, during, and the last week of intervention to examine potential changes in sedentary time and physical activity engagement. Participants undertake structural and functional (resting and task-based) MRI scans, neuropsychological tests, computerized behavioral measures, and neurobehavioral inventories at baseline and after the intervention. A 3-month follow-up assesses the long-term maintenance of any engendered behaviors from the intervention period. This study will assess the effectiveness of a novel behavioral intervention at reducing sedentarism in older adults and examine the neurobehavioral mechanisms underlying any such changes.

The study of how engagement in enriching cognitive, physical, and social activities in childhood impacts cognitive function decades later will advance our understanding of how modifiable lifestyle activities promote cognition across the lifespan. Eighty-eight healthy older adults (aged 60–80 years) returned a retrospective questionnaire regarding their participation in seven lifestyle activities (musical instrument playing, language learning, sports participation, art/dance lessons, scouting, volunteering, family vacations) before age 13 years. After controlling for current age, educational attainment, socioeconomic status of the mother, and current engagement in lifestyle activities, a greater number of activities were significantly associated with better vocabulary abilities, episodic memory, and fluid intelligence. The relationships with vocabulary and fluid intelligence were mediated by educational attainment. We postulate that engagement in a higher number of enriching early-life activities is a reflection of both one’s sociocontextual environment and engagement with that environment. This engagement leads to attributes relevant for educational aspirations/attainment, ultimately contributing to factors that have a lifespan impact on cognitive function.

The fraction of cases reported, known as ‘reporting’, is a key performance indicator in an outbreak response, and an essential factor to consider when modelling epidemics and assessing their impact on populations. Unfortunately, its estimation is inherently difficult, as it relates to the part of an epidemic which is, by definition, not observed.We introduce a simple statistical method for estimating reporting, initially developed for the response to Ebola in Eastern Democratic Republic of the Congo (DRC), 2018-2020. This approach uses transmission chain data typically gathered through case investigation and contact tracing, and uses the proportion of investigated cases with a known, reported infector as a proxy for reporting. Using simulated epidemics, we study how this method performs for different outbreak sizes and reporting levels. Results suggest that our method has low bias, reasonable precision, and despite sub-optimal coverage, usually provides estimates within close range (5-10%) of the true value.Being fast and simple, this method could be useful for estimating reporting in real-time in settings where person-to-person transmission is the main driver of the epidemic, and where case investigation is routinely performed as part of surveillance and contact tracing activities.Author summaryWhen responding to epidemics of infectious diseases, it is essential to estimate how many cases are not being reported. Unfortunately reporting, the proportion of cases actually observed, is difficult to estimate during an outbreak, as it typically requires large surveys to be conducted on the affected populations. Here, we introduce a method for estimating reporting from case investigation data, using the proportion of cases with a known, reported infector. We used simulations to test the performance of our approach by mimicking features of a recent Ebola epidemic in the Democratic Republic of the Congo. We found that despite some uncertainty in smaller outbreaks, our approach can be used to obtain informative ballpark estimates of reporting under most settings. This method is simple and computationally inexpensive, and can be used to inform the response to any epidemic in which transmission events can be uncovered by case investigation.

Information about a person’s available energy resources is integrated in daily behavioral choices that weigh motor costs against expected rewards. It has been posited that humans have an innate attraction towards effort minimization and that executive control is required to overcome this prepotent disposition. With sedentary behaviors increasing at the cost of millions of dollars spent in health care and productivity losses due to physical inactivity-related deaths, understanding the predictors of sedentary behaviors will improve future intervention development and precision medicine approaches. In 64 healthy older adults participating in a 6-month aerobic exercise intervention, we use neuroimaging (resting state functional connectivity), baseline measures of executive function and accelerometer measures of time spent sedentary to predict future changes in objectively measured time spent sedentary in daily life. Using cross-validation and bootstrap resampling, our results demonstrate that functional connectivity between 1) the anterior cingulate cortex and the supplementary motor area and 2) the right anterior insula and the left temporoparietal/temporooccipital junction, predict changes in time spent sedentary, whereas baseline cognitive, behavioral and demographic measures do not. Previous research has shown activation in and between the anterior cingulate and supplementary motor area as well as in the right anterior insula during effort avoidance and tasks that integrate motor costs and reward benefits in effort-based decision making. Our results add important knowledge toward understanding mechanistic associations underlying complex sedentary behaviors.

The fraction of cases reported, known as ‘reporting’, is a key performance indicator in an outbreak response, and an essential factor to consider when modelling epidemics and assessing their impact on populations. Unfortunately, its estimation is inherently difficult, as it relates to the part of an epidemic which is, by definition, not observed. We introduce a simple statistical method for estimating reporting, initially developed for the response to Ebola in Eastern Democratic Republic of the Congo (DRC), 2018–2020. This approach uses transmission chain data typically gathered through case investigation and contact tracing, and uses the proportion of investigated cases with a known, reported infector as a proxy for reporting. Using simulated epidemics, we study how this method performs for different outbreak sizes and reporting levels. Results suggest that our method has low bias, reasonable precision, and despite sub-optimal coverage, usually provides estimates within close range (5–10%) of the true value. Being fast and simple, this method could be useful for estimating reporting in real-time in settings where person-to-person transmission is the main driver of the epidemic, and where case investigation is routinely performed as part of surveillance and contact tracing activities.

MOSAIC is the planned multi-object spectrograph for the 39m Extremely Large Telescope (ELT). Conceived as a multi-purpose instrument, it offers both high multiplex and multi-IFU capabilities at a range of intermediate to high spectral resolving powers in the visible and the near-infrared. MOSAIC will enable unique spectroscopic surveys of the faintest sources, from the oldest stars in the Galaxy and beyond to the first populations of galaxies that completed the reionisation of the Universe--while simultaneously opening up a wide discovery space. In this contribution we present the status of the instrument ahead of Phase B, showcasing the key science cases as well as introducing the updated set of top level requirements and the adopted architecture. The high readiness level will allow MOSAIC to soon enter the construction phase, with the goal to provide the ELT community with a world-class MOS capability as soon as possible after the telescope first light., Accepted to SPIE

In health research, statistical methods are frequently used to address a wide variety of research questions. For almost every analytical challenge, different methods are available. But how do we choose between different methods and how do we judge whether the chosen method is appropriate for our specific study? Like in any science, in statistics, experiments can be run to find out which methods should be used under which circumstances. The main objective of this paper is to demonstrate that simulation studies, that is, experiments investigating synthetic data with known properties, are an invaluable tool for addressing these questions. We aim to provide a first introduction to simulation studies for data analysts or, more generally, for researchers involved at different levels in the analyses of health data, who (1) may rely on simulation studies published in statistical literature to choose their statistical methods and who, thus, need to understand the criteria of assessing the validity and relevance of simulation results and their interpretation; and/or (2) need to understand the basic principles of designing statistical simulations in order to efficiently collaborate with more experienced colleagues or start learning to conduct their own simulations. We illustrate the implementation of a simulation study and the interpretation of its results through a simple example inspired by recent literature, which is completely reproducible using the R-script available from online supplemental file 1.

Enriching early life experiences (e.g., sport, art, music, volunteering, language learning) during a critical period of brain development may promote structural and functional brain changes that are still present decades later (>60 years). We assessed whether a greater variety of enriching early life activities (EELA) before age 13 years were associated with individual differences in cortical and subcortical (hippocampus and amygdala) structure and function later in life (older adults aged 60–80 years). Results indicated no association between EELA and amygdala and hippocampus volumes, but higher functional connectivity between the amygdala and the insula was associated with more variety of EELA. EELA was not associated with cortical thickness controlling for sex, but sex-specific associations with the right pars opercularis were found. EELA was further associated with variations in functional connectivity patterns of the orbitofrontal cortex, driven by connecitivty to regions within the visual, somatosensory and limbic networks. Early life enriching activities appear to contribute to potential mechanisms of cognitive reserve (functional processes) more so than brain reserve (structure) later in life.

The aim of the current study was to examine the association between cardiorespiratory fitness and electroencephalogram-based neural oscillations, using midfrontal theta, during an inhibitory control task in children. One-hundred seventy-one school-aged children (mean age = 8.9 ± 0.6 years; 46% girls) were recruited. Cardiorespiratory fitness was assessed by a test of maximal oxygen consumption (VO2peak ) while inhibitory control performance was measured via a modified flanker task with an electroencephalogram. Behavioral findings demonstrated that higher cardiorespiratory fitness was associated with higher response accuracy regardless of task difficulty as well as lower response variability during trials with lower cognitive demand. Neuroelectric outcomes revealed that higher cardiorespiratory fitness was correlated with smaller modulation of theta (4-7 Hz) oscillatory power regardless of task difficulty. Collectively, the current findings indicate that higher cardiorespiratory fitness is associated with better performance on a task that modulates inhibitory control, signified by higher, and more stable, task performance. More importantly, higher childhood cardiorespiratory fitness is associated with better top-down control and cortical communication, as reflected by midfrontal theta. Such findings support the critical role of cardiorespiratory fitness in brain health during childhood.