Optimal survival analyses with prevalent and incident patients.

Period-prevalent cohorts are often used for their cost-saving potential in epidemiological studies of survival outcomes. Under this design, prevalent patients allow for evaluations of long-term survival outcomes without the need for long follow-up, whereas incident patients allow for evaluations of short-term survival outcomes without the issue of left-truncation. In most period-prevalent survival analyses from the existing literature, patients have been recruited to achieve an overall sample size, with little attention given to the relative frequencies of prevalent and incident patients and their statistical implications. Furthermore, there are no existing methods available to rigorously quantify the impact of these relative frequencies on estimation and inference and incorporate this information into study design strategies. To address these gaps, we develop an approach to identify the optimal mix of prevalent and incident patients that maximizes precision over the entire estimated survival curve, subject to a flexible weighting scheme. In addition, we prove that inference based on the weighted log-rank test or Cox proportional hazards model is most powerful with an entirely prevalent or incident cohort, and we derive theoretical formulas to determine the optimal choice. Simulations confirm the validity of the proposed optimization criteria and show that substantial efficiency gains can be achieved by recruiting the optimal mix of prevalent and incident patients. The proposed methods are applied to assess waitlist outcomes among kidney transplant candidates.

The association between study design and antidepressant effects in psychedelic-assisted therapy: A meta-analysis.

Different study designs of psychedelic trials may impact the blinding and expectance, leading to biased treatment effects. This study aimed to examine the association between antidepressant efficacy and study designs in psychedelic trials. Six databases were systematically searched. Eligible trials were required to investigate the efficacy of psychedelics (psilocybin, lysergic acid diethylamide [LSD], 3,4-Methylenedioxymethamphetamine [MDMA], and ayahuasca) in adult patients with depressive symptoms. We only considered oral psychedelic-assisted therapy without concomitant use of antidepressants. The primary outcome was the change in depressive symptoms. There were five study designs of psychedelic trials, including non-active-drug-as-placebo, active-drug-as-placebo, waitlist-as-control, fixed-order, and pre-post designs. In non-active-drug -as-placebo design, psilocybin (k = 4, Hedges' g [g] = 0.87, 95 % confidence intervals[CIs] = 0.58 to 1.16) and MDMA (k = 2, g = 0.65, 95%CIs = 0.26 to 1.05) were associated with large and medium effect sizes, respectively. In active-drug-as-placebo design, both psilocybin (k = 2, g = 0.71, 95%CIs = -0.01 to 1.43) and MDMA (k = 3, g = 0.53, 95%CIs = -0.23 to 1.28) were not statistically significant. In pre-post single-arm (k = 3, g = 2.51, 95%CIs = 1.00 to 4.02) and waitlist-as-control (k = 1, g = 2.88, 95%CIs = 1.75 to 4.00) designs, psilocybin showed a large effect size of antidepressant effect. Ayahuasca also showed a large effect size in both pre-post (k = 2, g = 1.88, 95%CIs = 1.18 to 2.57) and non-active-drug-as-placebo (k = 1, g = 1.60, 95%CIs = 0.84 to 2.36) designs. LSD was associated with a significant antidepressant effect only in non-active-drug-as-placebo design (k = 1, g = 1.49, 95%CIs = 0.80 to 2.17) but not in active-drug-as-placebo design (k = 1, g = 0.44, 95%CIs = -0.90 to 1.78). The antidepressant effects of psychedelics may be overestimated in studies with pre-post single-arm, non-active-drugs-as placebo, and waitlist-control designs. Restricted sample size, difficulty with establishing blinding for participants, and over expectancy limit the estimation of the antidepressant effect of psychedelic-assisted therapy.

Concept, study design, and baseline nighttime blood pressure control status of the WISDOM-Night Study using a wrist-type oscillometric home blood pressure monitoring device.

There is growing evidence that nocturnal hypertension is an independent risk factor for cardiovascular diseases, including heart failure. However, brachial blood pressure (BP) measurements during sleep might themselves disturb sleep quality. We initiated a nationwide, multicenter observational prospective study using a wrist-type oscillometric nighttime BP monitoring device with new algorithms to measure supine BP accurately without sleep disturbance. This study, named the Wrist ICT-based Sleep and Circadian Blood Pressure Monitoring Program-Night BP Study (WISDOM-Night Study), was designed to clarify the impact of wrist-measured daily nighttime BPs on cardiovascular prognosis (stroke, coronary artery disease, heart failure, etc.) using 7 days of BP measurements at 2:00 a.m., 3:00 a.m., 4:00 a.m., and 4 h after bedtime. A total of 2751 patients with one or more cardiovascular risk factors were recruited between March 2021 and March 2024 and are currently being followed up for 7 years. Additionally, 1416 of the WISDOM-Night Study-enrolled patients who also agreed to participate in the WISDOM-Hypertension-Mediated Organ Damage (HMOD) Study underwent echocardiography to evaluate the association between wrist-measured BP and left ventricular structure. Data from this WISDOM-Night Study should provide the prospective association between nighttime BP and cardiovascular disease and reveal the indexes of nighttime BP with clinical pathological relevance. This first report of the WISDOM-Night Study describes the study design, baseline characteristics, and BP control status.

Power and sample size calculations for testing the ratio of reproductive values in phylogenetic samples.

The quality of the inferences we make from pathogen sequence data is determined by the number and composition of pathogen sequences that make up the sample used to drive that inference. However, there remains limited guidance on how to best structure and power studies when the end goal is phylogenetic inference. One question that we can attempt to answer with molecular data is whether some people are more likely to transmit a pathogen than others. Here we present an estimator to quantify differential transmission, as measured by the ratio of reproductive numbers between people with different characteristics, using transmission pairs linked by molecular data, along with a sample size calculation for this estimator. We also provide extensions to our method to correct for imperfect identification of transmission linked pairs, overdispersion in the transmission process, and group imbalance. We validate this method via simulation and provide tools to implement it in an R package, phylosamp.

The Danish National Health Survey 2023: study design and participant characteristics.

AIM: To describe the design of the Danish National Health Survey (DNHS) 2023, participants' demographic characteristics and differences in demographic and selected health-related characteristics between respondents invited by web-mode and paper-mode. METHODS: A sample of 25,000 residents in Denmark aged 16 years or above was invited to participate in the DNHS 2023 using a mixed-mode approach (web/paper mode). Web-mode invited were additionally invited to participate in an accelerometer study. The self-administered questionnaire included 83 questions about health, health behaviour and morbidity. Descriptive statistics were used to describe characteristics associated with response and invitation mode. RESULTS: The response proportion was 40.8%. Non-response was more frequent among men, individuals of the youngest age groups, individuals with non-Western backgrounds, unmarried and individuals from densely populated areas. The response proportion was higher among web-mode invited (42.0%) than paper-mode invited (22.6%). Paper-mode invited respondents were more often women, aged 80 years or older, and widowed compared with web-mode invited respondents. CONCLUSIONS: The DNHS 2023 is a national health survey including adult residents in Denmark. Non-response was more pronounced among some subgroups; however, calibrated weights were calculated to minimise non-response bias. The survey is essential for public health surveillance and can be used in health planning and policy development. Furthermore, the data from the survey can be used for research on the population's health and health behaviour. For future waves of the DNHS, it should be considered whether resources should be used to invite people unsubscribed from digital-post due to the low response proportion.

How Do We Assess Controversies Using Evidence-Based Radiology?

At its best, the practice of medicine involves careful integration of experience and evidence. Generating evidence to address controversies in radiology - and translating such evidence to practice - requires appropriate selection of methods, and an understanding of the strengths, shortcomings, and biases inherent to different research designs and analyses. Equipped with such knowledge, the radiologic community can ensure that both research and clinical practice in our discipline excels, and that those questions that will be the most critical to answer will be formulated for successful investigation in the years to come.

Characteristic of clinical trials related to traumatic brain injury registered on ClinicalTrials.gov over the past two decades (2004-2023).

Objective: The aim of this report is to provide a comprehensive overview of clinical trials and protocols related to traumatic brain injury over the past two decades. Methods: We collected information on clinical trials related to traumatic brain injury (TBI) from the ClinicalTrials.gov database, identified key categorical variables, and assessed their characteristics. Results: A total of 367 TBI-related trials were identified for analysis. All identified trials were interventional clinical trials. Most trials were small-scale, with 75.2% enrolling 1-100 participants, and only about 20% were funded by industry or the National Institutes of Health (NIH). In most trials, participants were gender-neutral (96.5%), and the primary age group was adults and older adults (56.9%). Of all identified TBI trials, 78.2% were randomized, and 69.4% were blinded. Additionally, the primary purpose of 297 trials (80.9%) was treatment, with drug therapy as the most common intervention. A total of 153 trials (41.7%) were completed; however, only 58 trials submitted results to the registry. Furthermore, 81 trials (22.1%) were discontinued early, primarily due to recruitment problems. Clinical trials started between 2004 and 2013 reported a higher proportion of results compared with those started between 2014 and 2023 (35.1% vs. 11.1%, p < 0.001). In addition, between 2014 and 2023, there was an increase in trials for diagnostic purposes (2.4% vs. 6.5%, p < 0.001). Conclusion: Based on the data collected from the ClinicalTrials.gov, our study reveals that most clinical trials related to TBI focus on drug-related treatments, underreporting remains a significant concern, and greater emphasis should be placed on improving the publication and dissemination of clinical trial results.

Machine learning-based prediction of early neurological deterioration after intravenous thrombolysis for stroke: insights from a large multicenter study.

Background: This investigation seeks to ascertain the efficacy of various machine learning models in forecasting early neurological deterioration (END) following thrombolysis in patients with acute ischemic stroke (AIS). Methods: Employing data from the Shenyang Stroke Emergency Map database, this multicenter study compiled information on 7,570 AIS patients from 29 comprehensive hospitals who received thrombolytic therapy between January 2019 and December 2021. An independent testing cohort was constituted from 2,046 patients at the First People's Hospital of Shenyang. The dataset incorporated 15 pertinent clinical and therapeutic variables. The principal outcome assessed was the occurrence of END post-thrombolysis. Model development was executed using an 80/20 split for training and internal validation, employing classifiers like logistic regression with lasso regularization (lasso regression), support vector machine (SVM), random forest (RF), gradient-boosted decision tree (GBDT), and multi-layer perceptron (MLP). The model with the highest area under the curve (AUC) was utilized to delineate feature significance. Results: Baseline characteristics showed variability in END incidence between the training (n = 7,570; END incidence 22%) and external validation cohorts (n = 2,046; END incidence 10%; p < 0.001). Notably, all machine learning models demonstrated superior AUC values compared to the reference model, indicating their enhanced predictive capacity. The lasso regression model achieved the highest AUC at 0.829 (95% CI: 0.799-0.86; p < 0.001), closely followed by the MLP model with an AUC of 0.828 (95% CI: 0.799-0.858; p < 0.001). The SVM, RF, and GBDT models also showed commendable AUCs of 0.753, 0.797, and 0.774, respectively. Decision curve analysis revealed that the SVM and MLP models demonstrated a high net benefit. Feature importance analysis emphasized "Onset To Needle Time" and "Admission NIHSS Score" as significant predictors. Conclusion: Our research establishes the MLP and lasso regression as robust tools for predicting early neurological deterioration in acute ischemic stroke patients following thrombolysis. Their superior predictive accuracy, compared to traditional models, highlights the significant potential of machine learning approaches in refining prognosis and enhancing clinical decisions in stroke care management. This advancement paves the way for more tailored therapeutic strategies, ultimately aiming to improve patient outcomes in clinical practice.

Advancing collaborative research for health: why does collaboration matter?

The calls for health research to be collaborative are ubiquitous-even as part of a recent World Health Assembly resolution on clinical trials-yet the arguments in support of collaborative research have been taken for granted and are absent in the literature. This article provides three arguments to justify why health research ought to be collaborative and discusses trade-offs to be considered among the ethical values guiding each argument.

Power analyses to inform Duplex Sequencing study designs for MutaMouse liver and bone marrow.

Regulatory genetic toxicology testing is essential for identifying potentially mutagenic hazards. Duplex Sequencing (DS) is an error-corrected next-generation sequencing technology that provides substantial advantages for mutation analysis over conventional mutagenicity assays including: improved accuracy of mutation detection, ability to measure changes in mutation spectrum, and applicability across diverse biological models. To apply DS for regulatory toxicology testing, power analyses are required to determine suitable sample sizes and study designs. In this study, we explored study designs to achieve sufficient power for various effect sizes in chemical mutagenicity assessment. We collected data from MutaMouse bone marrow and liver samples that were analyzed by DS using TwinStrand's Mouse Mutagenesis Panel. Average duplex reads achieved in two separates studies on liver and bone marrow were 8.4 × 108 (± 7.4 × 107) and 9.5 × 108 (± 1.0 × 108), respectively. Baseline mean mutation frequencies (MF) were 4.6 × 10-8 (± 6.7 × 10-9) and 4.6 × 10-8 (± 1.1 × 10-8), with estimated standard deviations for the animal-to-animal random effect of 0.15 and 0.20, for liver and bone marrow, respectively. We conducted simulation analyses based on these empirically derived parameters. We found that a sample size of four animals per group is sufficient to obtain over 80% power to detect a two-fold change in MF relative to baseline. In addition, we estimated the minimal total number of informative duplex bases sequenced with different sample sizes required to retain power for various effect sizes. Our work provides foundational data for establishing suitable study designs for mutagenicity testing using DS.

How to Conduct Clinical Research in Cardiovascular Imaging: A Primer for Clinical Cardiologists and Researchers. A Statement of the European Association of Cardiovascular Imaging (EACVI) of the ESC.

This statement from the European Association of Cardiovascular Imaging (EACVI) of the ESC aims to address the fundamental principles that guide clinical research in the field of cardiovascular imaging. It provides clinical researchers, cardiology fellows, and Ph.D. students with a condensed, updated, and practical reference document to support them in designing, implementing, and conducting imaging protocols for clinical trials. Although the present article cannot replace formal research training and mentoring, it is recommended reading for any professional interested in becoming acquainted with or participating in clinical trials involving cardiovascular imaging.

The impact of a nurse's dual role on implementing an effectiveness study.

BACKGROUND: Reorganizing the Approach to Diabetes through the Application of Registries (RADAR) improved diabetes care and outcomes for First Nations people in Alberta, Canada. The nurse involved in the implementation of RADAR performed two roles in this model of care: research nurse and care coordinator. AIM: To describe the research nurse's dual role in the implementation and evaluation of RADAR. DISCUSSION: The research nurse not only documented and collected data in hard-to-reach communities as part of effective research, she also provided remote care coordination to support community healthcare providers using a culturally tailored registry to facilitate population-level care. This dual role required many qualities of nursing leadership and transformation. CONCLUSION: The research nurse's two roles contributed to the success of the intervention and were critical to the successful implementation of the model, creating valuable real-world evidence across diverse populations and settings. IMPLICATIONS: for practice Nurses are well placed to perform research duties alongside engagement and implementation activities. This can enhance the effectiveness and evaluation of healthcare interventions, particularly in community-based interventions within First Nations communities.

Heterogeneity of animal experiments and how to deal with it.

Heterogeneity of study samples is ubiquitous in animal experiments. Here, we discuss the different options of how to deal with heterogeneity in the statistical analysis of a single experiment. Specifically, data from different sub-groups (e.g. sex, strain, age cohorts) may be analysed separately, heterogenization factors may be ignored and data pooled for analysis, or heterogenization factors may be included as additional variables in the statistical model. The cost of ignoring a heterogenization factor is an inflated estimate of the variance and a consequent loss of statistical power. Therefore, it is usually preferable to include the heterogenization factor in the statistical model, especially if the heterogenization factor has been introduced intentionally (e.g. using both sexes). If heterogenization factors are included, they can be treated either as fixed factors in an analysis of variance design or sometimes as random effects in mixed effects regression models. Finally, for an appropriate sample size estimation, it is necessary to decide whether to treat heterogenization factors as nuisance variables, or whether the experiment should be powered to be able to detect not only the main effect of the treatment but also interactions between heterogenization factors and the treatment variable.

Prepared for the polycrisis? The need for complexity science and systems thinking to address global and national evidence gaps.

The Sustainable Development Goals are far off track. The convergence of global threats such as climate change, conflict and the lasting effects of the COVID-19 pandemic-among others-call for better data and research evidence that can account for the complex interactions between these threats. In the time of polycrisis, global and national-level data and research evidence must address complexity. Viewed through the lens of 'systemic risk', there is a need for data and research evidence that is sufficiently representative of the multiple interdependencies of global threats. Instead, current global published literature seems to be dominated by correlational, descriptive studies that are unable to account for complex interactions. The literature is geographically limited and rarely from countries facing severe polycrisis threats. As a result, country guidance fails to treat these threats interdependently. Applied systems thinking can offer more diverse research methods that are able to generate complex evidence. This is achievable through more participatory processes that will assist stakeholders in defining system boundaries and behaviours. Additionally, applied systems thinking can draw on known methods for hypothesising, modelling, visualising and testing complex system properties over time. Application is much needed for generating evidence at the global level and within national-level policy processes and structures.

Multilevel Longitudinal Functional Principal Component Model.

Sensor devices, such as accelerometers, are widely used for measuring physical activity (PA). These devices provide outputs at fine granularity (e.g., 10-100 Hz or minute-level), which while providing rich data on activity patterns, also pose computational challenges with multilevel densely sampled data, resulting in PA records that are measured continuously across multiple days and visits. On the other hand, a scalar health outcome (e.g., BMI) is usually observed only at the individual or visit level. This leads to a discrepancy in numbers of nested levels between the predictors (PA) and outcomes, raising analytic challenges. To address this issue, we proposed a multilevel longitudinal functional principal component analysis (mLFPCA) model to directly model multilevel functional PA inputs in a longitudinal study, and then implemented a longitudinal functional principal component regression (FPCR) to explore the association between PA and obesity-related health outcomes. Additionally, we conducted a comprehensive simulation study to examine the impact of imbalanced multilevel data on both mLFPCA and FPCR performance and offer guidelines for selecting optimal methods.

The Healthy User Effect in Pharmacoepidemiology.

The healthy user effect is a well-recognized bias in the field of pharmacoepidemiology and can be expected to overstate the effect of a preventive intervention when comparing long term users or "adherers" to non-users. Similar to the healthy worker effect observed in occupational epidemiology, the healthy user effect can be separated into a healthy initiator effect (baseline confounding) and a healthy adherer effect (selection bias). Restriction approaches and new user designs that implicitly condition on the indication and, similar healthy behaviors or health status can often mitigate the healthy initiator effect (confounding) or healthy adherer effect (selection bias) at the start of a study. Addressing the healthy adherer effect due to continued conditioning on adherence over the duration of a study is more challenging as methods to mitigate it require the ability to predict adherence, which is often difficult using databases common in pharmacoepidemiologic research. Here, we describe the healthy user effect, with supporting examples, and describe study design approaches available to pharmacoepidemiologists to mitigate the potential for bias.

Expert opinion on design and endpoints for studies on catheter ablation of atrial fibrillation.

INTRODUCTION: Catheter ablation of atrial fibrillation (AF) is frequently studied in randomized trials, observational and registry studies. The aim of this expert opinion is to provide guidance for clinicians and industry regarding the development of future clinical studies on catheter ablation of AF, implement lessons learned from previous studies, and promote a higher degree of consistency across studies. BACKGROUND: Studies on catheter ablation of AF may benefit from well-described definitions of endpoints and consistent methodology and documentation of outcomes related to efficacy, safety and cost-effectiveness. The availably of new, innovative technologies warrants further consideration about their application and impact on study design and the choice of endpoints. Moreover, recent insights gained from AF ablation studies suggest a reconsideration of some methodological aspects. METHODS: A panel of clinical experts on catheter ablation of AF and designing and conducting clinical studies developed an expert opinion on the design and endpoints for studies on catheter ablation of AF. Discussions within the expert panel with the aim to reach consensus on predefined topics were based on outcomes reported in the literature and experiences from recent clinical trials. RESULTS: A comprehensive set of recommendations is presented. Key elements include the documentation of clinical AF, medication during the study, repeated ablations and their effect on endpoint assessments, postablation blanking and the choice of rhythm-related and other endpoints. CONCLUSION: This expert opinion provides guidance and promotes consistency regarding design of AF catheter ablation studies and identified aspects requiring further research to optimize study design and methodology. CONDENSED ABSTRACT: Recent insights from studies on catheter ablation of atrial fibrillation (AF) and the availability of new innovative technologies warrant reconsideration of methodological aspects related to study design and the choice and assessment of endpoints. This expert opinion, developed by clinical experts on catheter ablation of AF provides a comprehensive set of recommendations related to these methodological aspects. The aim of this expert opinion is to provide guidance for clinicians and industry regarding the development of clinical studies, implement lessons learned from previous studies, and promote a higher degree of consistency across studies.

PATIENT RECRUITMENT USING ELECTRONIC HEALTH RECORDS UNDER SELECTION BIAS: A TWO-PHASE SAMPLING FRAMEWORK.

Electronic health records (EHRs) are increasingly recognized as a cost-effective resource for patient recruitment in clinical research. However, how to optimally select a cohort from millions of individuals to answer a scientific question of interest remains unclear. Consider a study to estimate the mean or mean difference of an expensive outcome. Inexpensive auxiliary covariates predictive of the outcome may often be available in patients' health records, presenting an opportunity to recruit patients selectively, which may improve efficiency in downstream analyses. In this paper we propose a two-phase sampling design that leverages available information on auxiliary covariates in EHR data. A key challenge in using EHR data for multiphase sampling is the potential selection bias, because EHR data are not necessarily representative of the target population. Extending existing literature on two-phase sampling design, we derive an optimal two-phase sampling method that improves efficiency over random sampling while accounting for the potential selection bias in EHR data. We demonstrate the efficiency gain from our sampling design via simulation studies and an application evaluating the prevalence of hypertension among U.S. adults leveraging data from the Michigan Genomics Initiative, a longitudinal biorepository in Michigan Medicine.

A worked example of contextualising and using reflexive thematic analysis in nursing research.

BACKGROUND: A researcher must consider their research question within their world view before selecting a technique appropriate for analysing their data. This will affect their choices of methodology and methods for collecting and analysing data. Reflexive thematic analysis (RTA) has become a go-to technique for qualitative nurse researchers. However, the justifications for using it and its application in the context of a wider approach are under-discussed. AIM: To rationalise the use of RTA within a wider philosophical-methodological-methods-analysis approach and provide nurse researchers with practical guidance about how to apply it to qualitative data. DISCUSSION: This article conceptually grounds the seminal work of Braun and Clarke (2006 ) and provides a process for rigorously and systematically analysing qualitative data. Researchers undertaking qualitative research must use a rigorous philosophical-methodological-method-analysis approach. Before selecting a technique appropriate for analysing their data, they must consider their research question within their own world view. This has implications for their choice of methodology and consequently the data collection methods and analysis techniques they use. Researchers should be mindful of RTA's conceptual roots when applying it. CONCLUSION: Transparent and rigorous data analysis leads to credible findings, supports evidence-based practice and contributes to the growing body of nursing research. Within the context of the wider philosophical-methodological-methods-analysis approach, RTA produces high-quality, credible findings when applied well. IMPLICATIONS: for practice This article can guide nursing students and novice researchers in choosing and applying RTA to their research.