Absolute risk from double nested case-control designs: cause-specific proportional hazards models with and without augmented estimating equations.

We estimate relative hazards and absolute risks (or cumulative incidence or crude risk) under cause-specific proportional hazards models for competing risks from double nested case-control (DNCC) data. In the DNCC design, controls are time-matched not only to cases from the cause of primary interest, but also to cases from competing risks (the phase-two sample). Complete covariate data are available in the phase-two sample, but other cohort members only have information on survival outcomes and some covariates. Design-weighted estimators use inverse sampling probabilities computed from Samuelsen-type calculations for DNCC. To take advantage of additional information available on all cohort members, we augment the estimating equations with a term that is unbiased for zero but improves the efficiency of estimates from the cause-specific proportional hazards model. We establish the asymptotic properties of the proposed estimators, including the estimator of absolute risk, and derive consistent variance estimators. We show that augmented design-weighted estimators are more efficient than design-weighted estimators. Through simulations, we show that the proposed asymptotic methods yield nominal operating characteristics in practical sample sizes. We illustrate the methods using prostate cancer mortality data from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Study of the National Cancer Institute.

Use of Statistics in Plant Cell Tissue Culture.

Statistics and experimental design are important tools for plant cell and tissue culture researchers and should be used when planning and conducting experiments as well as during the analysis and interpretation of experimental results. The chapter provides basic concepts important to the statistical analysis of data obtained from plant tissue culture experiments and illustrates the application of common statistical procedures to analyze binomial, count, and continuous data for experiments with different treatment factors as well as identifying trends of dosage treatment factors.

Prompt closure versus gradual weaning of external ventricular drain for hydrocephalus following aneurysmal subarachnoid haemorrhage: a statistical analysis plan for the DRAIN randomised clinical trial.

BACKGROUND: Insertion of an external ventricular drain (EVD) is a first-line treatment of acute hydrocephalus caused by aneurysmal subarachnoid haemorrhage (aSAH). Once the patient is clinically stable, the EVD is either removed or replaced by a permanent internal shunt. The optimal strategy for cessation of the EVD is unknown. Prompt closure carries a risk of acute hydrocephalus or redundant shunt implantations, whereas gradual weaning may increase the risk of EVD-related infections. METHODS: DRAIN (Danish RAndomised Trial of External Ventricular Drainage Cessation IN Aneurysmal Subarachnoid Haemorrhage) is an international multicentre randomised clinical trial comparing prompt closure versus gradual weaning of the EVD after aSAH. The primary outcome is a composite of VP-shunt implantation, all-cause mortality, or EVD-related infection. Secondary outcomes are serious adverse events excluding mortality and health-related quality of life (EQ-5D-5L). Exploratory outcomes are modified Rankin Scale, Fatigue Severity Scale, Glasgow Outcome Scale Extended, and length of stay in the neurointensive care unit and hospital. Outcome assessment will be performed 6 months after ictus. Based on the sample size calculation (event proportion 80% in the gradual weaning group, relative risk reduction 20%, alpha 5%, power 80%), 122 participants are required in each intervention group. Outcome assessment for the primary outcome, statistical analyses, and conclusion drawing will be blinded. Two independent statistical analyses and reports will be tracked using a version control system, and both will be published. Based on the final statistical report, the blinded steering group will formulate two abstracts. CONCLUSION: We present a pre-defined statistical analysis plan for the randomised DRAIN trial, which limits bias, p-hacking, and data-driven interpretations. This statistical analysis plan is accompanied by tables with simulated data, which increases transparency and reproducibility. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT03948256. Registered on May 13, 2019.

Multiply robust estimation of marginal structural models in observational studies subject to covariate-driven observations.

Electronic health records and other sources of observational data are increasingly used for drawing causal inferences. The estimation of a causal effect using these data not meant for research purposes is subject to confounding and irregularly-spaced covariate-driven observation times affecting the inference. A doubly-weighted estimator accounting for these features has previously been proposed that relies on the correct specification of two nuisance models used for the weights. In this work, we propose a novel consistent multiply robust estimator and demonstrate analytically and in comprehensive simulation studies that it is more flexible and more efficient than the only alternative estimator proposed for the same setting. It is further applied to data from the Add Health study in the United States to estimate the causal effect of therapy counseling on alcohol consumption in American adolescents.

Adopting Optimal Statistical Practices: Applied Researchers.

In keeping with this year's focus on how we might foster a culture of research that values and consistently adopts optimal statistical practices, this column entry highlights practices our applied researchers can take up that may help remedy the gap between recommended statistical practices and implementation. This installment specifically encourages increasing the transparency of analyses, teaming up with colleagues with quantitative expertise, and disseminating resources that highlight optimal practices. [J Nurs Educ. 2024;63(7):490-491.].

A hybrid digital parenting programme to prevent abuse of adolescents in Tanzania: statistical analysis plan for a pragmatic cluster randomised controlled trial.

BACKGROUND: Globally, violence against children poses substantial health and economic challenges, with estimated costs nearing USD 7 trillion. This prompts the urgent call for effective evidence-based interventions in preventing and mitigating violence against children. ParentApp is a mobile, open-source application designed to offer a remote version of the Parenting for Lifelong Health (PLH) programme. ParentApp is the first digital parenting intervention for caregivers of adolescents aged 10-17 years to be tested in low- and middle-income settings. METHODS: This study is a pragmatic, two-arm, cluster-randomised trial in Mwanza, Tanzania's urban and peri-urban areas. Assessments are set for baseline, 1 month post-intervention, and 12 months post-intervention. We randomised 80 clusters, each with about 30 caregiver-adolescent dyads, with a 1:1 ratio stratified by urban or peri-urban location. Both arms receive an entry-level smartphone preloaded with Kiswahili apps-ParentApp for intervention and WashApp control. The primary method of analysis will be generalised linear mixed-effects models with adjustment for person-level characteristics and multiple imputation. In three-level models, measurement waves are nested within a person, nested within a sub-ward. Regressions will constrain groups to be equal at baseline and include covariates for stratification, percentage of male caregivers, and individual-level characteristics. DISCUSSIONS: Preparations for the trial began in December 2022, including community mobilisation and sensitisation. Rolling recruitment, baseline data collection, and implementation onboarding took place between April and September 2023. One-month post-test data collection began in August 2023 and thus far achieved 97% and 94% retention rates for caregivers and adolescents respectively. Final post-test data collection will begin in September 2024, anticipated to run until April 2025. This SAP was submitted to the journal before the interim analysis to preserve scientific integrity under a superiority hypothesis testing framework. TRIAL REGISTRATION: The trial was registered on the Open Science Framework on 14 March 2023: https://doi.org/10.17605/OSF.IO/T9FXZ . The trial protocol was published in Trials 25, 119 (2024): Baerecke, L., Ornellas, A., Wamoyi, J. et al. A hybrid digital parenting programme to prevent abuse of adolescents in Tanzania: study protocol for a pragmatic cluster-randomised controlled trial. Trials 25, 119 (2024). https://doi.org/10.1186/s13063-023-07893-x .

Sample size calculation for comparing two ROC curves.

Biomarkers are key components of personalized medicine. In this paper, we consider biomarkers taking continuous values that are associated with disease status, called case and control. The performance of such a biomarker is evaluated by the area under the curve (AUC) of its receiver operating characteristic curve. Oftentimes, two biomarkers are collected from each subject to test if one has a larger AUC than the other. We propose a simple non-parametric statistical test for comparing the performance of two biomarkers. We also present a simple sample size calculation method for this test statistic. Our sample size formula requires specification of AUC values (or the standardized effect size of each biomarker between cases and controls together with the correlation coefficient between two biomarkers), prevalence of cases in the study population, type I error rate, and power. Through simulations, we show that the testing on two biomarkers controls type I error rate accurately and the proposed sample size closely maintains specified statistical power.

Smart data analysis V2: A user-friendly software for non-statisticians.

Data analysis can be accurate and reliable only if the underlying assumptions of the used statistical method are validated. Any violations of these assumptions can change the outcomes and conclusions of the analysis. In this study, we developed Smart Data Analysis V2 (SDA-V2), an interactive and user-friendly web application, to assist users with limited statistical knowledge in data analysis, and it can be freely accessed at https://jularatchumnaul.shinyapps.io/SDA-V2/. SDA-V2 automatically explores and visualizes data, examines the underlying assumptions associated with the parametric test, and selects an appropriate statistical method for the given data. Furthermore, SDA-V2 can assess the quality of research instruments and determine the minimum sample size required for a meaningful study. However, while SDA-V2 is a valuable tool for simplifying statistical analysis, it does not replace the need for a fundamental understanding of statistical principles. Researchers are encouraged to combine their expertise with the software's capabilities to achieve the most accurate and credible results.

Statistical analysis plan for cluster randomised trial to evaluate a community-level complementary food safety and hygiene and nutrition intervention in Mali: the MaaCiwara study.

BACKGROUND: Diarrheal disease is a significant cause of morbidity and mortality in under-fives in many low- and middle-income countries. Changes in food safety, hygiene practices, and nutrition around the weaning period may reduce the risk of disease and improve infant development. The MaaCiwara study aims to evaluate the effectiveness of a community-based educational intervention designed to improve food safety and hygiene behaviours, as well as child nutrition. This update article describes the statistical analysis plan for the MaaCiwara study in detail. METHODS AND DESIGN: The MaaCiwara study is a parallel group, two-arm, superiority cluster randomised controlled trial with baseline measures, involving 120 clusters of rural and urban communities. These clusters are randomised to either receive the community-based behaviour change intervention or to the control group. The study participants will be mother-child pairs, with children aged between 6 and 36 months. Data collection involves a day of observation and interviews with each participating mother-child pair, conducted at baseline, 4 months, and 15 months post-intervention. The primary analysis aims to estimate the effectiveness of the intervention on changes to complementary food safety and preparation behaviours, food and water contamination, and diarrhoea. The primary outcomes will be analysed generalised linear mixed models, at individual level, accounting for clusters and rural/urban status to estimate the difference in outcomes between the intervention and control groups. Secondary outcomes include maternal autonomy, enteric infection, nutrition, child anthropometry, and development scores. In addition, structural equation analysis will be conducted to examine the causal relationships between the different outcomes. TRIAL REGISTRATION: International Standard Randomised Controlled Trial Number (ISRCTN) register: ISRCTN14390796 . Registered on 13 December 2021.

A randomized, embedded, pragmatic, Bayesian clinical trial examining clinical decision support for high flow nasal cannula management in children with bronchiolitis: design and statistical analysis plan.

BACKGROUND: High flow nasal cannula (HFNC) has been increasingly adopted in the past 2 decades as a mode of respiratory support for children hospitalized with bronchiolitis. The growing use of HFNC despite a paucity of high-quality data regarding the therapy's efficacy has led to concerns about overutilization. We developed an electronic health record (EHR) embedded, quality improvement (QI) oriented clinical trial to determine whether standardized management of HFNC weaning guided by clinical decision support (CDS) results in a reduction in the duration of HFNC compared to usual care for children with bronchiolitis. METHODS: The design and summary of the statistical analysis plan for the REspiratory SupporT for Efficient and cost-Effective Care (REST EEC; "rest easy") trial are presented. The investigators hypothesize that CDS-coupled, standardized HFNC weaning will reduce the duration of HFNC, the trial's primary endpoint, for children with bronchiolitis compared to usual care. Data supporting trial design and eventual analyses are collected from the EHR and other real world data sources using existing informatics infrastructure and QI data sources. The trial workflow, including randomization and deployment of the intervention, is embedded within the EHR of a large children's hospital using existing vendor features. Trial simulations indicate that by assuming a true hazard ratio effect size of 1.27, equivalent to a 6-h reduction in the median duration of HFNC, and enrolling a maximum of 350 children, there will be a > 0.75 probability of declaring superiority (interim analysis posterior probability of intervention effect > 0.99 or final analysis posterior probability of intervention effect > 0.9) and a > 0.85 probability of declaring superiority or the CDS intervention showing promise (final analysis posterior probability of intervention effect > 0.8). Iterative plan-do-study-act cycles are used to monitor the trial and provide targeted education to the workforce. DISCUSSION: Through incorporation of the trial into usual care workflows, relying on QI tools and resources to support trial conduct, and relying on Bayesian inference to determine whether the intervention is superior to usual care, REST EEC is a learning health system intervention that blends health system operations with active evidence generation to optimize the use of HFNC and associated patient outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT05909566. Registered on June 18, 2023.

Addressing researcher degrees of freedom through minP adjustment.

When different researchers study the same research question using the same dataset they may obtain different and potentially even conflicting results. This is because there is often substantial flexibility in researchers' analytical choices, an issue also referred to as "researcher degrees of freedom". Combined with selective reporting of the smallest p-value or largest effect, researcher degrees of freedom may lead to an increased rate of false positive and overoptimistic results. In this paper, we address this issue by formalizing the multiplicity of analysis strategies as a multiple testing problem. As the test statistics of different analysis strategies are usually highly dependent, a naive approach such as the Bonferroni correction is inappropriate because it leads to an unacceptable loss of power. Instead, we propose using the "minP" adjustment method, which takes potential test dependencies into account and approximates the underlying null distribution of the minimal p-value through a permutation-based procedure. This procedure is known to achieve more power than simpler approaches while ensuring a weak control of the family-wise error rate. We illustrate our approach for addressing researcher degrees of freedom by applying it to a study on the impact of perioperative p a O 2 on post-operative complications after neurosurgery. A total of 48 analysis strategies are considered and adjusted using the minP procedure. This approach allows to selectively report the result of the analysis strategy yielding the most convincing evidence, while controlling the type 1 error-and thus the risk of publishing false positive results that may not be replicable.

On exact randomization-based covariate-adjusted confidence intervals.

Randomization-based inference using the Fisher randomization test allows for the computation of Fisher-exact P-values, making it an attractive option for the analysis of small, randomized experiments with non-normal outcomes. Two common test statistics used to perform Fisher randomization tests are the difference-in-means between the treatment and control groups and the covariate-adjusted version of the difference-in-means using analysis of covariance. Modern computing allows for fast computation of the Fisher-exact P-value, but confidence intervals have typically been obtained by inverting the Fisher randomization test over a range of possible effect sizes. The test inversion procedure is computationally expensive, limiting the usage of randomization-based inference in applied work. A recent paper by Zhu and Liu developed a closed form expression for the randomization-based confidence interval using the difference-in-means statistic. We develop an important extension of Zhu and Liu to obtain a closed form expression for the randomization-based covariate-adjusted confidence interval and give practitioners a sufficiency condition that can be checked using observed data and that guarantees that these confidence intervals have correct coverage. Simulations show that our procedure generates randomization-based covariate-adjusted confidence intervals that are robust to non-normality and that can be calculated in nearly the same time as it takes to calculate the Fisher-exact P-value, thus removing the computational barrier to performing randomization-based inference when adjusting for covariates. We also demonstrate our method on a re-analysis of phase I clinical trial data.

Incorporating nonparametric methods for estimating causal excursion effects in mobile health with zero-inflated count outcomes.

In mobile health, tailoring interventions for real-time delivery is of paramount importance. Micro-randomized trials have emerged as the "gold-standard" methodology for developing such interventions. Analyzing data from these trials provides insights into the efficacy of interventions and the potential moderation by specific covariates. The "causal excursion effect," a novel class of causal estimand, addresses these inquiries. Yet, existing research mainly focuses on continuous or binary data, leaving count data largely unexplored. The current work is motivated by the Drink Less micro-randomized trial from the UK, which focuses on a zero-inflated proximal outcome, i.e., the number of screen views in the subsequent hour following the intervention decision point. To be specific, we revisit the concept of causal excursion effect, specifically for zero-inflated count outcomes, and introduce novel estimation approaches that incorporate nonparametric techniques. Bidirectional asymptotics are established for the proposed estimators. Simulation studies are conducted to evaluate the performance of the proposed methods. As an illustration, we also implement these methods to the Drink Less trial data.

Generalized single index modeling of longitudinal data with multiple binary responses.

In health and clinical research, medical indices (eg, BMI) are commonly used for monitoring and/or predicting health outcomes of interest. While single-index modeling can be used to construct such indices, methods to use single-index models for analyzing longitudinal data with multiple correlated binary responses are underdeveloped, although there are abundant applications with such data (eg, prediction of multiple medical conditions based on longitudinally observed disease risk factors). This article aims to fill the gap by proposing a generalized single-index model that can incorporate multiple single indices and mixed effects for describing observed longitudinal data of multiple binary responses. Compared to the existing methods focusing on constructing marginal models for each response, the proposed method can make use of the correlation information in the observed data about different responses when estimating different single indices for predicting response variables. Estimation of the proposed model is achieved by using a local linear kernel smoothing procedure, together with methods designed specifically for estimating single-index models and traditional methods for estimating generalized linear mixed models. Numerical studies show that the proposed method is effective in various cases considered. It is also demonstrated using a dataset from the English Longitudinal Study of Aging project.

Statistical analysis of early cancer diagnosis requires new methods. / Statistisk analyse av tidligdiagnostikk av kreft krever nye metoder.

The methods for diagnosing cancer have traditionally been based on the concept that everything grows. However, immunotherapy and screening trials show that some tumours resolve spontaneously.

Missing data in amortized simulation-based neural posterior estimation.

Amortized simulation-based neural posterior estimation provides a novel machine learning based approach for solving parameter estimation problems. It has been shown to be computationally efficient and able to handle complex models and data sets. Yet, the available approach cannot handle the in experimental studies ubiquitous case of missing data, and might provide incorrect posterior estimates. In this work, we discuss various ways of encoding missing data and integrate them into the training and inference process. We implement the approaches in the BayesFlow methodology, an amortized estimation framework based on invertible neural networks, and evaluate their performance on multiple test problems. We find that an approach in which the data vector is augmented with binary indicators of presence or absence of values performs the most robustly. Indeed, it improved the performance also for the simpler problem of data sets with variable length. Accordingly, we demonstrate that amortized simulation-based inference approaches are applicable even with missing data, and we provide a guideline for their handling, which is relevant for a broad spectrum of applications.