BMC Medical Research Methodology最新文献

Background: Trial Sequential Analysis (TSA) is a statistical method to control random errors in systematic reviews with meta-analyses of randomised clinical trials. In our results from the Major Mistakes and Errors in Trial Sequential Analysis (METSA) project, we systematically assessed the use of TSA across all medical fields and found significant mistakes in the preplanning and reporting of most TSAs. This article provides a practical guide for authors of systematic review protocols on what to consider when planning Trial Sequential Analysis for dichotomous outcomes.

Methods: This practical guide has been developed based on the TSA manual, the recommendations published previously by Jakobsen and colleagues and Wetterslev and colleagues along with the findings from our recently published results from the METSA project.

Results: The following five parameters should be clearly defined in a publicly available protocol before the review is undertaken: 1) the proportion of participants with an event in the control group; 2) the relative risk reduction or increase in the experimental group; 3) the risk of type I errors (alpha); 4) the risk of type II errors (beta); and 5) the diversity of the meta-analysis. Improving the planning and reporting of these parameters will improve the interpretation, reproducibility, and validity of Trial Sequential Analysis results used in systematic reviews.

Conclusions: We hope this practical guide will aid in improving pre-registration and reporting of TSAs of dichotomous outcomes within systematic review protocols with meta-analysis of randomised clinical trials in the future.

Background: Longitudinal data can be used to study disease progression and are often collected at irregular intervals. When the assessment times are informative about the severity of the disease, regression analyses of the outcome trajectory over time based on Generalized Estimating Equations (GEEs) result in biased estimates of regression coefficients. Inverse-intensity weighted GEEs (IIW-GEEs) are a popular approach to account for informative assessment times and yield unbiased estimates of outcome model coefficients when the assessment times and outcomes are conditionally independent given previously observed data. However, a consequence of irregular assessment times is that some patients may have no follow-up assessments at all, and it is common practice to omit these patients from analyses when studying the outcome trajectory over time.

Methods: We show mathematically that IIW-GEEs yield biased estimates of regression coefficients when patients with no follow-up assessments are excluded from analyses. We design a simulation study to evaluate how the bias varies with sample size, assessment frequency, follow-up time, and the informativeness of the assessment time process. Using the STAR*D trial of treatments for major depressive disorder, we examine the extent of bias in practice.

Results: Our simulation results showed the bias incurred by omitting patients with no follow-up visits increased as visit frequency decreased and as the duration of follow-up decreased. In the STAR*D trial, omitting patients with no follow-up visits led to over-estimation of the rate of improvement in depressive symptoms.

Conclusions: Studies should be designed to ensure patients with no follow-up are included in the data. This can be achieved by a) creating inception cohorts; b) when taking sub-samples of existing cohorts, ensuring that patients without follow-up assessments are included; c) dropping exclusion criteria based on availability of follow-up visits.

Background: Advancements in medical treatment have significantly increased the likelihood of survival after childhood and adolescent cancer. However, this expanding group remains vulnerable to various late effects resulting from cancer itself or cancer treatment. It is crucial to implement consistent and systematic follow-up care procedures to promptly identify and address potential complications that may arise later in life.

Methods: We conducted 19 unstructured participant observations of follow-up appointments and 36 episodic narrative interviews with paediatric cancer survivors (diagnosed before age 18) and their informal caregivers. We analysed observational field notes and personal narratives on the "survivor pathway" from interview transcripts, applying the inductive narrative method to Yin's approach to case study development. Synthesising frequently discussed topics, we generated case studies to discuss with healthcare professionals and patient representatives in a focus group setting.

Results: We designed two case studies to capture the complexity of follow-up care organisation in paediatric cancer survivorship for further discussion in focus groups with healthcare professionals. One case study describes a typical 'survivor pathway' of an adult survivor of paediatric cancer, and another describes a survivor currently transitioning from paediatric to adult healthcare facilities.

Conclusions: Our objective is to examine real-life survivorship scenarios with the overall aim of suggesting improvements to the current structure of paediatric cancer follow-up care in the framework of a larger VersKiK-Study. We used both case studies as a basis for discussion in four focus groups (ca. 8 participants each) with healthcare providers involved in paediatric cancer follow-up and patient advocates.

Background: The longitudinal qualitative research (LQR) approach is an emerging design in nursing research which focuses on examining changes in experiences over specified timepoints. While some authors have tied this approach to a specific qualitative tradition such as phenomenology and case study, other authors have associated it with two or more qualitative methodologies. Yet, others have also argued it is untied to a specific qualitative tradition. Thus, there is palpable confusion regarding whether it is a methodology or merely a method. Additionally, its guiding paradigm or philosophical/ theoretical foundations remain poorly articulated or loosely defined within the broader qualitative research tradition.

Objective: This methodological discussion paper sought to examine the guiding paradigm/ philosophical underpinning, methodology, and methods unique to LQR to ground it within the broader qualitative research tradition. A secondary goal, perhaps more nuanced, is to generate further scholarly discussions regarding LQR and its application to nursing, health, and social care research.

Design: Methodological discussion FINDINGS: When the term "longitudinal" is applied to a qualitative methodology, the emphasis is on repeated data collection informed by that methodology's theoretical perspective. However, when LQR is used, then it is to be considered as a methodology characterised by a focus on change, meaning, and time grounded in context, an emphasis on participants' own reflections of their subjective experiences and the researchers understanding of them. LQR emphasises reflective, second-order perspective (the world as experienced and perceived/ understood). With the need to uncover change across time, its dynamics, and mechanisms, LQR is argued to be potentially underpinned by the critical realist theoretical/ philosophical stance. Methodologically, LQR lends itself to methodical flexibility and pluralism. Despite its strengths, some challenges are noteworthy including attrition, time and resource demands, data management, ethical considerations, researcher bias, analytical complexity, contextual changes, and issues of transferability.

Conclusions: LQR is a methodology fit for uncovering meaning, dynamics, and mechanisms of change over time and bound to specific contexts albeit its conduct requires careful planning and availability of adequate resources.

Background: To present an analytical framework for correcting misclassification when an imperfect test is used as an indicator of a disease in association studies, taking into account that part of the sample has joint test and disease data.

Methods: We explored two scenarios, depending on whether the disease is a covariate or the outcome. The analysis sample includes an internal validation sample where the disease status is known in addition to the test. Joint likelihood models taking into account classification errors and the possible selection of the validation sample not at random were used. Simulations were performed to evaluate the methods. We illustrated our framework using data from a multi-cohort COVID-19 serological study conducted in France between 2020 and 2021, with serology as the imperfect test and SARS-CoV-2 infection as the disease. The dataset included concomitant measurements of the serological test and the SARS-CoV-2 infection status determined using additional virologic methods (PCR, neutralization assay) in 7% participants. We estimated 1) the association between incident persistent symptoms defined as a symptom lasting at least 8 weeks (outcome) and infection (covariate) and 2) the association between infection (outcome) and several covariates. For comparison, we also estimated 'naïve' models using serology without correction or using the internal validation sample only, as well as models under different assumptions about the missingness pattern of the SARS-CoV-2 infection status.

Results: Simulations confirmed the methods' abilities to correct for misclassification and not at random selection of the validation sample. In the application, the estimated sensitivities and specificities of the serological test with respect to SARS-CoV-2 infection were 86.2%-87.7% and 95.8%-97.5%, respectively. Considering SARS-CoV-2 infection as a covariate, the corrected analysis identified a significant association between infection and persistent symptoms. Considering SARS-CoV-2 infection as the outcome, the corrected analysis identified an association between infection and age, gender and active smoking, but did not retrieve an association with living with at least one child at home and previous smoking, which were identified in the naïve analysis.

Conclusion: This methodological framework can be applied in association studies when an imperfect test is used as an indicator of a disease and the disease status has been validated in a subset of the sample. We extended previous works to deal with not at random selection of this validated sample.

Background: Irritable bowel syndrome (IBS) is one of the most common gut-brain interaction disorders. Most current IBS randomized controlled trials (RCTs) do not justify their choice of outcomes, and inappropriate outcome selection may obscure efficacy evaluation.

Objective: In this study, we systematically organize outcome-related information from IBS RCTs, encompassing two key components: categorizing outcomes into distinct domains, and summarizing the corresponding outcome measurement instruments for each domain. We also classify these outcome measures based on drug therapeutic mechanisms, IBS subtypes, and the research aims of RCTs, providing a reference basis for the comprehensive and accurate selection of outcome measures for IBS RCTs.

Search strategy: We developed a systematic search (PubMed, Embase, Cochrane Library; up to May 2023) with an information specialist, combining "IBS" and "RCT" concepts. Outcomes and their instruments were categorized into domains, stratified by IBS subtype and intervention.

Result: From 408 included studies, outcomes fell into four main domains: global improvement (76.96%), individual symptoms (64.46%), quality of life (43.63%), and psychological status (24.26%). Key instruments included IBS-SSS (global improvement), BSFS (stool consistency), IBS-QOL (quality of life), and HADS (psychological status). IBS-C emphasized defecatory straining, while IBS-D focused on defecatory urgency. Most psychological therapy uses VSI for psychological assessment. Gut-directed drug trials are rarely used for evaluation in the psychological domain. Only 3 of 146 long-term efficacy studies included recurrence rates.

Conclusion: Selecting appropriate outcome measures based on drug mechanisms, different IBS subtypes, and the specific research objectives of RCTs can allow for accurate efficacy evaluation. However, the placebo effect and long-term effect evaluation require more attention.

Trials that aim to optimise the implementation of an intervention are often complex, requiring multiple, combined strategies and requiring uptake on multiple levels. Previous implementation trials have optimised implementation strategies using multi-arm cluster randomised control trials (cRCTs) but can be inefficient and waste resources. Adaptive designs may potentially improve efficiency of these trials, but under what design features and trial properties is unknown. A simulation study was performed to assess under what conditions, if any, one or two interim adaptive designs offer increased efficiency compared to a 'fixed trial' approach for implementation cRCTs. A four-arm cRCT was simulated with varied trial properties, with a fixed design or an adaptive design (varying by number of interim analyses and timing of interim analyses) and modelled using Bayesian hierarchical models. The adaptive design allowed for stopping early for futility and dropping an arm for futility. The power, type 1 error rate, and adaptive design decisions were compared between the designs across trial properties. Both one and two interim adaptive designs offered power gains and a lower type 1 error rate compared to the fixed designs across most trial properties. A high intra-class correlation (ICC) of 0.2 led to adaptive trials dropping effective arms or incorrectly stopping for futility more frequently, with the incorrect decisions being compounded when two interim analyses were used. The rate of these incorrect decisions was reduced when the first interim analysis in the two interim designs was delayed. Adaptive designs can offer improved efficiency, more power, and reduce resource wastage for trials compared to fixed designs.

Background: Recruitment of participants for preventive health intervention studies remains a significant challenge: Approximately 19% of studies are discontinued due to insufficient participant numbers, and one in three extends its recruitment period. This study aimed to examine the impact of recruiting measures on the recruitment rate over time and to describe costs of these strategies to inform future study planning and optimize resource allocation.

Methods: A cluster-randomized controlled trial ("AOK-Family+" study) was conducted from April 2023 to June 2024 in southwestern Germany. The intervention focused on reducing lifestyle-related risk factors (LRRFs) among pregnant women and women planning a pregnancy. Analog recruitment strategies included e.g., printed magazines, flyers and digital recruitment strategies included e.g. social media ads, influencer marketing. For 255 interested women, contact details, recruitment source, and participation status were documented and analyzed. A segmented linear regression analysis was used to identify turning points in application trends. Direct costs were calculated based on internal project budget tracking.

Results: The targeted sample size was not reached despite substantial investment in recruitment measures. The highest number of applications resulted from analog strategies-especially printed AOK magazines (35.5%)-followed by influencer marketing (23.6%). The segmented linear regression analysis identified three significant increases in application rates, the first coinciding with the magazine distribution and the second with the influencer marketing on Instagram. Social media marketing showed a short-lived effect, with application rates dropping immediately after posts ended. Total costs amounted to 99.334 € equaling 389,54€ per application and 534,05€ per actually enrolled participant.

Conclusion: Health magazines proved to be the most cost-efficient and sustainable recruitment strategy. Influencer marketing led to high reach and initial spikes in engagement but had limited long-term impact. While digital measures generated many clicks, only a small fraction translated into study participation-indicating a pronounced intention-behavior gap and procedural barriers in the enrolment process. For future studies, a mixed-methods recruitment strategy is recommended combining wide digital outreach with personalized, trust-based communication, ideally through healthcare professionals such as gynecologists and midwives, to reach women in early pregnancy and reduce participation barriers.

Trial registration: The German Clinical Trials Register DRKS00027804. Registered on 2022/01/12.