BMC Medical Research Methodology最新文献

Background: The generalized additive model for location, scale and shape (GAMLSS) is a flexible regression model with a wide range of applications. In particular, it is the standard method to estimate age-specific percentile curves for clinical parameters for children and adolescents. Deriving international percentile curves requires large datasets that cover a diverse population. Such datasets are typically obtained by pooling data from multiple studies. However, due to ethical and legal constraints, physically sharing and pooling sensitive individual-level data might not always be permitted. Therefore, we aimed to develop a privacy-enhancing method to fit a GAMLSS.

Methods: We developed a federated version of the GAMLSS algorithm which allows to co-analyze data from different sources, without physically transferring the data. Instead, data are analyzed locally within their secure home environments and only non-disclosive analysis results are shared. We implemented our method in DataSHIELD, an open-source software infrastructure for federated analysis in R, and investigated its theoretical properties. Considering two different use cases, we applied our algorithm to physically separated epidemiological study data and compared its results with the ones obtained by fitting a GAMLSS to the physically-pooled data. Furthermore, we evaluated the runtime of the federated GAMLSS against the original GAMLSS algorithm for varying number of observations and DataSHIELD servers.

Results: We proved that, in theory, the federated GAMLSS yields identical results as the original GAMLSS algorithm, using the additivity of matrix multiplication in the fitting algorithm. Furthermore, we provided an implementation of the proposed algorithm and demonstrated that the federated GAMLSS implementation yielded the same results as the pooled GAMLSS in our examples, with only minor differences attributable to numerical computation. However, the runtime was more than 1000 times higher for fitting the federated compared to the pooled GAMLSS.

Conclusions: In this paper, we propose a privacy-enhancing federated GAMLSS that yields virtually identical results as the original GAMLSS algorithm, without the need to physically pool the data.

Background: In a meta-analysis where the effect size varies substantially between studies it is important to report the extent of the variation. Critically, we want to know if the treatment is always helpful or sometimes harmful. The statistic that addresses this is the prediction interval (PI), which gives the range of true effects for all studies comparable to those in the meta-analysis.

Methods: In addition to the PI's upper and lower limits, we propose to report the expected proportion of comparable studies that are expected to have an effect in a given range. If we define for example thresholds corresponding to minimal clinically important benefit and harm, we can report the expected proportion of comparable studies where the true effect is expected to exceed these thresholds.

Results: We apply our approach to two Cochrane Reviews assessing a dichotomous and a continuous outcome: caesarean section and health-related quality of life. This article shows how to plot the distribution of true study effects highlighting the expected proportion of comparable studies where the true effect is clinically beneficial or harmful. We also offer suggestions for how to report this information in scientific articles.

Conclusion: In addition to PIs, reporting the expected proportion of comparable studies with relevant benefit or harm as supplementary information could help physicians and other decision-makers to understand the potential utility of an intervention. However, these metrics must be interpreted with caution because the estimate of the between‑study heterogeneity [Formula: see text] may be imprecise when data are limited.

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.