BMC Medical Research Methodology最新文献

Background: Chronic illness impacts not only individuals affected by it, but also those who care for them. Care partnerships recognize that health conditions are often shared, dyadic experiences. Qualitative dyadic analysis, which foregrounds the dyad as the unit of analysis, is a method that can enhance understanding of illness as a joint experience. However, when perspectives of dyad members are collected separately, their subsequent analysis as a unit can be challenging.

Objective: To review and summarize qualitative literature where data have been collected through separate individual interviews with patient and care partner dyads and analyzed at the dyadic level.

Methods: A scoping review guided by Joanna Briggs Institute methodology was undertaken. Databases (Ovid's Medline, Embase, and PsycINFO; EBSCO CINAHL; and ProQuest Sociological Abstracts) were searched in February 2024. Eligible articles included peer-reviewed literature published in English from 2010 onwards documenting qualitative dyadic analysis of individual interviews collected from patient and care partner dyads. Title and abstracts were screened and the full text of all potentially eligible articles was reviewed by two independent reviewers. Data were extracted using a table and results were summarized using frequency counts and qualitative content analysis.

Results: 7,494 records were identified and screened. 113 reports of 112 unique studies fulfilled eligibility criteria and were included. Numerous methodologies and analytic methods were reported, many of which incorporated methods from different qualitative traditions, often with variable sequencing of analytic steps that were infrequently well described. Studies were not routinely conceptualized at the dyadic level and underlying epistemological assumptions were rarely discussed despite their essential role in grounding dyadic analysis.

Conclusions: When conducting qualitative dyadic analysis, researchers should consider dyadic study conceptualization from study outset. The purpose of the analysis, the analytic steps taken, and their alignment with underlying epistemology and other incorporated methodologies should be clearly documented and reported.

Background: Immortal time bias (ITB) represents a methodological challenge in evaluating treatment effects in observational studies using routinely collected data (RCD). However, the prevalence of ITB, the strategies used to address ITB and its impact remain inadequate. This study aimed to investigate how ITB was identified and handled in observational studies using RCD, and to assess its impact on treatment effect estimates.

Methods: A systematic search was performed in PubMed for observational studies published from 2018 to 2020 that used RCD to evaluate drug treatment effects. We examined the synchronization of three time points (eligibility, treatment assignment, and the start of follow-up) to identify ITB and assessed the risk of ITB. For low-risk studies, we summarized the handling approaches. For high-risk studies, we conducted quantitative bias analyses to correct for ITB and calculate ITB-controlled estimates. These ITB-controlled estimates were then compared with original estimates to quantify the impact of ITB.

Results: Among the 256 studies initially identified, 162 cohort studies with time-to-event outcomes were included. 13 studies (8.0%) lacked sufficient reporting to assess ITB. Of the remaining studies, 35 studies (21.6%) were classified as high risk for ITB, while 114 studies (70.4%) were classified as low risk, with 15 having naturally synchronized time points and 99 using design or analytical approaches to synchronize them. For the 99 low-risk studies, the commonly employed approaches were the active comparator new-user design and the time-varying exposure definition, accounting for 56.6% and 19.2%, respectively. Of the 35 high-risk studies, 16 studies that provided sufficient information for correction were included in the quantitative bias analyses. Among these, 4 studies (25%) showed statistically significant differences between ITB-controlled and original estimates, and 4 studies (25%) yielded conflicting conclusions regarding the statistical significance of these two estimates. Only 5 of the 35 high-risk studies (14.3%) discussed that the results may be affected by ITB.

Conclusions: ITB is a critical methodological issue in observational studies using RCD, with the potential to significantly distort conclusions. To enhance the validity of treatment effect estimates, researchers should thoroughly examine the presence of ITB and employ appropriate strategies to mitigate its impact.

Background: Moderate to severe Long COVID is estimated to impact as many as 10% of SARS-CoV-2 infected individuals, representing a chronic condition with a substantial public health burden. An expansive literature has identified over 200 persistent symptoms associated with a history of SARS-CoV-2 infection; yet, there remains to be a clear consensus on a syndrome definition. Long COVID thus represents a "negative-unlabeled" outcome where those without prior infection must be Long COVID "negative" but those with prior infection have unknown or "unlabeled" Long COVID status. Despite this lack of a gold standard definition or biomarker, developing and evaluating an approach to characterizing Long COVID is a critical first step in future studies of risk and resiliency factors, mechanisms of disease, and interventions for both treatment and prevention.

Methods: We recently applied a strategy for defining a numeric Long COVID research index (LCRI) using Lasso-penalized logistic regression, leveraging information on history of SARS-CoV-2 infection as a pseudo-label. In the current manuscript we formalize and evaluate this approach in a simulation framework for the occurrence of infection, Long COVID onset, and symptomatology. We evaluate its performance selecting symptoms associated with Long COVID and distinguishing individuals with Long COVID, in the presence of symptom correlations and demographic confounders. We compare the LCRI method to a simpler index defined by counting Long COVID symptoms, and assess these methods in a reanalysis of data on participants enrolled in the Adult Cohort of the Researching COVID to Enhance Recovery (RECOVER) study.

Results: Simulation results demonstrate that the Lasso-penalized LCRI methodology appropriately selects symptoms associated with Long COVID, and that the LCRI has high discriminatory power to distinguish Long COVID, outperforming symptom count. This performance was robust to correlation between symptoms, and weighting methods are shown to successfully address potential confounding by demographic characteristics. Analysis of RECOVER data showed the LCRI outperforming symptom count by misclassifying fewer uninfected individuals as having Long COVID.

Conclusions: As the LCRI is increasingly used to characterize LC in research settings, this paper represents an important step in understanding its operating characteristics and developing general methodology for settings with negative-unlabeled data.

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.