BMC Medical Research Methodology最新文献

Background: While acknowledging that theory can be critical to scientific progress, we are concerned about instances of its tendency to encroach on, or replace, deep engagement with data in qualitative research. We discuss conceptual frameworks' role in conducting and teaching qualitative research.

Methods: We address three questions about our attachment as researchers to theory through conceptual frameworks: (1) What do conceptual frameworks offer qualitative research?; (2) Why do researchers use and teach conceptual frameworks in qualitative research?; and (3) How can we practice and teach rigour while integrating conceptual frameworks in qualitative research?

Results: One way that theory may be misused in qualitative research is in the development and reliance on conceptual frameworks as a prescription for data collection and analysis. We suggest possible ways forward to ensure rigour while integrating frameworks in qualitative research, such as examining the evolution of our own theoretical perspectives.

Conclusions: We need to impart to our students the value of thinking deeply about their own data, of knowing what came before, and of taking the time and making an effort to unite these strands into novel and interesting results.

Background: Generalized propensity score (GPS) methods have become popular for estimating causal relationships between a continuous treatment and an outcome in observational studies with rich covariate information. The presence of rich covariates enhances the plausibility of the unconfoundedness assumption. Nonetheless, it is also crucial to ensure the correct specification of both marginal and conditional treatment distributions, beyond the assumption of unconfoundedness.

Method: We address limitations in existing GPS methods by extending balance-based approaches to high dimensions and introducing the Generalized Outcome-Adaptive LASSO and Doubly Robust Estimate (GOALDeR). This novel approach integrates a balance-based method that is robust to the misspecification of distributions required for GPS methods, a doubly robust estimator that is robust to the misspecification of models, and a variable selection technique for causal inference that ensures an unbiased and statistically efficient estimation.

Results: Simulation studies showed that GOALDeR was able to generate nearly unbiased estimates when either the GPS model or the outcome model was correctly specified. Notably, GOALDeR demonstrated greater precision and accuracy compared to existing methods and was slightly affected by the covariate correlation structure and ratio of sample size to covariate dimension. Real data analysis revealed no statistically significant dose-response relationship between epigenetic age acceleration and Alzheimer's disease.

Conclusion: In this study, we proposed GOALDeR as an advanced GPS method for causal inference in high dimensions, and empirically demonstrated that GOALDeR is doubly robust, with improved accuracy and precision compared to existing methods. The R package is available at https://github.com/QianGao-SXMU/GOALDeR .

Background: Research transparency is crucial for ensuring the relevance, integrity, and reliability of scientific findings. However, previous work indicates room for improvement across transparency practices. The primary objective of this study was to develop an extensible tool to provide individualized feedback and guidance for improved transparency across phases of a study. Our secondary objective was to assess the feasibility of implementing this tool to improve transparency in clinical trials.

Methods: We developed study-level "report cards" that combine tailored feedback and guidance to investigators across several transparency practices, including prospective registration, availability of summary results, and open access publication. The report cards were generated through an automated pipeline for scalability. We also developed an infosheet to summarize relevant laws, guidelines, and resources relating to transparency. To assess the feasibility of using these tools to improve transparency, we conducted a single-arm intervention study at Berlin's university medical center, the Charité - Universitätsmedizin Berlin. Investigators (n = 92) of 155 clinical trials were sent individualized report cards and the infosheet, and surveyed to assess their perceived usefulness. We also evaluated included trials for improvements in transparency following the intervention.

Results: Survey responses indicated general appreciation for the report cards and infosheet, with a majority of participants finding them helpful to build awareness of the transparency of their trial and transparency requirements. However, improvement on transparency practices was minimal and largely limited to linking publications in registries. Investigators also commented on various challenges associated with implementing transparency, including a lack of clarity around best practices and institutional hurdles.

Conclusions: This study demonstrates the potential of developing and using tools, such as report cards, to provide individualized feedback at scale to investigators on the transparency of their study. While these tools were positively received by investigators, the limited improvement in transparency practices suggests that awareness alone is likely not sufficient to drive improvement. Future research and implementation efforts may adapt the tools to further practices or research areas, and explore integrated approaches that combine the report cards with incentives and institutional support to effectively strengthen transparency in research.

Background: The potential value of large scale datasets is constrained by the ubiquitous problem of missing data, arising in either a structured or unstructured fashion. When imputation methods are proposed for large scale data, one limitation is the simplicity of existing evaluation methods. Specifically, most evaluations create synthetic data with only a simple, unstructured missing data mechanism which does not resemble the missing data patterns found in real data. For example, in the UK Biobank missing data tends to appear in blocks, because non-participation in one of the sub-studies leads to missingness for all sub-study variables.

Methods: We propose a tool for generating mixed type missing data mimicking key properties of a given real large scale epidemiological data set with both structured and unstructured missingness while accounting for informative missingness. The process involves identifying sub-studies using hierarchical clustering of missingness patterns and modelling the dependence of inter-variable correlation and co-missingness patterns.

Results: On the UK Biobank brain imaging cohort, we identify several large blocks of missing data. We demonstrate the use of our tool for evaluating several imputation methods, showing modest accuracy of imputation overall, with iterative imputation having the best performance. We compare our evaluations based on synthetic data to an exemplar study which includes variable selection on a single real imputed dataset, finding only small differences between the imputation methods though with iterative imputation leading to the most informative selection of variables.

Conclusions: We have created a framework for simulating large scale data with that captures the complexities of the inter-variable dependence as well as structured and unstructured informative missingness. Evaluations using this framework highlight the immense challenge of data imputation in this setting and the need for improved missing data methods.

Introduction: Chronic back pain (CBP) is a leading cause of disability worldwide and is commonly managed with pharmacological, non-pharmacological, and procedural interventions. However, adverse event (AE) reporting for these therapies often lacks transparency, raising concerns about the accuracy of safety data. This study aimed to quantify inconsistencies in AE reporting between ClinicalTrials.gov and corresponding randomized controlled trial (RCT) publications, emphasizing the importance of comprehensive safety reporting to improve clinical decision-making and patient care.

Methods: We retrospectively analyzed Phase 2-4 CBP RCTs registered on ClinicalTrials.gov from 2009 to 2023. Extracted data included AE reporting, trial sponsorship, and discrepancies in serious adverse events (SAEs), other adverse events (OAEs), mortality, and treatment-related withdrawals between registry entries and publications. Statistical analyses assessed reporting inconsistencies, following STROBE guidelines.

Results: A total of 114 registered trials were identified, with 40 (35.1%) corresponding publications. Among these, 67.5% were industry-sponsored. Only 4 (10%) publications fully reported adverse events (AEs) without discrepancies, while 36 (90%) contained at least one inconsistency compared to ClinicalTrials.gov. Discontinuation due to AEs was explicitly reported in 24 (60%) of ClinicalTrials.gov entries and in 30 (75%) of publications, with discrepancies in 16 trials (40%). Serious adverse events (SAEs) were reported differently in 15 (37.5%) publications; 80% reported fewer SAEs than ClinicalTrials.gov. Other adverse events (OAEs) showed discrepancies in 37 (92.5%) publications, with 43.2% reporting fewer and 54.1% reporting more OAEs.

Discussion: This study highlights pervasive discrepancies in AE reporting for CBP trials, undermining the reliability of published safety data. Inconsistent reporting poses risks to clinical decision-making and patient safety. Adopting standardized reporting guidelines, such as CONSORT Harms, and ensuring transparent updates in publications could enhance the accuracy and trustworthiness of safety data. Journals and regulatory bodies should enforce compliance and future efforts should develop mechanisms to monitor and correct reporting inconsistencies, enhancing the trustworthiness of safety data in clinical research.

Multimorbidity is characterized by the accrual of two or more long-term conditions (LTCs) in an individual. This state of health is increasingly prevalent and poses public health challenges. Adapting approaches to effectively analyse electronic health records is needed to better understand multimorbidity. We propose a novel unsupervised clustering approach to multiple time-to-event health records denoted as multiple state clustering analysis (MSCA). In MSCA, patients' pairwise dissimilarities are computed using patients' state matrices which are composed of multiple censored time-to-event indicators reflecting patients' health history. The use of state matrices enables the analysis of an arbitrary number of LTCs without reducing patients' health trajectories to a particular sequence of events. MSCA was applied to analyse multimorbidity associated with myocardial infarction using electronic health records of 26 LTCs, including conventional cardiovascular risk factors (CVRFs) such as diabetes and hypertension, collected from south London general practices between 2005 and 2021 in 5087 patients using the MSCA R library. We identified a typology of 11 clusters, characterised by age at onset of myocardial infarction, sequences of conventional CVRFs and non-conventional risk factors including physical and mental health conditions. Interestingly, multivariate analysis revealed that clusters were also associated with various combinations of socio-demographic characteristics including gender and ethnicity. By identifying meaningful sequences of LTCs associated with myocardial infarction and distinct socio-demographic characteristics, MSCA proves to be an effective approach to the analysis of electronic health records, with the potential to enhance our understanding of multimorbidity for improved prevention and management.

Background: Increasing transparency in clinical research is crucial to avoid misleading conclusions. Registering clinical trials prior to participant enrolment is mandatory, and the publication of trial protocols could further enhance transparency. However, the impact of protocol publication on primary outcomes (PO) and sample sizes (SS) remains unclear. This study aimed to determine the rates of trial protocol publication and registration for a sample of randomized controlled trials (RCTs) and to compare the consistency of published and registered PO and SS.

Methods: A search was conducted in MEDLINE via PubMed^® for RCT reports indexed in May and June 2023 across various medical specialties, focusing on general and high-impact factor journals. Data were extracted regarding trial registration, protocol publication, and comparisons were made between PO and SS in articles, registries, and published protocols.

Results: Out of 1119 references, 589 (52.6%) were RCTs. The corresponding protocol was published for 146 RCTs (24.8%) including 40 over 140 (28.6%) (6 without end date available) after the trial had ended. Sixty-two (42.4%) protocols were published before the trial conclusion, with no significant differences between PO and SS in published protocols and their corresponding articles. Five hundred and twenty-eight (89.6%) RCTs were registered, 225 over 510 (44%) were registered before the study start with no differences in PO and SS between article and registry. Articles published in generalist or high impact factor journals were associated with higher frequencies of published protocols and trial registration and a lower frequency of difference in PO and SS between articles, registries, and published protocols.

Conclusions: While publishing trial protocols may enhance transparency in peer-review process, the initial registered protocol alone appears sufficient for ensuring consistency in primary outcomes and sample sizes. Protocol publication does not seem to provide additional significant benefits in terms of outcome reporting.

Background and objectives: The National Institute for Health and Care Excellence (England's health technology assessment body) recommend the use of the average treatment effect (ATE) as an estimand for economic evaluations. However there is limited literature on methods to estimate the ATE, particularly in the case of survival outcomes. Single-arm trials and real-world data are playing an increasing role in health technology assessments, particularly in oncology/rare diseases, generating a need for new ATE estimation methods. This study aimed to present the adaptation and utility of this methodology for survival outcomes.

Methods: The approach is based on a "doubly robust" method combining matching with regression adjustment (Austin 2020) using a Weibull model (lowest Akaike information criteria [AIC] specification) to estimate counterfactual event times. As a case study, we compared mosunetuzumab versus rituximab/bendamustine, as a proxy for rituximab/chemotherapy, in 3L+ relapsed/refractory follicular lymphoma. Individual patient data for mosunetuzumab (NCT02500407) and a combination of two rituximab/bendamustine 3L+ follicular lymphoma cohorts (NCT02187861/NCT02257567) were used. Endpoints included overall survival (OS) and progression-free survival (PFS). Sensitivity analyses were performed to test robustness to different distributional assumptions (log-normal, log-logistic and exponential) or model specifications (second, third and fourth lowest AIC) for event times.

Results: The case study found improved PFS (hazard ratio [HR] 0.43 [95% confidence interval (CI): 0.13, 0.91]) and OS (HR 0.30 [95% CI: 0.05, 5.28]) for mosunetuzumab. Consistent findings (HR range 0.25-0.47 and 0.21-0.50 with all CIs excluding/including 1 for PFS/OS, respectively) were observed in sensitivity analyses.

Discussion/conclusions: The proposed adaptation expands the range of available approaches for the estimation of the (local) ATE for survival outcomes in health technology assessments using "doubly robust" methods. This approach appeared relatively robust to modelling decisions in our case study.

Background: Population-adjusted indirect comparison using parametric Simulated Treatment Comparison (STC) has had limited application to survival outcomes in unanchored settings. Matching-Adjusted Indirect Comparison (MAIC) is commonly used but does not account for violation of proportional hazards or enable extrapolations of survival. We developed and applied a novel methodology for STC in unanchored settings. We compared overall survival (OS) and progression-free survival (PFS) of lenvatinib plus pembrolizumab (LEN + PEM) against nivolumab plus ipilimumab (NIVO + IPI), pembrolizumab plus axitinib (PEM + AXI), avelumab plus axitinib (AVE + AXI), and nivolumab plus cabozontanib (NIVO + CABO) in patients with advanced renal cell carcinoma (RCC). Unanchored comparison was necessitated as the control groups differed in their use of PD-1/PD-L1 rescue therapy.

Methods: We fit covariate-adjusted survival models to individual patient data from phase 3 trial of LEN + PEM, including standard parametric distributions and Royston-Parmar spline models with up to 3 knots. We used these models to predict OS and PFS in the population of comparator treatments. The base case model was selected by minimum Akaike Information Criterion (AIC). Treatment effects were measured using difference in restricted mean survival time (RMST), over shortest follow-up of input trials, and hazard ratios at 6, 12, 18, and 24 months.

Results: The survival model with the lowest AIC was 1-knot spline odds for OS and log-logistic for PFS. Difference in RMST OS was 6.90 months (95% CI: 1.95, 11.36), 5.31 (3.58, 7.28), 5.99 (1.82, 9.42), and 11.59 (8.41, 15.38) versus NIVO + IPI (over 64.8 months follow-up), AVE + AXI (46.7 months), PEM + AXI (64.8 months), NIVO + CABO (53.0 months), respectively. Difference in RMST PFS was 4.50 months (95% CI: 0.92, 8.26), 8.23 (5.60, 10.57), 5.38 (2.06, 9.09), and 4.58 (0.09, 9.44) versus NIVO + IPI (over 57.8 months), AVE + AXI (44.9 months), PEM + AXI (57.8 months), NIVO + CABO (23.8 months), respectively. Hazard ratios indicated strong evidence of greater OS and PFS on LEN + PEM at most timepoints.

Conclusions: We developed and applied a novel methodology for comparing survival outcomes in unanchored settings using STC. Pending investigation with a simulation study or further examples, this methodology could be used for clinical decision-making and, if long-term data are available, inform economic models designed to extrapolate outcomes for the evaluation of lifetime cost-effectiveness.

Trial registration: NCT02811861 (registered: 23/06/2016).