BMC Medical Research Methodology最新文献

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).

Background: Effective interventions to reduce drowning incidents require accurate and reliable data for scientific analysis. However, the lack of high-quality evidence and the variability in drowning terminology, definitions, and outcomes present significant challenges in assessing studies to inform drowning guidelines. Many drowning reports use inappropriate classifications for drowning incidents, which significantly contributes to the underreporting of drowning. In particular, non-fatal drowning incidents are underreported because many countries do not routinely collect this data.

The danish drowning cohort: The Danish Drowning Cohort was established in 2016 to facilitate research to improve preventative, rescue, and treatment interventions to reduce the incidence, mortality, and morbidity of drowning. The Danish Drowning Cohort contains nationwide data on all fatal and non-fatal drowning incidents treated by the Danish Emergency Medical Services. Data are extracted from the Danish prehospital electronic medical record using a text-search algorithm (Danish Drowning Formula) and a manual validation process. The WHO definition of drowning, supported by the clarification statement for non-fatal drowning, is used as the case definition to identify drowning. All drowning patients are included, including unwitnessed incidents, non-conveyed patients, patients declared dead prehospital, or patients with obvious clinical signs of irreversible death. This method allows syndromic surveillance and monitors a nationwide cohort of fatal and non-fatal drowning incidents in near-real time to inform future prevention strategies. The Danish Drowning Cohort complies with the Utstein style for drowning reporting guidelines. The 30-day mortality is obtained through the Civil Personal Register to differentiate between fatal and non-fatal drowning incidents. In addition to prehospital data, new data linkages with other Danish registries via the patient's civil registration number will enable the examination of various additional factors associated with drowning risk.

Conclusion: The Danish Drowning Cohort contains nationwide prehospital data on all fatal and non-fatal drowning incidents treated by the Danish Emergency Medical Service. It is a basis for all research on drowning in Denmark and may improve preventative, rescue, and treatment interventions to reduce the incidence, mortality, and morbidity of drowning.

Background: Propensity Score Matching (PSM) stands as a widely embraced method in comparative effectiveness research. PSM crafts matched datasets, mimicking some attributes of randomized designs, from observational data. In a valid PSM design where all baseline confounders are measured and matched, the confounders would be balanced, allowing the treatment status to be considered as if it were randomly assigned. Nevertheless, recent research has unveiled a different facet of PSM, termed "the PSM paradox". As PSM approaches exact matching by progressively pruning matched sets in order of decreasing propensity score distance, it can paradoxically lead to greater covariate imbalance, heightened model dependence, and increased bias, contrary to its intended purpose.

Methods: We used analytic formula, simulation, and literature to demonstrate that this paradox stems from the misuse of metrics for assessing chance imbalance and bias.

Results: Firstly, matched pairs typically exhibit different covariate values despite having identical propensity scores. However, this disparity represents a "chance" difference and will average to zero over a large number of matched pairs. Common distance metrics cannot capture this "chance" nature in covariate imbalance, instead reflecting increasing variability in chance imbalance as units are pruned and the sample size diminishes. Secondly, the largest estimate among numerous fitted models, because of uncertainty among researchers over the correct model, was used to determine statistical bias. This cherry-picking procedure ignores the most significant benefit of matching design-reducing model dependence based on its robustness against model misspecification bias.

Conclusions: We conclude that the PSM paradox is not a legitimate concern and should not stop researchers from using PSM designs.