BMC MEDICAL RESEARCH METHODOLOGY 是一本开放获取期刊，发表有关医疗保健研究方法论的原创同行评审研究文章。特别鼓励有关流行病学研究、临床试验和荟萃分析/系统评价方法的文章，以及对方法选择与研究结果之间关联的实证研究。 BMC 医学研究方法论的目的不是发表描述科学方法或技术的文章：这些文章应直接提交给涵盖相关生物医学主题领域的 BMC 期刊。

BMC Medical Research Methodology is an open-access journal that publishes original peer-reviewed research articles on healthcare research methodology. The journal particularly encourages submissions related to epidemiological studies, clinical trials, and meta-analysis/systematic review methods, as well as empirical research exploring the relationship between methodological choices and research outcomes. The aim of BMC Medical Research Methodology is not to publish articles that merely describe scientific methods or techniques; such articles should be submitted directly to BMC journals that cover relevant biomedical subject areas.

BMC Medical Research Methodology 是一本开放获取期刊，专注于发表医疗保健研究方法论方面的原创同行评审研究文章。期刊特别鼓励关于流行病学研究、临床试验以及荟萃分析/系统评价方法的文章，同时也欢迎探讨方法选择与研究结果之间关系的实证研究。BMC Medical Research Methodology 的目标不是发表描述科学方法或技术的文章，这类文章应直接提交给涵盖相关生物医学主题的 BMC 期刊。

BMC Medical Research Methodology

Abstract Background Machine learning models promise to support diagnostic predictions, but may not perform well in new settings. Selecting the best model for a new setting without available data is challenging. We aimed to investigate the transportability by calibration and discrimination of prediction models for cognitive impairment in simulated external settings with different distributions of demographic and clinical characteristics. Methods We mapped and quantified relationships between variables associated with cognitive impairment using causal graphs, structural equation models, and data from the ADNI study. These estimates were then used to generate datasets and evaluate prediction models with different sets of predictors. We measured transportability to external settings under guided interventions on age, APOE ε4, and tau-protein, using performance differences between internal and external settings measured by calibration metrics and area under the receiver operating curve (AUC). Results Calibration differences indicated that models predicting with causes of the outcome were more transportable than those predicting with consequences. AUC differences indicated inconsistent trends of transportability between the different external settings. Models predicting with consequences tended to show higher AUC in the external settings compared to internal settings, while models predicting with parents or all variables showed similar AUC. Conclusions We demonstrated with a practical prediction task example that predicting with causes of the outcome results in better transportability compared to anti-causal predictions when considering calibration differences. We conclude that calibration performance is crucial when assessing model transportability to external settings.

Jana Fehr

Marco Piccininni

Tobias Kurth

Stefan Konigorski

Assessing the transportability of clinical prediction models for cognitive impairment using causal models

Navigating the world of qualitative thematic analysis can be challenging. This is compounded by the fact that detailed descriptions of methods are often omitted from qualitative discussions. While qualitative research methodologies are now mature, there often remains a lack of fine detail in their description both at submitted peer reviewed article level and in textbooks. As one of research’s aims is to determine the relationship between knowledge and practice through the demonstration of rigour, more detailed descriptions of methods could prove useful. Rigour in quantitative research is often determined through detailed explanation allowing replication, but the ability to replicate is often not considered appropriate in qualitative research. However, a well described qualitative methodology could demonstrate and ensure the same effect. This article details the codebook development which contributed to thematic analysis of qualitative data. This analysis formed part of a mixed methods multiphase design research project, with both qualitative and quantitative inquiry and involving the convergence of data and analyses. This design consisted of three distinct phases: quantitative, qualitative and implementation phases. This article is aimed at researchers and doctoral students new to thematic analysis by describing a framework to assist their processes. The detailed description of the methods used supports attempts to utilise the thematic analysis process and to determine rigour to support the establishment of credibility. This process will assist practitioners to be confident that the knowledge and claims contained within research are transferable to their practice. The approach described within this article builds on, and enhances, current accepted models.

Kate Roberts

Anthony Dowell

Jing-Bao Nie

Attempting rigour and replicability in thematic analysis of qualitative research data; a case study of codebook development

尝试在定性研究数据的主题分析中实现严谨性和可重复性：代码本开发的案例研究

Medical practitioners use survival models to explore and understand the relationships between patients’ covariates (e.g. clinical and genetic features) and the effectiveness of various treatment options. Standard survival models like the linear Cox proportional hazards model require extensive feature engineering or prior medical knowledge to model treatment interaction at an individual level. While nonlinear survival methods, such as neural networks and survival forests, can inherently model these high-level interaction terms, they have yet to be shown as effective treatment recommender systems. We introduce DeepSurv, a Cox proportional hazards deep neural network and state-of-the-art survival method for modeling interactions between a patient’s covariates and treatment effectiveness in order to provide personalized treatment recommendations. We perform a number of experiments training DeepSurv on simulated and real survival data. We demonstrate that DeepSurv performs as well as or better than other state-of-the-art survival models and validate that DeepSurv successfully models increasingly complex relationships between a patient’s covariates and their risk of failure. We then show how DeepSurv models the relationship between a patient’s features and effectiveness of different treatment options to show how DeepSurv can be used to provide individual treatment recommendations. Finally, we train DeepSurv on real clinical studies to demonstrate how it’s personalized treatment recommendations would increase the survival time of a set of patients. The predictive and modeling capabilities of DeepSurv will enable medical researchers to use deep neural networks as a tool in their exploration, understanding, and prediction of the effects of a patient’s characteristics on their risk of failure.

Jared L. Katzman

Uri Shaham

Alexander Cloninger

Jonathan Bates

Tingting Jiang

Yuval Kluger

BMC Med Res Methodol

DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network

DeepSurv：基于Cox比例风险深度神经网络的个性化治疗推荐系统

Abstract Background Projection of future cancer incidence is an important task in cancer epidemiology. The results are of interest also for biomedical research and public health policy. Age-Period-Cohort (APC) models, usually based on long-term cancer registry data (&gt; 20 yrs), are established for such projections. In many countries (including Germany), however, nationwide long-term data are not yet available. General guidance on statistical approaches for projections using rather short-term data is challenging and software to enable researchers to easily compare approaches is lacking. Methods To enable a comparative analysis of the performance of statistical approaches to cancer incidence projection, we developed an R package (incAnalysis), supporting in particular Bayesian models fitted by Integrated Nested Laplace Approximations (INLA). Its use is demonstrated by an extensive empirical evaluation of operating characteristics (bias, coverage and precision) of potentially applicable models differing by complexity. Observed long-term data from three cancer registries (SEER-9, NORDCAN, Saarland) was used for benchmarking. Results Overall, coverage was high (mostly &gt; 90%) for Bayesian APC models (BAPC), whereas less complex models showed differences in coverage dependent on projection-period. Intercept-only models yielded values below 20% for coverage. Bias increased and precision decreased for longer projection periods (&gt; 15 years) for all except intercept-only models. Precision was lowest for complex models such as BAPC models, generalized additive models with multivariate smoothers and generalized linear models with age x period interaction effects. Conclusion The incAnalysis R package allows a straightforward comparison of cancer incidence rate projection approaches. Further detailed and targeted investigations into model performance in addition to the presented empirical results are recommended to derive guidance on appropriate statistical projection methods in a given setting.

Maximilian Knoll

Jennifer Furkel

Jürgen Debus

Amir Abdollahi

André Karch

Christian Stock

An R package for an integrated evaluation of statistical approaches to cancer incidence projection

用于癌症发病率预测统计方法综合评估的R包

Mediation analysis methodology underwent many advancements throughout the years, with the most recent and important advancement being the development of causal mediation analysis based on the counterfactual framework. However, a previous review showed that for experimental studies the uptake of causal mediation analysis remains low. The aim of this paper is to review the methodological characteristics of mediation analyses performed in observational epidemiologic studies published between 2015 and 2019 and to provide recommendations for the application of mediation analysis in future studies.
We searched the MEDLINE and EMBASE databases for observational epidemiologic studies published between 2015 and 2019 in which mediation analysis was applied as one of the primary analysis methods. Information was extracted on the characteristics of the mediation model and the applied mediation analysis method.
We included 174 studies, most of which applied traditional mediation analysis methods (n= 123, 70.7%). Causal mediation analysis was not often used to analyze more complicated mediation models, such as multiple mediator models. Most studies adjusted their analyses for measured confounders, but did not perform sensitivity analyses for unmeasured confounders and did not assess the presence of an exposure-mediator interaction.
To ensure a causal interpretation of the effect estimates in the mediation model, we recommend that researchers use causal mediation analysis and assess the plausibility of the causal assumptions. The uptake of causal mediation analysis can be enhanced through tutorial papers that demonstrate the application of causal mediation analysis, and through the development of software packages that facilitate the causal mediation analysis of relatively complicated mediation models.

Judith J. M. Rijnhart

Sophia J. Lamp

Matthew J. Valente

David P. MacKinnon

Jos W. R. Twisk

Martijn W. Heymans

Mediation analysis methods used in observational research: a scoping review and recommendations

观察性研究中使用的调解分析方法：范围综述与建议

Undetected atrial fibrillation (AF) poses a significant risk of stroke and cardiovascular mortality. However, diagnosing AF in real-time can be challenging as the arrhythmia is often not captured instantly. To address this issue, a deep-learning model was developed to diagnose AF even during periods of arrhythmia-free windows. The proposed method introduces a novel approach that integrates clinical data and electrocardiograms (ECGs) using a colorization technique. This technique recolors ECG images based on patients' demographic information while preserving their original characteristics and incorporating color correlations from statistical data features. Our primary objective is to enhance atrial fibrillation (AF) detection by fusing ECG images with demographic data for colorization. To ensure the reliability of our dataset for training, validation, and testing, we rigorously maintained separation to prevent cross-contamination among these sets. We designed a Dual-input Mixed Neural Network (DMNN) that effectively handles different types of inputs, including demographic and image data, leveraging their mixed characteristics to optimize prediction performance. Unlike previous approaches, this method introduces demographic data through color transformation within ECG images, enriching the diversity of features for improved learning outcomes. The proposed approach yielded promising results on the independent test set, achieving an impressive AUC of 83.4%. This outperformed the AUC of 75.8% obtained when using only the original signal values as input for the CNN. The evaluation of performance improvement revealed significant enhancements, including a 7.6% increase in AUC, an 11.3% boost in accuracy, a 9.4% improvement in sensitivity, an 11.6% enhancement in specificity, and a substantial 25.1% increase in the F1 score. Notably, AI diagnosis of AF was associated with future cardiovascular mortality. For clinical application, over a median follow-up of 71.6 ± 29.1 months, high-risk AI-predicted AF patients exhibited significantly higher cardiovascular mortality (AF vs. non-AF; 47 [18.7%] vs. 34 [4.8%]) and all-cause mortality (176 [52.9%] vs. 216 [26.3%]) compared to non-AF patients. In the low-risk group, AI-predicted AF patients showed slightly elevated cardiovascular (7 [0.7%] vs. 1 [0.3%]) and all-cause mortality (103 [9.0%] vs. 26 [6.4%]) than AI-predicted non-AF patients during six-year follow-up. These findings underscore the potential clinical utility of the AI model in predicting AF-related outcomes. This study introduces an ECG colorization approach to enhance atrial fibrillation (AF) detection using deep learning and demographic data, improving performance compared to ECG-only methods. This method is effective in identifying high-risk and low-risk populations, providing valuable features for future AF research and clinical applications, as well as benefiting ECG-based classification studies.

Wei-Wen Chen

Chih-Min Liu

Chien-Chao Tseng

Ching-Chun Huang

I-Chien Wu

Pei-Fen Chen

Shih-Lin Chang

Yenn-Jiang Lin

Li-Wei Lo

Fa-Po Chung

Tze-Fan Chao

Ta-Chuan Tuan

Jo-Nan Liao

Chin-Yu Lin

Ting-Yung Chang

Ling Kuo

Cheng-I Wu

Shin-Huei Liu

Jacky Chung-Hao Wu

Yu-Feng Hu

Shih-Ann Chen

Henry Horng-Shing Lu

Identifying the presence of atrial fibrillation during sinus rhythm using a dual-input mixed neural network with ECG coloring technology

使用带有ECG染色技术的双输入混合神经网络识别窦性心律下的房颤存在

Abstract Background Propensity score matching has become a popular method for estimating causal treatment effects in non-randomized studies. However, for time-to-event outcomes, the estimation of hazard ratios based on propensity scores can be challenging if omitted or unobserved covariates are present. Not accounting for such covariates could lead to treatment estimates, differing from the estimate of interest. However, researchers often do not know whether (and, if so, which) covariates will cause this divergence. Methods To address this issue, we extended a previously described method, Dynamic Landmarking , which was originally developed for randomized trials. The method is based on successively deletion of sorted observations and gradually fitting univariable Cox models. In addition, the balance of observed, but omitted covariates can be measured by the sum of squared z-differences. Results By simulation we show, that Dynamic Landmarking provides a good visual tool for detecting and distinguishing treatment effect estimates underlying built-in selection or confounding bias. We illustrate the approach with a data set from cardiac surgery and provide some recommendations on how to use and interpret Dynamic Landmarking in propensity score matched studies. Conclusion Dynamic Landmarking is a useful post-hoc diagnosis tool for visualizing whether an estimated hazard ratio could be distorted by confounding or built-in selection bias.

Alexandra Strobel

Andreas Wienke

Jan Gummert

Sabine Bleiziffer

Oliver Kuss

Built-in selection or confounder bias? Dynamic Landmarking in matched propensity score analyses

内置选择还是混杂偏倚？匹配倾向评分分析中的动态界标法

Abstract Background The choice of a single primary outcome in randomised trials can be difficult, especially in mental health where interventions may be complex and target several outcomes simultaneously. We carried out a systematic review to assess the quality of the analysis and reporting of multiple outcomes in mental health RCTs, comparing approaches with current CONSORT and other regulatory guidance. Methods The review included all late-stage mental health trials published between 1st January 2019 to 31st December 2020 in 9 leading medical and mental health journals. Pilot and feasibility trials, non-randomised trials, and early phase trials were excluded. The total number of primary, secondary and other outcomes was recorded, as was any strategy used to incorporate multiple primary outcomes in the primary analysis. Results There were 147 included mental health trials. Most trials (101/147) followed CONSORT guidance by specifying a single primary outcome with other outcomes defined as secondary and analysed in separate statistical analyses, although a minority (10/147) did not specify any outcomes as primary. Where multiple primary outcomes were specified (33/147), most (26/33) did not correct for multiplicity, contradicting regulatory guidance. The median number of clinical outcomes reported across studies was 8 (IQR 5–11 ). Conclusions Most trials are correctly following CONSORT guidance. However, there was little consideration given to multiplicity or correlation between outcomes even where multiple primary outcomes were stated. Trials should correct for multiplicity when multiple primary outcomes are specified or describe some other strategy to address the multiplicity. Overall, very few mental health trials are taking advantage of multiple outcome strategies in the primary analysis, especially more complex strategies such as multivariate modelling. More work is required to show these exist, aid interpretation, increase efficiency and are easily implemented. Registration Our systematic review protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO) on 11th January 2023 (CRD42023382274).

Dominic Stringer

Mollie Payne

Ben Carter

Richard Emsley

The analysis and reporting of multiple outcomes in mental health trials: a methodological systematic review

精神健康试验中多重结局的分析与报告：一项方法学系统评价

In cohort studies with time-to-event outcomes, covariates of interest often have values that change over time. The classical Cox regression model can handle time-dependent covariates but assumes linear effects on the log hazard function, which can be limiting in practice. Furthermore, when multiple correlated covariates are studied, it is of great interest to model their joint effects by allowing a flexible functional form and to delineate their relative contributions to survival risk.

Myeonggyun Lee

Andrea B. Troxel

Sophia Kwon

George Crowley

Theresa Schwartz

Rachel Zeig-Owens

David J. Prezant

Anna Nolan

Mengling Liu

Partial-linear single-index Cox regression models with multiple time-dependent covariates

部分线性单指标Cox回归模型与多时间依赖协变量

Liver injury from drug-drug interactions (DDIs), notably with anti-tuberculosis drugs such as isoniazid, poses a significant safety concern. Electronic medical records contain comprehensive clinical information and have gained increasing attention as a potential resource for DDI detection. However, a substantial portion of adverse drug reaction (ADR) information is hidden in unstructured narrative text, which has yet to be efficiently harnessed, thereby introducing bias into the research. There is a significant need for an efficient framework for the DDI assessment. Using a Chinese natural language processing (NLP) model, we extracted 25,130 adverse drug reaction (ADR) records, dividing them into sets for training an automated normalization model. The trained models, in conjunction with liver function laboratory tests, were used to thoroughly and efficiently identify liver injury cases. Ultimately, we applied a case-control study design to detect DDI signals increasing isoniazid's liver injury risk. The Logistic Regression model demonstrated stable and superior performance in classification task. Based on laboratory criteria and NLP, we identified 128 liver injury cases among a cohort of 3,209 patients treated with isoniazid. Preliminary screening of 113 drug combinations with isoniazid highlighted 20 potential signal drugs, with antibacterials constituting 25%. Sensitivity analysis confirmed the robustness of signal drugs, especially in cardiac therapy and antibacterials. Our NLP and machine learning approach effectively identifies isoniazid-related DDIs that increase the risk of liver injury, identifying 20 signal drugs, mainly antibacterials. Further research is required to validate these DDI signals.

Junlong Ma

Heng Chen

Ji Sun

Juanjuan Huang

Gefei He

Guoping Yang

Efficient analysis of drug interactions in liver injury: a retrospective study leveraging natural language processing and machine learning

药物相互作用在肝损伤中的高效分析：一项利用自然语言处理和机器学习的回顾性研究

Abbreviation	BMC Med Res Methodol
Journal Impact	3.76
Quartiles(Global)	HEALTH CARE SCIENCES & SERVICES(Q1)

ISSN	1471-2288
h-index	163

Publication Information	Publisher: BioMed Central Ltd，Publishing cycle: Irregular，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2001，Proportion of original research papers: 91.27%，Self Citation Rate:2.60%， Gold OA Rate: 99.66%
Journal Fees	$2990Details
Average review cycle	网友分享经验：一般,3-8周

BMC Medical Research Methodology

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