BMC Medical Research Methodology最新文献

{"title":"A prediction approach to COVID-19 time series with LSTM integrated attention mechanism and transfer learning.","authors":"Bin Hu, Yaohui Han, Wenhui Zhang, Qingyang Zhang, Wen Gu, Jun Bi, Bi Chen, Lishun Xiao","doi":"10.1186/s12874-024-02433-w","DOIUrl":"10.1186/s12874-024-02433-w","url":null,"abstract":"<p><strong>Background: </strong>The prediction of coronavirus disease in 2019 (COVID-19) in broader regions has been widely researched, but for specific areas such as urban areas the predictive models were rarely studied. It may be inaccurate to apply predictive models from a broad region directly to a small area. This paper builds a prediction approach for small size COVID-19 time series in a city.</p><p><strong>Methods: </strong>Numbers of COVID-19 daily confirmed cases were collected from November 1, 2022 to November 16, 2023 in Xuzhou city of China. Classical deep learning models including recurrent neural network (RNN), long and short-term memory (LSTM), gated recurrent unit (GRU) and temporal convolutional network (TCN) are initially trained, then RNN, LSTM and GRU are integrated with a new attention mechanism and transfer learning to improve the performance. Ten times ablation experiments are conducted to show the robustness of the performance in prediction. The performances among the models are compared by the mean absolute error, root mean square error and coefficient of determination.</p><p><strong>Results: </strong>LSTM outperforms than others, and TCN has the worst generalization ability. Thus, LSTM is integrated with the new attention mechanism to construct an LSTMATT model, which improves the performance. LSTMATT is trained on the smoothed time series curve through frequency domain convolution augmentation, then transfer learning is adopted to transfer the learned features back to the original time series resulting in a TLLA model that further improves the performance. RNN and GRU are also integrated with the attention mechanism and transfer learning and their performances are also improved, but TLLA still performs best.</p><p><strong>Conclusions: </strong>The TLLA model has the best prediction performance for the time series of COVID-19 daily confirmed cases, and the new attention mechanism and transfer learning contribute to improve the prediction performance in the flatten part and the jagged part, respectively.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"323"},"PeriodicalIF":3.9,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11686916/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142906191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting kidney graft function and failure among kidney transplant recipients.","authors":"Yi Yao, Brad C Astor, Wei Yang, Tom Greene, Liang Li","doi":"10.1186/s12874-024-02445-6","DOIUrl":"10.1186/s12874-024-02445-6","url":null,"abstract":"<p><strong>Background: </strong>Graft loss is a major health concern for kidney transplant (KTx) recipients. It is of clinical interest to develop a prognostic model for both graft function, quantified by estimated glomerular filtration rate (eGFR), and the risk of graft failure. Additionally, the model should be dynamic in the sense that it adapts to accumulating longitudinal information, including time-varying at-risk population, predictor-outcome association, and clinical history. Finally, the model should also properly account for the competing risk by death with a functioning graft. A model with the features above is not yet available in the literature and is the focus of this research.</p><p><strong>Methods: </strong>We built and internally validated a prediction model on 3,893 patients from the Wisconsin Allograft Recipient Database (WisARD) who had a functioning graft 6 months after kidney transplantation. The landmark analysis approach was used to build a proof-of-concept dynamic prediction model to address the aforementioned methodological issues: the prediction of graft failure, accounted for competing risk of death, as well as the future eGFR value, are updated at each post-transplant time. We used 21 predictors including recipient characteristics, donor characteristics, transplant-related and post-transplant factors, longitudinal eGFR, hospitalization, and rejection history. A sensitivity analysis explored a less conservative variable selection rule that resulted in a more parsimonious model with reduced predictors.</p><p><strong>Results: </strong>For prediction up to the next 1 to 5 years, the model achieved high accuracy in predicting graft failure, with the AUC between 0.80 and 0.95, and moderately high accuracy in predicting eGFR, with the root mean squared error between 10 and 18 mL/min/1.73m2 and 70%-90% of predicted eGFR falling within 30% of the observed eGFR. The model demonstrated substantial accuracy improvement compared to a conventional prediction model that used only baseline predictors.</p><p><strong>Conclusion: </strong>The model outperformed conventional prediction model that used only baseline predictors. It is a useful tool for patient counseling and clinical management of KTx and is currently available as a web app.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"324"},"PeriodicalIF":3.9,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11687162/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142906241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward value-based care using cost mining: cost aggregation and visualization across the entire colorectal cancer patient pathway.","authors":"Maura Leusder, Sven Relijveld, Derya Demirtas, Jon Emery, Michelle Tew, Peter Gibbs, Jeremy Millar, Victoria White, Michael Jefford, Fanny Franchini, Maarten IJzerman","doi":"10.1186/s12874-024-02446-5","DOIUrl":"10.1186/s12874-024-02446-5","url":null,"abstract":"<p><strong>Background: </strong>The aim of this study is to develop a method we call \"cost mining\" to unravel cost variation and identify cost drivers by modelling integrated patient pathways from primary care to the palliative care setting. This approach fills an urgent need to quantify financial strains on healthcare systems, particularly for colorectal cancer, which is the most expensive cancer in Australia, and the second most expensive cancer globally.</p><p><strong>Methods: </strong>We developed and published a customized algorithm that dynamically estimates and visualizes the mean, minimum, and total costs of care at the patient level, by aggregating activity-based healthcare system costs (e.g. DRGs) across integrated pathways. This extends traditional process mining approaches by making the resulting process maps actionable and informative and by displaying cost estimates. We demonstrate the method by constructing a unique dataset of colorectal cancer pathways in Victoria, Australia, using records of primary care, diagnosis, hospital admission and chemotherapy, medication, health system costs, and life events to create integrated colorectal cancer patient pathways from 2012 to 2020.</p><p><strong>Results: </strong>Cost mining with the algorithm enabled exploration of costly integrated pathways, i.e. drilling down in high-cost pathways to discover cost drivers, for 4246 cases covering approx. 4 million care activities. Per-patient CRC pathway costs ranged from $10,379 AUD to $41,643 AUD, and varied significantly per cancer stage such that e.g. chemotherapy costs in one cancer stage are different to the same chemotherapy regimen in a different stage. Admitted episodes were most costly, representing 93.34% or $56.6 M AUD of the total healthcare system costs covered in the sample.</p><p><strong>Conclusions: </strong>Cost mining can supplement other health economic methods by providing contextual, sequence and timing-related information depicting how patients flow through complex care pathways. This approach can also facilitate health economic studies informing decision-makers on where to target care improvement or to evaluate the consequences of new treatments or care delivery interventions. Through this study we provide an approach for hospitals and policymakers to leverage their health data infrastructure and to enable real time patient level cost mining.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"321"},"PeriodicalIF":3.9,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11681630/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142891622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The necessity of validity diagnostics when drawing causal inferences from observational data: lessons from a multi-database evaluation of the risk of non-infectious uveitis among patients exposed to Remicade^®.","authors":"James Weaver, Erica A Voss, Guy Cafri, Kathleen Beyrau, Michelle Nashleanas, Robert Suruki","doi":"10.1186/s12874-024-02428-7","DOIUrl":"10.1186/s12874-024-02428-7","url":null,"abstract":"<p><strong>Background: </strong>Autoimmune disorders have primary manifestations such as joint pain and bowel inflammation but can also have secondary manifestations such as non-infectious uveitis (NIU). A regulatory health authority raised concerns after receiving spontaneous reports for NIU following exposure to Remicade^®, a biologic therapy with multiple indications for which alternative therapies are available. In assessment of this clinical question, we applied validity diagnostics to support observational data causal inferences.</p><p><strong>Methods: </strong>We assessed the risk of NIU among patients exposed to Remicade^® compared to alternative biologics. Five databases, four study populations, and four analysis methodologies were used to estimate 80 potential treatment effects, with 20 pre-specified as primary. The study populations included inflammatory bowel conditions Crohn's disease or ulcerative colitis (IBD), ankylosing spondylitis (AS), psoriatic conditions plaque psoriasis or psoriatic arthritis (PsO/PsA), and rheumatoid arthritis (RA). We conducted four analysis strategies intended to address limitations of causal estimation using observational data and applied four diagnostics with pre-specified quantitative rules to evaluate threats to validity from observed and unobserved confounding. We also qualitatively assessed post-propensity score matching representativeness, and bias susceptibility from outcome misclassification. We fit Cox proportional-hazards models, conditioned on propensity score-matched sets, to estimate the on-treatment risk of NIU among Remicade^® initiators versus alternatives. Estimates from analyses that passed four validity tests were assessed.</p><p><strong>Results: </strong>Of the 80 total analyses and the 20 analyses pre-specified as primary, 24% and 20% passed diagnostics, respectively. Among patients with IBD, we observed no evidence of increased risk for NIU relative to other similarly indicated biologics (pooled hazard ratio [HR] 0.75, 95% confidence interval [CI] 0.38-1.40). For patients with RA, we observed no increased risk relative to similarly indicated biologics, although results were imprecise (HR: 1.23, 95% CI 0.14-10.47).</p><p><strong>Conclusions: </strong>We applied validity diagnostics on a heterogenous, observational setting to answer a specific research question. The results indicated that safety effect estimates from many analyses would be inappropriate to interpret as causal, given the data available and methods employed. Validity diagnostics should always be used to determine if the design and analysis are of sufficient quality to support causal inferences. The clinical implications of our findings on IBD suggests that, if an increased risk exists, it is unlikely to be greater than 40% given the 1.40 upper bound of the pooled HR confidence interval.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"322"},"PeriodicalIF":3.9,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11681708/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142892081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploratory interviews with Australian clinical research staff on how they communicate with participants.","authors":"Gudrun Wells, Janelle Bowden, Duncan Colyer, Eleonora Kay, Sarah Lukeman, Lyndsay Newett, Lisa Eckstein","doi":"10.1186/s12874-024-02417-w","DOIUrl":"10.1186/s12874-024-02417-w","url":null,"abstract":"<p><strong>Background: </strong>The connection between participants and their research team can affect how safe, informed, and respected a participant feels, and their willingness to complete a research project. Communication between researchers and participants is key to developing this connection, but there is little published work evaluating how communication during clinical research is conducted.</p><p><strong>Purpose: </strong>This paper explores what communications happen (and how) with research participants in Australia post consenting to participate in clinical research. It provides reflections from Australians working in clinical research about their current strategies, or those they would like to use, to communicate with research participants.</p><p><strong>Methods: </strong>This exploratory, qualitative descriptive study reports findings associated with twenty semi-structured interviews that were undertaken with people who work in clinical research in Australia (such as staff in participant facing, site management, or sponsor representative roles). These interviews were conducted and analysed inductively using thematic analysis.</p><p><strong>Findings: </strong>Research staff reported using a range of communication strategies which varied in implementation, uptake, and suitability between clinical research studies and sites. Four major themes were identified in the interviews: [1] staff use innovative pragmatism to communicate; [2] staff tailor the communication strategies to fit the participants' context; [3] the site, its systems, and staff training all impact communication; [4] successful communication requires collaboration between stakeholders.</p><p><strong>Conclusion: </strong>There are a variety of communication strategies, methods and activities research staff currently employ with trial participants, which vary in purpose, method, resources required, and suitability between studies and sites. Thorough consideration of the participants' contexts and the capacity of research sites is crucial for the design of studies which allow for effective communication between the research team and participants. The authors encourage those developing clinical research projects to involve site staff and consumer representatives early in planning for communication with participants.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"319"},"PeriodicalIF":3.9,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11670412/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142892077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of missing data imputation methods on univariate blood pressure time series data analysis and forecasting with ARIMA and LSTM.","authors":"Nicholas Niako, Jesus D Melgarejo, Gladys E Maestre, Kristina P Vatcheva","doi":"10.1186/s12874-024-02448-3","DOIUrl":"10.1186/s12874-024-02448-3","url":null,"abstract":"<p><strong>Background: </strong>Missing observations within the univariate time series are common in real-life and cause analytical problems in the flow of the analysis. Imputation of missing values is an inevitable step in every incomplete univariate time series. Most of the existing studies focus on comparing the distributions of imputed data. There is a gap of knowledge on how different imputation methods for univariate time series affect the forecasting performance of time series models. We evaluated the prediction performance of autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) network models on imputed time series data using ten different imputation techniques.</p><p><strong>Methods: </strong>Missing values were generated under missing completely at random (MCAR) mechanism at 10%, 15%, 25%, and 35% rates of missingness using complete data of 24-h ambulatory diastolic blood pressure readings. The performance of the mean, Kalman filtering, linear, spline, and Stineman interpolations, exponentially weighted moving average (EWMA), simple moving average (SMA), k-nearest neighborhood (KNN), and last-observation-carried-forward (LOCF) imputation techniques on the time series structure and the prediction performance of the LSTM and ARIMA models were compared on imputed and original data.</p><p><strong>Results: </strong>All imputation techniques either increased or decreased the data autocorrelation and with this affected the forecasting performance of the ARIMA and LSTM algorithms. The best imputation technique did not guarantee better predictions obtained on the imputed data. The mean imputation, LOCF, KNN, Stineman, and cubic spline interpolations methods performed better for a small rate of missingness. Interpolation with EWMA and Kalman filtering yielded consistent performances across all scenarios of missingness. Disregarding the imputation methods, the LSTM resulted with a slightly better predictive accuracy among the best performing ARIMA and LSTM models; otherwise, the results varied. In our small sample, ARIMA tended to perform better on data with higher autocorrelation.</p><p><strong>Conclusions: </strong>We recommend to the researchers that they consider Kalman smoothing techniques, interpolation techniques (linear, spline, and Stineman), moving average techniques (SMA and EWMA) for imputing univariate time series data as they perform well on both data distribution and forecasting with ARIMA and LSTM models. The LSTM slightly outperforms ARIMA models, however, for small samples, ARIMA is simpler and faster to execute.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"320"},"PeriodicalIF":3.9,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11670515/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142892064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying the presence of atrial fibrillation during sinus rhythm using a dual-input mixed neural network with ECG coloring technology.","authors":"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","doi":"10.1186/s12874-024-02421-0","DOIUrl":"10.1186/s12874-024-02421-0","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;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 ","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"318"},"PeriodicalIF":3.9,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11665121/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142881113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The analysis and reporting of multiple outcomes in mental health trials: a methodological systematic review.","authors":"Dominic Stringer, Mollie Payne, Ben Carter, Richard Emsley","doi":"10.1186/s12874-024-02451-8","DOIUrl":"10.1186/s12874-024-02451-8","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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 ).</p><p><strong>Conclusions: </strong>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.</p><p><strong>Registration: </strong>Our systematic review protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO) on 11th January 2023 (CRD42023382274).</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"317"},"PeriodicalIF":3.9,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11662570/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142871332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Built-in selection or confounder bias? Dynamic Landmarking in matched propensity score analyses.","authors":"Alexandra Strobel, Andreas Wienke, Jan Gummert, Sabine Bleiziffer, Oliver Kuss","doi":"10.1186/s12874-024-02444-7","DOIUrl":"10.1186/s12874-024-02444-7","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"316"},"PeriodicalIF":3.9,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11662801/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142871317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Addressing treatment switching in the ALTA-1L trial with g-methods: exploring the impact of model specification.","authors":"Amani Al Tawil, Sean McGrath, Robin Ristl, Ulrich Mansmann","doi":"10.1186/s12874-024-02437-6","DOIUrl":"10.1186/s12874-024-02437-6","url":null,"abstract":"<p><strong>Background: </strong>Treatment switching in randomized clinical trials introduces challenges in performing causal inference. Intention To Treat (ITT) analyses often fail to fully capture the causal effect of treatment in the presence of treatment switching. Consequently, decision makers may instead be interested in causal effects of hypothetical treatment strategies that do not allow for treatment switching. For example, the phase 3 ALTA-1L trial showed that brigatinib may have improved Overall Survival (OS) compared to crizotinib if treatment switching had not occurred. Their sensitivity analysis using Inverse Probability of Censoring Weights (IPCW), reported a Hazard Ratio (HR) of 0.50 (95% CI, 0.28-0.87), while their initial ITT analysis estimated an HR of 0.81 (0.53-1.22).</p><p><strong>Methods: </strong>We used a directed acyclic graph to depict the clinical setting of the ALTA-1L trial in the presence of treatment switching, illustrating the concept of treatment-confounder feedback and highlighting the need for g-methods. In a re-analysis of the ALTA-1L trial data, we used IPCW and the parametric g-formula to adjust for baseline and time-varying covariates to estimate the effect of two hypothetical treatment strategies on OS: \"always treat with brigatinib\" versus \"always treat with crizotinib\". We conducted various sensitivity analyses using different model specifications and weight truncation approaches.</p><p><strong>Results: </strong>Applying the IPCW approach in a series of sensitivity analyses yielded Cumulative HRs (cHRs) ranging between 0.38 (0.12, 0.98) and 0.73 (0.45,1.22) and Risk Ratios (RRs) ranging between 0.52 (0.32, 0.98) and 0.79 (0.54,1.17). Applying the parametric g-formula resulted in cHRs ranging between 0.61 (0.38,0.91) and 0.72 (0.43,1.07) and RRs ranging between 0.71 (0.48,0.94) and 0.79 (0.54,1.05).</p><p><strong>Conclusion: </strong>Our results consistently indicated that our estimated ITT effect estimate (cHR: 0.82 (0.51,1.22) may have underestimated brigatinib's benefit by around 10-45 percentage points (using IPCW) and 10-20 percentage points (using the parametric g-formula) across a wide range of model choices. Our analyses underscore the importance of performing sensitivity analyses, as the result from a single analysis could potentially stand as an outlier in a whole range of sensitivity analyses.</p><p><strong>Trial registration: </strong>Clinicaltrials.gov Identifier: NCT02737501 on April 14, 2016.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"314"},"PeriodicalIF":3.9,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11660711/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142871316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}