BMC Medical Research Methodology最新文献

{"title":"Assessing the representativeness of large medical data using population stability index.","authors":"Sheng-Chieh Lu, Wenye Song, Andre Pfob, Chris Gibbons","doi":"10.1186/s12874-025-02474-9","DOIUrl":"10.1186/s12874-025-02474-9","url":null,"abstract":"<p><strong>Background: </strong>Understanding sample representativeness is key to interpreting findings from epidemiological research and applying these findings to broader populations. Though techniques for assessing sample representativeness are available, they rely on access to raw data detailing the population of interest which are often not readily available and may not be suitable for comparing large datasets. In reality, population-based data are often only available in an aggregated format. In this study, we aimed to examine the capability of population stability index (PSI), a popular metric to assess data drift for artificial intelligence studies, in detecting sample differences using population-based data.</p><p><strong>Method: </strong>We obtained United States cancer statistics from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database. We queried the SEER 17-registry research database to obtain cancer count data by age, sex, and cancer site groups from the rate sessions of the SEER*State incidence database for 2000 and 2015 - 2020. We then calculated PSI scores to estimate yearly data distribution shift from 2015 to 2020 for each variable. We compared the PSI results to the Chi-Square and Cramér's V tests for the same comparisons.</p><p><strong>Results: </strong>Scores for PSI comparing age, sex, and cancer site distribution between years ranged widely from 2.96 to less than 0.01. In line with our expectations, we found moderate to substantial differences in cancer population characteristics between 2000 and all other included years using PSI. Despite small effect sizes (Cramér's V 0.01 - 0.09), Chi-Square tests were significant for most comparisons, indicating likely type-I error caused by our large sample.</p><p><strong>Conclusions: </strong>Population stability index can be used to examine sample differences in healthcare studies where only binned data are available or where large datasets may reduce the reliability of other metrics. Inclusion of PSI in epidemiological research will give greater confidence that results are representative of the general population.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"44"},"PeriodicalIF":3.9,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11844046/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143472093","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":"Comprehensive implementations of multiple imputation using retrieved dropouts for continuous endpoints.","authors":"Shuai Wang, Pamela F Schwartz, James P Mancuso","doi":"10.1186/s12874-025-02494-5","DOIUrl":"10.1186/s12874-025-02494-5","url":null,"abstract":"<p><strong>Background: </strong>In the metabolic disease area, there has been a long-time debate against using mixed models for repeated measures (MMRM) as the primary analysis of longitudinal continuous endpoints. As missing data arising from missing not at random assumptions are not addressed in MMRM, multiple imputation based on specific assumptions has been brought into play. Among many missing not at random assumptions with varying degrees of conservativeness, multiple imputation based on retrieved dropouts (MIRD) has been accepted by regulatory agencies in several type 2 diabetes and chronic weight management products in recent years, marking the beginning of a new standard for analysis of longitudinal data in this disease area.</p><p><strong>Methods: </strong>On top of the established MIRD approach of which the imputation is based on last on-treatment data of retrieved dropout (RD)s, we propose a new class of MIRD approaches utilizing all available data from RDs. The imputation implementation can be one-step Markov Chain Monte Carlo (MCMC) or two-step (creating monotone missingness, followed by regression approach). ANCOVA can be applied to the complete dataset post imputation and Rubin's rule can be used to combine all estimates into a single estimate. Simulation studies in a wide range of scenarios are conducted to understand the type-I error and power rates of the new class versus the established MIRD approach and other reference statistical methods such as MMRM.</p><p><strong>Results: </strong>Overall, the new class has very similar performance compared to the established MIRD approach based on last on-treatment data. What's more interesting is the one-step MCMC approach has better controlled type-I error and is more powerful than the established MIRD approach in certain scenarios with the difference gradually diminishing with larger sample size. The data analyses based on two real phase 3 datasets further manifest the power conclusions, with the results based on the new class applied to the larger of the two datasets almost identical to that of on-study MMRM.</p><p><strong>Conclusions: </strong>We have presented comprehensive implementations of the MIRD approach for continuous endpoints in a longitudinal setting that fully fit within the strategy of treatment policy. The proposed new class based on all observed data of RDs is proved to be as powerful as the established MIRD approach based on last on-treatment visit in most scenarios. The one-step MCMC approach is more powerful than the established MIRD approach in certain scenarios. Since the new class involves less programming derivation of additional flags, it's anticipated to be more easily implemented in clinical trial reporting.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"47"},"PeriodicalIF":3.9,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11846319/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143472141","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":"A systematic review of mediation analysis frameworks in studies examining the determinants of cardiometabolic outcomes in people living with HIV.","authors":"Peter Vanes Ebasone, Nasheeta Peer, Anastase Dzudie, Merveille Foaleng, Johney Melpsa, Andre Pascal Kengne","doi":"10.1186/s12874-025-02498-1","DOIUrl":"10.1186/s12874-025-02498-1","url":null,"abstract":"<p><strong>Introduction: </strong>Mediation analysis provides a more flexible mechanistic view of the causal relationship between HIV-related factors and cardiometabolic diseases. However, there is limited evidence on how mediation analysis is implemented in this specific research area. We aimed to describe the frameworks used in mediation analysis and examine how these analyses are conducted and reported in studies focusing on cardiometabolic outcomes among people living with HIV (PLWH).</p><p><strong>Methods: </strong>Following the PRISMA 2020 Guidelines, we comprehensively searched Medline, CINAHL, Africa-Wide Information and SCOPUS to identify observational studies that employed mediation analysis before October 2023. Two reviewers independently screened studies for eligibility. One reviewer performed data extraction, and two others reviewed the extracted information.</p><p><strong>Results: </strong>Nine studies met the inclusion criteria, predominantly focusing on the mediation effects of weight and obesity-related factors on the relationship between HIV serostatus, ART, and cardiometabolic outcomes. The review revealed a diverse application of both traditional and causal mediation frameworks. However, inconsistencies and gaps in reporting were noted, particularly in handling missing data, detailing identifiability assumptions, and the use of sensitivity analyses.</p><p><strong>Conclusion: </strong>While some studies of cardiometabolic risks among PLWH have embraced causal mediation frameworks, their overall application remains limited. In addition, we identified notable inconsistencies and gaps in reporting practices. To advance the field, researchers should not only integrate more rigorous causal mediation methods but also closely follow established reporting guidelines, such as the AGReMA Statement, to ensure greater transparency, reliability, and impact of future research.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"41"},"PeriodicalIF":3.9,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11844112/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143466941","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":"Conceptual framework as a guide to choose appropriate imputation method for missing values in a clinical structured dataset.","authors":"Marziyeh Afkanpour, Diyana Tehrany Dehkordy, Mehri Momeni, Hamed Tabesh","doi":"10.1186/s12874-025-02496-3","DOIUrl":"10.1186/s12874-025-02496-3","url":null,"abstract":"<p><strong>Background: </strong>Missing data is a common challenge in structured datasets, and numerous methods are available for imputing these missing values. While all of these imputation methods address the issue of incomplete data, it is important to note that some methods perform better than others in terms of their effectiveness. A thorough data analysis can help a researcher identify a given dataset's most appropriate imputation approach, leading to more reliable analytical results. The primary objective of this study is to develop a conceptual framework that integrates various data imputation methods.</p><p><strong>Methods: </strong>This study was conducted in two main steps. First, we defined the conceptual components and their interrelationships by identifying and categorizing primary concepts through a secondary analysis of our previous systematic review, which examined 58 studies to uncover influential factors for selecting optimal imputation methods. Second, we analyzed the implementation process, focusing on the properties of missing values and selecting appropriate imputation techniques while verifying the underlying assumptions according to the estimand framework from the ICH E9(R1) Guideline to ensure unbiased estimates and enhance the credibility of our findings.</p><p><strong>Results: </strong>The findings from the secondary analysis suggest that the primary concepts of the developed conceptual framework directly influence the selection of appropriate imputation methods.</p><p><strong>Conclusions: </strong>This integrated structure will enable researchers to select the most suitable imputation method based on the specific characteristics and conditions of the dataset under investigation. By employing the appropriate imputation method, the study aims to enhance the overall quality and trustworthiness of the analytical outcomes derived from the research dataset.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"43"},"PeriodicalIF":3.9,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11843774/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143466943","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":"Validation of HES coding for the detection of major bleeding events: insights from the ROBOT-ACS study.","authors":"Liam Mullen, Rod Stables","doi":"10.1186/s12874-025-02503-7","DOIUrl":"10.1186/s12874-025-02503-7","url":null,"abstract":"<p><strong>Background: </strong>Increasingly research studies use HES (Hospital Episode Statistics) data to report clinical outcomes. No data exists on the performance of individual ICD-10 (International Classification of Diseases 10th Revision) diagnostic codes for identifying major bleeding events. Data from the ROBOT-ACS study provide a unique opportunity to assess this compared to conventionally adjudicated bleeding by standard definitions.</p><p><strong>Methods: </strong>A secondary analysis was performed on the 1172 HES records from ROBOT-ACS follow up data containing bleeding or anaemia codes. The 213 adjudicated major bleeds in ROBOT-ACS served as the gold standard comparator. Individual bleeding codes, and groups by type, were assessed for their positive predictive value (PPV).</p><p><strong>Results: </strong>The PPV of most codes for major bleeding were poor, generally < 50%. The best performing group of codes were relating to intracranial haemorrhage. 26 of 213 adjudicated bleeding events in ROBOT-ACS would be missed if anaemia codes were not considered.</p><p><strong>Conclusions: </strong>The performance of diagnostic ICD-10 codes in HES, without further interrogation, for determining major bleeding events is poor. Whilst sensitivity is likely to be favourable, differentiating major bleeding is challenging. Options for using HES data to determine bleeding in cardiovascular studies would be either a hybrid approach, with HES screening followed by records review, or creating a new definition of significant bleeding using data more readily available in HES.</p><p><strong>Trial registration: </strong>ROBOT-ACS is registered on clinicaltrials.gov. Unique identifier: NCT02484924. Registered 30/6/2015.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"42"},"PeriodicalIF":3.9,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11844057/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143466945","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":"Joint modelling of longitudinal data: a scoping review of methodology and applications for non-time to event data.","authors":"Rehema K Ouko, Mavuto Mukaka, Eric O Ohuma","doi":"10.1186/s12874-025-02485-6","DOIUrl":"10.1186/s12874-025-02485-6","url":null,"abstract":"<p><strong>Background: </strong>Joint models are powerful statistical models that allow us to define a joint likelihood for quantifying the association between two or more outcomes. Joint modelling has been shown to reduce bias in parameter estimates, increase the efficiency of statistical inference by incorporating the correlation between measurements, and allow borrowing of information in cases where data is missing for variables of interest. Most joint modelling methods and applications involve time-to-event data. There is less awareness about the amount of literature available for joint models of non-time-to-event data. Therefore, this review's main objective is to summarise the current state of joint modelling of non-time-to-event longitudinal data.</p><p><strong>Methods: </strong>We conducted a search in PubMed, Embase, Medline, Scopus, and Web of Science following the PRISMA-ScR guidelines for articles published up to 28 January 2024. Studies were included if they focused on joint modelling of non-time-to-event longitudinal data and published in English. Exclusions were made for time-to-event articles, conference abstracts, book chapters, and studies without full text. We extracted information on statistical methods, association structure, estimation methods, software, etc. RESULTS: We identified 4,681 studies from the search. After removing 2,769 duplicates, 1,912 were reviewed by title and abstract, and 190 underwent full-text review. Ultimately, 74 studies met inclusion criteria and spanned from 2001 to 2024, with the majority (64 studies; 86%) published between 2014 and 2024. Most joint models were based on a frequentist approach (48 studies; 65%) and applied a linear mixed-effects model. The random effect was the most commonly applied association structure for linking two sub-models (63 studies; 85%). Estimation of model parameters was commonly done using Markov Chain Monte Carlo with Gibbs sampler algorithm (10 studies; 38%) for the Bayesian approach, whereas maximum likelihood was the most common (33 studies; 68.75%) for the frequentist approach. Most studies used R statistical software (33 studies; 40%) for analysis.</p><p><strong>Conclusion: </strong>A wide range of methods for joint-modelling non-time-to-event longitudinal data exist and have been applied to various areas. An exponential increase in the application of joint modelling of non-time-to-event longitudinal data has been observed in the last decade. There is an opportunity to leverage potential benefits of joint modelling for non-time-to-event longitudinal data for reducing bias in parameter estimates, increasing efficiency of statistical inference by incorporating the correlation between measurements, and allowing borrowing of information in cases with missing data.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"40"},"PeriodicalIF":3.9,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11831847/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143439935","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":"Patient partner engagement in the publication process: challenges and possible solutions.","authors":"Krista E Cooksey, Mark Neuman, Mara Bollini, Bethany Pennington, Hugo de O Campos, Kathleen Oberst, Melissa Wurst, Mary C Politi","doi":"10.1186/s12874-025-02495-4","DOIUrl":"10.1186/s12874-025-02495-4","url":null,"abstract":"<p><p>Patient engagement in research is gaining traction as an international standard, and often requirement, of many health research funding agencies. Drivers of this increase include patient interest, increased attention to and recognition of the value of patients' voices in research, and more patients leading or partnering in the conduct research. Patient engagement includes collaborating and providing insights into research question and study design, and may extend to the publication process. When patients contribute to publications, they can bring unique perspectives that may enhance the impact, reach, and utility of the research in real-world contexts. Currently, there is limited systematic guidance to support patient partners as they navigate this complex publication process. As a result, it can be difficult for patient partners to understand when and how they should be included as authors, how to collaborate in the writing process, and how to complete mandatory tasks during the submission process. In this paper, we review barriers and facilitators within existing publication practices and offer recommendations to ensure that the scientific publication process is more transparent and accessible for patient partners.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"39"},"PeriodicalIF":3.9,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11829384/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143424839","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":"An algorithm to assess importance of predictors in systematic reviews of prediction models: a case study with simulations.","authors":"Ruohua Yan, Chen Wang, Chao Zhang, Xiaohang Liu, Dong Zhang, Xiaoxia Peng","doi":"10.1186/s12874-025-02492-7","DOIUrl":"10.1186/s12874-025-02492-7","url":null,"abstract":"<p><strong>Background: </strong>How to assess the importance of predictors in systematic reviews (SR) of prediction models remains largely unknown. The commonly used indicators of importance for predictors in individual models include parameter estimates, information entropy, etc., but they cannot be quantitatively synthesized through meta-analysis.</p><p><strong>Methods: </strong>We explored the synthesis method of the importance indicators in a simulation study, which mainly solved the following four methodological issues: (1) whether to synthesize the original values of the importance indicators or the importance ranks; (2) whether to normalize the importance ranks to a same dimension; (3) whether and how to impute the missing values in importance ranks; and (4) whether to weight the importance indicators according to the sample size of the model during synthesis. Then we used an empirical SR to illustrate the feasibility and validity of the synthesis method.</p><p><strong>Results: </strong>According to the simulation experiments, we found that ranking or normalizing the values of the importance indicators had little impact on the synthesis results, while imputation of missing values in the importance ranks had a great impact on the synthesis results due to the incorporation of variable frequency. Moreover, the results of means and weighted means of the importance indicators were similar. In consideration of accuracy and interpretability, synthesis of the normalized importance ranks by weighted mean was recommended. The synthesis method was used in the SR of prediction models for acute kidney injury. The importance assessment results were approved by experienced nephrologists, which further verified the reliability of the synthesis method.</p><p><strong>Conclusions: </strong>An importance assessment of predictors should be included in SR of prediction models, using the weighted mean of importance ranks normalized to a same dimension in different models.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"38"},"PeriodicalIF":3.9,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11827416/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143424818","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 reliance on conceptual frameworks in qualitative research - a way forward.","authors":"Joanna E M Sale, Leslie Carlin","doi":"10.1186/s12874-025-02461-0","DOIUrl":"10.1186/s12874-025-02461-0","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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?</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"36"},"PeriodicalIF":3.9,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11823033/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143412806","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":"A doubly robust estimator for continuous treatments in high dimensions.","authors":"Qian Gao, Jiale Wang, Ruiling Fang, Hongwei Sun, Tong Wang","doi":"10.1186/s12874-025-02488-3","DOIUrl":"10.1186/s12874-025-02488-3","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Method: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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 .</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"35"},"PeriodicalIF":3.9,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11823051/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143413417","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}