BMC Medical Research Methodology最新文献

{"title":"Pitfalls of single-study external validation illustrated with a model predicting functional outcome after aneurysmal subarachnoid hemorrhage.","authors":"Jordi de Winkel, Carolien C H M Maas, Bob Roozenbeek, David van Klaveren, Hester F Lingsma","doi":"10.1186/s12874-024-02280-9","DOIUrl":"10.1186/s12874-024-02280-9","url":null,"abstract":"<p><strong>Background: </strong>Prediction models are often externally validated with data from a single study or cohort. However, the interpretation of performance estimates obtained with single-study external validation is not as straightforward as assumed. We aimed to illustrate this by conducting a large number of external validations of a prediction model for functional outcome in subarachnoid hemorrhage (SAH) patients.</p><p><strong>Methods: </strong>We used data from the Subarachnoid Hemorrhage International Trialists (SAHIT) data repository (n = 11,931, 14 studies) to refit the SAHIT model for predicting a dichotomous functional outcome (favorable versus unfavorable), with the (extended) Glasgow Outcome Scale or modified Rankin Scale score, at a minimum of three months after discharge. We performed leave-one-cluster-out cross-validation to mimic the process of multiple single-study external validations. Each study represented one cluster. In each of these validations, we assessed discrimination with Harrell's c-statistic and calibration with calibration plots, the intercepts, and the slopes. We used random effects meta-analysis to obtain the (reference) mean performance estimates and between-study heterogeneity (I²-statistic). The influence of case-mix variation on discriminative performance was assessed with the model-based c-statistic and we fitted a \"membership model\" to obtain a gross estimate of transportability.</p><p><strong>Results: </strong>Across 14 single-study external validations, model performance was highly variable. The mean c-statistic was 0.74 (95%CI 0.70-0.78, range 0.52-0.84, I² = 0.92), the mean intercept was -0.06 (95%CI -0.37-0.24, range -1.40-0.75, I² = 0.97), and the mean slope was 0.96 (95%CI 0.78-1.13, range 0.53-1.31, I² = 0.90). The decrease in discriminative performance was attributable to case-mix variation, between-study heterogeneity, or a combination of both. Incidentally, we observed poor generalizability or transportability of the model.</p><p><strong>Conclusions: </strong>We demonstrate two potential pitfalls in the interpretation of model performance with single-study external validation. With single-study external validation. (1) model performance is highly variable and depends on the choice of validation data and (2) no insight is provided into generalizability or transportability of the model that is needed to guide local implementation. As such, a single single-study external validation can easily be misinterpreted and lead to a false appreciation of the clinical prediction model. Cross-validation is better equipped to address these pitfalls.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11308226/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141905838","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 simple and effective method for simulating nested exchangeable correlated binary data for longitudinal cluster randomised trials.","authors":"Rhys A Bowden, Jessica Kasza, Andrew B Forbes","doi":"10.1186/s12874-024-02285-4","DOIUrl":"10.1186/s12874-024-02285-4","url":null,"abstract":"<p><strong>Background: </strong>Simulation is an important tool for assessing the performance of statistical methods for the analysis of data and for the planning of studies. While methods are available for the simulation of correlated binary random variables, all have significant practical limitations for simulating outcomes from longitudinal cluster randomised trial designs, such as the cluster randomised crossover and the stepped wedge trial designs. For these trial designs as the number of observations in each cluster increases these methods either become computationally infeasible or their range of allowable correlations rapidly shrinks to zero.</p><p><strong>Methods: </strong>In this paper we present a simple method for simulating binary random variables with a specified vector of prevalences and correlation matrix. This method allows for the outcome prevalence to change due to treatment or over time, and for a 'nested exchangeable' correlation structure, in which observations in the same cluster are more highly correlated if they are measured in the same time period than in different time periods, and where different individuals are measured in each time period. This means that our method is also applicable to more general hierarchical clustered data contexts, such as students within classrooms within schools. The method is demonstrated by simulating 1000 datasets with parameters matching those derived from data from a cluster randomised crossover trial assessing two variants of stress ulcer prophylaxis.</p><p><strong>Results: </strong>Our method is orders of magnitude faster than the most well known general simulation method while also allowing a much wider range of correlations than alternative methods. An implementation of our method is available in an R package NestBin.</p><p><strong>Conclusions: </strong>This simulation method is the first to allow for practical and efficient simulation of large datasets of binary outcomes with the commonly used nested exchangeable correlation structure. This will allow for much more effective testing of designs and inference methods for longitudinal cluster randomised trials with binary outcomes.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11308151/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141905913","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":"Regression without regrets -initial data analysis is a prerequisite for multivariable regression.","authors":"Georg Heinze, Mark Baillie, Lara Lusa, Willi Sauerbrei, Carsten Oliver Schmidt, Frank E Harrell, Marianne Huebner","doi":"10.1186/s12874-024-02294-3","DOIUrl":"10.1186/s12874-024-02294-3","url":null,"abstract":"<p><p>Statistical regression models are used for predicting outcomes based on the values of some predictor variables or for describing the association of an outcome with predictors. With a data set at hand, a regression model can be easily fit with standard software packages. This bears the risk that data analysts may rush to perform sophisticated analyses without sufficient knowledge of basic properties, associations in and errors of their data, leading to wrong interpretation and presentation of the modeling results that lacks clarity. Ignorance about special features of the data such as redundancies or particular distributions may even invalidate the chosen analysis strategy. Initial data analysis (IDA) is prerequisite to regression analyses as it provides knowledge about the data needed to confirm the appropriateness of or to refine a chosen model building strategy, to interpret the modeling results correctly, and to guide the presentation of modeling results. In order to facilitate reproducibility, IDA needs to be preplanned, an IDA plan should be included in the general statistical analysis plan of a research project, and results should be well documented. Biased statistical inference of the final regression model can be minimized if IDA abstains from evaluating associations of outcome and predictors, a key principle of IDA. We give advice on which aspects to consider in an IDA plan for data screening in the context of regression modeling to supplement the statistical analysis plan. We illustrate this IDA plan for data screening in an example of a typical diagnostic modeling project and give recommendations for data visualizations.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11308558/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141905839","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":"Development of a diagnostic predictive model for determining child stunting in Malawi: a comparative analysis of variable selection approaches.","authors":"Jonathan Mkungudza, Halima S Twabi, Samuel O M Manda","doi":"10.1186/s12874-024-02283-6","DOIUrl":"10.1186/s12874-024-02283-6","url":null,"abstract":"<p><strong>Background: </strong>Childhood stunting is a major indicator of child malnutrition and a focus area of Global Nutrition Targets for 2025 and Sustainable Development Goals. Risk factors for childhood stunting are well studied and well known and could be used in a risk prediction model for assessing whether a child is stunted or not. However, the selection of child stunting predictor variables is a critical step in the development and performance of any such prediction model. This paper compares the performance of child stunting diagnostic predictive models based on predictor variables selected using a set of variable selection methods.</p><p><strong>Methods: </strong>Firstly, we conducted a subjective review of the literature to identify determinants of child stunting in Sub-Saharan Africa. Secondly, a multivariate logistic regression model of child stunting was fitted using the identified predictors on stunting data among children aged 0-59 months in the Malawi Demographic Health Survey (MDHS 2015-16) data. Thirdly, several reduced multivariable logistic regression models were fitted depending on the predictor variables selected using seven variable selection algorithms, namely backward, forward, stepwise, random forest, Least Absolute Shrinkage and Selection Operator (LASSO), and judgmental. Lastly, for each reduced model, a diagnostic predictive model for the childhood stunting risk score, defined as the child propensity score based on derived coefficients, was calculated for each child. The prediction risk models were assessed using discrimination measures, including area under-receiver operator curve (AUROC), sensitivity and specificity.</p><p><strong>Results: </strong>The review identified 68 predictor variables of child stunting, of which 27 were available in the MDHS 2016-16 data. The common risk factors selected by all the variable selection models include household wealth index, age of the child, household size, type of birth (singleton/multiple births), and birth weight. The best cut-off point on the child stunting risk prediction model was 0.37 based on risk factors determined by the judgmental variable selection method. The model's accuracy was estimated with an AUROC value of 64% (95% CI: 60%-67%) in the test data. For children residing in urban areas, the corresponding AUROC was AUC = 67% (95% CI: 58-76%), as opposed to those in rural areas, AUC = 63% (95% CI: 59-67%).</p><p><strong>Conclusion: </strong>The derived child stunting diagnostic prediction model could be useful as a first screening tool to identify children more likely to be stunted. The identified children could then receive necessary nutritional interventions.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11308741/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141905915","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 gap between statistical and clinical significance: time to pay attention to clinical relevance in patient-reported outcome measures of insomnia.","authors":"Zongshi Qin, Yidan Zhu, Dong-Dong Shi, Rumeng Chen, Sen Li, Jiani Wu","doi":"10.1186/s12874-024-02297-0","DOIUrl":"10.1186/s12874-024-02297-0","url":null,"abstract":"<p><strong>Background: </strong>Appropriately defining and using the minimal important change (MIC) and the minimal clinically important difference (MCID) are crucial for determining whether the results are clinically significant. The aim of this study is to survey the status of randomized controlled trials (RCTs) for insomnia interventions to assess the inclusion and interpretation of MIC/MCID values.</p><p><strong>Methods: </strong>We conducted a cross-sectional study to survey the status of RCTs for insomnia interventions to assess the inclusion and appropriate interpretation of MIC/MCID values. A literature search was conducted by searching the main sleep medicine journals indexed in PubMed, the Excerpta Medica Database (EMBASE), and the Cochrane Central Register of Controlled Trials (CENTRAL) to identify a broad range of search terms. We included RCTs with no restriction on the intervention. The included studies used the Insomnia Severity Index (ISI) or the Pittsburgh Sleep Quality Index (PSQI) questionnaire as the outcome measures.</p><p><strong>Results: </strong>81 eligible studies were identified, and more than one-third of the included studies used MIC/MCID (n = 31, 38.3%). Among them, 21 studies with ISI as the outcome used MIC defined as a relative decrease ranging from 3 to 8 points. The most frequently used MIC value was a 6-point decrease (n = 7), followed by 8-point (n = 6) and 7-point decrease (n = 4), a 4 to 5-points decrease (n = 3), and a 30% reduction from baseline; 6 studies used MCID values, ranging from 2.8 to 4 points. The most frequently used MCID value was a 4-point decrease in the ISI (n = 4). 4 studies with PSQI as the outcome used a 3-point change as the MIC (n = 2) and a 2.5 to 2.7-point difference as MCID (n = 2). 4 non-inferiority design studies considered interval estimation when drawing clinically significant conclusions in their MCID usage.</p><p><strong>Conclusions: </strong>The lack of consistent MIC/MCID interpretation and usage in outcome measures for insomnia highlights the urgent need for further efforts to address this issue and improve reporting practices.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11308508/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141905840","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":"Using joint probability density to create most informative unidimensional indices: a new method using pain and psychiatric severity as examples.","authors":"Siamak Noorbaloochi, Barbara A Clothier, Maureen Murdoch","doi":"10.1186/s12874-024-02299-y","DOIUrl":"10.1186/s12874-024-02299-y","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Dimension reduction methods do not always reduce their underlying indicators to a single composite score. Furthermore, such methods are usually based on optimality criteria that require discarding some information. We suggest, under some conditions, to use the joint probability density function (joint pdf or JPD) of p-dimensional random variable (the p indicators), as an index or a composite score. It is proved that this index is more informative than any alternative composite score. In two examples, we compare the JPD index with some alternatives constructed from traditional methods.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We develop a probabilistic unsupervised dimension reduction method based on the probability density of multivariate data. We show that the conditional distribution of the variables given JPD is uniform, implying that the JPD is the most informative scalar summary under the most common notions of information. B. We show under some widely plausible conditions, JPD can be used as an index. To use JPD as an index, in addition to having a plausible interpretation, all the random variables should have approximately the same direction(unidirectionality) as the density values (codirectionality). We applied these ideas to two data sets: first, on the 7 Brief Pain Inventory Interference scale (BPI-I) items obtained from 8,889 US Veterans with chronic pain and, second, on a novel measure based on administrative data for 912 US Veterans. To estimate the JPD in both examples, among the available JPD estimation methods, we used its conditional specifications, identified a well-fitted parametric model for each factored conditional (regression) specification, and, by maximizing the corresponding likelihoods, estimated their parameters. Due to the non-uniqueness of conditional specification, the average of all estimated conditional specifications was used as the final estimate. Since a prevalent common use of indices is ranking, we used measures of monotone dependence [e.g., Spearman's rank correlation (rho)] to assess the strength of unidirectionality and co-directionality. Finally, we cross-validate the JPD score against variance-covariance-based scores (factor scores in unidimensional models), and the \"person's parameter\" estimates of (Generalized) Partial Credit and Graded Response IRT models. We used Pearson Divergence as a measure of information and Shannon's entropy to compare uncertainties (informativeness) in these alternative scores.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;An unsupervised dimension reduction was developed based on the joint probability density (JPD) of the multi-dimensional data. The JPD, under regularity conditions, may be used as an index. For the well-established Brief Pain Interference Inventory (BPI-I (the short form with 7 Items) and for a new mental health severity index (MoPSI) with 6 indicators, we estimated the JPD scoring. We compared, assuming unidimensionality, factor scores, Person's sco","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11301985/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141896682","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":"jmBIG: enhancing dynamic risk prediction and personalized medicine through joint modeling of longitudinal and survival data in big routinely collected data.","authors":"Atanu Bhattacharjee, Bhrigu Kumar Rajbongshi, Gajendra K Vishwakarma","doi":"10.1186/s12874-024-02289-0","DOIUrl":"10.1186/s12874-024-02289-0","url":null,"abstract":"<p><p>We have introduced the R package jmBIG to facilitate the analysis of large healthcare datasets and the development of predictive models. This package provides a comprehensive set of tools and functions specifically designed for the joint modelling of longitudinal and survival data in the context of big data analytics. The jmBIG package offers efficient and scalable implementations of joint modelling algorithms, allowing for integrating large-scale healthcare datasets.By utilizing the capabilities of jmBIG, researchers and analysts can effectively handle the challenges associated with big healthcare data, such as high dimensionality and complex relationships between multiple outcomes.With the support of jmBIG, analysts can seamlessly fit Bayesian joint models, generate predictions, and evaluate the performance of the models. The package incorporates cutting-edge methodologies and harnesses the computational capabilities of parallel computing to accelerate the analysis of large-scale healthcare datasets significantly. In summary, jmBIG empowers researchers to gain deeper insights into disease progression and treatment response, fostering evidence-based decision-making and paving the way for personalized healthcare interventions that can positively impact patient outcomes on a larger scale.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11301890/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141896681","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":"Evaluating quasi-experimental approaches for estimating epidemiological efficacy of non-randomised field trials: applications in Wolbachia interventions for dengue.","authors":"Jo Yi Chow, Lin Geng, Somya Bansal, Borame Sue Lee Dickens, Lee Ching Ng, Ary Anthony Hoffmann, Jue Tao Lim","doi":"10.1186/s12874-024-02291-6","DOIUrl":"10.1186/s12874-024-02291-6","url":null,"abstract":"<p><strong>Background: </strong>Wolbachia symbiosis in Aedes aegypti is an emerging biocontrol measure against dengue. However, assessing its real-world efficacy is challenging due to the non-randomised, field-based nature of most intervention studies. This research re-evaluates the spatial-temporal impact of Wolbachia interventions on dengue incidence using a large battery of quasi-experimental methods and assesses each method's validity.</p><p><strong>Methods: </strong>A systematic search for Wolbachia intervention data was conducted via PUBMED. Efficacy was reassessed using commonly-used quasi-experimental approaches with extensive robustness checks, including geospatial placebo tests and a simulation study. Intervention efficacies across multiple study sites were computed using high-resolution aggregations to examine heterogeneities across sites and study periods. We further designed a stochastic simulation framework to assess the methods' ability to estimate intervention efficacies (IE).</p><p><strong>Results: </strong>Wolbachia interventions in Singapore, Malaysia, and Brazil significantly decreased dengue incidence, with reductions ranging from 48.17% to 69.19%. IEs varied with location and duration. Malaysia showed increasing efficacy over time, while Brazil exhibited initial success with subsequent decline, hinting at operational challenges. Singapore's strategy was highly effective despite partial saturation. Simulations identified Synthetic Control Methods (SCM) and its variant, count Synthetic Control Method (cSCM), as superior in precision, with the smallest percentage errors in efficacy estimation. These methods also demonstrated robustness in placebo tests.</p><p><strong>Conclusions: </strong>Wolbachia interventions exhibit consistent protective effects against dengue. SCM and cSCM provided the most precise and robust estimates of IEs, validated across simulated and real-world settings.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11302844/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141896680","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":"crossnma: An R package to synthesize cross-design evidence and cross-format data using network meta-analysis and network meta-regression.","authors":"Tasnim Hamza, Guido Schwarzer, Georgia Salanti","doi":"10.1186/s12874-023-02130-0","DOIUrl":"10.1186/s12874-023-02130-0","url":null,"abstract":"<p><strong>Background: </strong>Although aggregate data (AD) from randomised clinical trials (RCTs) are used in the majority of network meta-analyses (NMAs), other study designs (e.g., cohort studies and other non-randomised studies, NRS) can be informative about relative treatment effects. The individual participant data (IPD) of the study, when available, are preferred to AD for adjusting for important participant characteristics and to better handle heterogeneity and inconsistency in the network.</p><p><strong>Results: </strong>We developed the R package crossnma to perform cross-format (IPD and AD) and cross-design (RCT and NRS) NMA and network meta-regression (NMR). The models are implemented as Bayesian three-level hierarchical models using Just Another Gibbs Sampler (JAGS) software within the R environment. The R package crossnma includes functions to automatically create the JAGS model, reformat the data (based on user input), assess convergence and summarize the results. We demonstrate the workflow within crossnma by using a network of six trials comparing four treatments.</p><p><strong>Conclusions: </strong>The R package crossnma enables the user to perform NMA and NMR with different data types in a Bayesian framework and facilitates the inclusion of all types of evidence recognising differences in risk of bias.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11299362/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141892868","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":"Interactive molecular causal networks of hypertension using a fast machine learning algorithm MRdualPC.","authors":"Jack Kelly, Xiaoguang Xu, James M Eales, Bernard Keavney, Carlo Berzuini, Maciej Tomaszewski, Hui Guo","doi":"10.1186/s12874-024-02229-y","DOIUrl":"10.1186/s12874-024-02229-y","url":null,"abstract":"<p><strong>Background: </strong>Understanding the complex interactions between genes and their causal effects on diseases is crucial for developing targeted treatments and gaining insight into biological mechanisms. However, the analysis of molecular networks, especially in the context of high-dimensional data, presents significant challenges.</p><p><strong>Methods: </strong>This study introduces MRdualPC, a computationally tractable algorithm based on the MRPC approach, to infer large-scale causal molecular networks. We apply MRdualPC to investigate the upstream causal transcriptomics influencing hypertension using a comprehensive dataset of kidney genome and transcriptome data.</p><p><strong>Results: </strong>Our algorithm proves to be 100 times faster than MRPC on average in identifying transcriptomics drivers of hypertension. Through clustering, we identify 63 modules with causal driver genes, including 17 modules with extensive causal networks. Notably, we find that genes within one of the causal networks are associated with the electron transport chain and oxidative phosphorylation, previously linked to hypertension. Moreover, the identified causal ancestor genes show an over-representation of blood pressure-related genes.</p><p><strong>Conclusions: </strong>MRdualPC has the potential for broader applications beyond gene expression data, including multi-omics integration. While there are limitations, such as the need for clustering in large gene expression datasets, our study represents a significant advancement in building causal molecular networks, offering researchers a valuable tool for analyzing big data and investigating complex diseases.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11295895/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141878350","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}