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Clinical Trials (London, England) Jun 2023An ongoing cluster-randomized trial for the prevention of arboviral diseases utilizes covariate-constrained randomization to balance two treatment arms across four...
BACKGROUND
An ongoing cluster-randomized trial for the prevention of arboviral diseases utilizes covariate-constrained randomization to balance two treatment arms across four specified covariates and geographic sector. Each cluster is within a census tract of the city of Mérida, Mexico, and there were 133 eligible tracts from which to select 50. As some selected clusters may have been subsequently found unsuitable in the field, we desired a strategy to substitute new clusters while maintaining covariate balance.
METHODS
We developed an algorithm that successfully identified a subset of clusters that maximized the average minimum pairwise distance between clusters in order to reduce contamination and balanced the specified covariates both before and after substitutions were made.
SIMULATIONS
Simulations were performed to explore some limitations of this algorithm. The number of selected clusters and eligible clusters were varied along with the method of selecting the final allocation pattern.
CONCLUSION
The algorithm is presented here as a series of optional steps that can be added to the standard covariate-constrained randomization process in order to achieve spatial dispersion, cluster subsampling, and cluster substitution. Simulation results indicate that these extensions can be used without loss of statistical validity, given a sufficient number of clusters included in the trial.
Topics: Humans; Cluster Analysis; Random Allocation; Research Design; Computer Simulation; Algorithms
PubMed: 36932663
DOI: 10.1177/17407745231160556 -
BMC Infectious Diseases Dec 2022Current tuberculosis (TB) regimen development pathways are slow and in urgent need of innovation. We investigated novel phase IIc and seamless phase II/III trials...
BACKGROUND
Current tuberculosis (TB) regimen development pathways are slow and in urgent need of innovation. We investigated novel phase IIc and seamless phase II/III trials utilizing multi-arm multi-stage and Bayesian response adaptive randomization trial designs to select promising combination regimens in a platform adaptive trial.
METHODS
Clinical trial simulation tools were built using predictive and validated parametric survival models of time to culture conversion (intermediate endpoint) and time to TB-related unfavorable outcome (final endpoint). This integrative clinical trial simulation tool was used to explore and optimize design parameters for aforementioned trial designs.
RESULTS
Both multi-arm multi-stage and Bayesian response adaptive randomization designs were able to reliably graduate desirable regimens in ≥ 95% of trial simulations and reliably stop suboptimal regimens in ≥ 90% of trial simulations. Overall, adaptive phase IIc designs reduced patient enrollment by 17% and 25% with multi-arm multi-stage and Bayesian response adaptive randomization designs respectively compared to the conventional sequential approach, while seamless designs reduced study duration by 2.6 and 3.5 years respectively (typically ≥ 8.5 years for standard sequential approach).
CONCLUSIONS
In this study, we demonstrate that adaptive trial designs are suitable for TB regimen development, and we provide plausible design parameters for a platform adaptive trial. Ultimately trial design and specification of design parameters will depend on clinical trial objectives. To support decision-making for clinical trial designs in contemporary TB regimen development, we provide a flexible clinical trial simulation tool that can be used to explore and optimize design features and parameters.
Topics: Humans; Bayes Theorem; Random Allocation; Research Design; Tuberculosis; Computer Simulation
PubMed: 36494644
DOI: 10.1186/s12879-022-07846-w -
European Review For Medical and... Dec 202330-day readmission after hip fracture surgery in the elderly is common and costly. A predictive tool to identify high-risk patients could significantly improve outcomes....
OBJECTIVE
30-day readmission after hip fracture surgery in the elderly is common and costly. A predictive tool to identify high-risk patients could significantly improve outcomes. This study aims to develop and validate a risk nomogram for 30-day readmission after hip fracture surgery in geriatric patients.
PATIENTS AND METHODS
We retrospectively analyzed 1,249 geriatric hip fracture patients (≥60 years) undergoing surgery at Dandong Central Hospital from October 2011 to October 2023. Using a 7:3 ratio, patients were randomly divided into training (n=877) and validation (n=372) sets. Independent risk factors for 30-day readmission were identified using LASSO regression and logistic regression in the training set. A nomogram was constructed using the identified predictors. Finally, the C-index, ROC curve, calibration curve, and decision curve analysis were used to validate the model in the training and validation sets respectively.
RESULTS
The nomogram was developed based on the 8 predictors of age, prior stroke, chronic liver disease, treatment, uric acid (UA), total protein (TP), albumin (ALB), and pneumonia that were found to be independently associated with 30-day readmission. The nomogram showed good discrimination with a C-index of 0.88 in the training set and 0.84 in the validation set. Calibration curves exhibited good agreement between predicted and observed outcomes. Decision curve analysis demonstrated clinical utility.
CONCLUSIONS
We developed and validated a nomogram incorporating eight clinical variables to accurately predict the individualized risk of 30-day readmission after hip fracture surgery in elderly patients. The model demonstrated favorable discrimination, calibration, and clinical utility. It can help to identify high-risk patients needing additional interventions to prevent avoidable hospital readmissions.
Topics: Aged; Humans; Albumins; Hip Fractures; Nomograms; Patient Readmission; Retrospective Studies; Random Allocation
PubMed: 38095399
DOI: 10.26355/eurrev_202312_34590 -
Frontiers in Public Health 2021We aimed to establish and validate a risk assessment system that combines demographic and clinical variables to predict the 3-year risk of incident diabetes in Chinese...
We aimed to establish and validate a risk assessment system that combines demographic and clinical variables to predict the 3-year risk of incident diabetes in Chinese adults. A 3-year cohort study was performed on 15,928 Chinese adults without diabetes at baseline. All participants were randomly divided into a training set ( = 7,940) and a validation set ( = 7,988). XGBoost method is an effective machine learning technique used to select the most important variables from candidate variables. And we further established a stepwise model based on the predictors chosen by the XGBoost model. The area under the receiver operating characteristic curve (AUC), decision curve and calibration analysis were used to assess discrimination, clinical use and calibration of the model, respectively. The external validation was performed on a cohort of 11,113 Japanese participants. In the training and validation sets, 148 and 145 incident diabetes cases occurred. XGBoost methods selected the 10 most important variables from 15 candidate variables. Fasting plasma glucose (FPG), body mass index (BMI) and age were the top 3 important variables. And we further established a stepwise model and a prediction nomogram. The AUCs of the stepwise model were 0.933 and 0.910 in the training and validation sets, respectively. The Hosmer-Lemeshow test showed a perfect fit between the predicted diabetes risk and the observed diabetes risk ( = 0.068 for the training set, = 0.165 for the validation set). Decision curve analysis presented the clinical use of the stepwise model and there was a wide range of alternative threshold probability spectrum. And there were almost no the interactions between these predictors (most -values for interaction >0.05). Furthermore, the AUC for the external validation set was 0.830, and the Hosmer-Lemeshow test for the external validation set showed no statistically significant difference between the predicted diabetes risk and observed diabetes risk ( = 0.824). We established and validated a risk assessment system for characterizing the 3-year risk of incident diabetes.
Topics: Adult; China; Cohort Studies; Diabetes Mellitus; Humans; Machine Learning; Random Allocation; Risk Assessment
PubMed: 34268283
DOI: 10.3389/fpubh.2021.626331 -
Clinical and Experimental Medicine Feb 2024Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, and patients with HCC have a poor prognosis and low survival rates. Establishing a...
Development and evaluation of nomograms and risk stratification systems to predict the overall survival and cancer-specific survival of patients with hepatocellular carcinoma.
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, and patients with HCC have a poor prognosis and low survival rates. Establishing a prognostic nomogram is important for predicting the survival of patients with HCC, as it helps to improve the patient's prognosis. This study aimed to develop and evaluate nomograms and risk stratification to predict overall survival (OS) and cancer-specific survival (CSS) in HCC patients. Data from 10,302 patients with initially diagnosed HCC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2017. Patients were randomly divided into the training and validation set. Kaplan-Meier survival, LASSO regression, and Cox regression analysis were conducted to select the predictors of OS. Competing risk analysis, LASSO regression, and Cox regression analysis were conducted to select the predictors of CSS. The validation of the nomograms was performed using the concordance index (C-index), the Akaike information criterion (AIC), the Bayesian information criterion (BIC), Net Reclassification Index (NRI), Discrimination Improvement (IDI), the receiver operating characteristic (ROC) curve, calibration curves, and decision curve analyses (DCAs). The results indicated that factors including age, grade, T stage, N stage, M stage, surgery, surgery to lymph node (LN), Alpha-Fetal Protein (AFP), and tumor size were independent predictors of OS, whereas grade, T stage, surgery, AFP, tumor size, and distant lymph node metastasis were independent predictors of CSS. Based on these factors, predictive models were built and virtualized by nomograms. The C-index for predicting 1-, 3-, and 5-year OS were 0.788, 0.792, and 0.790. The C-index for predicting 1-, 3-, and 5-year CSS were 0.803, 0.808, and 0.806. AIC, BIC, NRI, and IDI suggested that nomograms had an excellent predictive performance with no significant overfitting. The calibration curves showed good consistency of OS and CSS between the actual observation and nomograms prediction, and the DCA showed great clinical usefulness of the nomograms. The risk stratification of OS and CSS was built that could perfectly classify HCC patients into three risk groups. Our study developed nomograms and a corresponding risk stratification system predicting the OS and CSS of HCC patients. These tools can assist in patient counseling and guiding treatment decision making.
Topics: Humans; alpha-Fetoproteins; Bayes Theorem; Carcinoma, Hepatocellular; Liver Neoplasms; Nomograms; Prognosis; Random Allocation
PubMed: 38413421
DOI: 10.1007/s10238-024-01296-1 -
Movement Ecology Jun 2021When assessing connectivity, it is crucial to rely on accurate modeling frameworks that consider species movement preferences and patterns. One important aspect is the...
BACKGROUND
When assessing connectivity, it is crucial to rely on accurate modeling frameworks that consider species movement preferences and patterns. One important aspect is the level of randomness or unpredictability in the route selection. In this respect, traditional approaches (based on least-cost path or circuit theory) consider species movements unrealistically as totally deterministic or as totally random. A recent approach (randomized shortest path) advocates for choosing intermediate levels of randomness through a single parameter. This parameter may be optimized by validating connectivity surfaces developed from different levels of randomness against observed movement data. However, connectivity models are seldom validated, and it is still unclear how to approach this task. To address this knowledge gap, this paper aims at comparing different validation methods to infer the optimal randomness level in connectivity studies. Additionally, we aimed to disentangle the practical consequences of applying traditional connectivity approaches versus using an optimized level of movement randomness when delineating corridors.
METHODS
These objectives were accomplished through the study case of the Iberian lynx, an endangered species whose maintenance and recovery depend on the current connectivity among its population nuclei. We firstly determined a conductance surface based on point selection functions accounting for the behavioral state (territorial or exploratory) of individuals. Secondly, we identified the level of randomness that better fits lynxes' movements with independent GPS locations and different validation techniques. Lastly, we delineated corridors between lynx population nuclei through a) the randomized shortest path approach and the extreme and optimal levels of randomness of each validation method, and b) the traditional connectivity approaches.
RESULTS
According to all used validation methodologies, models with intermediate levels of randomness outperformed those with extreme randomness levels representing totally deterministic or random movements. We found differences in the optimal randomness level among validation methods but similar results in the delineation of corridors. Our results also revealed that models with extreme randomness levels (deterministic and random walk) of the randomized path approach provided equivalent corridor networks to those from traditional approaches. Moreover, these corridor networks calculated with traditional approaches showed notable differences in patterns from the corridor network calculated with an optimized randomness level.
CONCLUSIONS
Here we presented a connectivity model with a solid biological basis that calibrates the level of movement randomness and is supported by comprehensive validation methods. It is thus a step forward in the search and evaluation of connectivity approaches that lead to improved, efficient, and successful management actions.
PubMed: 34187578
DOI: 10.1186/s40462-021-00273-7 -
NeuroImage Apr 2021Intracranial stereoelectroencephalography (sEEG) provides unsurpassed sensitivity and specificity for human neurophysiology. However, functional mapping of brain...
Intracranial stereoelectroencephalography (sEEG) provides unsurpassed sensitivity and specificity for human neurophysiology. However, functional mapping of brain functions has been limited because the implantations have sparse coverage and differ greatly across individuals. Here, we developed a distributed, anatomically realistic sEEG source-modeling approach for within- and between-subject analyses. In addition to intracranial event-related potentials (iERP), we estimated the sources of high broadband gamma activity (HBBG), a putative correlate of local neural firing. Our novel approach accounted for a significant portion of the variance of the sEEG measurements in leave-one-out cross-validation. After logarithmic transformations, the sensitivity and signal-to-noise ratio were linearly inversely related to the minimal distance between the brain location and electrode contacts (slope≈-3.6). The signa-to-noise ratio and sensitivity in the thalamus and brain stem were comparable to those locations at the vicinity of electrode contact implantation. The HGGB source estimates were remarkably consistent with analyses of intracranial-contact data. In conclusion, distributed sEEG source modeling provides a powerful neuroimaging tool, which facilitates anatomically-normalized functional mapping of human brain using both iERP and HBBG data.
Topics: Acoustic Stimulation; Adult; Drug Resistant Epilepsy; Electrodes, Implanted; Electroencephalography; Female; Humans; Male; Middle Aged; Random Allocation; Stereotaxic Techniques
PubMed: 33454414
DOI: 10.1016/j.neuroimage.2021.117746 -
Acta Bio-medica : Atenei Parmensis May 2020Foot-and-Ankle-Disability-Index (FADI) is one of the most widely used evaluation questionnaires for this anatomical district, but an italian validated version lacks and...
BACKGROUND AND AIM OF THE WORK
Foot-and-Ankle-Disability-Index (FADI) is one of the most widely used evaluation questionnaires for this anatomical district, but an italian validated version lacks and is necessary to properly evaluate italian people. In fact a correct interpretation of the items by patients is essential to obtain a precise subjective response, making the questionnaire valid to evaluate patients' satisfaction and wellness. Our purpose was to translate and culturally adapt into Italian the FADI questionnaire, and to check its reproducibility and validity.
MATERIALS AND METHODS
The original english version of FADI questionnaire was translated into Italian and checked for medical part coherence. It was submitted to 10 italian randomized patients to verify a correct cultural adaptation, and then to other 50 randomized patients operated at their ankle or hallux to assess intra- and inter-observer reproducibility by the Pearson's-Correlation-Coefficient (PCC) and the Intra-Class-Correlation (ICC) coefficient. Moreover, Short-Form-36 (SF36) questionnaire for Quality-of-Life and Visual-Analogue-Scale (VAS) for pain were also administered to the same 60 people and compared to italian-FADI to perform validation analysis by PCC and ICC coefficient.
RESULTS
Cultural adaptation of the translated version of the scale resulted good in terms of understandability by patients. An optimal correlation of the inter- and intra-observer reproducibility was obtained. The correlation obtained between FADI and SF-36 as well as between FADI and VAS indicates success in the validation process.
CONCLUSIONS
Validation of the FADI italian version has been performed successfully, its use can be considered appropriate and is indicated in italian clinical practice. (www.actabiomedica.it).
Topics: Adult; Aged; Aged, 80 and over; Ankle; Cultural Characteristics; Disability Evaluation; Female; Foot; Humans; Italy; Male; Middle Aged; Random Allocation; Reproducibility of Results; Surveys and Questionnaires; Translations
PubMed: 32555091
DOI: 10.23750/abm.v91i4-S.9544 -
Scientific Reports Oct 2019The aim of this retrospective study was to develop and validate a nomogram for predicting the risk of post-operative pulmonary infection (POI) in gastric cancer (GC)...
The aim of this retrospective study was to develop and validate a nomogram for predicting the risk of post-operative pulmonary infection (POI) in gastric cancer (GC) patients following radical gastrectomy. 2469 GC patients who underwent radical gastrectomy were enrolled, and randomly divided into the development and validation groups. The nomogram was constructed based on prognostic factors using logistic regression analysis, and was internally and crossly validated by bootstrap resampling and the validation dataset, respectively. Concordance index (C-index) value and calibration curve were used for estimating the predictive accuracy and discriminatory capability. Sixty-five (2.63%) patients developed POI within 30 days following surgery, with higher rates of requiring intensive care and longer post-operative hospital stays. The nomogram showed that open operation, chronic obstructive pulmonary disease (COPD), intra-operative blood transfusion, tumor located at upper and/or middle third and longer operation time (≥4 h) in a descending order were significant contributors to POI risk. The C-index value for the model was 0.756 (95% CI: 0.675-0.837), and calibration curves showed good agreement between nomogram predictions and actual observations. In conclusion, a nomogram based on these factors could accurately and simply provide a picture tool to predict the incidence of POI in GC patients undergoing radical gastrectomy.
Topics: Adult; Aged; Aged, 80 and over; Blood Transfusion; Female; Gastrectomy; Humans; Infections; Lung Diseases; Male; Middle Aged; Nomograms; Postoperative Complications; Postoperative Period; Prognosis; Prospective Studies; Pulmonary Disease, Chronic Obstructive; Random Allocation; Regression Analysis; Reproducibility of Results; Retrospective Studies; Stomach Neoplasms; Young Adult
PubMed: 31601989
DOI: 10.1038/s41598-019-51227-4 -
Trials Aug 2018Institutional review boards must guarantee the ethical acceptability of a randomized controlled trial before it is conducted. However, some may regard an unbalanced... (Comparative Study)
Comparative Study Review
BACKGROUND
Institutional review boards must guarantee the ethical acceptability of a randomized controlled trial before it is conducted. However, some may regard an unbalanced randomization ratio as reflecting an absence of uncertainty between the groups being compared. The objective was to assess institutional review board members' perceptions of whether unbalanced randomization in randomized controlled trials is justified and ethically acceptable.
METHODS
Institutional review board members worldwide completed a survey involving clinical vignettes modeling situations classically advocated to explain the use of unbalanced randomization. Institutional review board members were asked whether unbalanced randomization was justified and ethically sound. Answers were collected by using visual analog scales. Data were analyzed by principal component analysis, and a hierarchical ascending classification was created. Verbatim answers were assessed by qualitative content analysis.
RESULTS
We analyzed responses from 148 institutional review board members. Three classes of respondents were identified: class 1 (n = 58; 39.2%), mostly skeptics who disagreed with unbalanced randomization, whatever the justification; class 2 (n = 46; 31.1%), believers who considered that unbalanced randomization was acceptable whatever the justification, except cost; and class 3 (n = 44; 29.7%), circumstantial believers for whom unbalanced randomization may be justified for methodological and safety issues but not cost or ethical issues. When institutional review board members were asked whether unbalanced randomization respected the equipoise principle, the mean quotation was low (4.5 ± 3.3 out of 10), especially for class 1 members.
CONCLUSIONS
Institutional review board members perceive unbalanced randomization heterogeneously in terms of its justification and its ethical validity.
Topics: Adult; Attitude of Health Personnel; Epidemiologists; Ethicists; Ethics Committees, Research; Female; Health Knowledge, Attitudes, Practice; Humans; Male; Middle Aged; Perception; Philosophy, Medical; Random Allocation; Randomized Controlled Trials as Topic; Surgeons; Therapeutic Equipoise; Uncertainty
PubMed: 30107812
DOI: 10.1186/s13063-018-2822-1