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1.
Sci Rep ; 14(1): 6433, 2024 Mar 18.
Article in English | MEDLINE | ID: mdl-38499738

ABSTRACT

In this study, we suggest an optimal imputation strategy for the elevated estimation of the population mean of the primary variable utilizing the known auxiliary parameters for the missing observations. Under this strategy, we suggest a new modified Searls type estimator, and we study its sampling properties, mainly bias and mean squared error (MSE), for an approximation of order one. The introduced estimator is compared theoretically with the estimators of population mean in competition under the imputation method. The efficiency conditions for the introduced estimator to be more efficient than the estimators in the competition are derived. To be sure about the efficiencies, these efficiency conditions are verified through the three natural populations. We have also conducted a simulation study and generated an artificial population with the same parameters as a natural population. The estimator with minimum MSE and the highest Percentage Relative Efficiency (PRE) is recommended for practical use in different areas of applications.

2.
Sci Rep ; 14(1): 4368, 2024 02 22.
Article in English | MEDLINE | ID: mdl-38388653

ABSTRACT

The potential contribution of the paper is the use of the propensity score matching method for updating censored observations within the context of multi-state model featuring two competing risks.The competing risks are modelled using cause-specific Cox proportional hazard model.The simulation findings demonstrate that updating censored observations tends to lead to reduced bias and mean squared error for all estimated parameters in the risk of cause-specific Cox model.The results for a chemoradiotherapy real dataset are consistent with the simulation results.


Subject(s)
Propensity Score , Proportional Hazards Models , Computer Simulation
3.
Cancer Biomark ; 38(4): 413-424, 2023.
Article in English | MEDLINE | ID: mdl-37980650

ABSTRACT

BACKGROUND: The initiation biomarker-driven trials have revolutionized oncology drug development by challenging the traditional phased approach and introducing basket studies. Notable successes in non-small cell lung cancer (NSCLC) with ALK, ALK/ROS1, and EGFR inhibitors have prompted the need to expand this approach to other cancer sites. OBJECTIVES: This study explores the use of dose response modeling and time-to-event algorithms on the biomarker molecular targeted agent (MTA). By simulating subgroup identification in MTA-related time-to-event data, the study aims to develop statistical methodology supporting biomarker-driven trials in oncology. METHODS: A total of n patients are selected assigned for different doses. A dataset is prepared to mimic the situation on Subgroup Identification of MTA for time to event data analysis. The response is measured through MTA. The MTA value is also measured through ROC. The Markov Chain Monte Carlo (MCMC) techniques are prepared to perform the proposed algorithm. The analysis is carried out with a simulation study. The subset selection is performed through the Threshold Limit Value (TLV) by the Bayesian approach. RESULTS: The MTA is observed with range 12-16. It is expected that there is a marginal level shift of the MTA from pre to post-treatment. The Cox time-varying model can be adopted further as causal-effect relation to establishing the MTA on prolonging the survival duration. The proposed work in the statistical methodology to support the biomarker-driven trial for oncology research. CONCLUSION: This study extends the application of biomarker-driven trials beyond NSCLC, opening possibilities for implementation in other cancer sites. By demonstrating the feasibility and efficacy of utilizing MTA as a biomarker, the research lays the foundation for refining and validating biomarker use in clinical trials. These advancements aim to enhance the precision and effectiveness of cancer treatments, ultimately benefiting patients.


Subject(s)
Carcinoma, Non-Small-Cell Lung , Lung Neoplasms , Humans , Carcinoma, Non-Small-Cell Lung/drug therapy , Carcinoma, Non-Small-Cell Lung/genetics , Lung Neoplasms/drug therapy , Lung Neoplasms/genetics , Protein-Tyrosine Kinases/therapeutic use , Bayes Theorem , Proto-Oncogene Proteins , Biomarkers/analysis , Receptor Protein-Tyrosine Kinases/therapeutic use , Molecular Targeted Therapy/methods
4.
Cancer Biomark ; 34(2): 319-328, 2022.
Article in English | MEDLINE | ID: mdl-35001879

ABSTRACT

BACKGROUND: HER2, ER, PR, and ERBB2 play a vital role in treating breast cancer. These are significant predictive and prognosis biomarkers of breast cancer. OBJECTIVE: We aim to obtain a unique biomarker-specific prediction on overall survival to know their survival and death risk. METHODS: Survival analysis is performed on classified data using Classification and Regression Tree (CART) analysis. Hazard ratio and Confidence Interval are computed using MLE and the Bayesian approach with the CPH model for univariate and multivariable illustrations. Validation of CART is executed with the Brier score, and accuracy and sensitivity are obtained using the k-nn classifier. RESULTS: Utilizing CART analysis, the cut-off value of continuous-valued biomarkers HER2, ER, PR, and ERBB2 are obtained as 14.707, 8.128, 13.153, and 6.884, respectively. Brier score of CART is 0.16 towards validation of methodology. Survival analysis gives a demonstration of the survival estimates with significant statistical strategies. CONCLUSIONS: Patients with breast cancer are at low risk of death, whose HER2 value is below its cut-off value, and ER, PR, and ERBB2 values are greater than their cut-off values. This comparison is with the patient having the opposite side of these cut-off values for the same biomarkers.


Subject(s)
Breast Neoplasms , Bayes Theorem , Biomarkers, Tumor , Female , Humans , Prognosis , Receptor, ErbB-2 , Receptors, Estrogen , Receptors, Progesterone
5.
BMC Infect Dis ; 21(1): 84, 2021 Jan 19.
Article in English | MEDLINE | ID: mdl-33468070

ABSTRACT

BACKGROUND: Diabetes is a risk factor for infection with coronaviruses. This study describes the demographic, clinical data, and outcomes of critically ill patients with diabetes and Middle East Respiratory Syndrome (MERS). METHODS: This retrospective cohort study was conducted at 14 hospitals in Saudi Arabia (September 2012-January 2018). We compared the demographic characteristics, underlying medical conditions, presenting symptoms and signs, management and clinical course, and outcomes of critically ill patients with MERS who had diabetes compared to those with no diabetes. Multivariable logistic regression analysis was performed to determine if diabetes was an independent predictor of 90-day mortality. RESULTS: Of the 350 critically ill patients with MERS, 171 (48.9%) had diabetes. Patients with diabetes were more likely to be older, and have comorbid conditions, compared to patients with no diabetes. They were more likely to present with respiratory failure requiring intubation, vasopressors, and corticosteroids. The median time to clearance of MERS-CoV RNA was similar (23 days (Q1, Q3: 17, 36) in patients with diabetes and 21.0 days (Q1, Q3: 10, 33) in patients with no diabetes). Mortality at 90 days was higher in patients with diabetes (78.9% versus 54.7%, p < 0.0001). Multivariable regression analysis showed that diabetes was an independent risk factor for 90-day mortality (odds ratio, 2.09; 95% confidence interval, 1.18-3.72). CONCLUSIONS: Half of the critically ill patients with MERS have diabetes; which is associated with more severe disease. Diabetes is an independent predictor of mortality among critically patients with MERS.


Subject(s)
Coronavirus Infections/complications , Diabetes Complications/epidemiology , Diabetes Mellitus/epidemiology , Adrenal Cortex Hormones , Adult , Age Factors , Aged , Bronchoalveolar Lavage Fluid/virology , Cohort Studies , Comorbidity , Coronavirus Infections/epidemiology , Coronavirus Infections/mortality , Critical Illness , Female , Humans , Male , Middle Aged , Middle East Respiratory Syndrome Coronavirus/genetics , Middle East Respiratory Syndrome Coronavirus/isolation & purification , Nasopharynx/virology , Respiratory Insufficiency/etiology , Respiratory Insufficiency/mortality , Retrospective Studies , Risk Factors , Saudi Arabia/epidemiology , Sputum/virology , Trachea/virology
6.
Stat Med ; 39(28): 4201-4217, 2020 12 10.
Article in English | MEDLINE | ID: mdl-32844489

ABSTRACT

Identification of biomarkers is an emerging area in oncology. In this article, we develop an efficient statistical procedure for the classification of protein markers according to their effect on cancer progression. A high-dimensional time-course dataset of protein markers for 80 patients motivates us for developing the model. The threshold value is formulated as a level of a marker having maximum impact on cancer progression. The classification algorithm technique for high-dimensional time-course data is developed and the algorithm is validated by comparing random components using both proportional hazard and accelerated failure time frailty models. The study elucidates the application of two separate joint modeling techniques using auto regressive-type model and mixed effect model for time-course data and proportional hazard model for survival data with proper utilization of Bayesian methodology. Also, a prognostic score is developed on the basis of few selected genes with application on patients. This study facilitates to identify relevant biomarkers from a set of markers.


Subject(s)
Algorithms , Medical Oncology , Bayes Theorem , Biomarkers , Humans , Proportional Hazards Models
7.
BMC Med Res Methodol ; 20(1): 209, 2020 08 12.
Article in English | MEDLINE | ID: mdl-32787822

ABSTRACT

BACKGROUND: As the whole world is experiencing the cascading effect of a new pandemic, almost every aspect of modern life has been disrupted. Because of health emergencies during this period, widespread fear has resulted in compromised patient safety, especially for patients with cancer. It is very challenging to treat such cancer patients because of the complexity of providing care and treatment, along with COVID-19. Hence, an effective treatment comparison strategy is needed. We need to have a handy tool to understand cancer progression in this unprecedented scenario. Linking different events of cancer progression is the need of the hour. It is a huge challenge for the development of new methodology. METHODS: This article explores the time lag effect and makes a statistical inference about the best experimental arm using Accelerated Failure Time (AFT) model and regression methods. The work is presented as the occurrence of other events as a hazard rate after the first event (relapse). The time lag effect between the events is linked and analysed. RESULTS: The results were presented as a comprehensive analytical strategy by joining all disease progression. An AFT model applied with the transition states, and the dependency structure between the gap times was used by the auto-regression model. The effects of arms were compared using the coefficient of auto-regression and accelerated failure time (AFT) models. CONCLUSIONS: We provide the solutions to overcome the issue with intervals between two consecutive events in motivating head and neck cancer (HNC) data. COVID-19 is not going to leave us soon. We have to conduct several cancer clinical trials in the presence of COVID-19. A comprehensive analytical strategy to analyse cancer clinical trial data during COVID-19 pandemic is presented.


Subject(s)
Algorithms , Coronavirus Infections/prevention & control , Head and Neck Neoplasms/therapy , Medical Oncology/methods , Models, Theoretical , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Bayes Theorem , Betacoronavirus/physiology , COVID-19 , Coronavirus Infections/complications , Coronavirus Infections/virology , Disease Progression , Head and Neck Neoplasms/complications , Head and Neck Neoplasms/diagnosis , Humans , Kaplan-Meier Estimate , Markov Chains , Monte Carlo Method , Neoplasm Recurrence, Local , Pneumonia, Viral/complications , Pneumonia, Viral/virology , SARS-CoV-2
8.
Math Biosci ; 305: 96-101, 2018 11.
Article in English | MEDLINE | ID: mdl-30194959

ABSTRACT

Background and ObjectiveBayesian State Space models are recent advancement in stochastic modeling which capture the randomness of a hidden background process by scrutinizing the prior knowledge and likelihood of observed data. This article elucidate the scope of Bayesian state space modeling on predicting the future expression values of a longitudinal micro array data. MethodsThe study conveniently makes use of longitudinally collected clinical trial data (GSE30531) from NCBI Gene Expression Omnibus (GEO) data repository. Multiple testing methodology using t-test is used for selecting differentially expressed genes between groups for fitting the model. The parameter values of the predictive model and future expression levels are estimated by drawing samples from the posterior joint distribution using a stochastic Markov Chain Monte Carlo (MCMC) algorithm which relies on Gibbs Sampling. The study also made an attempt to get estimates and its 95% Credible Interval through assumptions of different covariance structures like Variance Components, First order Auto Regressive and Unstructured variance-covariance structure to showcase the flexibility of the algorithm. Results72 Distinct genes with significantly different expression levels where selected for model fitting. Parameter estimates showed almost similar trends under different covariance structure assumption. Cross tabulation of gene frequencies having minimum credible interval under each covariance structure and study group showed a significant P value of 0.02. ConclusionsPresent study reveals that Bayesian state space models can be effectively used to explain and predict a complex data like gene expression data.


Subject(s)
Bayes Theorem , Gene Expression Profiling/statistics & numerical data , Models, Genetic , Algorithms , Genetic Markers , Humans , Markov Chains , Mathematical Concepts , Monte Carlo Method , Oligonucleotide Array Sequence Analysis/statistics & numerical data , Stochastic Processes
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