Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Statistical Methodology (14)
- Biostatistics (13)
- Statistical Theory (13)
- Statistical Models (7)
- Survival Analysis (7)
-
- Medicine and Health Sciences (6)
- Epidemiology (4)
- Public Health (4)
- Clinical Epidemiology (2)
- Diseases (2)
- Multivariate Analysis (2)
- Analytical, Diagnostic and Therapeutic Techniques and Equipment (1)
- Applied Statistics (1)
- Biomedical Engineering and Bioengineering (1)
- Categorical Data Analysis (1)
- Disease Modeling (1)
- Engineering (1)
- Longitudinal Data Analysis and Time Series (1)
- Male Urogenital Diseases (1)
- Microarrays (1)
- Therapeutics (1)
- Keyword
-
- ANCOVA; cross validation; efficiency augmentation; Mayo PBC data; semi-parametric efficiency (1)
- Asymptotic Theory (1)
- Asymptotics; Augmented kernel estimating equations; Double robustness; Efficiency; Inverse probability weighted kernel estimating equations; Kernel smoothing (1)
- Biomarker; clinical outcome; diagnosis; incremental prediction accuracy; K-fold cross validation; prediction; subgroup analysis (1)
- Bivariate beta; conditional permutation test; erythropiesis-stimulating agents; logit-normal; two-level hierarchical model (1)
-
- Causal inference; complier average causal effect; Multi-arm trials; Non-compliance; Principal compliance; Principal stratification (1)
- Causal inference; instrumental variables; measurement error; non-compliance; prior information; structural models; weak identifability (1)
- Clinical trail; Cox model; nonparametric estimation; presonalized medicine; perturbation-resampling method; stratified medicine; subgroup analysis; survival analysis (1)
- Conditional power; frailty model; adaptive design (1)
- Cox's model; Nonparametric function estimation; Personalized medicine; Perturbation-resampling method; Stratified medicine; Subgroup analysis; Survival analysis (1)
- Cramer-Von Mises Statistics (1)
- Cross-training-evaluation; Personalized medicine; Prediction; Stratified medicine; Subgroup analysis; Variable selection. (1)
- Cure Model (1)
- Direct effect; indirect effect; instability; inverse probability; weighting; pathway; structural nested model; surrogate marker (1)
- Discrminant analysis; Nonparametric function estimation; Prediction; Receiver operating characteristics curve (1)
- Empirical Bayes (1)
- Equivalence study; Event driven study; Kaplan-Meier curve; Non-inferiority trial; Post-market study; Proportional hazards estimate (1)
- Gene-expession (1)
- Generalized Linear Model (1)
- Global Clinical Trials (1)
- Hierarchical Model (1)
- Highest Posterior Density Region (1)
- Meta analysis; cardiovascular toxicity; combining 2 x 2 tables; continuity correction for zero events (1)
- Meta-analysis (1)
- Mixed Model (1)
- Mixture model (1)
- PSA (1)
- Predicted Duration (1)
- Random effects (1)
- Testosterone (1)
Articles 1 - 19 of 19
Full-Text Articles in Clinical Trials
Studying The Optimal Scheduling For Controlling Prostate Cancer Under Intermittent Androgen Suppression, Sunil K. Dhar, Hans R. Chaudhry, Bruce G. Bukiet, Zhiming Ji, Nan Gao, Thomas W. Findley
Studying The Optimal Scheduling For Controlling Prostate Cancer Under Intermittent Androgen Suppression, Sunil K. Dhar, Hans R. Chaudhry, Bruce G. Bukiet, Zhiming Ji, Nan Gao, Thomas W. Findley
Harvard University Biostatistics Working Paper Series
This retrospective study shows that the majority of patients’ correlations between PSA and Testosterone during the on-treatment period is at least 0.90. Model-based duration calculations to control PSA levels during off-treatment are provided. There are two pairs of models. In one pair, the Generalized Linear Model and Mixed Model are both used to analyze the variability of PSA at the individual patient level by using the variable “Patient ID” as a repeated measure. In the second pair, Patient ID is not used as a repeated measure but additional baseline variables are included to analyze the variability of PSA.
The Myth Of Making Inferences For An Overall Treatment Efficacy With Data From Multiple Comparative Studies Via Meta-Analysis, Takahiro Hasegawa, Brian Claggett, Lu Tian, Scott D. Solomon, Marc A. Pfeffer, Lee-Jen Wei
The Myth Of Making Inferences For An Overall Treatment Efficacy With Data From Multiple Comparative Studies Via Meta-Analysis, Takahiro Hasegawa, Brian Claggett, Lu Tian, Scott D. Solomon, Marc A. Pfeffer, Lee-Jen Wei
Harvard University Biostatistics Working Paper Series
Meta analysis techniques, if applied appropriately, can provide a summary of the totality of evidence regarding an overall difference between a new treatment and a control group using data from multiple comparative clinical studies. The standard meta analysis procedures, however, may not give a meaningful between-group difference summary measure or identify a meaningful patient population of interest, especially when the fixed effect model assumption is not met. Moreover, a single between-group comparison measure without a reference value obtained from patients in the control arm would likely not be informative enough for clinical decision making. In this paper, we propose a …
Effectively Selecting A Target Population For A Future Comparative Study, Lihui Zhao, Lu Tian, Tianxi Cai, Brian Claggett, L. J. Wei
Effectively Selecting A Target Population For A Future Comparative Study, Lihui Zhao, Lu Tian, Tianxi Cai, Brian Claggett, L. J. Wei
Harvard University Biostatistics Working Paper Series
When comparing a new treatment with a control in a randomized clinical study, the treatment effect is generally assessed by evaluating a summary measure over a specific study population. The success of the trial heavily depends on the choice of such a population. In this paper, we show a systematic, effective way to identify a promising population, for which the new treatment is expected to have a desired benefit, using the data from a current study involving similar comparator treatments. Specifically, with the existing data we first create a parametric scoring system using multiple covariates to estimate subject-specific treatment differences. …
On The Covariate-Adjusted Estimation For An Overall Treatment Difference With Data From A Randomized Comparative Clinical Trial, Lu Tian, Tianxi Cai, Lihui Zhao, L. J. Wei
On The Covariate-Adjusted Estimation For An Overall Treatment Difference With Data From A Randomized Comparative Clinical Trial, Lu Tian, Tianxi Cai, Lihui Zhao, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Estimating Subject-Specific Treatment Differences For Risk-Benefit Assessment With Competing Risk Event-Time Data, Brian Claggett, Lihui Zhao, Lu Tian, Davide Castagno, L. J. Wei
Estimating Subject-Specific Treatment Differences For Risk-Benefit Assessment With Competing Risk Event-Time Data, Brian Claggett, Lihui Zhao, Lu Tian, Davide Castagno, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Stratifying Subjects For Treatment Selection With Censored Event Time Data From A Comparative Study, Lihui Zhao, Tianxi Cai, Lu Tian, Hajime Uno, Scott D. Solomon, L. J. Wei
Stratifying Subjects For Treatment Selection With Censored Event Time Data From A Comparative Study, Lihui Zhao, Tianxi Cai, Lu Tian, Hajime Uno, Scott D. Solomon, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Estimating Causal Effects In Trials Involving Multi-Treatment Arms Subject To Non-Compliance: A Bayesian Frame-Work, Qi Long, Roderick J. Little, Xihong Lin
Estimating Causal Effects In Trials Involving Multi-Treatment Arms Subject To Non-Compliance: A Bayesian Frame-Work, Qi Long, Roderick J. Little, Xihong Lin
Harvard University Biostatistics Working Paper Series
No abstract provided.
Nonparametric Regression With Missing Outcomes Using Weighted Kernel Estimating Equations, Lu Wang, Andrea Rotnitzky, Xihong Lin
Nonparametric Regression With Missing Outcomes Using Weighted Kernel Estimating Equations, Lu Wang, Andrea Rotnitzky, Xihong Lin
Harvard University Biostatistics Working Paper Series
No abstract provided.
Utilizing The Integrated Difference Of Two Survival Functions To Quantify The Treatment Contrast For Designing, Monitoring And Analyzing A Comparative Clinical Study, Lihui Zhao, Lu Tian, Hajime Uno, Scott D. Solomon, Marc A. Pfeffer, J. S. Schindler, L. J. Wei
Utilizing The Integrated Difference Of Two Survival Functions To Quantify The Treatment Contrast For Designing, Monitoring And Analyzing A Comparative Clinical Study, Lihui Zhao, Lu Tian, Hajime Uno, Scott D. Solomon, Marc A. Pfeffer, J. S. Schindler, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Graphical Procedures For Evaluating Overall And Subject-Specific Incremental Values From New Predictors With Censored Event Time Data, Hajime Uno, Tianxi Cai, Lu Tian, L. J. Wei
Graphical Procedures For Evaluating Overall And Subject-Specific Incremental Values From New Predictors With Censored Event Time Data, Hajime Uno, Tianxi Cai, Lu Tian, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
A Comparison Of Methods For Estimating The Causal Effect Of A Treatment In Randomized Clinical Trials Subject To Noncompliance, Rod Little, Qi Long, Xihong Lin
A Comparison Of Methods For Estimating The Causal Effect Of A Treatment In Randomized Clinical Trials Subject To Noncompliance, Rod Little, Qi Long, Xihong Lin
Harvard University Biostatistics Working Paper Series
No abstract provided.
Nonparametric Inference Procedure For Percentiles Of The Random Effect Distribution In Meta Analysis, Rui Wang, Lu Tian, Tianxi Cai, L. J. Wei
Nonparametric Inference Procedure For Percentiles Of The Random Effect Distribution In Meta Analysis, Rui Wang, Lu Tian, Tianxi Cai, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Estimation Of Controlled Direct Effects, Sylvie Goetgeluk, Stijn Vansteelandt, Els Goetghebeur
Estimation Of Controlled Direct Effects, Sylvie Goetgeluk, Stijn Vansteelandt, Els Goetghebeur
Harvard University Biostatistics Working Paper Series
No abstract provided.
Correcting Instrumental Variables Estimators For Systematic Measurement Error, Stijn Vansteelandt, Manoochehr Babanezhad, Els Goetghebeur
Correcting Instrumental Variables Estimators For Systematic Measurement Error, Stijn Vansteelandt, Manoochehr Babanezhad, Els Goetghebeur
Harvard University Biostatistics Working Paper Series
No abstract provided.
Effectively Combining Independent 2 X 2 Tables For Valid Inferences In Meta Analysis With All Available Data But No Artificial Continuity Corrections For Studies With Zero Events And Its Application To The Analysis Of Rosiglitazone's Cardiovascular Disease Related Event Data, Lu Tian, Tianxi Cai, Nikita Piankov, Pierre-Yves Cremieux, L. J. Wei
Effectively Combining Independent 2 X 2 Tables For Valid Inferences In Meta Analysis With All Available Data But No Artificial Continuity Corrections For Studies With Zero Events And Its Application To The Analysis Of Rosiglitazone's Cardiovascular Disease Related Event Data, Lu Tian, Tianxi Cai, Nikita Piankov, Pierre-Yves Cremieux, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Identifying Patients Who Need Additional Biomarkers For Better Prediction Of Health Outcome Or Diagnosis Of Clinical Phenotype, Lu Tian, Tianxi Cai, L. J. Wei
Identifying Patients Who Need Additional Biomarkers For Better Prediction Of Health Outcome Or Diagnosis Of Clinical Phenotype, Lu Tian, Tianxi Cai, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Designed Extension Of Survival Studies: Application To Clinical Trials With Unrecognized Heterogeneity, Yi Li, Mei-Chiung Shih, Rebecca A. Betensky
Designed Extension Of Survival Studies: Application To Clinical Trials With Unrecognized Heterogeneity, Yi Li, Mei-Chiung Shih, Rebecca A. Betensky
Harvard University Biostatistics Working Paper Series
It is well known that unrecognized heterogeneity among patients, such as is conferred by genetic subtype, can undermine the power of randomized trial, designed under the assumption of homogeneity, to detect a truly beneficial treatment. We consider the conditional power approach to allow for recovery of power under unexplained heterogeneity. While Proschan and Hunsberger (1995) confined the application of conditional power design to normally distributed observations, we consider more general and difficult settings in which the data are in the framework of continuous time and are subject to censoring. In particular, we derive a procedure appropriate for the analysis of …
The Optimal Confidence Region For A Random Parameter, Hajime Uno, Lu Tian, L.J. Wei
The Optimal Confidence Region For A Random Parameter, Hajime Uno, Lu Tian, L.J. Wei
Harvard University Biostatistics Working Paper Series
Under a two-level hierarchical model, suppose that the distribution of the random parameter is known or can be estimated well. Data are generated via a fixed, but unobservable realization of this parameter. In this paper, we derive the smallest confidence region of the random parameter under a joint Bayesian/frequentist paradigm. On average this optimal region can be much smaller than the corresponding Bayesian highest posterior density region. The new estimation procedure is appealing when one deals with data generated under a highly parallel structure, for example, data from a trial with a large number of clinical centers involved or genome-wide …
A Nonparametric Comparison Of Conditional Distributions With Nonnegligible Cure Fractions, Yi Li, Jin Feng
A Nonparametric Comparison Of Conditional Distributions With Nonnegligible Cure Fractions, Yi Li, Jin Feng
Harvard University Biostatistics Working Paper Series
No abstract provided.