Statistical Methods in Clinical Trials

Theoretical and numerical investigation of the consistency ofmodel comparisons in pharmacometrics

This paper investigates the consistency of likelihood ratio tests in pharmacometrics by examining the continuity of likelihood functions under parameter fixation in population models.

Exact formula of the total quasi-steady state approximation in competitive target-mediated drug disposition

This paper derives an exact formula for the competitive qTMDD model, improving computational speed and enabling accurate pharmacological parameter estimation in real-world clinical data.

Aggregate data modelling: a fast implementation for fitting pharmacometrics models to summary-level data in R

The paper implements an aggregate data modelling framework in R, introducing an efficient algorithm that significantly speeds up pharmacometric model fitting while maintaining accuracy.

A surrogate workflow for virtual population development: designing an amenable proxy for a hypothesis-test inspired nonlinear goodness-of-fit

The paper introduces a surrogate workflow for faster virtual population development by using a convex quadratic objective function as a proxy for nonlinear goodness-of-fit, improving efficiency in calibration.

Bonferroni's correction, not Tukey's, should be used to control the total number of false positives when making multiple pairwise comparisons in experiments with few replicates.

The paper shows that Bonferroni correction is more effective than Tukey's test in controlling false positives with few replicates in multiple pairwise comparisons.

MERIT: controlling Monte-Carlo error rate in large-scale Monte-Carlo hypothesis testing

MERIT improves the statistical efficiency of Monte-Carlo hypothesis testing by controlling the Monte-Carlo error rate, outperforming the GH method in sensitivity and specificity.

Statistical Assessment of Biomarker Replicability using MAJAR Method

The paper introduces MAJAR, a statistical framework to assess biomarker replicability in meta-analysis, showing improved power and robustness compared to existing methods.

Permutation-based group sequential analyses for cognitive neuroscience

This paper introduces a permutation-based sequential analysis method for neuroimaging studies to improve statistical power and reduce sample size requirements.

A Bayesian Precision Medicine Framework for Calibrating Individualized Therapeutic Indices in Cancer

This paper introduces a Bayesian framework to improve drug response prediction in cancer by integrating pharmacogenomic data across patients and cell lines.

CombPDX: a unified statistical framework for evaluating drug synergism in patient-derived xenografts

CombPDX evaluates drug synergism in PDX models using tumor growth curves and provides statistical metrics and inference procedures for combination effects.

Application of machine learning methods in clinical trials for precision medicine

This paper applies machine learning to response-adaptive randomization in clinical trials, showing improved treatment assignment and response rates.

MultipleTesting.com: a tool for life science researchers for multiple hypothesis testing correction

MultipleTesting.com provides an online tool for multiple hypothesis testing correction with examples and a guide to select appropriate statistical methods.

Accurate error control in high dimensional association testing using conditional false discovery rates

This paper introduces a new method for controlling type-1 error rates in high-dimensional association testing using conditional false discovery rates, showing improved power over existing methods.

Graphing and reporting heterogeneous treatment effects through reference classes

The paper introduces a method to analyze heterogeneous treatment effects using reference classes in RCTs, showing increased treatment effect in higher risk strata of a malaria trial.

Machine learning for randomised controlled trials: identifying treatment effect heterogeneity with strict control of type I error

This paper develops ML methods for RCTs to identify treatment effect heterogeneity while controlling type I error, finding no crossover subgroups but significant noncrossover TEH in artesunate treatment.

Genetic variability and potential effects on clinical trial outcomes: perspectives in Parkinson’s disease

This study simulates clinical trials to show genetic differences between trial arms can lead to false negatives and positives, especially in small samples, and suggests genetic balancing improves outcomes.

A Bayesian Zero-Inflated Binomial Regression and Its Application in Dose-Finding Study

This paper develops a Bayesian zero-inflated binomial regression model to improve dose-finding in clinical trials by accounting for excess zeros and sudden jumps in dose-limiting toxicities.

A practical guide to methods controlling false discoveries in computational biology

This paper benchmarks classic and modern false discovery rate methods in computational biology, finding modern methods with informative covariates offer modest power gains without sacrificing FDR control.

Designing robust N-of-1 studies for precision medicine

The paper develops a stochastic time series model to optimize N-of-1 study designs for precision medicine, showing how design parameters affect treatment efficacy detection.

The harmonic mean p-value for combining dependent tests

The paper introduces the harmonic mean p-value (HMP) to combine dependent tests, improving statistical power while controlling the family-wise error rate in large-scale hypothesis testing.

A direct approach to estimating false discovery rates conditional on covariates

The paper proposes a regression framework to estimate false discovery rates conditional on covariates and applies it to genome-wide association studies.

Designing fecal microbiota transplant trials that account for differencesin donor stool efficacy

The paper develops a Bayesian allocation strategy to improve statistical power in FMT trials by accounting for donor stool efficacy differences.