Data Analysis Planning and Reporting for Confirmatory Multi-Lab Preclinical Trials: A Tutorial.

Confirmatory multi-lab preclinical trials are a powerful experimental strategy to enable decisions to transition from preclinical to clinical settings. With their complexity, such study designs pose several challenges in statistical planning, analysis, and reporting of experiments. To address these, we convened an expert group of biostatisticians and biomedical scientists currently involved in such trials to summarize in a tutorial the most common challenges and offer general guidance. Furthermore, we incorporated statistical advice from existing clinical trials' guidelines and adapted it into recommendations for preclinical trials. We describe strategies on key topics such as calculating sample sizes, handling differences between centers, and selecting relevant covariates. Additionally, we give guidance on statistical methods to account for lab effects and proper reporting of analyses. We embed this in a general discussion on remaining open questions to advance the analysis of preclinical confirmatory studies. The provided general, non-case-specific guidance serves as a conversation starter between biomedical scientists and statisticians to develop robust statistical analysis strategies for confirmatory multi-lab preclinical trials.

Authors

Institutions

Publication Details

Journal
Open Access CRIS of the University of Bern
Published
2026-07-28
DOI
https://doi.org/10.48620/99820
Primary Topic
Statistical Methods in Clinical Trials
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Data Analysis Planning and Reporting for Confirmatory Multi-Lab Preclinical Trials: A Tutorial.

Frank Konietschke, Juliane C. Wilcke, Anne‐Laure Boulesteix, Bernhard Voelkl et al.
Open Access CRIS of the University of Bern
Statistical Methods in Clinical Trials
article

Data Analysis Planning and Reporting for Confirmatory Multi-Lab Preclinical Trials: A Tutorial.

Frank Konietschke, Juliane C. Wilcke, Anne‐Laure Boulesteix, Bernhard Voelkl, Nicole Ellenbach, Leonard Held, Bernhard Haller, Sophie K. Piper, Lars Björn Riecken, Robbert Emprechtinger, Benjamin V. Ineichen, María Arroyo-Araujo, Clarissa F D Carneiro, Ulf Toelch
article en

Abstract

Confirmatory multi-lab preclinical trials are a powerful experimental strategy to enable decisions to transition from preclinical to clinical settings. With their complexity, such study designs pose several challenges in statistical planning, analysis, and reporting of experiments. To address these, we convened an expert group of biostatisticians and biomedical scientists currently involved in such trials to summarize in a tutorial the most common challenges and offer general guidance. Furthermore, we incorporated statistical advice from existing clinical trials' guidelines and adapted it into recommendations for preclinical trials. We describe strategies on key topics such as calculating sample sizes, handling differences between centers, and selecting relevant covariates. Additionally, we give guidance on statistical methods to account for lab effects and proper reporting of analyses. We embed this in a general discussion on remaining open questions to advance the analysis of preclinical confirmatory studies. The provided general, non-case-specific guidance serves as a conversation starter between biomedical scientists and statisticians to develop robust statistical analysis strategies for confirmatory multi-lab preclinical trials.

Open Access CRIS of the University of Bern
Animal Welfare Institute (US), Institute of Clinical Research (US)
Openalex Percentile: Top 6%
Statistical Methods in Clinical Trials
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.