Dataset Information

Protocol for a factorial randomised controlled trial, embedded within WHiTE 8 COPAL, of an Enhanced Trainee Principal Investigator Package and Additional Digital Nudge to increase recruitment rates.

ABSTRACT: Recruitment remains an issue when conducting randomised controlled trials (RCTs) with a significant proportion of studies failing to reach their target sample size. Studies evaluating interventions to improve recruitment aimed specifically at recruiters to the trial are limited in number. This factorial RCT will evaluate the effectiveness of an educational intervention to trainee principal investigators and a positive reinforcement intervention via an email nudge on increasing recruitment. The targeted recruiters will be in 20 centres nationally recruiting to one large orthopaedic randomised controlled trial, WHiTE 8 COPAL. Centres will be randomised via minimisation to one of four groups. The primary outcome is recruitment rate in the first six months that a centre is actively recruiting, with data being analysed via a Poisson regression model. Results will be presented as adjusted incidence rate ratios with 95% confidence intervals. Secondary outcomes relate to the feasibility and logistics of running the interventions. We will also collect feedback regarding the educational programme set out for the trainee principal investigators. The study started in August 2018 with the anticipation of the primary objective endpoint by October 2019. The results of this study will be used to inform the design of future RCTs, particularly in orthopaedics in the UK, where the role of Trainee Principal Investigators is now a consistent one across different trials. Trial registration: 11600053, ISRCTN, 20/08/2018; SWAT 67, Northern Ireland Hub for Trials Methodology Research SWAT repository, 01/10/2017.

SUBMITTER: Agni N

PROVIDER: S-EPMC6733373 | biostudies-other | 2019

REPOSITORIES: biostudies-other

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Protocol for a factorial randomised controlled trial, embedded within WHiTE 8 COPAL, of an Enhanced Trainee Principal Investigator Package and Additional Digital Nudge to increase recruitment rates.

Agni Nickil N Fairhurst Caroline C McDaid Catriona C Reed Mike M Torgerson David D

F1000Research 20190722

Recruitment remains an issue when conducting randomised controlled trials (RCTs) with a significant proportion of studies failing to reach their target sample size. Studies evaluating interventions to improve recruitment aimed specifically at recruiters to the trial are limited in number. This factorial RCT will evaluate the effectiveness of an educational intervention to trainee principal investigators and a positive reinforcement intervention via an email nudge on increasing recruitment. The t ...[more]

PMID: 31543957

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Project description:Background and aim: Analysis of data obtained from genome wide gene expression experiments is challenging, due to the huge amount of variables, management of the data and the need for multivariate analysis. We here present the R package: pcaGoPromoter that facilitates the interpretation of genome wide expression data to overcome these problems. In a first step principal component analysis is applied to overview any differences between the observations and possible groupings. The next step is interpretation of the principal components with respect to both biological function and involvement of predicted transcription factor binding sites. The robustness of the results is evaluated using cross validation. Illustrative plots of PCA score plots and Gene Ontology terms are available. To illustrate the functionality of the R package, we designed a serum stimulation experiment, where the main biological outcome is well documented. Results: Samples from the serum stimulation experiment were analyzed using the Affymetrix Human Genome U133 Plus 2.0 chip. The array data were analyzed by the tools of the pcaGoPromoter package, which resulted in a clear separation of the observations into the three experimental groups - controls, serum only and serum with inhibitor. The functional annotation of the axes in the PCA score plot showed the expected serum promoted biological processes such as cell cycle progression and the predicted involvement of the expected transcription factors including E2F. In addition unexpected results, e.g. the cholesterol synthesis in serum depleted cells and NF-?B activation in inhibitor treated cells were uncovered. Conclusion: The pcaGoPromoter R package provides a collection of tools for analyzing gene expression data. It works with any platform using gene symbols or Entrez Ids as probe identifiers. In addition support for several popular Affymetrix GeneChip platforms is provided. The tools give an overview of the data via principal component analysis, functional interpretation by Gene Ontology terms (biological processes), and indication of involvement of possible transcription factors. Thus, pcaGoPromoter structures the high-dimensional data of gene expression experiments and can be applied to generate hypotheses for further exploration. A serum starved human monocyte cell line (ATCC® Number: CRL-9853) was stimulated by serum in the presence or absence of the specific Erk-1/2 inhibitor U0126.

Project description:Background and aim: Analysis of data obtained from genome wide gene expression experiments is challenging, due to the huge amount of variables, management of the data and the need for multivariate analysis. We here present the R package: pcaGoPromoter that facilitates the interpretation of genome wide expression data to overcome these problems. In a first step principal component analysis is applied to overview any differences between the observations and possible groupings. The next step is interpretation of the principal components with respect to both biological function and involvement of predicted transcription factor binding sites. The robustness of the results is evaluated using cross validation. Illustrative plots of PCA score plots and Gene Ontology terms are available. To illustrate the functionality of the R package, we designed a serum stimulation experiment, where the main biological outcome is well documented. Results: Samples from the serum stimulation experiment were analyzed using the Affymetrix Human Genome U133 Plus 2.0 chip. The array data were analyzed by the tools of the pcaGoPromoter package, which resulted in a clear separation of the observations into the three experimental groups - controls, serum only and serum with inhibitor. The functional annotation of the axes in the PCA score plot showed the expected serum promoted biological processes such as cell cycle progression and the predicted involvement of the expected transcription factors including E2F. In addition unexpected results, e.g. the cholesterol synthesis in serum depleted cells and NF-κB activation in inhibitor treated cells were uncovered. Conclusion: The pcaGoPromoter R package provides a collection of tools for analyzing gene expression data. It works with any platform using gene symbols or Entrez Ids as probe identifiers. In addition support for several popular Affymetrix GeneChip platforms is provided. The tools give an overview of the data via principal component analysis, functional interpretation by Gene Ontology terms (biological processes), and indication of involvement of possible transcription factors. Thus, pcaGoPromoter structures the high-dimensional data of gene expression experiments and can be applied to generate hypotheses for further exploration.

Dataset Information

Protocol for a factorial randomised controlled trial, embedded within WHiTE 8 COPAL, of an Enhanced Trainee Principal Investigator Package and Additional Digital Nudge to increase recruitment rates.

Publications

Protocol for a factorial randomised controlled trial, embedded within WHiTE 8 COPAL, of an Enhanced Trainee Principal Investigator Package and Additional Digital Nudge to increase recruitment rates.

Similar Datasets

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