Dataset for a randomised factorial experiment to optimise an information leaflet for women with breast cancer

Sophie M C Green; Samuel G Smith

doi:10.3310/nihropenres.13547.1

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Dataset for a randomised factorial experiment to optimise an information leaflet for women with breast cancer

[version 1; peer review: 2 approved]

Sophie M C Green ¹, Samuel G Smith¹

PUBLISHED 30 May 2024

Author details Author details

¹ Leeds Institute of Health Sciences, University of Leeds, Leeds, England, LS2 9LU, UK

Sophie M C Green
Roles: Conceptualization, Data Curation, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Writing – Original Draft Preparation, Writing – Review & Editing

Samuel G Smith
Roles: Conceptualization, Funding Acquisition, Investigation, Methodology, Resources, Supervision, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

Abstract

Background

Adherence to adjuvant endocrine therapy (AET) is low in women with breast cancer, which increases the risk of recurrence and mortality. A consistently reported barrier to adherence is low perceived necessity of AET and high concerns. Existing interventions to support medication beliefs have mixed effectiveness and rarely target medication beliefs specifically. We developed an information leaflet with five candidate components aiming to increase necessity beliefs about AET and reduce concerns; (1) diagrams explaining how AET works; (2) icon arrays displaying the benefits of AET; (3) information about the prevalence of side-effects; (4) answers to common concerns and (5) quotes and pictures from breast cancer survivors. Guided by the multiphase optimisation strategy (MOST), we aimed to optimise the content of the information leaflet. We planned for the dataset to be open access to provide an exemplar for other investigators to use.

Methods

The content of the leaflet was optimised in a fully powered online 2⁵ factorial experiment. Each candidate component of the leaflet was operationalised as a factor with two levels; on vs off or enhanced vs basic. Healthy women (n=1604) completed the beliefs about medicines questionnaire and were randomised to view one of 32 versions of the information leaflet. The 32 versions comprised unique combinations of the factor levels corresponding to the five candidate intervention components. Time spent on the information leaflet page of the survey was recorded. After viewing the information leaflet, participants completed the beliefs about medicines questionnaire again, a true/false questionnaire assessing their objective knowledge of AET, a subjective rating of their knowledge of AET, and a questionnaire evaluating their satisfaction with the information they received.

Importance of this dataset

The factorial dataset provides the opportunity for other investigators interested in using the MOST framework to learn about complex factorial designs, using a real dataset.

Plain Language Summary

Most women with breast cancer are treated with adjuvant endocrine therapy (AET) to reduce the chance of breast cancer coming back. However, many women do not take the medication as recommended. Women’s beliefs about the medication are a common reason for not taking AET. Some women do not think AET will help them, and some women have lots of concerns about AET. At the moment, we do not know the best way to change women’s beliefs about AET. Therefore, we ran a study to help us understand what combination of information might help change women’s beliefs about AET.

We developed a written information leaflet with five parts; (1) diagrams about how AET works; (2) visual figures of the benefits of AET; (3) information about how likely each side-effect is; (4) answers to common concerns about AET; and (5) pictures and quotes from women who have taken AET. In an online survey, 1,604 healthy women answered questions about their beliefs about the medication. Each woman was shown one version of the information leaflet picked at random. There were 32 possible versions of the information leaflet, which contained unique combinations of the five parts of the leaflet. After women read the leaflet, they were asked to complete the same questionnaire about their beliefs about the medication. They were also asked questions about how satisfied they were with the information they received, true or false questions about AET to assess their knowledge after reading the leaflet, and a rating of how informed they felt about AET. We also recorded how long women spent looking at the leaflet. One of our aims was to make the dataset from this experiment openly available so other scientists could use it to learn how to conduct similar experiments.

Keywords

factorial, intervention optimisation, multiphase optimisation strategy, breast cancer, information leaflet

Corresponding author: Sophie M C Green

Competing interests: No competing interests were disclosed.

Grant information: This project is funded by the National Institute for Health and Care Research under a NIHR Advanced Fellowship to Prof Samuel Smith (Grant Reference Number NIHR300588). Smith also acknowledges funding support from a Yorkshire Cancer Research University Academic Fellowship. S.G. acknowledges receipt of a Health and Behavior International Collaborative Research Award, sponsored by the International Behavioral Trials Network. The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health and Social Care. The funders had no role in the design of the study, data collection, analysis, interpretation of data, and in the writing of this manuscript.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2024 Green SMC and Smith SG. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Green SMC and Smith SG. Dataset for a randomised factorial experiment to optimise an information leaflet for women with breast cancer [version 1; peer review: 2 approved]. NIHR Open Res 2024, 4:32 (https://doi.org/10.3310/nihropenres.13547.1) First published: 30 May 2024, 4:32 (https://doi.org/10.3310/nihropenres.13547.1) Latest published: 30 May 2024, 4:32 (https://doi.org/10.3310/nihropenres.13547.1)

Introduction

Adjuvant endocrine therapy (e.g., tamoxifen, anastrozole, letrozole, exemestane) reduces breast cancer recurrence and mortality in women with early-stage (I-III) breast cancer^1,2. However, up to three-quarters of women do not take AET as prescribed^3–5. A consistently cited barrier to AET adherence is medication beliefs. Low perceived necessity of AET and high concerns surrounding AET (e.g., concerns of potential side-effects) have been associated with lower adherence^6–10.

Educational interventions are commonly used to address medication beliefs¹¹. However, evidence is mixed regarding the effectiveness of such interventions in changing medication beliefs^11,12. Moreover, medication beliefs are often targeted within a larger complex intervention aiming to support AET adherence. Therefore, the effectiveness of components specifically targeting medication beliefs is unclear^11,12.

To increase necessity beliefs and reduce concerns in women prescribed AET, we developed a theory-informed multicomponent educational information leaflet intervention¹³. The information leaflet had five candidate components; (1) diagrams explaining how AET works; (2) icon arrays stating the benefits of AET; (3) side-effect prevalence information; (4) answers to common concerns; and (5) quotes and pictures from other breast cancer survivors¹³.

Typically, an information leaflet intervention may be evaluated using a parallel group randomised controlled trial (RCT), comparing whether the leaflet as a whole is more effective than a suitable comparator, such as usual care. However, in a parallel group RCT, effects of the individual components of the leaflet, and interactions between intervention components cannot be estimated¹⁴. It is possible some intervention components could be redundant, or could have a negative effect on beliefs about AET, meaning the effectiveness and efficiency of the leaflet may be compromised¹⁴.

The multiphase optimisation strategy (MOST) is an engineering-inspired framework aiming to optimise complex interventions to balance effectiveness with efficiency¹⁴. MOST proposes an optimisation phase prior to definitive evaluation of complex interventions. In the optimisation phase, highly efficient and fully powered experimental designs, such as factorial designs, can be used to estimate the individual and combined effects of intervention components¹⁴. Empirical data from an optimisation trial can be used to select an optimised intervention¹⁵; for example, selecting an intervention comprising only intervention components estimated to contribute to a positive effect on beliefs about AET. The MOST framework therefore offers the potential to balance intervention effectiveness with efficiency.

As the MOST framework is a novel approach to optimising and evaluating complex interventions, there are relatively few open access examples of data of optimisation trials. Open access datasets of factorial experiments would be useful to provide scientists learning about the framework with some real data to practice analysing and interpreting the data. Our aim was to create a dataset of our factorial experiment that is available for other investigators to use. The original aim of the factorial experiment leading to creation of this dataset was to optimise the content of an information leaflet intervention targeting beliefs about AET¹³.

Methods

Patient and Public Involvement

A panel of four women prescribed AET for breast cancer were involved in this project. The panel were recruited in 2021 via a local charity supporting people affected by cancer. Through regular (approximately quarterly) meetings with the investigator(s), the panel had input into the design of the study (i.e., targeting medication beliefs), the targets of the intervention components and the content and design of the intervention components making up the information leaflet. They additionally provided quotes of their motivations for taking AET and advised on wording of the scenario presented at the beginning of the survey to provide context around being prescribed AET^13,16. The panel did not contribute to the conduct or recruitment of the study. After further evaluation of the information leaflet in a larger trial¹⁷, the panel will be consulted on methods of dissemination of study results.

Ethical approval

Ethical approval was granted from the University of Leeds School of Medicine ethical review board (MREC 21-033, 21/03/2022). Written informed consent was obtained electronically from all participants. All procedures, including the informed consent process, were conducted in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000.

Full methods and a detailed description of the development of the candidate intervention components are described elsewhere^13,18.

Candidate intervention components

The information leaflet was made up of five candidate intervention components, each operationalised as factors with two levels: (1) diagrams explaining how AET works (levels: on/off); (2) icon arrays visually displaying the benefits of taking AET in terms of reduced recurrence and mortality (levels: enhanced/basic); (3) information about potential side-effects from AET and their prevalence (levels: enhanced/basic); (4) answers to common concerns women have about taking AET (levels: on/off); (5) quotes and pictures from breast cancer survivors, stating their motivations for taking AET (levels: on/off).

Participants

Participants were required to be female, over 18 and able to read English. A market research company recruited participants via sending the survey link to potential respondents in the UK. A total of 1,604 women completed the survey. One participant was excluded due to being under 18.

Design

We used a 2⁵ (2x2x2x2x2) factorial design (Table 1). After completing basic demographic information, participants were shown a scenario asking them to imagine they had been diagnosed with breast cancer and prescribed AET. This scenario is available elsewhere¹³. Participants could not proceed to the next page of the survey until 30 seconds had passed.

Table 1. Experimental conditions in 2⁵ factorial design and number randomized to each condition.

	Constant Component	Diagrams	Benefits	Side-effects	Common concerns	Patient input	Number randomised
1	Yes	Yes	Enhanced	Enhanced	Yes	Yes	55
2	Yes	Yes	Enhanced	Enhanced	Yes	No	54
3	Yes	Yes	Enhanced	Enhanced	No	Yes	53
4	Yes	Yes	Enhanced	Enhanced	No	No	38
5	Yes	Yes	Enhanced	Basic	Yes	Yes	53
6	Yes	Yes	Enhanced	Basic	Yes	No	56
7	Yes	Yes	Enhanced	Basic	No	Yes	47
8	Yes	Yes	Enhanced	Basic	No	No	58
9	Yes	Yes	Basic	Enhanced	Yes	Yes	45
10	Yes	Yes	Basic	Enhanced	Yes	No	57
11	Yes	Yes	Basic	Enhanced	No	Yes	42
12	Yes	Yes	Basic	Enhanced	No	No	50
13	Yes	Yes	Basic	Basic	Yes	Yes	54
14	Yes	Yes	Basic	Basic	Yes	No	41
15	Yes	Yes	Basic	Basic	No	Yes	49
16	Yes	Yes	Basic	Basic	No	No	63
17	Yes	No	Enhanced	Enhanced	Yes	Yes	45
18	Yes	No	Enhanced	Enhanced	Yes	No	55
19	Yes	No	Enhanced	Enhanced	No	Yes	56
20	Yes	No	Enhanced	Enhanced	No	No	42
21	Yes	No	Enhanced	Basic	Yes	Yes	61
22	Yes	No	Enhanced	Basic	Yes	No	52
23	Yes	No	Enhanced	Basic	No	Yes	54
24	Yes	No	Enhanced	Basic	No	No	58
25	Yes	No	Basic	Enhanced	Yes	Yes	44
26	Yes	No	Basic	Enhanced	Yes	No	51
27	Yes	No	Basic	Enhanced	No	Yes	40
28	Yes	No	Basic	Enhanced	No	No	50
29	Yes	No	Basic	Basic	Yes	Yes	46
30	Yes	No	Basic	Basic	Yes	No	39
31	Yes	No	Basic	Basic	No	Yes	43
32	Yes	No	Basic	Basic	No	No	52

Note. Each component had two levels: on vs off, or enhanced vs basic.

This table was taken directly from Green et al.,¹³.

Participants completed the beliefs about medicines questionnaire. They were then randomised to one of 32 experimental conditions. Each condition corresponded to a unique version of the information leaflet made up of different combinations of the factor levels corresponding to the five intervention components. All possible combinations of factor levels made up the 32 experimental conditions. Participants could not proceed with the survey until three minutes had passed. After viewing the leaflet, participants were asked the same questions about their beliefs about AET, in addition to true or false questions about their knowledge of AET, their subjective knowledge of AET and their satisfaction with information they had received about AET. The questionnaires are available online (DOI 10.17605/OSF.IO/3CZ9Q)¹⁹.

Outcome measures

Participant demographics. Age, marital status, education level, ethnicity, menopausal status, previous breast cancer diagnoses and any close relations diagnosed with breast cancer were collected at the beginning of the survey. For women diagnosed with breast cancer, further questions about the stage and whether they had been prescribed AET were asked.

Beliefs about Medication Questionnaire-AET (BMQ-AET). The BMQ-AET is a 10-item scale used to assess specific medication beliefs²⁰. It is made up of two subscales; necessity and concerns, each with five items. Concern scores were subtracted from necessity scores to create a BMQ differential score. A BMQ differential score was calculated for the BMQ completed before and after viewing the information leaflet.

Satisfaction with information about Medicines (SIMS). A modified version of the SIMS was used²¹. Seven of the original SIMS items were removed due to not being relevant. One item was added asking about the benefits of taking AET. Participants were asked to rate their satisfaction with 11 different aspects of information about AET on a 5-point scale; too much, about right, too little, none received or none needed. Responses of “about right” and “none needed” indicated satisfaction with information and scored 1, while all other responses indicated dissatisfaction and scored 0. A total score was calculated (0–11).

Objective knowledge. Eight true or false items assessed objective knowledge of AET, relating to the mechanisms, benefits and side-effects of AET. Items answered correctly were scored as 1, items answered incorrectly were scored as 0. A total knowledge score was calculated.

Subjective knowledge. One item assessed participants subjective knowledge of AET; “How informed do you feel about hormone therapy for women with breast cancer”. Participants answered on a 0 (not very well informed at all) to 10 (very well informed) subscale.

Engagement. To observe engagement with the information leaflet, the length of time participants spent on the information leaflet page of the survey was recorded.

Statistical considerations

Missing data. There was no missing data as all survey items were mandatory. Any non-completed responses were not recorded.

Sample size. A sample size of 1,524 was required to detect an effect size of 0.15, with power of 0.9 and alpha set to 0.1. Alpha was set to 0.1 as a decision-priority approach was taken in which Type I and Type II errors are equally detrimental to selecting an optimised information leaflet¹⁵. Assuming 5% of participants would be speed responders (not completing the survey correctly), we increased the sample size to 1,604. The ‘MOST’ package in R Studio version 4.2.0 was used to calculate sample size^22,23.

Data cleaning

All data cleaning was conducted using R Studio, in R Statistical Software version 4.2.0²³. R packages used to clean the data included ‘tidyverse’ version 2.0.0²⁴, ‘dplyr’ version 1.0.10²⁵, ‘tibble’ version 3.2.1²⁶ and ‘descr’ version 1.1.8²⁷. Data cleaning involved four main steps: (1) renaming variables; (2) scoring the questionnaires; (3) effect coding the factors relating to the five intervention components (i.e., -1, +1); and (4) exclusion of participant that was under 18. The raw dataset and R code used to clean the data are available at https://doi.org/10.5518/1467²⁸.

Consent

Written informed consent was obtained electronically from all participants. All procedures, including the informed consent process, were conducted in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000.

Data availability

Underlying data

The dataset is available in a restricted access data repository. The data is restricted access due to data protection concerns; the data is not directly identifiable but could be viewed as indirectly identifiable. Ethical approval was granted to share the dataset open access in the University of Leeds data repository. All participants consented to their data to be stored in this way, and to be available for use in other research.

Repository Name: Restricted Access Data Repository Leeds

Request to access the data set can be made at: https://doi.org/10.5518/1467²⁸

The dataset contains the following files:

Raw data- the raw dataset prior to any data cleaning (csv file).
Clean dataset- the dataset after data cleaning (csv file).
R script for data cleaning- the code used to clean the raw dataset (R script).
Data notes- explanation of the variables in the cleaned dataset (PDF).

Extended data

Open Science Framework: Extended data for ‘Dataset for a randomised factorial experiment to optimise an information leaflet for women with breast cancer’, https://doi.org/10.17605/OSF.IO/3CZ9Q¹⁹

This project contains the following files:

Information Leaflet Survey V1.1.pdf
Information Leaflet PIS V1.2.pdf

Data are available under a Creative Commons Attribution 4.0 International Licence (CC-BY 4.0).

Acknowledgements

We would like to thank our patient contributors for their input into the development of the information leaflet, and to Health Creatives for designing the leaflet.

Faculty Opinions recommended

References

1. Early Breast Cancer Trialists' Collaborative Group (EBCTCG), Davies C, Godwin J, et al.: Relevance of breast cancer hormone receptors and other factors to the efficacy of adjuvant tamoxifen: patient-level meta-analysis of randomised trials. Lancet. 2011; 378(9793): 771–784. PubMed Abstract | Publisher Full Text | Free Full Text
2. Early Breast Cancer Trialists Collaborative Group: Aromatase inhibitors versus tamoxifen in early breast cancer: patient-level meta-analysis of the randomised trials. Lancet. 2015; 386(10001): 1341–1352. PubMed Abstract | Publisher Full Text
3. Hershman DL, Kushi LH, Shao T, et al.: Early discontinuation and nonadherence to adjuvant hormonal therapy in a cohort of 8,769 early-stage breast cancer patients. J Clin Oncol. 2010; 28(27): 4120–4128. PubMed Abstract | Publisher Full Text | Free Full Text
4. Murphy CC, Bartholomew LK, Carpentier MY, et al.: Adherence to adjuvant hormonal therapy among breast cancer survivors in clinical practice: a systematic review. Breast Cancer Res Treat. 2012; 134(2): 459–478. PubMed Abstract | Publisher Full Text | Free Full Text
5. Huiart L, Bouhnik AD, Rey D, et al.: Early discontinuation of tamoxifen intake in younger women with breast cancer: is it time to rethink the way it is prescribed? Eur J Cancer. 2012; 48(13): 1939–1946. PubMed Abstract | Publisher Full Text
6. Toivonen KI, Williamson TM, Carlson LE, et al.: Potentially modifiable factors associated with adherence to Adjuvant Endocrine Therapy among breast cancer survivors: a systematic review. Cancers (Basel). 2020; 13(1): 107. PubMed Abstract | Publisher Full Text | Free Full Text
7. Moon Z, Moss-Morris R, Hunter MS, et al.: Barriers and facilitators of adjuvant hormone therapy adherence and persistence in women with breast cancer: a systematic review. Patient Prefer Adherence. 2017; 11(1): 305–322. PubMed Abstract | Publisher Full Text | Free Full Text
8. Moon Z, Moss-Morris R, Hunter MS, et al.: Understanding tamoxifen adherence in women with breast cancer: a qualitative study. Br J Health Psychol. 2017; 22(4): 978–997. PubMed Abstract | Publisher Full Text
9. Cahir C, Dombrowski SU, Kelly CM, et al.: Women’s experiences of hormonal therapy for breast cancer: exploring influences on medication-taking behaviour. Support Care Cancer. 2015; 23(11): 3115–3130. PubMed Abstract | Publisher Full Text
10. Cahir C, Guinan E, Dombrowski SU, et al.: Identifying the determinants of adjuvant hormonal therapy medication taking behaviour in women with stages I-III breast cancer: a systematic review and meta-analysis. Patient Educ Couns. 2015; 98(12): 1524–1539. PubMed Abstract | Publisher Full Text
11. Bright EE, Finkelstein LB, Nealis MS, et al.: A systematic review and meta-analysis of interventions to promote Adjuvant Endocrine Therapy adherence among breast cancer survivors. J Clin Oncol. 2023; 41(28): 4548–4561. PubMed Abstract | Publisher Full Text | Free Full Text
12. Sheils E, Tillett W, James D, et al.: Changing medication-related beliefs: a systematic review and meta-analysis of randomized controlled trials. Health Psychol. 2024; 43(3): 155–170. PubMed Abstract | Publisher Full Text
13. Green SMC, Hall LH, French DP, et al.: Optimization of an information leaflet to influence medication beliefs in women with breast cancer: a randomized factorial experiment. Ann Behav Med. 2023; 57(11): 988–1000. PubMed Abstract | Publisher Full Text | Free Full Text
14. Collins LM: Optimization of behavioral, biobehavioral, and biomedical interventions: the Multiphase Optimization Strategy (MOST). Cham: Springer International Publishing, 2018. Publisher Full Text
15. Collins LM: The completion of the optimization phase. In: Optimization of Behavioral, Biobehavioral, and Biomedical Interventions: The Multiphase Optimization Strategy (MOST). L.M. Collins, Editor. Springer International Publishing: Cham, 2018; 227–266. Publisher Full Text
16. Green SMC, French DP, Graham CD, et al.: Supporting Adjuvant Endocrine Therapy adherence in women with breast cancer: the development of a complex behavioural intervention using Intervention Mapping guided by the Multiphase Optimisation Strategy. BMC Health Serv Res. 2022; 22(1): 1081. PubMed Abstract | Publisher Full Text | Free Full Text
17. Smith SG, Green SMC, Ellison R, et al.: Refining and Optimising a behavioural intervention to Support Endocrine Therapy Adherence (ROSETA) in UK women with breast cancer: protocol for a pilot fractional factorial trial. BMJ Open. 2023; 13(2): e069971. PubMed Abstract | Publisher Full Text | Free Full Text
18. Green SMC, et al.: Decision-making in the multiphase optimization strategy (MOST): Applying decision analysis for intervention value efficiency (DAIVE) to optimize an information leaflet to promote key antecedents of medication adherence. Transl Behav Med. , 2024. Publisher Full Text
19. Green SMC, Smith SG: Optimisation of an information leaflet to support medication beliefs in women with breast cancer. Open Science Framework. 2024. Publisher Full Text
20. Brett J, Hulbert-Williams NJ, Fenlon D, et al.: Psychometric properties of the Beliefs about Medicine Questionnaire-Adjuvant Endocrine Therapy (BMQ-AET) for women taking AETs following early-stage breast cancer. Health Psychol Open. 2017; 4(2): 2055102917740469. PubMed Abstract | Publisher Full Text | Free Full Text
21. Horne R, Hankins M, Jenkins R: The Satisfaction with Information about Medicines Scale (SIMS): a new measurement tool for audit and research. BMJ Quality & Safety. 2001; 10(3): 135–140.
22. Collins LM, Huang L, Dziak J: MOST: Multiphase Optimization Strategy. R package version 0.1.2. 2022. Reference Source
23. R Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing: Vienna, Austria, 2022. Reference Source
24. Wickham H, Averick M, Bryan J, et al.: Welcome to the Tidyverse. J Open Source Softw. 2019; 4(43): 1686. Publisher Full Text
25. Wickham H, François R, Henry L, et al.: _dplyr: A grammar of data manipulation_.. R package version 1.0.10. 2022. Reference Source
26. Müller K, Wickham H: tibble: Simple data frames. R package version 3.2.1. 2023. Reference Source
27. Aquino J, Enzmann D, Schwartz M, et al.: descr: Descriptive statistics. R package version 1.1.8. 2023. Reference Source
28. Green SMC, Smith SG: Dataset for a randomised factorial experiment to optimise an information leaflet for women with breast cancer. 2024. http://www.doi.org/10.5518/1467

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Version 1

VERSION 1 PUBLISHED 30 May 2024

Author details Author details

¹ Leeds Institute of Health Sciences, University of Leeds, Leeds, England, LS2 9LU, UK

Sophie M C Green
Roles: Conceptualization, Data Curation, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Writing – Original Draft Preparation, Writing – Review & Editing

Samuel G Smith
Roles: Conceptualization, Funding Acquisition, Investigation, Methodology, Resources, Supervision, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

This project is funded by the National Institute for Health and Care Research under a NIHR Advanced Fellowship to Prof Samuel Smith (Grant Reference Number NIHR300588). Smith also acknowledges funding support from a Yorkshire Cancer Research University Academic Fellowship. S.G. acknowledges receipt of a Health and Behavior International Collaborative Research Award, sponsored by the International Behavioral Trials Network. The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health and Social Care. The funders had no role in the design of the study, data collection, analysis, interpretation of data, and in the writing of this manuscript.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Published: 30 May 2024, 4:32

https://doi.org/10.3310/nihropenres.13547.1

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© 2024 Green SMC and Smith SG. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Green SMC and Smith SG. Dataset for a randomised factorial experiment to optimise an information leaflet for women with breast cancer [version 1; peer review: 2 approved]. NIHR Open Res 2024, 4:32 (https://doi.org/10.3310/nihropenres.13547.1)

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PUBLISHED 30 May 2024

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Reviewer Report 16 Jul 2024

Caroline Charlton, Northumbria University, Newcastle upon Tyne, England, UK

Approved

https://doi.org/10.3310/nihropenres.14705.r32251

Thank you for the opportunity to review this exciting article. This article is a great example of extending open science practices and knowledge sharing, outlining an interesting and novel methodology for intervention optimisation, and allowing for other researchers interested within ... Continue reading

Thank you for the opportunity to review this exciting article. This article is a great example of extending open science practices and knowledge sharing, outlining an interesting and novel methodology for intervention optimisation, and allowing for other researchers interested within intervention optimisation to utilise the shared dataset and learn more about the MOST framework.

The rationale for this article is clearly outlined, and the reported work is clear, concise, and technically sound. The methods provide a good overview of how the dataset was created, with links provided to previously published resources to further aid understanding on the intervention design and the decisions made during this process. Outcomes measures are clearly stated, although I would be interested to know whether there were any controls/measures in places regarding the engagement outcome, as the length of time spent on the leaflet survey page may not be indicative of actual engagement (e.g., the participant may have done something else whilst waiting to be allowed to move forward in the survey). The dataset is presented with clear instructions on how to access it and what files are contained within the dataset.

Overall, a great article which is important for sharing the novel methodology utilised. I would recommend this article to be indexed without any need for revision.

Is the rationale for creating the dataset(s) clearly described?

Yes
Are the protocols appropriate and is the work technically sound?

Yes
Are sufficient details of methods and materials provided to allow replication by others?

Yes
Are the datasets clearly presented in a useable and accessible format?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Health literacy, cancer screening, intervention design and evaluation. I recently published a paper evaluating the NHS cervical screening leaflet.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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Reviewer Report 13 Jul 2024

Emily A. Walsh, University of Miami, Coral Gables, Florida, USA

Approved

https://doi.org/10.3310/nihropenres.14705.r32110

This article, Dataset for a randomised factorial experiment to optimise an information leaflet for women with breast cancer, summarized a study of 1604 health women who completed a single, brief leaflet-based intervention to promote knowledge of adjuvant endocrine therapy. Authors ... Continue reading

This article, Dataset for a randomised factorial experiment to optimise an information leaflet for women with breast cancer, summarized a study of 1604 health women who completed a single, brief leaflet-based intervention to promote knowledge of adjuvant endocrine therapy. Authors concisely described the methods and procedures of the study, which was based at the University of Leeds, and consisted of recruiting women via a paid market research company. This public dataset would offer open access to data for researchers to practice and examine questions related to a Multiphase Optimization Strategy, or MOST approach, which is a novel and compelling analytic strategy used to identify which components of an intervention offer the most effect. Authors note the lack of current public datasets available to analyze via a MOST approach, and thus, this dataset will provide meaningful and impactful opportunities to researchers practicing and utilizing this approach. Notably, authors recruited healthy women to partake in this study on knowledge of a cancer treatment, which may slightly impact generalizability of related findings of this dataset versus a patient sample of women with breast cancer. However, the benefits of this dataset outweigh this minor consideration, which should be stated in subsequent, related publications from this dataset.

Is the rationale for creating the dataset(s) clearly described?

Yes
Are the protocols appropriate and is the work technically sound?

Yes
Are sufficient details of methods and materials provided to allow replication by others?

Yes
Are the datasets clearly presented in a useable and accessible format?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Psycho-oncology, clinical psychology, breast cancer, adjuvant endocrine therapy adherence and medication management

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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Emily A. Walsh, University of Miami, Coral Gables, USA
Caroline Charlton, Northumbria University, Newcastle upon Tyne, UK

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Reviewer Report

9 Views

16 Jul 2024 | for Version 1

Caroline Charlton, Northumbria University, Newcastle upon Tyne, England, UK

9 Views Cite this report Responses(0)

Approved

Thank you for the opportunity to review this exciting article. This article is a great example of extending open science practices and knowledge sharing, outlining an interesting and novel methodology for intervention optimisation, and allowing for other researchers interested within intervention optimisation to utilise the shared dataset and learn more about the MOST framework.

The rationale for this article is clearly outlined, and the reported work is clear, concise, and technically sound. The methods provide a good overview of how the dataset was created, with links provided to previously published resources to further aid understanding on the intervention design and the decisions made during this process. Outcomes measures are clearly stated, although I would be interested to know whether there were any controls/measures in places regarding the engagement outcome, as the length of time spent on the leaflet survey page may not be indicative of actual engagement (e.g., the participant may have done something else whilst waiting to be allowed to move forward in the survey). The dataset is presented with clear instructions on how to access it and what files are contained within the dataset.

Overall, a great article which is important for sharing the novel methodology utilised. I would recommend this article to be indexed without any need for revision.

Is the rationale for creating the dataset(s) clearly described?

Yes
Are the protocols appropriate and is the work technically sound?

Yes
Are sufficient details of methods and materials provided to allow replication by others?

Yes
Are the datasets clearly presented in a useable and accessible format?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Health literacy, cancer screening, intervention design and evaluation. I recently published a paper evaluating the NHS cervical screening leaflet.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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Reviewer Report

8 Views

13 Jul 2024 | for Version 1

Emily A. Walsh, University of Miami, Coral Gables, Florida, USA

8 Views Cite this report Responses(0)

Approved

This article, Dataset for a randomised factorial experiment to optimise an information leaflet for women with breast cancer, summarized a study of 1604 health women who completed a single, brief leaflet-based intervention to promote knowledge of adjuvant endocrine therapy. Authors concisely described the methods and procedures of the study, which was based at the University of Leeds, and consisted of recruiting women via a paid market research company. This public dataset would offer open access to data for researchers to practice and examine questions related to a Multiphase Optimization Strategy, or MOST approach, which is a novel and compelling analytic strategy used to identify which components of an intervention offer the most effect. Authors note the lack of current public datasets available to analyze via a MOST approach, and thus, this dataset will provide meaningful and impactful opportunities to researchers practicing and utilizing this approach. Notably, authors recruited healthy women to partake in this study on knowledge of a cancer treatment, which may slightly impact generalizability of related findings of this dataset versus a patient sample of women with breast cancer. However, the benefits of this dataset outweigh this minor consideration, which should be stated in subsequent, related publications from this dataset.

Is the rationale for creating the dataset(s) clearly described?

Yes
Are the protocols appropriate and is the work technically sound?

Yes
Are sufficient details of methods and materials provided to allow replication by others?

Yes
Are the datasets clearly presented in a useable and accessible format?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Psycho-oncology, clinical psychology, breast cancer, adjuvant endocrine therapy adherence and medication management

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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[1] 1. Early Breast Cancer Trialists' Collaborative Group (EBCTCG), Davies C, Godwin J, et al.: Relevance of breast cancer hormone receptors and other factors to the efficacy of adjuvant tamoxifen: patient-level meta-analysis of randomised trials. Lancet. 2011; 378(9793): 771–784. PubMed Abstract | Publisher Full Text | Free Full Text

[2] 2. Early Breast Cancer Trialists Collaborative Group: Aromatase inhibitors versus tamoxifen in early breast cancer: patient-level meta-analysis of the randomised trials. Lancet. 2015; 386(10001): 1341–1352. PubMed Abstract | Publisher Full Text

[3] 3. Hershman DL, Kushi LH, Shao T, et al.: Early discontinuation and nonadherence to adjuvant hormonal therapy in a cohort of 8,769 early-stage breast cancer patients. J Clin Oncol. 2010; 28(27): 4120–4128. PubMed Abstract | Publisher Full Text | Free Full Text

[4] 4. Murphy CC, Bartholomew LK, Carpentier MY, et al.: Adherence to adjuvant hormonal therapy among breast cancer survivors in clinical practice: a systematic review. Breast Cancer Res Treat. 2012; 134(2): 459–478. PubMed Abstract | Publisher Full Text | Free Full Text

[5] 5. Huiart L, Bouhnik AD, Rey D, et al.: Early discontinuation of tamoxifen intake in younger women with breast cancer: is it time to rethink the way it is prescribed? Eur J Cancer. 2012; 48(13): 1939–1946. PubMed Abstract | Publisher Full Text

[6] 6. Toivonen KI, Williamson TM, Carlson LE, et al.: Potentially modifiable factors associated with adherence to Adjuvant Endocrine Therapy among breast cancer survivors: a systematic review. Cancers (Basel). 2020; 13(1): 107. PubMed Abstract | Publisher Full Text | Free Full Text

[7] 7. Moon Z, Moss-Morris R, Hunter MS, et al.: Barriers and facilitators of adjuvant hormone therapy adherence and persistence in women with breast cancer: a systematic review. Patient Prefer Adherence. 2017; 11(1): 305–322. PubMed Abstract | Publisher Full Text | Free Full Text

[8] 8. Moon Z, Moss-Morris R, Hunter MS, et al.: Understanding tamoxifen adherence in women with breast cancer: a qualitative study. Br J Health Psychol. 2017; 22(4): 978–997. PubMed Abstract | Publisher Full Text

[9] 9. Cahir C, Dombrowski SU, Kelly CM, et al.: Women’s experiences of hormonal therapy for breast cancer: exploring influences on medication-taking behaviour. Support Care Cancer. 2015; 23(11): 3115–3130. PubMed Abstract | Publisher Full Text

[10] 10. Cahir C, Guinan E, Dombrowski SU, et al.: Identifying the determinants of adjuvant hormonal therapy medication taking behaviour in women with stages I-III breast cancer: a systematic review and meta-analysis. Patient Educ Couns. 2015; 98(12): 1524–1539. PubMed Abstract | Publisher Full Text

[11] 11. Bright EE, Finkelstein LB, Nealis MS, et al.: A systematic review and meta-analysis of interventions to promote Adjuvant Endocrine Therapy adherence among breast cancer survivors. J Clin Oncol. 2023; 41(28): 4548–4561. PubMed Abstract | Publisher Full Text | Free Full Text

[12] 12. Sheils E, Tillett W, James D, et al.: Changing medication-related beliefs: a systematic review and meta-analysis of randomized controlled trials. Health Psychol. 2024; 43(3): 155–170. PubMed Abstract | Publisher Full Text

[13] 13. Green SMC, Hall LH, French DP, et al.: Optimization of an information leaflet to influence medication beliefs in women with breast cancer: a randomized factorial experiment. Ann Behav Med. 2023; 57(11): 988–1000. PubMed Abstract | Publisher Full Text | Free Full Text

[14] 14. Collins LM: Optimization of behavioral, biobehavioral, and biomedical interventions: the Multiphase Optimization Strategy (MOST). Cham: Springer International Publishing, 2018. Publisher Full Text

[15] 15. Collins LM: The completion of the optimization phase. In: Optimization of Behavioral, Biobehavioral, and Biomedical Interventions: The Multiphase Optimization Strategy (MOST). L.M. Collins, Editor. Springer International Publishing: Cham, 2018; 227–266. Publisher Full Text

[16] 16. Green SMC, French DP, Graham CD, et al.: Supporting Adjuvant Endocrine Therapy adherence in women with breast cancer: the development of a complex behavioural intervention using Intervention Mapping guided by the Multiphase Optimisation Strategy. BMC Health Serv Res. 2022; 22(1): 1081. PubMed Abstract | Publisher Full Text | Free Full Text

[17] 17. Smith SG, Green SMC, Ellison R, et al.: Refining and Optimising a behavioural intervention to Support Endocrine Therapy Adherence (ROSETA) in UK women with breast cancer: protocol for a pilot fractional factorial trial. BMJ Open. 2023; 13(2): e069971. PubMed Abstract | Publisher Full Text | Free Full Text

[18] 18. Green SMC, et al.: Decision-making in the multiphase optimization strategy (MOST): Applying decision analysis for intervention value efficiency (DAIVE) to optimize an information leaflet to promote key antecedents of medication adherence. Transl Behav Med. , 2024. Publisher Full Text

[19] 19. Green SMC, Smith SG: Optimisation of an information leaflet to support medication beliefs in women with breast cancer. Open Science Framework. 2024. Publisher Full Text

[20] 20. Brett J, Hulbert-Williams NJ, Fenlon D, et al.: Psychometric properties of the Beliefs about Medicine Questionnaire-Adjuvant Endocrine Therapy (BMQ-AET) for women taking AETs following early-stage breast cancer. Health Psychol Open. 2017; 4(2): 2055102917740469. PubMed Abstract | Publisher Full Text | Free Full Text

[21] 21. Horne R, Hankins M, Jenkins R: The Satisfaction with Information about Medicines Scale (SIMS): a new measurement tool for audit and research. BMJ Quality & Safety. 2001; 10(3): 135–140.

[22] 22. Collins LM, Huang L, Dziak J: MOST: Multiphase Optimization Strategy. R package version 0.1.2. 2022. Reference Source

[23] 23. R Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing: Vienna, Austria, 2022. Reference Source

[24] 24. Wickham H, Averick M, Bryan J, et al.: Welcome to the Tidyverse. J Open Source Softw. 2019; 4(43): 1686. Publisher Full Text

[25] 25. Wickham H, François R, Henry L, et al.: _dplyr: A grammar of data manipulation_.. R package version 1.0.10. 2022. Reference Source

[26] 26. Müller K, Wickham H: tibble: Simple data frames. R package version 3.2.1. 2023. Reference Source

[27] 27. Aquino J, Enzmann D, Schwartz M, et al.: descr: Descriptive statistics. R package version 1.1.8. 2023. Reference Source

[28] 28. Green SMC, Smith SG: Dataset for a randomised factorial experiment to optimise an information leaflet for women with breast cancer. 2024. http://www.doi.org/10.5518/1467

Dataset for a randomised factorial experiment to optimise an information leaflet for women with breast cancer

Abstract

Background

Methods

Importance of this dataset

Plain Language Summary

Keywords

Introduction

Methods

Patient and Public Involvement

Ethical approval

Candidate intervention components

Participants

Design

Table 1. Experimental conditions in 2⁵ factorial design and number randomized to each condition.

Outcome measures

Statistical considerations

Data cleaning

Consent

Data availability

Underlying data

Extended data

Acknowledgements

References

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Dataset for a randomised factorial experiment to optimise an information leaflet for women with breast cancer

Abstract

Background

Methods

Importance of this dataset

Plain Language Summary

Keywords

Introduction

Methods

Patient and Public Involvement

Ethical approval

Candidate intervention components

Participants

Design

Table 1. Experimental conditions in 25 factorial design and number randomized to each condition.

Outcome measures

Statistical considerations

Data cleaning

Consent

Data availability

Underlying data

Extended data

Acknowledgements

References

Comments on this article Comments (0)

Open Peer Review

Comments on this article Comments (0)

Open Peer Review

Reviewer Status

Reviewer Reports

Comments on this article

Competing Interests Policy

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Are you an NIHR-funded researcher?

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Table 1. Experimental conditions in 2⁵ factorial design and number randomized to each condition.