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Statistical methods, outcome structures, and research designs in hybrid type II effectiveness-implementation studies: protocol for a scoping review [version 1; peer review: awaiting peer review]
Дата публикации: 13-08-2026 09:04:36
Background Hybrid type II effectiveness-implementation studies evaluate health intervention and implementation strategy effectiveness simultaneously. While the emphasis on each “side” varies within hybrid type II studies, the focus of this review is those that attempt a more-or-less equivalent dual emphasis. This version of type II studies spans many research designs including cluster randomized trials, sequential multiple assignment randomized trials (SMARTs), stepped wedge designs, pre-post and quasi-experimental designs, and program evaluation approaches. This bivariate structure introduces statistical challenges, including multiple testing and clustering. In addition, when outcomes are modeled as trajectories over time, dose, or implementation-strategy intensity, additional considerations arise related to effect estimation and sample-size determination. Although methodological solutions exist, the extent to which they are applied and the consistency with which authors justify them, remain unclear. This scoping review will describe the statistical methods, outcome structures, and research designs used in hybrid type II effectiveness–implementation studies. Methods This protocol follows JBI guidance (Peters et al., 2022) and will be reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) framework. We will search Embase, Medline, Scopus, and Web of Science for either: (a) hybrid type II effectiveness-implementation studies or (b) methodological guidance on their design or analysis. There will be no search limits related to publication type (e.g., protocols). Data will be extracted by two independent reviewers. The statistical methods employed will be categorised and other characteristics, such as implementation strategy used, will be extracted. The guidance of included methodological studies will be narratively summarised. Discussion This review will map the gaps between methodological availability and applications, with particular attention to designs generating trajectory outcomes. Findings will inform the planning and reporting of future hybrid type II studies and identify priorities for methodological development in this growing area of implementation science.
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Parsons M, Akinyemi O(, Golchi S et al. Statistical methods, outcome structures, and research designs in hybrid type II effectiveness-implementation studies: protocol for a scoping review [version 1; peer review: awaiting peer review]. F1000Research 2026, 15:1366 (https://doi.org/10.12688/f1000research.187738.1)
Study Protocol
[version 1; peer review: awaiting peer review]
https://orcid.org/0009-0007-4175-1352
1,2, Olajesutofunmi (Ola) Akinyemi2,3, Shirin Golchi3, [...] Rachael Laritz4, Rebecca Legnick-Hall5, Justin D. Smith5, Kathryn Hyzak6, Lawrence Mbuagbaw1,7, Guillaume Fontainehttps://orcid.org/0000-0002-7806-814X
1,2,8,9https://orcid.org/0009-0007-4175-1352
1,2, Olajesutofunmi (Ola) Akinyemi2,3, [...] Shirin Golchi3, Rachael Laritz4, Rebecca Legnick-Hall5, Justin D. Smith5, Kathryn Hyzak6, Lawrence Mbuagbaw1,7, Guillaume Fontainehttps://orcid.org/0000-0002-7806-814X
1,2,8,91 Methods Think Tank, Canadian Institutes of Health Research Pan-Canadian HIV/AIDS and Sexually-Transmitted and Blood Borne Infections Clinical Trials Research Network, Montréal, Québec, Canada
2 Ingram School of Nursing, McGill University, Montréal, Québec, Canada
3 Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montréal, Québec, Canada
4 Centre for Nursing Research, Jewish General Hospital, Montréal, Quebec, Canada
5 Department of Population Health Sciences, Division of Systems Innovation and Research, Spencer Fox Eccles School of Medicine, The University of Utah, Salt Lake City, Utah, USA
6 Department of Physical Medicine and Rehabilitation, College of Medicine, The Ohio State University, Columbus, Ohio, USA
7 Department of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
8 Department of Global and Public Health, Faculty of Medicine and Health Sciences, McGill University, Montréal, Québec, Canada
9 Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Montréal, Québec, Canada
Marc Parsons
Roles: Conceptualization, Data Curation, Investigation, Methodology, Project Administration, Software, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing
Olajesutofunmi (Ola) Akinyemi
Roles: Data Curation, Investigation, Validation, Writing – Review & Editing
Shirin Golchi
Roles: Methodology, Supervision, Writing – Review & Editing
Rachael Laritz
Roles: Data Curation, Investigation, Validation, Writing – Review & Editing
Rebecca Legnick-Hall
Roles: Methodology, Writing – Review & Editing
Justin D. Smith
Roles: Methodology, Writing – Review & Editing
Kathryn Hyzak
Roles: Methodology, Writing – Review & Editing
Lawrence Mbuagbaw
Roles: Methodology, Supervision, Writing – Review & Editing
Guillaume Fontaine
Roles: Conceptualization, Investigation, Methodology, Project Administration, Supervision, Validation, Writing – Review & Editing
OPEN PEER REVIEW
REVIEWER STATUS AWAITING PEER REVIEW
Abstract
Background
Hybrid type II effectiveness-implementation studies evaluate health intervention and implementation strategy effectiveness simultaneously. While the emphasis on each “side” varies within hybrid type II studies, the focus of this review is those that attempt a more-or-less equivalent dual emphasis. This version of type II studies spans many research designs including cluster randomized trials, sequential multiple assignment randomized trials (SMARTs), stepped wedge designs, pre-post and quasi-experimental designs, and program evaluation approaches. This bivariate structure introduces statistical challenges, including multiple testing and clustering. In addition, when outcomes are modeled as trajectories over time, dose, or implementation-strategy intensity, additional considerations arise related to effect estimation and sample-size determination. Although methodological solutions exist, the extent to which they are applied and the consistency with which authors justify them, remain unclear. This scoping review will describe the statistical methods, outcome structures, and research designs used in hybrid type II effectiveness–implementation studies.
MethodsThis protocol follows JBI guidance (Peters et al., 2022) and will be reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) framework. We will search Embase, Medline, Scopus, and Web of Science for either: (a) hybrid type II effectiveness-implementation studies or (b) methodological guidance on their design or analysis. There will be no search limits related to publication type (e.g., protocols). Data will be extracted by two independent reviewers. The statistical methods employed will be categorised and other characteristics, such as implementation strategy used, will be extracted. The guidance of included methodological studies will be narratively summarised.
DiscussionThis review will map the gaps between methodological availability and applications, with particular attention to designs generating trajectory outcomes. Findings will inform the planning and reporting of future hybrid type II studies and identify priorities for methodological development in this growing area of implementation science.
Keywords
hybrid effectiveness-implementation trials; clinical trials; implementation science; methodological review; scoping review; sample size estimation; multivariable statistics; trajectory outcomes
Corresponding author: Marc Parsons Competing interests: No competing interests were disclosed.
Grant information: This research was partially funded by the Canadian Institutes of Health Research Pan-Canadian HIV/AIDS and Sexually-Transmitted and Blood Borne Infections Clinical Trials Research Network (CIHR-CTN+).
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Copyright: © 2026 Parsons M et al. 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: Parsons M, Akinyemi O(, Golchi S et al. Statistical methods, outcome structures, and research designs in hybrid type II effectiveness-implementation studies: protocol for a scoping review [version 1; peer review: awaiting peer review]. F1000Research 2026, 15:1366 (https://doi.org/10.12688/f1000research.187738.1) First published: 13 Aug 2026, 15:1366 (https://doi.org/10.12688/f1000research.187738.1) Latest published: 13 Aug 2026, 15:1366 (https://doi.org/10.12688/f1000research.187738.1)
Background
Hybrid effectiveness-implementation studies were introduced to shorten the gap between establishing whether an intervention works and how it can best be implemented in routine practice (Curran et al., 2012). Over the past decade, hybrid studies have become a central part of implementation research. In contemporary usage, hybrid type I studies primarily emphasize intervention effectiveness while gathering implementation information; hybrid type II studies have a shared emphasis on both intervention effectiveness and implementation strategy effectiveness, which may or may not be formally operationalized as co-primary outcomes or formally as equivalent in their emphasis; and hybrid type III studies emphasize the effectiveness of implementation strategies while also measuring intervention outcomes as a secondary objective (Curran et al., 2012, 2022).
The simultaneous evaluation of both intervention and implementation strategy effectiveness in a hybrid type II study creates a distinctive set of research design and associated statistical considerations that vary depending on how the bivariate structure is operationalized. Hybrid type II studies span a wide range of research designs, including parallel cluster randomized trials, sequential multiple assignment randomized trials (SMARTs), stepped wedge and roll-out designs and their variants (e.g., factorial, matched-pair, roll-out implementation optimization), pre-post and interrupted time series studies, configurational analyses, and pilot and program-evaluation designs (Brown et al., 2017; Curran et al., 2012, 2022). This breadth matters for the present review because statistical and design considerations are not uniform across these design types. In hybrid type II studies, investigators must make design decisions that account for two often interdependent domains simultaneously, including how to specify target estimands, conduct hypothesis testing, estimate bivariate effects, address multiplicity, and determine adequate sample sizes for each outcome.
Hybrid effectiveness-implementation studies often evaluate effectiveness and implementation using single-point outcomes, such as a mean difference, proportion, or rate assessed at a specified follow-up time. Past methodological work has addressed hypothesis testing as well as power and sample-size determination for studies with bivariate single-point outcomes, particularly in cluster-randomized settings. Owen et al. (2025) identified several methods proposed in the methodological literature, considering them in the specific context of hybrid type II effectiveness-implementation studies, with a particular focus on cluster-randomized variants. They grouped these into five categories: p-value adjustments for multiple testing (e.g. Bonferroni correction, Sidak Method, etc.) (Sankoh et al., 1997; Sidak, 1967); combined outcomes methods, where the two outcomes are combined into a single index to facilitate the analysis (Chi, 2005); a single combined test approach, where the test statistics for the two outcomes are combined into a single statistic (O’Brien, 1984; Pocock et al., 1987); a disjunctive test approach, where both test statistics are simultaneously tested to assess whether the intervention affects at least one outcome (Yang et al., 2023); and a conjunctive test, where the test considers whether the intervention affects both outcomes (Yang et al., 2023).
However, many hybrid type II designs inherently generate trajectory outcomes. Specifically, stepped wedge designs produce time-as-trajectory data, SMART designs generate intensity-as-trajectory data typically on the implementation-strategy side, and dose-finding studies generate dose-trajectory data typically on the intervention side. In these cases, modeling one or both outcomes as trajectories over ordered levels of an independent variable is necessary. For instance, this could be time (e.g. days since the intervention), dose (e.g. dose of an implementation strategy), cumulative exposure (e.g. number of interventions in a given time window), or implementation-strategy intensity (e.g. level of training). For example, Magidson et al. (2025) used a stepped wedge design to assess implementation and effectiveness outcomes related to a training program to reduce stigma towards substance use and depression in community health workers. In this study, outcomes were measured at three timepoints: pre-training, 3 months post-training, and 6 months post-training. The longitudinal effect of the implementation strategy was explicitly modelled. There was moderate evidence of a waning effect on depression stigma (an effectiveness outcome) and fidelity (an implementation outcome) due to the intervention over time. Table 1 highlights examples of this study and other hybrid type II effectiveness-implementation studies of trajectory-based outcomes.
Table 1. Examples of hybrid type II effectiveness-implementation studies of trajectory outcomes.Author (year)Study designIntervention(s) /implementation strategiesIndependent variable(s) (levels)Co-primary outcomesEffectivenessImplementationMagidson et al. (2025)Stepped wedge trialCommunity health worker anti-stigma training, with social contact (lived experience) and clinical supervisionMonths since training (pre- training, 3 post-, and 6 post-training)Stigma towards substance use; depression score (Social Distance Scale)Proctor model outcomes (fidelity, acceptability, etc.)Szeszulski et al. (2025)Cluster randomized factorial trialMulticomponent program to promote physical activity and healthy eating in school environmentsLevel of implementation support including additional resources, school-to-school mentoring, and enhanced engagement opportunities (8 levels)Metabolic syndrome, cardiovascular fitness, fruit and vegetable consumption, and body mass index (BMI)Number of programs which were chosen by schoolteachers and the number of sessions taughtRantala et al. (2024)Pre-post studyWorkplace choice-architecture (nudge) modifications for healthy eating and physical activityNumber of implementation strategies employed (1–9 strategies)Self-reported food consumption and physical activity patternsSelf-reported perception of and response to the implemented strategies
Compared to studies of single-point estimates, the statistical analysis underpinning studies of trajectory outcomes is less straightforward. This is due to issues surrounding multiple testing as well as the complexity induced in modelling correlations between trajectory outcomes over dose levels. Another obstacle lies in the determination of the levels of the independent intervention variable to include. Luckily, methods for power and sample size calculations, as well as hypothesis testing of bivariate trajectory outcomes exist, including parametric and non-parametric models (Brunner et al., 2017; Ghosh et al., 2025; Xu et al., 2023). However, some shortcomings exist, including difficulty capturing non-linear trends and poor performance when integrating information across multiple outcomes (Ghosh et al., 2025). While the underlying statistical methodology may exist, it is ultimately not clear if and how these methods have been applied in hybrid type II effectiveness-implementation studies of trajectory outcomes.
Considering the complex structure of hybrid effectiveness-implementation studies discussed above, recent scoping reviews have described various characteristics of these studies as applied in practice. Liu et al. (2026) conducted a review of all three types of hybrid effectiveness-implementation studies. They found that clinical effectiveness may have greater emphasis in certain studies, including those employing a type II approach, suggesting that the labeling and justification of hybrid types are variable in practice. This review was not specifically focused on statistical methods, however. Further, this finding may be related to the design type used within the hybrid type II model. For example, in studies using a quasi-experimental design, researchers may place a greater emphasis on effectiveness outcomes out of necessity to enhance the rigour of the overarching study. This is particularly relevant for pilot and feasibility studies (Pearson et al., 2020). Other narrative and scoping reviews of hybrid effectiveness-implementation studies have also been conducted in specific clinical contexts such as traumatic brain injury (Hyzak et al., 2024), infectious disease (Clack et al., 2025), renal disease (Cervantes et al., 2025), and mental health (Chen et al., 2024; Lorente-Català & García-Palacios, 2026). A narrative but non-systematic search of the relevant methodological literature was conducted by Owen et al. (2025). As discussed above, they identified statistical methods commonly outlined in the methodological literature. However, to the best of our knowledge, to date, no scoping review of hybrid type II studies has been conducted to describe their specific statistical and design approaches as well as to describe how authors justify and report the use of a hybrid type II design. We will conduct a scoping review to (a) describe the statistical methods, outcome structures and research designs employed in hybrid type II effectiveness-implementation studies; (b) identify how studies of trajectory outcomes determine and justify which levels of the independent variable to include; and (c) characterize how authors justify the hybrid type II designation and operationalize the bivariate outcome structure (e.g., co-primary, combined index, disjunctive/conjunctive testing).
Objectives
The primary objective of this scoping review is to describe the statistical and design approaches currently employed in published hybrid type II effectiveness-implementation studies with a stated equivalent emphasis on both endpoints, including, where applicable, the methods used to incorporate trajectory outcomes.
This review has two secondary objectives: (1) to describe how hybrid type II effectiveness-implementation studies of trajectory outcomes determine and justify the levels of the independent variable included in their designs; and (2) to characterize how authors justify the hybrid type II designation and operationalize the bivariate effectiveness-implementation structure.
Methods
This protocol was developed in accordance with the methodological guidance of Peters et al. (2022) and will be reported using relevant items from the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) (Tricco et al., 2018). Any deviations from the protocol will be documented and reported in the final review.
Search strategy
A preliminary search strategy was developed and implemented in Embase to determine feasibility (Appendix 1). This preliminary search resulted in 1,353 records retrieved. This was determined to be a feasible number from a practical standpoint while also providing enough breadth to capture potentially important studies. Using this search, we developed a complete search strategy in consultation with a research librarian to identify published, peer-reviewed journal articles or other research products relating to hybrid type II effectiveness-implementation studies.
We aim to identify two types of research outputs: hybrid type II effectiveness-implementation studies (and protocols for these studies), as well as methodological studies outlining guidance on the statistical analysis and other design characteristics of these studies.
We will search Embase, Medline, Scopus, and Web of Science. The search terms used will remain consistent between the information sources, aside from formatting adjustments specific to each database. Exact duplicates will be identified and discarded. Duplicate research published in different formats (e.g., conference abstract later published as a journal article) will be linked and considered as a single record. Published protocols of identified studies, where available, will also be linked with their originating studies. We will exclude records not published in either English or French.
We will include records published since 1 March 2012. This coincides with the work of Curran et al. (2012), which first formalised the hybrid effectiveness-implementation study framework. To maintain as broad a scope as possible, we will not limit the type of research report to include in this review. As such, we will consider published articles, conference abstracts, protocols, and pilot studies. This is to ensure that we capture the most information on the current state of statistical practice in the determination of study design parameters in hybrid type II effectiveness-implementation studies of trajectories.
In addition to the database search, we will conduct forward citation tracking of the original Curran et al. (2012) paper as well as a well-cited follow-up paper (Curran et al., 2022). This will be performed in Web of Science to identify additional biomedical and methodological studies that explicitly build on its framework. Citation tracking will be documented as a supplementary search method. Records identified through citation tracking will be screened using the same eligibility criteria as other records retrieved through the main search.
Reverse-citation searches will be conducted on included methodological papers using Web of Science to identify studies that may have been missed by our initial search. If any scoping or methodological reviews on related topics are identified, we will search their included studies for eligible records. Similarly, reference lists of included biomedical studies will be examined to identify relevant methodological reports.
Study selection
After completing initial database searches, identified studies will be uploaded to Covidence. All duplicates will be removed. The first stage of screening will involve assessing eligibility of identified records based only on title and abstract by two reviewers working independently (Appendix 2). Records will be classified as either not eligible, possibly eligible, or eligible. Records coded in either one of the two latter categories by at least one reviewer will move on to full-text screening at the second stage. Full-text screening will be conducted in Covidence by two independent reviewers.
Inclusion/exclusion forms for both stages of screening will be piloted with an initial sample of abstracts to ensure consistency between both reviewers. Interrater reliability ( κ ) of at least 0.75 will be considered sufficient for the screening process in this review. If there are discrepancies between reviewers at the second stage of screening, we will involve additional reviewers to facilitate a consensus. Study selection will be presented in a PRISMA-ScR flow diagram (Tricco et al., 2018).
Data extraction
We have developed a preliminary list of data items to be extracted (Appendix 3). We will pilot our data extraction form with a random sample of 5 included articles. Two independent reviewers will extract data in duplicate. The data extraction form will be adapted after the pilot process as well as throughout data extraction, as needed. Changes to the original form will be explicitly noted and will be included as updates to the review registration. We will extract bibliographic details of included studies (author, publication year) as well as other publication factors such as number of authors, country, and institutional affiliation using bibliometric data from the initial search, where available. Depending on study type, we will manually extract the following information: study objective (including primary and secondary distinctions as well as the clinical and implementation outcome(s) considered); study design (e.g. cluster randomized controlled trial, pre-post trial, etc.); record type (e.g. journal article, protocol, conference abstract, etc.); implementation strategy and clinical/health intervention of interest, target population; statistical model(s) used to estimate and/or test effect sizes; method used to estimate sample size, whether the study was randomised and, if so, the level of randomisation (e.g. patients, sites, etc.), whether a dual design was employed (Stevens et al., 2023); whether a trajectory or single-point effect was estimated; how levels of the independent intervention variable were determined for trajectory outcomes; whether relationships between effectiveness and implementation outcomes were empirically examined, including the analytical approach and main finding; whether relationships among implementation outcomes were examined; whether an outcome framework or taxonomy was used; the measurement source and level for the primary effectiveness and implementation outcomes; the number and relative timing of measurement occasions for each primary outcome; and the statistical software employed, as appropriate. For methodological studies, we will also extract the area(s) to which the guidance applies (e.g., power calculation, trajectory estimation).
Journal impact factor (JIF) for the journals of published articles will be obtained from Clarivate’s Journal Citations Reports online tool by publication year. Impact factor has been shown to be associated with the quality of reporting in other clinical studies (Mbuagbaw et al., 2020). If JIF is not available for the year of publication, the value for the nearest year will be extracted, with a preference for an earlier rather than later year. Other study design characteristics, such as sample size and number of study arms, will be extracted for biomedical studies.
Analysis
Study characteristics such as author, year of publication, study design, and statistical methods in included records will be summarised using descriptive statistics (n and %). For numerical publication factors (JIF), number of authors, and sample size, we will calculate median and interquartile range (IQR). We will present the number of studies published by year and trends in study characteristics over time using histograms and scatterplots. Data visualisations and descriptive tables will be stratified by study type.
The primary outcome of interest in this review, the statistical methods employed, will be coded in each included study. Depending on the methods identified, we will group them into categories based on characteristics such as statistical paradigm (e.g., frequentist or Bayesian) and estimation method (e.g., analytic versus numerical). As an a priori starting framework, we will code single-point bivariate methods using the five categories identified by (Owen et al., 2025), i.e., p-value adjustments for multiple testing, combined-outcome (single-index) methods, single combined-test approaches, disjunctive tests, and conjunctive tests. We will inductively add or refine categories as needed to accommodate methods not covered by this scheme (including those used for trajectory outcomes). This categorisation will be conducted in consultation with experts in clinical trial design and sample size estimation.
To further inform the coding of these methods, studies will also be categorized by the methodological studies included in their reference lists to identify patterns and trends in their application. Network graphs showing the relationships between the citation patterns of included studies will also be generated to highlight any patterns or trends. The above will also be conducted to examine the statistical methods used in trajectory estimation, a secondary objective of this review.
We will outline the findings of this review in terms of the study objectives through a narrative summary. This will be stratified into two parts, depending on data availability: a review of the published studies and protocols to describe the state of current statistical practice and a review of the methodological studies to summarise the state of current statistical knowledge. This summary will highlight gaps in the research to improve the analytical quality of hybrid effectiveness-implementation studies of trajectory outcomes and provide direction for future methodological work.
Discussion
Current research and development on the statistical methods for power determination, hypothesis testing, and effect size estimation for studies of bivariate outcomes provide practical guidance to researchers (Owen et al., 2025). However, it is not clear how these methods are used in practice. We will summarise current practice in the statistical analysis of hybrid type II effectiveness-implementation studies that have a stated equivalent emphasis on both outcomes using a scoping review framework. This will provide an overview of the current state of the statistical practice of published studies in this field. This review will also specifically outline gaps in the statistical literature on this topic with the aim of providing avenues for future methodological work.
Hybrid type II effectiveness-implementation studies are a growing field in biomedical research more generally and in implementation science more specifically. Past work has demonstrated that these study designs may have issues in terms of reporting, justification, and practical use of the hybrid type II methodology (Curran et al., 2022; Liu et al., 2026). Further, it is not clear if researchers in this field have kept up pace specifically with recent methodological developments in the statistical analysis of bivariate trial outcomes. With concrete and relevant guidance, researchers will be better equipped to plan and conduct hybrid effectiveness-implementation studies of trajectory outcomes.
Limitations
A limitation of this review is that it does not involve critical assessment of the appropriateness of individual statistical methods used in the included biomedical studies. Ideally, we would be able to determine if the methods were applied correctly and rigorously in each study. Currently, no formalised guidance exists on the correct reporting of hybrid effectiveness-implementation studies. A further limitation is that, while it is fairly straightforward to capture hybrid type II effectiveness-implementation studies in a literature search, it is less so for capturing those which specifically consist of trajectory outcomes. As such, we decided to not limit our search just to studies employing these outcomes. Rather, we include this as a data extraction item in order to capture these specific studies. However, it is conceivable that some studies may not have been captured in our search as there is no common reporting guideline for the identification of studies of trajectory outcomes. Relatedly, our reliance on explicit “type II” terminology may exclude studies that adopt an equal-emphasis effectiveness-implementation study without using that label. Similarly, studies that are labeled only as a hybrid without stating the type will be missed as will type I and type III studies that align with type II defining characteristics but are inaccurately labeled.
As in all meta-studies, there may be a risk of publication bias in the proposed review. However, it is not possible to ascertain its direction or its magnitude. Despite this, as the target population of this meta-study is principally published research, the effect of publication bias on results may be limited. The issue of publication bias has not been widely explored in the case of methodological reviews (Mbuagbaw et al., 2020). We will not assess the risk of bias of included studies.
Ethics and consent
Ethics approval is not required for the proposed review as this does not include the analysis of primary data derived from human subjects. Only secondary data will be analysed. All included data will be extracted from publicly available sources.
Research findings will be disseminated through conference presentations and publication in open-access peer-reviewed journals. Extracted data will be publicly available through an open data repository. Any deviations or adaptations of the study protocol will be transparently reported as updates to the original study registration.
Data availability statement
No data are associated with this article. Data generated in this review will be publicly available through the Open Science Framework repository and are available through a CC-BY licence (OSF registration number: 10.17605/OSF.IO/SA2XU).
Acknowledgements
We would like to thank Alenda (Alen) Dwiadila Matra Putra at McGill University for his assistance with this project.
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This research was partially funded by the Canadian Institutes of Health Research Pan-Canadian HIV/AIDS and Sexually-Transmitted and Blood Borne Infections Clinical Trials Research Network (CIHR-CTN+).
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
© 2026 Parsons M et al. 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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