Toward a framework for the design, implementation, and reporting of methodology scoping reviews

Addressing missing outcome data in randomised controlled trials: A methodological scoping review

BACKGROUND

Missing outcome data is common in trials, and robust methods to address this are needed. Most trial reports currently use methods applicable under a missing completely at random assumption (MCAR), although this strong assumption can often be inappropriate.

OBJECTIVE

To identify and summarise current literature on the analytical methods for handling missing outcome data in randomised controlled trials (RCTs), emphasising methods appropriate for data missing at random (MAR) or missing not at random (MNAR).

STUDY DESIGN AND SETTING

We conducted a methodological scoping review and identified papers through searching four databases (MEDLINE, Embase, CENTRAL, and CINAHL) from January 2015 to March 2023. We also performed forward and backward citation searching. Eligible papers discussed methods or frameworks for handling missing outcome data in RCTs or simulation studies with an RCT design.

RESULTS

From 1878 records screened, our search identified 101 eligible papers. 90 (89%) papers described specific methods for addressing missing outcome data and 11 (11%) described frameworks for overall methodological approach. Of the 90 methods papers, 30 (33%) described methods under the MAR assumption, 48 (53%) explored methods under the MNAR assumption and 11 (12%) discussed methods under a hybrid of MAR and MNAR assumptions. Control-based methods under the MNAR assumption were the most common method explored, followed by multiple imputation under the MAR assumption.

CONCLUSION

This review provides guidance on available analytic approaches for handling missing outcome data, particularly under the MNAR assumption. These findings may support trialists in using appropriate methods to address missing outcome data.

Palliative Care Interventions for Persons With Neurodegenerative Disease: A Scoping Review of Clinical Trial Study Design Features

Background: Within palliative care research, best practice guidelines to conduct scientifically rigorous clinical trials for neurodegenerative diseases are underexplored. This patient population experiences unique challenges, including fluctuations in cognitive capacity, care partner (CP) and proxy involvement, and high adverse events (AEs), that necessitate special consideration when designing clinical trials. Objective: The objective of this study was to describe and identify clinical trial design features that have been documented in studies involving a neuropalliative intervention for persons with neurodegenerative diseases, highlighting features that have been adapted for this unique patient population. Design: We conducted a scoping review of clinical trials with a neuropalliative intervention for persons with neurodegenerative disease. We searched Cochrane, Web of Science, EMBASE, Scopus, and PubMed (MEDLINE) databases for articles published in English between 1950 and 2023. Two reviewers screened, extracted, and synthesized data from the included articles. A third reviewer adjudicated instances of conflict. The data were analyzed using a thematic framework approach. Results: Of 1025 texts, 44 articles were included. Seven study design features were analyzed: (1) consent, (2) proxies and CPs, (3) recruitment strategies, (4) retention strategies, (5) choice of comparator, (6) AEs, and (7) internal validity. This scoping review found disparities in study design features around structured consent, proxies and CPs, comparators, and AEs. Conclusions: To date, neuropalliative care clinical trials have had varied study designs and the majority of research has focused on dementia. Research guideline development for high-quality neuropalliative care clinical trials is greatly needed across the range of neurodegenerative diseases. To increase the scientific rigor of clinical trials and neuropalliative care, we recommend a standardized capacity assessment for consent, defining conditions for the CP, proxy, and AEs, systematizing appropriate comparators, and outlining preemptive recruitment and retention strategies to address the broader unpredictable challenges of palliative care research.

A scoping review finds a growing trend in studies validating multimorbidity patterns and identifies five broad types of validation methods

OBJECTIVES

Multimorbidity, the presence of two or more long-term conditions, is a growing public health concern. Many studies use analytical methods to discover multimorbidity patterns from data. We aimed to review approaches used in published literature to validate these patterns.

STUDY DESIGN AND SETTING

We systematically searched PubMed and Web of Science for studies published between July 2017 and July 2023 that used analytical methods to discover multimorbidity patterns.

RESULTS

Out of 31,617 studies returned by the searches, 172 were included. Of these, 111 studies (64%) conducted validation, the number of studies with validation increased from 53.13% (17 out of 32 studies) to 71.25% (57 out of 80 studies) in 2017-2019 to 2022-2023, respectively. Five types of validation were identified: assessing the association of multimorbidity patterns with clinical outcomes (n = 79), stability across subsamples (n = 26), clinical plausibility (n = 22), stability across methods (n = 7) and exploring common determinants (n = 2). Some studies used multiple types of validation.

CONCLUSION

The number of studies conducting a validation of multimorbidity patterns is clearly increasing. The most popular validation approach is assessing the association of multimorbidity patterns with clinical outcomes. Methodological guidance on the validation of multimorbidity patterns is needed.

Effects of COVID-19 on malaria elimination initiatives in sub-Saharan Africa: a scoping review protocol

INTRODUCTION

The occurrence of the COVID-19 pandemic significantly impacted health systems, resulting in varied outcomes of different variables in terms of health. Due to the nature of the causative organism that is spread mainly in the air, the disease rapidly spread to numerous countries, leading to a series of mitigation measures being proposed and implemented, including but not limited to travel restrictions, decongesting and in some instances closure of workplaces and schools and banning of social gatherings. This could have negatively impacted implementing strategies meant to ensure the effective management of malaria, hoping to eliminate it in different countries in sub-Saharan Africa (SSA). This review seeks to explore the effect of the COVID-19 pandemic on malaria elimination initiatives in SSA.

METHODS AND ANALYSIS

An exploratory scoping review will be conducted on literature (searched using keywords and a search strategy) sources published in English on Web of Science, Cochrane Library, PUBMED, Dimensions, ProQuest, Scopus and African Journals Online. These would then be imported to Rayyan Software for screening for possible inclusion. The JBI Guidelines on Reviews, Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist would guide the data collection, extraction and analysis from the accessed literature. Furthermore, charting, trends and developing themes would ensure the findings are presented comprehensively and yet understandable. The data collection and analysis process leading to the final submission of a review paper to a journal will be conducted from September 2023 to February 2024.

ETHICS AND DISSEMINATION

An application for ethical approval was lodged with the Health Sciences Research Ethics Committee at the University of the Free State in Bloemfontein, South Africa. This ethics committee granted ethics clearance (ethics number: UFS-HSD2022/1754). Results will be communicated through peer-reviewed publications, presentations, conferences, workshops and other means and forums to reach the critical stakeholders.

Digital health interventions and quality of home-based primary care for older adults: A scoping review protocol

Introduction

The use of digital health interventions has expanded, particularly in home-based primary care (HBPC), following the increase in the older adult population and the need to respond to the higher demand of chronic conditions, weakness and loss of autonomy of this population. There was an even greater demand with COVID-19 and subsequent isolation/social distancing measures for this risk group. The objective of this study is to map and identify the uses and types of digital health interventions and their reported impacts on the quality of HBPC for older adults worldwide.

Methods and analysis

This is a scoping review protocol which will enable a rigorous, transparent and reliable synthesis of knowledge. The review will be developed from the theoretical perspective of Arksey and O'malley, with updates by Levac and Peters and respective collaborators based on the Joanna Briggs Institute manual, and guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). Data from white literature will be extracted from multidisciplinary health databases such as: the Virtual Health Library, LILACS, MEDLINE/PubMed, Scopus, Web of Science, Cinahl and Embase; while Google Scholar will be used for gray literature. No date limit or language restrictions will be determined. The quantitative data will be analyzed through descriptive statistics and qualitative data through thematic analysis. The results will be submitted to stakeholder consultation for preliminary sharing of the study and will later be disseminated through publication in open access scientific journals, scientific events and academic and community journals. The full scoping review report will present the main impacts, challenges, opportunities and gaps found in publications related to the use of digital technologies in primary home care.

Discussion

The organization of this protocol will increase the methodological rigor, quality, transparency and accuracy of scoping reviews, reducing the risk of bias.

Use of mixed methods research in intervention studies to increase young people's interest in STEM: A systematic methodological review

Introduction

Mixed methods research intervention studies integrate quantitative evaluation approaches, such as randomized controlled trials and quasi-experimental designs, with qualitative research to evaluate the effectiveness, efficacy, or other results of an intervention or program. These types of studies, which have attracted growing attention in recent years, enhance the scope and rigor of the evaluation. While various frameworks that summarize the justifications for carrying out these types of studies and provide implementation guidance have been published in the last few years in the health sciences, we do not know whether such frameworks have been properly implemented in the social and educational sciences. This review examined the methodological features and reporting practices of mixed methods intervention studies aimed at increasing young people's interest in STEM.

Methods

A systematic search was carried out in APA PsycNET, ERIC, ProQuest, Scopus, and Web of Science, and a hand search in 20 journals. We included peer-reviewed English-language articles that reported intervention studies with a quantitative component measuring outcomes specific to increasing secondary school students' interest in STEM fields, a qualitative component conducted before, during, or after the quantitative component, and evidence of integration of both components. Qualitative content analysis and ideal-type analysis were used to synthesize the findings.

Results

We found 34 studies; the majority published in the last ten years. Several patterns of mixed methods application were described in these studies, illustrating the unique insights that can be gained by employing this methodology. The reporting quality of the included studies was generally adequate, especially regarding the justification for using a mixed methods intervention design and the integration of the quantitative and qualitative components. Nonetheless, a few reporting issues were observed, such as a lack of detail in the presentation of the mixed methods design, an inadequate description of the qualitative sampling and analysis techniques, and the absence of joint displays for representing integration.

Discussion

Authors must pay attention to these issues to ensure that the insights obtained by the use of mixed methods research are effectively communicated.

Adverse drug event detection using natural language processing: A scoping review of supervised learning methods

To reduce adverse drug events (ADEs), hospitals need a system to support them in monitoring ADE occurrence routinely, rapidly, and at scale. Natural language processing (NLP), a computerized approach to analyze text data, has shown promising results for the purpose of ADE detection in the context of pharmacovigilance. However, a detailed qualitative assessment and critical appraisal of NLP methods for ADE detection in the context of ADE monitoring in hospitals is lacking. Therefore, we have conducted a scoping review to close this knowledge gap, and to provide directions for future research and practice. We included articles where NLP was applied to detect ADEs in clinical narratives within electronic health records of inpatients. Quantitative and qualitative data items relating to NLP methods were extracted and critically appraised. Out of 1,065 articles screened for eligibility, 29 articles met the inclusion criteria. Most frequent tasks included named entity recognition (n = 17; 58.6%) and relation extraction/classification (n = 15; 51.7%). Clinical involvement was reported in nine studies (31%). Multiple NLP modelling approaches seem suitable, with Long Short Term Memory and Conditional Random Field methods most commonly used. Although reported overall performance of the systems was high, it provides an inflated impression given a steep drop in performance when predicting the ADE entity or ADE relation class. When annotating corpora, treating an ADE as a relation between a drug and non-drug entity seems the best practice. Future research should focus on semi-automated methods to reduce the manual annotation effort, and examine implementation of the NLP methods in practice.

Use of Digital Health Interventions in Sub-Saharan Africa for Health Systems Strengthening Over the Last 10 Years: A Scoping Review Protocol

Background

Digital Health Interventions (DHIs) refers to the utilization of digital and mobile technology to support the health system in service delivery. Over the recent years, advanced computing, genomics, and artificial intelligence are considered part of digital health. In the context of the World Health Organization (WHO) global strategy 2020-2025, digital health is defined as "the field of knowledge and practice associated with the development and use of digital technologies to improve health." The scoping review protocol details the procedure for developing a comprehensive list of DHIs in Sub-Saharan Africa and documenting their roles in strengthening health systems.

Method and Analysis

A scoping review will be done according to the Joanne Briggs institute reviewers manual and following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) checklist and explanation. The protocol has been registered at the Open Science Framework (OSF) database at https://osf.io/5kzq7. The review will include DHIs conceptualized/developed/designed, adapted, piloted, deployed, scaled up, and addressing health challenges in Sub-Saharan Africa. We will retrieve data from the global DHI repository-the WHO Digital Health Atlas (DHA)- and supplement it with information from the WHO eHealth Observatory, eHealth Survey (2015), and eHealth country profiles report. Additional searches will be conducted in four (4) electronic databases: PubMed, HINARI-Reasearch4Life, Cochrane Library, and Google Scholar. The review will also include gray literature and reference lists of selected studies. Data will be organized in conceptual categories looking at digital health interventions' distinct function toward achieving health sector objectives.

Discussion

Sub-Saharan Africa is an emerging powerhouse in DHI innovations with rapid expansion and evolvement. The enthusiasm for digital health has experienced challenges including an escalation of short-lived digital health interventions, duplication, and minimal documentation of evidence on their impact on the health system. Efficient use of resources is important when striving toward the use digital health interventions in health systems strengthening. This can be achieved through documenting successes and lessons learnt over time.

Conclusion

The review will provide the evidence to guide further investments in DHIs, avoid duplication, circumvent barriers, focus on gaps, and scale-up successful interventions.

Discordance and concordance on perception of quality care at end of life between older patients, caregivers and clinicians: a scoping review

BACKGROUND

This scoping review aimed to investigate the presence of discordance or concordance in the perceptions of end-of-life (EOL) care quality between consumers (i.e. patients aged over 60 in their last years of life and/or their informal caregivers) and clinicians, to inform further improvements in end-of-life care service delivery.

METHODS

A scoping review of qualitative and quantitative studies was systematically undertaken by searching for English language publications in MEDLINE database and manual reference search of eligible articles. Thematic analysis was employed to identify and extract common concordance and discordance themes leading to the development of analytical constructs. Articles were eligible for inclusion if they reported on consumers' (i.e. older patients aged 60 + years in their final years of life and/or their informal caregivers) and clinicians' (doctors, nurses, social workers, etc.) perspectives on quality of medical, surgical or palliative/supportive care administered to older adults in the last year of life across all healthcare settings.

RESULTS

Of the 2736 articles screened, 21 articles were included. Four themes identified concordance between consumers' and clinicians' perceptions of care quality: holistic patient care; coordinated care that facilitated EOL; the role of family at EOL; and impact of prognostic uncertainty on care planning. Three themes emerged for discordance of perceptions: understanding the patient needs at EOL; capacity of healthcare system/providers to accommodate family needs; and knowledge and communication of active or palliative care at EOL.

CONCLUSIONS

While progress has been made on promoting patient autonomy and respecting the family role in representing patient's best interest, gaps remain in terms of care coordination, communication of prognosis, public understanding of the meaning of goals of care including de-escalation of management and enactment of advance care directives by clinicians for people with diminished decision capacity. Public understanding of the meaning of "comfort" care and the need to prevent over-treatment are essential for their satisfaction with care and their ability to embrace the concept of a good death.

A scoping review of causal methods enabling predictions under hypothetical interventions

BACKGROUND

The methods with which prediction models are usually developed mean that neither the parameters nor the predictions should be interpreted causally. For many applications, this is perfectly acceptable. However, when prediction models are used to support decision making, there is often a need for predicting outcomes under hypothetical interventions.

AIMS

We aimed to identify published methods for developing and validating prediction models that enable risk estimation of outcomes under hypothetical interventions, utilizing causal inference. We aimed to identify the main methodological approaches, their underlying assumptions, targeted estimands, and potential pitfalls and challenges with using the method. Finally, we aimed to highlight unresolved methodological challenges.

METHODS

We systematically reviewed literature published by December 2019, considering papers in the health domain that used causal considerations to enable prediction models to be used for predictions under hypothetical interventions. We included both methodologies proposed in statistical/machine learning literature and methodologies used in applied studies.

RESULTS

We identified 4919 papers through database searches and a further 115 papers through manual searches. Of these, 87 papers were retained for full-text screening, of which 13 were selected for inclusion. We found papers from both the statistical and the machine learning literature. Most of the identified methods for causal inference from observational data were based on marginal structural models and g-estimation.

CONCLUSIONS

There exist two broad methodological approaches for allowing prediction under hypothetical intervention into clinical prediction models: (1) enriching prediction models derived from observational studies with estimated causal effects from clinical trials and meta-analyses and (2) estimating prediction models and causal effects directly from observational data. These methods require extending to dynamic treatment regimes, and consideration of multiple interventions to operationalise a clinical decision support system. Techniques for validating 'causal prediction models' are still in their infancy.

Informative presence and observation in routine health data: A review of methodology for clinical risk prediction

Objective

Informative presence (IP) is the phenomenon whereby the presence or absence of patient data is potentially informative with respect to their health condition, with informative observation (IO) being the longitudinal equivalent. These phenomena predominantly exist within routinely collected healthcare data, in which data collection is driven by the clinical requirements of patients and clinicians. The extent to which IP and IO are considered when using such data to develop clinical prediction models (CPMs) is unknown, as is the existing methodology aiming at handling these issues. This review aims to synthesize such existing methodology, thereby helping identify an agenda for future methodological work.

Materials and Methods

A systematic literature search was conducted by 2 independent reviewers using prespecified keywords.

Results

Thirty-six articles were included. We categorized the methods presented within as derived predictors (including some representation of the measurement process as a predictor in the model), modeling under IP, and latent structures. Including missing indicators or summary measures as predictors is the most commonly presented approach amongst the included studies (24 of 36 articles).

Discussion

This is the first review to collate the literature in this area under a prediction framework. A considerable body relevant of literature exists, and we present ways in which the described methods could be developed further. Guidance is required for specifying the conditions under which each method should be used to enable applied prediction modelers to use these methods.

Conclusions

A growing recognition of IP and IO exists within the literature, and methodology is increasingly becoming available to leverage these phenomena for prediction purposes. IP and IO should be approached differently in a prediction context than when the primary goal is explanation. The work included in this review has demonstrated theoretical and empirical benefits of incorporating IP and IO, and therefore we recommend that applied health researchers consider incorporating these methods in their work.

A methods review of posttrial follow-up studies of cardiovascular prevention finds potential biases in estimating legacy effects

OBJECTIVES

The objective of the study was to assess the methods used, and potential for bias, in posttrial studies of cardiovascular disease (CVD) where legacy effects may be estimated.

STUDY DESIGN AND SETTING

We undertook a methods review of posttrial studies after randomized controlled trials (RCTs) of interventions to prevent CVD. For each included article, we extracted information on important aspects of the design and analysis of the study, and on the reporting of legacy effects.

RESULTS

Of 2,622 retrieved articles, 46 were included in the review: 13 on blood glucose control, 13 on blood pressure control, and 20 on blood lipid control. The median duration for the RCT and posttrial follow-up studies was 5.0 and 5.7 years, respectively. At least 80% of initial RCT participants were enrolled in the posttrial study in 32 of the reports. Most reports used both linkage to routine administrative data sources and active data collection for the posttrial study. Of the 46 included articles, the authors assessed and reported posttrial covariate balance in 29 and made statistical adjustments in the analysis for potential confounding in 25. Posttrial results were reported separately to overall results (from time of randomization) in 21 articles. Legacy effects were claimed in 19 reports, of which 16 could be justified on the basis of the posttrial results.

CONCLUSION

Posttrial studies may provide valuable information for investigating legacy effects, but better reporting of results is needed to realize their full potential. Robust methods of data collection and analysis may address the risk of selection and confounding biases in posttrial studies.