research design for comparative study

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Comparative Research Designs and Methods

Taught in English

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Instructor: Dirk Berg Schlosser

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Skills you'll gain

• Research Methods
• Qualitative Comparative Analysis (QCA)
• comparative research
• Macro-quantitative methods

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There are 5 modules in this course

Emile Durkheim, one of the founders of modern empirical social science, once stated that the comparative method is the only one that suits the social sciences. But Descartes already had reminded us that “comparaison n’est pas raison”, which means that comparison is not reason (or theory) by itself.

This course provides an introduction and overview of systematic comparative analyses in the social sciences and shows how to employ this method for constructive explanation and theory building. It begins with comparisons of very few cases and specific “most similar” and “most different” research designs. A major part is then devoted to the often occurring situation of dealing with a small number of highly complex cases, for example when comparing EU member states. Latin American political systems, or particular policy areas. In response to this complexity, new approaches and software have been developed in recent years (“Qualitative Comparative Analysis”, QCA, and related methods). These procedures are able to reduce complexity and to arrive at “configurational” solutions based on set theory and Boolean algebra, which are more meaningful in this context than the usual broad-based statistical methods. In the last section, these methods are contrasted with more common statistical comparative methods at the macro-level of states or societies and the respective strengths and weaknesses are discussed. Some basic quantitative or qualitative methodological training is probably useful to get more out of the course, but participants with little methodological training should find no major obstacles to follow.

An introduction to Comparative Research

This module presents fundamental notions of comparative research designs. To begin with, you will be introduced to multi-dimensional matters. Subsequently, you will delve into John Stuart Mill’s methods and limitations.

What's included

6 videos 10 readings 2 quizzes

6 videos • Total 41 minutes

• Multi-dimensional substance matter • 7 minutes • Preview module
• The plastic matter of social sciences • 9 minutes
• Linking levels of analysis • 6 minutes
• Mill's canons • 5 minutes
• Mill‘s methods: Pop's Seafood Platter • 4 minutes
• Mill's methods: limitations • 7 minutes

10 readings • Total 31 minutes

• Three Fundamental notions • 3 minutes
• Multi-dimensional substance matter • 4 minutes
• The plastic matter of social sciences • 4 minutes
• Qualitative Comparative Analysis • 3 minutes
• Linking levels of analysis • 2 minutes
• Coleman's "bathtub" • 3 minutes
• Pop's Seafood Platter • 3 minutes
• Mill's limitations • 3 minutes
• Mill's methods and recent advances • 1 minute

2 quizzes • Total 30 minutes

• Challenge yourself: Epistemological foundations of the social sciences • 15 minutes
• Challenge yourself: Mill's canon • 15 minutes

Comparative Research Designs

This module presents further advances in comparative research designs. To begin with, you will be introduced to case selection and types of research designs. Subsequently, you will delve into most similar and most different designs (MSDO/MDSO) and observe their operationalization.

6 videos 6 readings 2 quizzes

6 videos • Total 48 minutes

• Further advances • 7 minutes • Preview module
• Overview • 7 minutes
• Major steps of research process • 6 minutes
• MSDO/MDSO application • 8 minutes
• Operationalizing similarities and dissimilarities • 9 minutes
• Analysis and interpretation • 8 minutes

6 readings • Total 30 minutes

• Further advances • 4 minutes
• Overview of comparative research designs • 4 minutes
• Selection of variables and cases • 5 minutes
• Operationalizing similarities and dissimilarities • 5 minutes
• Analysis and interpretation • 6 minutes
• Challenge yourself: Further Advances, Comparative Research Designs • 15 minutes
• Challenge yourself: Most similar and most different designs • 15 minutes

QCA Analysis

This module presents Boolean Algebra and the main steps of QCA. The first lesson will introduce basic features of QCA and provide an example of such analysis. The second lesson will focus on QCA applications, troubleshooting, Multi-Value QCA (mv-QCA), and more specific features of QCA.

6 videos 7 readings 2 quizzes

6 videos • Total 40 minutes

• QCA Basics • 8 minutes • Preview module
• QCA Analysis • 9 minutes
• Simple paper and pencil example • 5 minutes
• Troubleshooting Contradictions • 6 minutes
• Threshold setting, necessary and sufficient conditions • 4 minutes
• Multi-Value QCA (mv-QCA) • 6 minutes

7 readings • Total 41 minutes

• QCA Basics • 7 minutes
• QCA Analysis pt.1 • 5 minutes
• QCA Analysis pt.2 • 7 minutes
• Simple paper and pencil example • 7 minutes
• Troubleshooting Contradictions (C) • 6 minutes
• Threshold setting, necessary and sufficient conditions • 3 minutes
• Challenge yourself: Introduction to Boolean Algebra, main steps of QCA • 15 minutes
• Challenge yourself: QCA applications, troubleshooting, Multi-Value QCA (mv-QCA) • 15 minutes

Fuzzy set analyses

This module presents the basic features of the fuzzy set analyses and application, and analyzes in greater depth QCA. The first lesson will introduce basic features of fuzzy set analyses and provide examples of such analysis. The second lesson will focus on fuzzy set applications, its purposes and advantages, and explores more specific features of QCA.

6 videos • Total 37 minutes

• Fuzzy sets • 6 minutes • Preview module
• Calculation of necessary and sufficient conditions • 3 minutes
• Fuzzy sets. Relationship between condition and outcome as in a triangular scatterplot • 3 minutes
• Principles • 6 minutes
• Lipset's conditions • 7 minutes
• Conclusions • 9 minutes

7 readings • Total 75 minutes

• Fuzzy sets • 10 minutes
• Calculation of necessary and sufficient conditions • 10 minutes
• Fuzzy sets. Relationship between condition and outcome as in a triangular scatterplot • 10 minutes
• Principles • 10 minutes
• Cases • 5 minutes
• Examples • 15 minutes
• Conclusions • 15 minutes
• Challenge yourself: Fuzzy set analyses, basic features • 15 minutes
• Challenge yourself: Fuzzy set applications (fs/qca) • 15 minutes

Macro-quantitative (statistical): Methods and perspectives

This module presents the macro-quantitative (statistical) methods by giving examples of recent research employing them. It analyzes the regression analysis and the various ways of analyzing data. Moreover, it concludes the course and opens to further perspectives on comparative research designs and methods.

6 videos • Total 45 minutes

• Data • 7 minutes • Preview module
• Examples • 6 minutes
• Regression analysis • 6 minutes
• Summary • 8 minutes
• Contrasting macro qualitative and quantitative methods • 8 minutes
• Continuing debates: prospects • 6 minutes

6 readings • Total 75 minutes

• Data • 10 minutes
• Examples • 10 minutes
• Regression analysis • 10 minutes
• Summary • 15 minutes
• Contrasting macro qualitative and quantitative methods • 15 minutes
• Continuing debates, prospects • 15 minutes
• Challenge yourself: Macro-quantitative (statistical) Methods • 15 minutes
• Challenge yourself: Conclusions and Perspectives • 15 minutes

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• Knowledge Base

Methodology

• Types of Research Designs Compared | Guide & Examples

Types of Research Designs Compared | Guide & Examples

Published on June 20, 2019 by Shona McCombes . Revised on June 22, 2023.

When you start planning a research project, developing research questions and creating a  research design , you will have to make various decisions about the type of research you want to do.

There are many ways to categorize different types of research. The words you use to describe your research depend on your discipline and field. In general, though, the form your research design takes will be shaped by:

• The type of knowledge you aim to produce
• The type of data you will collect and analyze
• The sampling methods , timescale and location of the research

This article takes a look at some common distinctions made between different types of research and outlines the key differences between them.

Table of contents

Types of research aims, types of research data, types of sampling, timescale, and location, other interesting articles.

The first thing to consider is what kind of knowledge your research aims to contribute.

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The next thing to consider is what type of data you will collect. Each kind of data is associated with a range of specific research methods and procedures.

Finally, you have to consider three closely related questions: how will you select the subjects or participants of the research? When and how often will you collect data from your subjects? And where will the research take place?

Keep in mind that the methods that you choose bring with them different risk factors and types of research bias . Biases aren’t completely avoidable, but can heavily impact the validity and reliability of your findings if left unchecked.

Choosing between all these different research types is part of the process of creating your research design , which determines exactly how your research will be conducted. But the type of research is only the first step: next, you have to make more concrete decisions about your research methods and the details of the study.

Read more about creating a research design

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Cite this Scribbr article

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McCombes, S. (2023, June 22). Types of Research Designs Compared | Guide & Examples. Scribbr. Retrieved April 9, 2024, from https://www.scribbr.com/methodology/types-of-research/

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Characteristics of a Comparative Research Design

Hannah richardson, 28 jun 2018.

Comparative research essentially compares two groups in an attempt to draw a conclusion about them. Researchers attempt to identify and analyze similarities and differences between groups, and these studies are most often cross-national, comparing two separate people groups. Comparative studies can be used to increase understanding between cultures and societies and create a foundation for compromise and collaboration. These studies contain both quantitative and qualitative research methods.

Explore this article

• Comparative Quantitative
• Comparative Qualitative
• When to Use It
• When Not to Use It

1 Comparative Quantitative

Quantitative, or experimental, research is characterized by the manipulation of an independent variable to measure and explain its influence on a dependent variable. Because comparative research studies analyze two different groups -- which may have very different social contexts -- it is difficult to establish the parameters of research. Such studies might seek to compare, for example, large amounts of demographic or employment data from different nations that define or measure relevant research elements differently.

However, the methods for statistical analysis of data inherent in quantitative research are still helpful in establishing correlations in comparative studies. Also, the need for a specific research question in quantitative research helps comparative researchers narrow down and establish a more specific comparative research question.

2 Comparative Qualitative

Qualitative, or nonexperimental, is characterized by observation and recording outcomes without manipulation. In comparative research, data are collected primarily by observation, and the goal is to determine similarities and differences that are related to the particular situation or environment of the two groups. These similarities and differences are identified through qualitative observation methods. Additionally, some researchers have favored designing comparative studies around a variety of case studies in which individuals are observed and behaviors are recorded. The results of each case are then compared across people groups.

3 When to Use It

Comparative research studies should be used when comparing two people groups, often cross-nationally. These studies analyze the similarities and differences between these two groups in an attempt to better understand both groups. Comparisons lead to new insights and better understanding of all participants involved. These studies also require collaboration, strong teams, advanced technologies and access to international databases, making them more expensive. Use comparative research design when the necessary funding and resources are available.

4 When Not to Use It

Do not use comparative research design with little funding, limited access to necessary technology and few team members. Because of the larger scale of these studies, they should be conducted only if adequate population samples are available. Additionally, data within these studies require extensive measurement analysis; if the necessary organizational and technological resources are not available, a comparative study should not be used. Do not use a comparative design if data are not able to be measured accurately and analyzed with fidelity and validity.

• 1 San Jose State University: Selected Issues in Study Design
• 2 University of Surrey: Social Research Update 13: Comparative Research Methods

About the Author

Hannah Richardson has a Master's degree in Special Education from Vanderbilt University and a Bacheor of Arts in English. She has been a writer since 2004 and wrote regularly for the sports and features sections of "The Technician" newspaper, as well as "Coastwach" magazine. Richardson also served as the co-editor-in-chief of "Windhover," an award-winning literary and arts magazine. She is currently teaching at a middle school.

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Comparative effectiveness research for the clinician researcher: a framework for making a methodological design choice

Cylie m. williams.

1 Peninsula Health, Community Health, PO Box 52, Frankston, Melbourne, Victoria 3199 Australia

2 Monash University, School of Physiotherapy, Melbourne, Australia

3 Monash Health, Allied Health Research Unit, Melbourne, Australia

Elizabeth H. Skinner

4 Western Health, Allied Health, Melbourne, Australia

Alicia M. James

Jill l. cook, steven m. mcphail.

5 Queensland University of Technology, School of Public Health and Social Work, Brisbane, Australia

Terry P. Haines

Comparative effectiveness research compares two active forms of treatment or usual care in comparison with usual care with an additional intervention element. These types of study are commonly conducted following a placebo or no active treatment trial. Research designs with a placebo or non-active treatment arm can be challenging for the clinician researcher when conducted within the healthcare environment with patients attending for treatment.

A framework for conducting comparative effectiveness research is needed, particularly for interventions for which there are no strong regulatory requirements that must be met prior to their introduction into usual care. We argue for a broader use of comparative effectiveness research to achieve translatable real-world clinical research. These types of research design also affect the rapid uptake of evidence-based clinical practice within the healthcare setting.

This framework includes questions to guide the clinician researcher into the most appropriate trial design to measure treatment effect. These questions include consideration given to current treatment provision during usual care, known treatment effectiveness, side effects of treatments, economic impact, and the setting in which the research is being undertaken.

Comparative effectiveness research compares two active forms of treatment or usual care in comparison with usual care with an additional intervention element. Comparative effectiveness research differs from study designs that have an inactive control, such as a ‘no-intervention’ or placebo group. In pharmaceutical research, trial designs in which placebo drugs are tested against the trial medication are often labeled ‘Phase III’ trials. Phase III trials aim to produce high-quality evidence of intervention efficacy and are important to identify potential side effects and benefits. Health outcome research with this study design involves the placebo being non-treatment or a ‘sham’ treatment option [ 1 ].

Traditionally, comparative effectiveness research is conducted following completion of a Phase III placebo control trial [ 2 – 4 ]. It is possible that comparative effectiveness research might not determine whether one treatment has clinical beneficence, because the comparator treatment might be harmful, irrelevant, or ineffective. This is unless the comparator treatment has already demonstrated superiority to a placebo [ 2 ]. Moreover, comparing an active treatment to an inactive control will be more likely to produce larger effect sizes than a comparison of two active treatments [ 5 ], requiring smaller sample sizes and lower costs to establish or refute the effectiveness of a treatment. Historically, then, treatments only become candidates for comparative effectiveness research to establish superiority, after a treatment has demonstrated efficacy against an inactive control.

Frequently, the provision of health interventions precedes development of the evidence base directly supporting their use [ 6 ]. Some service-provision contexts are highly regulated and high standards of evidence are required before an intervention can be provided (such as pharmacological interventions and device use). However, this is not universally the case for all services that may be provided in healthcare interventions. Despite this, there may be expectation from the individual patient and the public that individuals who present to a health service will receive some form of care deemed appropriate by treating clinicians, even in the absence of research-based evidence supporting this. This expectation may be amplified in publicly subsidized health services (as is largely the case in Canada, the UK, Australia, and many other developed nations) [ 7 – 9 ]. If a treatment is already widely employed by health professionals and is accepted by patients as a component of usual care, then it is important to consider the ethics and practicality of attempting a placebo or no-intervention control trial in this context. In this context, comparative effectiveness research could provide valuable insights to treatment effectiveness, disease pathophysiology, and economic efficiency in service delivery, with greater research feasibility than the traditional paradigm just described. Further, some authors have argued that studies with inactive control groups are used when comparative effectiveness research designs are more appropriate [ 10 ]. We propose and justify a framework for conducting research that argues for the broader use of comparative effectiveness research to achieve more feasible and translatable real-world clinical research.

This debate is important for the research community; particularly those engaged in the planning and execution of research in clinical practice settings, particularly in the provision of non-pharmacological, non-device type interventions. The ethical, preferential, and pragmatic implications from active versus inactive comparator selection in clinical trials not only influence the range of theoretical conclusions that could be drawn from a study, but also the lived experiences of patients and their treating clinical teams. The comparator selection will also have important implications for policy and practice when considering potential translation into clinical settings. It is these implications that affect the clinical researcher’s methodological design choice and justification.

The decision-making framework takes the form of a decision tree (Fig.  1 ) to determine when a comparative effectiveness study can be justified and is particularly relevant to the provision of services that do not have a tight regulatory framework governing when an intervention can be used as part of usual care. This framework is headed by Level 1 questions (demarcated by a question within an oval), which feed into decision nodes (demarcated by rectangles), which end in decision points (demarcated by diamonds). Each question is discussed with clinical examples to illustrate relevant points.

Comparative effectiveness research decision-making framework. Treatment A represents any treatment for a particular condition, which may or may not be a component of usual care to manage that condition. Treatment B is used to represent our treatment of interest. Where the response is unknown, the user should choose the NO response

Treatment A is any treatment for a particular condition that may or may not be a component of usual care to manage that condition. Treatment B is our treatment of interest. The framework results in three possible recommendations: that either (i) a study design comparing Treatment B with no active intervention could be used, or (ii) a study design comparing Treatment A, Treatment B and no active intervention should be used, or (iii) a comparative effectiveness study (Treatment A versus Treatment B) should be used.

Level 1 questions

Is the condition of interest being managed by any treatment as part of usual care either locally or internationally.

Researchers first need to identify what treatments are being offered as usual care to their target patient population to consider whether to perform a comparative effectiveness research (Treatment A versus B) or use a design comparing Treatment B with an inactive control. Usual care has been shown to vary across healthcare settings for many interventions [ 11 , 12 ]; thus, researchers should understand that usual care in their context might not be usual care universally. Consequently, researchers must consider what comprises usual care both in their local context and more broadly.

If there is no usual care treatment, then it is practical to undertake a design comparing Treatment B with no active treatment (Fig.  1 , Exit 1). If there is strong evidence of treatment effectiveness, safety, and cost-effectiveness of Treatment A that is not a component of usual care locally, this treatment should be considered for inclusion in the study. This situation can occur from delayed translation of research evidence into practice, with an estimated 17 years to implement only 14 % of research in evidence-based care [ 13 ]. In this circumstance, although it may be more feasible to use a Treatment B versus no active treatment design, the value of this research will be very limited, compared with comparative effectiveness research of Treatment A versus B. If the condition is currently being treated as part of usual care, then the researcher should consider the alternate Level 1 question for progression to Level 2.

As an example, prevention of falls is a safety priority within all healthcare sectors and most healthcare services have mitigation strategies in place. Evaluation of the effectiveness of different fall-prevention strategies within the hospital setting would most commonly require a comparative design [ 14 ]. A non-active treatment in this instance would mean withdrawal of a service that might be perceived as essential, a governmental health priority, and already integrated in the healthcare system.

Is there evidence of Treatment A’s effectiveness compared with no active intervention beyond usual care?

If there is evidence of Treatment A’s effectiveness compared with a placebo or no active treatment, then we progress to Question 3. If Treatment A has limited evidence, a comparative effectiveness research design of Treatment B versus no active treatment design can be considered. By comparing Treatment A with Treatment B, researchers would generate relevant research evidence for their local healthcare setting (is Treatment B superior to usual care or Treatment A?) and other healthcare settings that use Treatment A as their usual care. This design may be particularly useful when the local population is targeted and extrapolation of research findings is less relevant.

For example, the success of chronic disease management programs (Treatment A) run in different Aboriginal communities were highly influenced by unique characteristics and local cultures and traditions [ 15 ]. Therefore, taking Treatment A to an urban setting or non-indigenous setting with those unique characteristics will render Treatment A ineffectual. The use of Treatment A may also be particularly useful in circumstances where the condition of interest has an uncertain etiology and the competing treatments under consideration address different pathophysiological pathways. However, if Treatment A has limited use beyond the research location and there are no compelling reasons to extrapolate findings more broadly applicable, then Treatment B versus no active control design may be suitable.

The key points clinical researchers should consider are:

• The commonality of the treatment within usual care
• The success of established treatments in localized or unique population groups only
• Established effectiveness of treatments compared with placebo or no active treatment

Level 2 questions

Do the benefits of treatment a exceed the side effects when compared with no active intervention beyond usual care.

Where Treatment A is known to be effective, yet produces side effects, the severity, risk of occurrence, and duration of the side effects should be considered before it is used as a comparator for Treatment B. If the risk or potential severity of Treatment A is unacceptably high or is uncertain, and there are no other potential comparative treatments available, a study design comparing Treatment B with no active intervention should be used (Fig.  1 , Exit 2). Whether Treatment A remains a component of usual care should also be considered. If the side effects of Treatment A are considered acceptable, comparative effectiveness research may still be warranted.

The clinician researcher may also be challenged when the risk of the Treatment A and risk of Treatment B are unknown or when one is marginally more risky than the other [ 16 ]. Unknown risk comparison between the two treatments when using this framework should be considered as uncertain and the design of Treatment A versus Treatment B or Treatment B versus no intervention or a three-arm trial investigating Treatment A, B and no intervention is potentially justified (Fig.  1 , Exit 3).

A good example of risk comparison is the use of exercise programs. Walking has many health benefits, particularly for older adults, and has also demonstrated benefits in reducing falls [ 17 ]. Exercise programs inclusive of walking training have been shown to prevent falls but brisk walking programs for people at high risk of falls can increase the number of falls experienced [ 18 ]. The pragmatic approach of risk and design of comparative effectiveness research could better demonstrate the effect than a placebo (no active treatment) based trial.

• Risk of treatment side effects (including death) in the design
• Acceptable levels of risk are present for all treatments

Level 3 question

Does treatment a have a sufficient overall net benefit, when all costs and consequences or benefits are considered to deem it superior to a ‘no active intervention beyond usual care’ condition.

Simply being effective and free of unacceptable side effects is insufficient to warrant Treatment A being the standard for comparison. If the cost of providing Treatment A is so high that it renders its benefits insignificant compared with its costs, or Treatment A has been shown not to be cost-effective, or the cost-effectiveness is below acceptable thresholds, it is clear that Treatment A is not a realistic comparator. Some have advocated for a cost-effectiveness (cost-utility) threshold of $50,000 per quality-adjusted life year gained as being an appropriate threshold, though there is some disagreement about this and different societies might have different capacities to afford such a threshold [ 19 ]. Based on these considerations, one should further contemplate whether Treatment A should remain a component of usual care. If no other potential comparative treatments are available, a study design comparing Treatment B with no active intervention is recommended (Fig.  1 , Exit 4).

If Treatment A does have demonstrated efficacy, safety, and cost-effectiveness compared with no active treatment, it is unethical to pursue a study design comparing Treatment B with no active intervention, where patients providing consent are being asked to forego a safe and effective treatment that they otherwise would have received. This is an unethical approach and also unfeasible, as the recruitment rates could be very poor. However, Treatment A may be reasonable to include as a comparison if it is usually purchased by the potential participant and is made available through the trial.

The methodological design of a diabetic foot wound study illustrates the importance of health economics [ 20 ]. This study compared the outcomes of Treatment A (non-surgical sharps debridement) with Treatment B (low-frequency ultrasonic debridement). Empirical evidence supports the need for wound care and non-intervention would place the patient at risk of further wound deterioration, potentially resulting in loss of limb loss or death [ 21 ]. High consumable expenses and increased short-term time demands compared with low expense and longer term decreased time demands must also be considered. The value of information should also be considered, with the existing levels of evidence weighed up against the opportunity cost of using research funds for another purpose in the context of the probability that Treatment A is cost-effective [ 22 ].

• Economic evaluation and effect on treatment
• Understanding the health economics of treatment based on effectiveness will guide clinical practice
• Not all treatment costs are known but establishing these can guide evidence-based practice or research design

Level 4 question

Is the patient (potential participant) presenting to a health service or to a university- or research-administered clinic.

If Treatment A is not a component of usual care, one of three alternatives is being considered by the researcher: (i) conducting a comparative effectiveness study of Treatment B in addition to usual care versus usual care alone, (ii) introducing Treatment A to usual care for the purpose of the trial and then comparing it with Treatment B in addition to usual care, (iii) conducting a trial of Treatment B versus no active control. If the researcher is considering option (i), usual care should itself be considered to be Treatment A, and the researcher should return to Question 2 in our framework.

There is a recent focus on the importance of health research conducted by clinicians within health service settings as distinct from health research conducted by university-based academics within university settings [ 23 , 24 ]. People who present to health services expect to receive treatment for their complaint, unlike a person responding to a research trial advertisement, where it is clearly stated that participants might not receive active treatment. It is in these circumstances that option (ii) is most appropriate.

Using research designs (option iii) comparing Treatment B with no active control within a health service setting poses challenges to clinical staff caring for patients, as they need to consider the ethics of enrolling patients into a study who might not receive an active treatment (Fig.  1 , Exit 4). This is not to imply that the use of a non-active control is unethical. Where there is no evidence of effectiveness, this should be considered within the study design and in relation to the other framework questions about the risk and use of the treatment within usual care. Clinicians will need to establish the effectiveness, safety, and cost-effectiveness of the treatments and their impact on other health services, weighed against their concern for the patient’s well-being and the possibility that no treatment will be provided [ 25 ]. This is referred to as clinical equipoise.

Patients have a right to access publicly available health interventions, regardless of the presence of a trial. Comparing Treatment B with no active control is inappropriate, owing to usual care being withheld. However, if there is insufficient evidence that usual care is effective, or sufficient evidence that adverse events are likely, the treatment is prohibitive to implement within clinical practice, or the cost of the intervention is significant, a sham or placebo-based trial should be implemented.

Comparative effectiveness research evaluating different treatment options of heel pain within a community health service [ 26 ] highlighted the importance of the research setting. Children with heel pain who attended the health service for treatment were recruited for this study. Children and parents were asked on enrollment if they would participate if there were a potential assignment to a ‘no-intervention’ group. Of the 124 participants, only 7 % ( n  = 9) agreed that they would participate if placed into a group with no treatment [ 26 ].

• The research setting can impact the design of research
• Clinical equipoise challenges clinicians during recruitment into research in the healthcare setting
• Patients enter a healthcare service for treatment; entering a clinical trial is not the presentation motive

This framework describes and examines a decision structure for comparator selection in comparative effectiveness research based on current interventions, risk, and setting. While scientific rigor is critical, researchers in clinical contexts have additional considerations related to existing practice, patient safety, and outcomes. It is proposed that when trials are conducted in healthcare settings, a comparative effectiveness research design should be the preferred methodology to placebo-based trial design, provided that evidence for treatment options, risk, and setting have all been carefully considered.

Authors’ contributions

CMW and TPH drafted the framework and manuscript. All authors critically reviewed and revised the framework and manuscript and approved the final version of the manuscript.

Competing interests

The authors declare that they have no competing interests.

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ORIGINAL RESEARCH article

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Climate Change and Urban Resilience

Charting Climate Adaptation Integration in Smart Building Rating Systems: A Comparative Study Provisionally Accepted

• 1 Munich University of Applied Sciences, Germany
• 2 EU Business School, Germany

The final, formatted version of the article will be published soon.

As the world is engulfed with the growing impacts of climate change, the integration of climate adaptation measures into building performance requirements is essential. In the era of the 4th industrial revolution, smart buildings are expected to be the next frontier in the realm of building rating systems after sustainability-based one. Smart buildings can play a pivotal role in addressing the evolving challenges of changing climate due to their temporal and spatial cross-scale nature.Methods: This study assesses the integration of climate hazard adaptation options within four prominent smart building rating systems (SBRS). Using a sectoral analysis approach and a 4-point Likert scale, we systematically evaluate the extent to which these rating systems incorporate climate adaptation measures directly or indirectly across multiple building sectors. We identify strengths and weaknesses in each system's approach, highlighting areas where adaptation options are more profoundly addressed and sectors that require further attention.The evaluation results reveal variations in the comprehensiveness of climate adaptation integration among the smart building rating systems. The SRBS show a high level of integration of climate adaptation measures in the urban sectors intrinsically tied to the smart building paradigm, such as communication sector, and the human wellbeing and organization sector. Nevertheless, the study also revealed that SBRS almost universally fall short in covering other vital domains such as building envelope and structure, water and sanitation, and blue and green infrastructure.Discussions: Complementing the SBRS with sustainability rating systems (GBRS) can effectively address the limitations in climate adaptation integration within SBRS. Moreover, the inherent interconnectedness of smart buildings with their surrounding infrastructure and the broader urban environment underscores the importance of the cross-scale consideration in the building rating domain in general and in climate related topics in particular, this interconnectedness also highlights a smart building's reliance on its surrounding context for optimal functionality and the interdependency between the building and urban scale.

Keywords: Smart Building, Climate resilience, climate adaptation, sustainability, Building rating systems

Received: 07 Nov 2023; Accepted: 08 Apr 2024.

Copyright: © 2024 Khoja and Danylenko. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Ahmed Khoja, Munich University of Applied Sciences, Munich, Germany

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A comparative study of ensemble machine learning models for compressive strength prediction in recycled aggregate concrete and parametric analysis

• Original Paper
• Published: 07 April 2024

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• Pobithra Das 1 ,
• Abul Kashem 1 , 2 ,
• Jasim Uddin Rahat 1 &
• Rezaul Karim 3  

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Nowadays, recycled aggregate concrete (RAC) has been most extensively applied in the construction industry as a sustainable resource to decrease carbon dioxide emissions and construction waste. Predicting the compressive strength (CS) of RAC is crucial to understanding the behavior and performance of this environment-friendly (EF) concrete. This paper developed the models for forecasting the CS of RAC materials using hybrid machine learning (ML) models and ML with hyperparameter tuning techniques. The RAC experimental datasets were collected from the research literature, where the datasets were utilized for the 70% training and 30% testing phases of the models. This study used some renowned AI models such as XGBoost (Extreme Gradient-Boosting), GBM (Gradient Boosting Machine), RF (Random Forest), and the hybrid GBM–XGBoost model. The ensemble GBM–XGBoost algorithm showed the highest level of accuracy for CS prediction, with \({R}^{2}=\) 0.982 for the training stage and \({R}^{2}=\) 0.793 for the testing stage. The evaluation of the statistical indicators of AI algorithms revealed that the ensemble GBM–XBR had a more accurate prediction. The SHapley Additive exPlainations (SHAP) analysis showed that the effective water–cement ratio (We/C), nominal maximum RCA size, and replacement ratio positively correlated with the CS of RAC, which were the most significant parameters. The partial dependence plots (PDP) study displayed the optimal quantity of each parameter, which could help in mix design to achieve a targeted CS. Furthermore, the output of both the SHAP and PDP analyses could assist researchers and the industry in determining the quality of raw ingredients when preparing RAC.

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Pobithra Das, Abul Kashem & Jasim Uddin Rahat

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Pobithra Das: supervision, conceptualization, data analysis, writing—original draft, data curation, software, formal analysis, methodology, writing—review and editing. Abul Kashem: conceptualization, writing, data curation, data analysis, software, methodology, writing—review and editing. Jasim Uddin Rahat: writing, data curation, and methodology. Rezaul Karim: writing, writing—review and editing.

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Das, P., Kashem, A., Rahat, J.U. et al. A comparative study of ensemble machine learning models for compressive strength prediction in recycled aggregate concrete and parametric analysis. Multiscale and Multidiscip. Model. Exp. and Des. (2024). https://doi.org/10.1007/s41939-024-00409-3

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Received : 29 December 2023

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DOI : https://doi.org/10.1007/s41939-024-00409-3

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