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Acta Obstetricia Et Gynecologica... Apr 2018A randomized controlled trial is a prospective, comparative, quantitative study/experiment performed under controlled conditions with random allocation of interventions... (Review)
Review
A randomized controlled trial is a prospective, comparative, quantitative study/experiment performed under controlled conditions with random allocation of interventions to comparison groups. The randomized controlled trial is the most rigorous and robust research method of determining whether a cause-effect relation exists between an intervention and an outcome. High-quality evidence can be generated by performing an randomized controlled trial when evaluating the effectiveness and safety of an intervention. Furthermore, randomized controlled trials yield themselves well to systematic review and meta-analysis providing a solid base for synthesizing evidence generated by such studies. Evidence-based clinical practice improves patient outcomes and safety, and is generally cost-effective. Therefore, randomized controlled trials are becoming increasingly popular in all areas of clinical medicine including perinatology. However, designing and conducting an randomized controlled trial, analyzing data, interpreting findings and disseminating results can be challenging as there are several practicalities to be considered. In this review, we provide simple descriptive guidance on planning, conducting, analyzing and reporting randomized controlled trials.
Topics: Gynecology; Humans; Obstetrics; Randomized Controlled Trials as Topic; Research Design
PubMed: 29377058
DOI: 10.1111/aogs.13309 -
Social Science & Medicine (1982) Jan 2022To review empirical studies that assess saturation in qualitative research in order to identify sample sizes for saturation, strategies used to assess saturation, and...
OBJECTIVE
To review empirical studies that assess saturation in qualitative research in order to identify sample sizes for saturation, strategies used to assess saturation, and guidance we can draw from these studies.
METHODS
We conducted a systematic review of four databases to identify studies empirically assessing sample sizes for saturation in qualitative research, supplemented by searching citing articles and reference lists.
RESULTS
We identified 23 articles that used empirical data (n = 17) or statistical modeling (n = 6) to assess saturation. Studies using empirical data reached saturation within a narrow range of interviews (9-17) or focus group discussions (4-8), particularly those with relatively homogenous study populations and narrowly defined objectives. Most studies had a relatively homogenous study population and assessed code saturation; the few outliers (e.g., multi-country research, meta-themes, "code meaning" saturation) needed larger samples for saturation.
CONCLUSIONS
Despite varied research topics and approaches to assessing saturation, studies converged on a relatively consistent sample size for saturation for commonly used qualitative research methods. However, these findings apply to certain types of studies (e.g., those with homogenous study populations). These results provide strong empirical guidance on effective sample sizes for qualitative research, which can be used in conjunction with the characteristics of individual studies to estimate an appropriate sample size prior to data collection. This synthesis also provides an important resource for researchers, academic journals, journal reviewers, ethical review boards, and funding agencies to facilitate greater transparency in justifying and reporting sample sizes in qualitative research. Future empirical research is needed to explore how various parameters affect sample sizes for saturation.
Topics: Data Collection; Focus Groups; Humans; Qualitative Research; Research Design; Sample Size
PubMed: 34785096
DOI: 10.1016/j.socscimed.2021.114523 -
Research in Nursing & Health Feb 2017Qualitative description (QD) is a term that is widely used to describe qualitative studies of health care and nursing-related phenomena. However, limited discussions... (Review)
Review
Qualitative description (QD) is a term that is widely used to describe qualitative studies of health care and nursing-related phenomena. However, limited discussions regarding QD are found in the existing literature. In this systematic review, we identified characteristics of methods and findings reported in research articles published in 2014 whose authors identified the work as QD. After searching and screening, data were extracted from the sample of 55 QD articles and examined to characterize research objectives, design justification, theoretical/philosophical frameworks, sampling and sample size, data collection and sources, data analysis, and presentation of findings. In this review, three primary findings were identified. First, although there were some inconsistencies, most articles included characteristics consistent with the limited available QD definitions and descriptions. Next, flexibility or variability of methods was common and effective for obtaining rich data and achieving understanding of a phenomenon. Finally, justification for how a QD approach was chosen and why it would be an appropriate fit for a particular study was limited in the sample and, therefore, in need of increased attention. Based on these findings, recommendations include encouragement to researchers to provide as many details as possible regarding the methods of their QD studies so that readers can determine whether the methods used were reasonable and effective in producing useful findings. © 2016 Wiley Periodicals, Inc.
Topics: Humans; Qualitative Research; Research Design
PubMed: 27686751
DOI: 10.1002/nur.21768 -
Journal of Advanced Nursing Apr 2021The aim of this study is to discuss the available methodological resources and best-practice guidelines for the development and completion of scoping reviews relevant to... (Review)
Review
AIM
The aim of this study is to discuss the available methodological resources and best-practice guidelines for the development and completion of scoping reviews relevant to nursing and midwifery policy, practice, and research.
DESIGN
Discussion Paper.
DATA SOURCES
Scoping reviews that exemplify best practice are explored with reference to the recently updated JBI scoping review guide (2020) and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Scoping Review extension (PRISMA-ScR).
IMPLICATIONS FOR NURSING AND MIDWIFERY
Scoping reviews are an increasingly common form of evidence synthesis. They are used to address broad research questions and to map evidence from a variety of sources. Scoping reviews are a useful form of evidence synthesis for those in nursing and midwifery and present opportunities for researchers to review a broad array of evidence and resources. However, scoping reviews still need to be conducted with rigour and transparency.
CONCLUSION
This study provides guidance and advice for researchers and clinicians who are preparing to undertake an evidence synthesis and are considering a scoping review methodology in the field of nursing and midwifery.
IMPACT
With the increasing popularity of scoping reviews, criticism of the rigour, transparency, and appropriateness of the methodology have been raised across multiple academic and clinical disciplines, including nursing and midwifery. This discussion paper provides a unique contribution by discussing each component of a scoping review, including: developing research questions and objectives; protocol development; developing eligibility criteria and the planned search approach; searching and selecting the evidence; extracting and analysing evidence; presenting results; and summarizing the evidence specifically for the fields of nursing and midwifery. Considerations for when to select this methodology and how to prepare a review for publication are also discussed. This approach is applied to the disciplines of nursing and midwifery to assist nursing and/or midwifery students, clinicians, researchers, and academics.
Topics: Female; Humans; Midwifery; Pregnancy; Research Design; Research Personnel; Students
PubMed: 33543511
DOI: 10.1111/jan.14743 -
The Journal of Investigative Dermatology Nov 2016Systematic reviews are increasingly utilized in the medical literature to summarize available evidence on a research question. Like other studies, systematic reviews are... (Review)
Review
Systematic reviews are increasingly utilized in the medical literature to summarize available evidence on a research question. Like other studies, systematic reviews are at risk for bias from a number of sources. A systematic review should be based on a formal protocol developed and made publicly available before the conduct of the review; deviations from a protocol with selective presentation of data can result in reporting bias. Evidence selection bias occurs when a systematic review does not identify all available data on a topic. This can arise from publication bias, where data from statistically significant studies are more likely to be published than those that are not statistically significant. Systematic reviews are also susceptible to bias that arises in any of the included primary studies, each of which needs to be critically appraised. Finally, competing interests can lead to bias in favor of a particular intervention. Awareness of these sources of bias is important for authors and consumers of the scientific literature as they conduct and read systematic reviews and incorporate their findings into clinical practice and policy making.
Topics: Dermatology; Disease Management; Humans; Research Design; Selection Bias; Skin Diseases
PubMed: 27772550
DOI: 10.1016/j.jid.2016.08.021 -
BMJ (Clinical Research Ed.) Mar 2022To determine the comparative effectiveness and safety of psychological interventions for chronic low back pain. (Meta-Analysis)
Meta-Analysis
OBJECTIVE
To determine the comparative effectiveness and safety of psychological interventions for chronic low back pain.
DESIGN
Systematic review with network meta-analysis.
DATA SOURCES
Medline, Embase, PsycINFO, Cochrane Central Register of Controlled Trials, Web of Science, SCOPUS, and CINAHL from database inception to 31 January 2021.
ELIGIBILITY CRITERIA FOR STUDY SELECTION
Randomised controlled trials comparing psychological interventions with any comparison intervention in adults with chronic, non-specific low back pain. Two reviewers independently screened studies, extracted data, and assessed risk of bias and confidence in the evidence. Primary outcomes were physical function and pain intensity. A random effects network meta-analysis using a frequentist approach was performed at post-intervention (from the end of treatment to <2 months post-intervention); and at short term (≥2 to <6 months post-intervention), mid-term (≥6 to <12 months post-intervention), and long term follow-up (≥12 months post-intervention). Physiotherapy care was the reference comparison intervention. The design-by-treatment interaction model was used to assess global inconsistency and the Bucher method was used to assess local inconsistency.
RESULTS
97 randomised controlled trials involving 13 136 participants and 17 treatment nodes were included. Inconsistency was detected at short term and mid-term follow-up for physical function, and short term follow-up for pain intensity, and were resolved through sensitivity analyses. For physical function, cognitive behavioural therapy (standardised mean difference 1.01, 95% confidence interval 0.58 to 1.44), and pain education (0.62, 0.08 to 1.17), delivered with physiotherapy care, resulted in clinically important improvements at post-intervention (moderate quality evidence). The most sustainable effects of treatment for improving physical function were reported with pain education delivered with physiotherapy care, at least until mid-term follow-up (0.63, 0.25 to 1.00; low quality evidence). No studies investigated the long term effectiveness of pain education delivered with physiotherapy care. For pain intensity, behavioural therapy (1.08, 0.22 to 1.94), cognitive behavioural therapy (0.92, 0.43 to 1.42), and pain education (0.91, 0.37 to 1.45), delivered with physiotherapy care, resulted in clinically important effects at post-intervention (low to moderate quality evidence). Only behavioural therapy delivered with physiotherapy care maintained clinically important effects on reducing pain intensity until mid-term follow-up (1.01, 0.41 to 1.60; high quality evidence).
CONCLUSIONS
For people with chronic, non-specific low back pain, psychological interventions are most effective when delivered in conjunction with physiotherapy care (mainly structured exercise). Pain education programmes (low to moderate quality evidence) and behavioural therapy (low to high quality evidence) result in the most sustainable effects of treatment; however, uncertainty remains as to their long term effectiveness. Although inconsistency was detected, potential sources were identified and resolved.
SYSTEMATIC REVIEW REGISTRATION
PROSPERO CRD42019138074.
Topics: Adult; Cognitive Behavioral Therapy; Humans; Low Back Pain; Network Meta-Analysis; Psychosocial Intervention; Research Design
PubMed: 35354560
DOI: 10.1136/bmj-2021-067718 -
Preventive Medicine Jun 2017Excellent medication adherence contributes to decreases in morbidity, mortality, and health care costs. Although researchers have tested many interventions to increase... (Meta-Analysis)
Meta-Analysis Review
Excellent medication adherence contributes to decreases in morbidity, mortality, and health care costs. Although researchers have tested many interventions to increase adherence, results are sometimes conflicting and often unclear. This systematic review applied meta-analytic procedures to integrate primary research that tested medication adherence interventions. Comprehensive searching completed in 2015 located 771 published and unpublished intervention studies with adherence behavior outcomes. Random-effects model analysis calculated standardized mean difference effect sizes. Meta-analytic moderator analyses examined the association between adherence effect sizes and sample, design, and intervention characteristics. Analyses were conducted in 2016. A standardized mean difference effect size of 0.290 comparing treatment and control groups was calculated. Moderator analyses revealed larger effect sizes for habit-based and behavioral-targeted (vs. cognitive-focused) interventions. The most effective interventions were delivered face-to-face, by pharmacists, and administered directly to patients. Effect sizes were smaller in studies with older and homeless participants. Risks of bias were common; effect sizes were significantly lower among studies with masked data collectors and intention-to-treat analyses. The largest effect sizes were reported by studies using medication electronic event monitoring and pill count medication adherence measures. Publication bias was present. This most comprehensive review to date documented that, although interventions can increase adherence, much room remains for improvement. Findings suggest health care providers should focus intervention content on behavioral strategies, especially habit-based interventions, more so than cognitive strategies designed to change knowledge and beliefs.
Topics: Bias; Humans; Medication Adherence; Research Design
PubMed: 28315760
DOI: 10.1016/j.ypmed.2017.03.008 -
BMC Pediatrics Nov 2021Motor deficiencies are observed in a large number of children with ADHD. Especially fine motor impairments can lead to academic underachievement, low self-esteem and... (Review)
Review
BACKGROUND
Motor deficiencies are observed in a large number of children with ADHD. Especially fine motor impairments can lead to academic underachievement, low self-esteem and frustration in affected children. Despite these far-reaching consequences, fine motor deficiencies have remained widely undertreated in the ADHD population. The aim of this review was to systematically map the evidence on existing training programs for remediating fine motor impairments in children with ADHD and to assess their effectiveness.
METHODS
The scoping review followed the PRISMA-ScR guidelines. In March 2020, PsycINFO, MEDLINE (PubMed), Web of Science, Google Scholar and The Cochrane Database of Systematic Reviews were searched for evidence. The eligibility criteria and the data charting process followed the PICO framework, complemented by study design. The investigated population included children with a formal ADHD diagnosis (either subtype) or elevated ADHD symptoms aged between 4 and 12 years, both on and off medication. All training interventions aiming at improving fine motor skills, having a fine motor component or fine motor improvements as a secondary outcome were assessed for eligibility; no comparators were specified.
RESULTS
Twelve articles were included in the final report, comprising observational and experimental studies as well as a review. Both offline and online or virtual training interventions were reported, often accompanied by physical activity and supplemented by training sessions at home. The training programs varied in length and intensity, but generally comprised several weeks and single or multiple training sessions per week. All interventions including more than one session were effective in the treatment of fine motor deficiencies in children with ADHD and had a wide range of additional positive outcomes. The effects could be maintained at follow-up.
CONCLUSIONS
Fine motor training in children with ADHD can be very effective and multiple approaches including specific fine motor and cognitive training components, some kind of physical activity, feedback mechanisms, or multimodal treatments can be successful. Training programs need to be tailored to the specific characteristics of the ADHD population. A mHealth approach using serious games could be promising in this context due to its strong motivational components.
Topics: Attention Deficit Disorder with Hyperactivity; Child; Child, Preschool; Educational Status; Humans; Research Design
PubMed: 34736439
DOI: 10.1186/s12887-021-02916-5 -
BMJ (Clinical Research Ed.) Mar 2020To systematically examine the design, reporting standards, risk of bias, and claims of studies comparing the performance of diagnostic deep learning algorithms for...
OBJECTIVE
To systematically examine the design, reporting standards, risk of bias, and claims of studies comparing the performance of diagnostic deep learning algorithms for medical imaging with that of expert clinicians.
DESIGN
Systematic review.
DATA SOURCES
Medline, Embase, Cochrane Central Register of Controlled Trials, and the World Health Organization trial registry from 2010 to June 2019.
ELIGIBILITY CRITERIA FOR SELECTING STUDIES
Randomised trial registrations and non-randomised studies comparing the performance of a deep learning algorithm in medical imaging with a contemporary group of one or more expert clinicians. Medical imaging has seen a growing interest in deep learning research. The main distinguishing feature of convolutional neural networks (CNNs) in deep learning is that when CNNs are fed with raw data, they develop their own representations needed for pattern recognition. The algorithm learns for itself the features of an image that are important for classification rather than being told by humans which features to use. The selected studies aimed to use medical imaging for predicting absolute risk of existing disease or classification into diagnostic groups (eg, disease or non-disease). For example, raw chest radiographs tagged with a label such as pneumothorax or no pneumothorax and the CNN learning which pixel patterns suggest pneumothorax.
REVIEW METHODS
Adherence to reporting standards was assessed by using CONSORT (consolidated standards of reporting trials) for randomised studies and TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) for non-randomised studies. Risk of bias was assessed by using the Cochrane risk of bias tool for randomised studies and PROBAST (prediction model risk of bias assessment tool) for non-randomised studies.
RESULTS
Only 10 records were found for deep learning randomised clinical trials, two of which have been published (with low risk of bias, except for lack of blinding, and high adherence to reporting standards) and eight are ongoing. Of 81 non-randomised clinical trials identified, only nine were prospective and just six were tested in a real world clinical setting. The median number of experts in the comparator group was only four (interquartile range 2-9). Full access to all datasets and code was severely limited (unavailable in 95% and 93% of studies, respectively). The overall risk of bias was high in 58 of 81 studies and adherence to reporting standards was suboptimal (<50% adherence for 12 of 29 TRIPOD items). 61 of 81 studies stated in their abstract that performance of artificial intelligence was at least comparable to (or better than) that of clinicians. Only 31 of 81 studies (38%) stated that further prospective studies or trials were required.
CONCLUSIONS
Few prospective deep learning studies and randomised trials exist in medical imaging. Most non-randomised trials are not prospective, are at high risk of bias, and deviate from existing reporting standards. Data and code availability are lacking in most studies, and human comparator groups are often small. Future studies should diminish risk of bias, enhance real world clinical relevance, improve reporting and transparency, and appropriately temper conclusions.
STUDY REGISTRATION
PROSPERO CRD42019123605.
Topics: Algorithms; Bias; Deep Learning; Diagnostic Imaging; Humans; Image Processing, Computer-Assisted; Physicians; Randomized Controlled Trials as Topic; Research Design
PubMed: 32213531
DOI: 10.1136/bmj.m689 -
Journal of the National Cancer Institute Aug 2017: Propensity score (PS) analysis is increasingly being used in observational studies, especially in some cancer studies where random assignment is not feasible. This... (Review)
Review
BACKGROUND
: Propensity score (PS) analysis is increasingly being used in observational studies, especially in some cancer studies where random assignment is not feasible. This systematic review evaluates the use and reporting quality of PS analysis in oncology studies.
METHODS
: We searched PubMed to identify the use of PS methods in cancer studies (CS) and cancer surgical studies (CSS) in major medical, cancer, and surgical journals over time and critically evaluated 33 CS published in top medical and cancer journals in 2014 and 2015 and 306 CSS published up to November 26, 2015, without earlier date limits. The quality of reporting in PS analysis was evaluated. It was also compared over time and among journals with differing impact factors. All statistical tests were two-sided.
RESULTS
More than 50% of the publications with PS analysis from the past decade occurred within the past two years. Of the studies critically evaluated, a considerable proportion did not clearly provide the variables used to estimate PS (CS 12.1%, CSS 8.8%), incorrectly included non baseline variables (CS 3.4%, CSS 9.3%), neglected the comparison of baseline characteristics (CS 21.9%, CSS 15.6%), or did not report the matching algorithm utilized (CS 19.0%, CSS 36.1%). In CSS, the reporting of the matching algorithm improved in 2014 and 2015 ( P = .04), and the reporting of variables used to estimate PS was better in top surgery journals ( P = .008). However, there were no statistically significant differences for the inclusion of non baseline variables and reporting of comparability of baseline characteristics.
CONCLUSIONS
The use of PS in cancer studies has dramatically increased recently, but there is substantial room for improvement in the quality of reporting even in top journals. Herein we have proposed reporting guidelines for PS analyses that are broadly applicable to different areas of medical research that will allow better evaluation and comparison across studies applying this approach.
Topics: Algorithms; Biomedical Research; Guidelines as Topic; Humans; Journal Impact Factor; Neoplasms; Periodicals as Topic; Propensity Score; Research Design; Research Report
PubMed: 28376195
DOI: 10.1093/jnci/djw323