Reporting sample size calculations for randomized controlled trials published in nursing journals: A cross-sectional study

Poor sample size reporting quality and insufficient sample size in economic evaluations conducted alongside pragmatic trials: a cross-sectional survey

OBJECTIVES

Economic evaluations based on well-designed and -conducted pragmatic randomized controlled trials (pRCTs) can provide valuable evidence on the cost-effectiveness of interventions, enhancing the relevance and applicability of findings to healthcare decision-making. However, economic evaluation outcomes are seldom taken into consideration during the process of sample size calculation in pragmatic trials. The reporting quality of sample size and information on its calculation in economic evaluations that are well-suited to pRCTs remain unknown. This study aims to assess the reporting quality of sample size and estimate the power values of economic evaluations in pRCTs.

STUDY DESIGN AND SETTING

We conducted a cross-sectional survey using data of pRCTs available from PubMed and OVID from 1 January 2010 to 24 April 2022. Two groups of independent reviewers identified articles; three groups of reviewers each extracted the data. Descriptive statistics presented the general characteristics of included studies. Statistical power analyses were performed on clinical and economic outcomes with sufficient data.

RESULTS

The electronic search identified 715 studies and 152 met the inclusion criteria. Of these, 26 were available for power analysis. Only 9 out of 152 trials (5.9%) considered economic outcomes when estimating sample size, and only one adjusted the sample size accordingly. Power values for trial-based economic evaluations and clinical trials ranged from 2.56% to 100% and 3.21%-100%, respectively. Regardless of the perspectives, in 14 out of the 26 studies (53.8%), the power values of economic evaluations for quality-adjusted life years (QALYs) were lower than those of clinical trials for primary endpoints (PEs). In 11 out of the 24 (45.8%) and in 8 out of the 13 (61.5%) studies, power values of economic evaluations for QALYs were lower than those of clinical trials for PEs from the healthcare and societal perspectives, respectively. Power values of economic evaluations for non-QALYs from the healthcare and societal perspectives were potentially higher than those of clinical trials in 3 out of the 4 studies (75%). The power values for economic outcomes in Q1 were not higher than those for other journal impact factor quartile categories.

CONCLUSION

Theoretically, pragmatic trials with concurrent economic evaluations can provide real-world evidence for healthcare decision makers. However, in pRCT-based economic evaluations, limited consideration, and inadequate reporting of sample-size calculations for economic outcomes could negatively affect the results' reliability and generalisability. We thus recommend that future pragmatic trials with economic evaluations should report how sample sizes are determined or adjusted based on the economic outcomes in their protocols to enhance their transparency and evidence quality.

Clinical Effectiveness of a 3-Step Versus a 6-Step Hand Hygiene Technique: A Randomized Controlled Cross-over Study

Background

The aim of this study is to assess the clinical effectiveness of the 3-step hand hygiene (HH) technique (3-HT) compared with the 6-step HH technique (6-HT; World Health Organization 6-step technique) using an alcohol-based hand rub.

Methods

A randomized controlled crossover trial was conducted from November to December 2023 in 10 wards of a tertiary A-level hospital according to CONSORT guidelines. The 240 healthcare workers (HCWs) were randomly divided into the 3-HT intervention group or the 6-HT control group. The trial was conducted in 2 stages, and the effectiveness of each indicator in the 2 groups was compared after a washout period of 2 weeks.

Results

Compared with the 6-HT, the 3-HT has demonstrated significant superiority in all indicators of HH compliance as well as the accuracy rate of HH practices. The total HH median times for the 3-HT and 6-HT were 16.00 (interquartile range, 15.00-20.00) and 32.50 (30.00-40.00) seconds, respectively (P < .05). The reduction factors for bacterial colony-forming unit counts did not differ and the colony-forming unit counts were not significantly different. Bacillus, Staphylococcus, and Micrococcus were detected before and after the use of hand rubs. HCWs preferred the 3-HT over the 6-HT. There were no significant difference in healthcare-associated infections rate between the 2 techniques.

Conclusions

The 3-HT was significantly superior to the 6-HT in terms of the HH eligibility rate, compliance, and HH time. The safety and feasibility of the 3-HT were verified by assessing microorganism count.

Predicting non-improvement of symptoms in daily mental healthcare practice using routinely collected patient-level data: a machine learning approach

Objectives

Anxiety and mood disorders greatly affect the quality of life for individuals worldwide. A substantial proportion of patients do not sufficiently improve during evidence-based treatments in mental healthcare. It remains challenging to predict which patients will or will not benefit. Moreover, the limited research available on predictors of treatment outcomes comes from efficacy RCTs with strict selection criteria which may limit generalizability to a real-world context. The current study evaluates the performance of different machine learning (ML) models in predicting non-improvement in an observational sample of patients treated in routine specialized mental healthcare.

Methods

In the current longitudinal exploratory prediction study diagnosis-related, sociodemographic, clinical and routinely collected patient-reported quantitative outcome measures were acquired during treatment as usual of 755 patients with a primary anxiety, depressive, obsessive compulsive or trauma-related disorder in a specialized outpatient mental healthcare center. ML algorithms were trained to predict non-response (< 0.5 standard deviation improvement) in symptomatic distress 6 months after baseline. Different models were trained, including models with and without early change scores in psychopathology and well-being and models with a trimmed set of predictor variables. Performance of trained models was evaluated in a hold-out sample (30%) as a proxy for unseen data.

Results

ML models without early change scores performed poorly in predicting six-month non-response in the hold-out sample with Area Under the Curves (AUCs) < 0.63. Including early change scores slightly improved the models' performance (AUC range: 0.68-0.73). Computationally-intensive ML models did not significantly outperform logistic regression (AUC: 0.69). Reduced prediction models performed similar to the full prediction models in both the models without (AUC: 0.58-0.62 vs. 0.58-0.63) and models with early change scores (AUC: 0.69-0.73 vs. 0.68-0.71). Across different ML algorithms, early change scores in psychopathology and well-being consistently emerged as important predictors for non-improvement.

Conclusion

Accurately predicting treatment outcomes in a mental healthcare context remains challenging. While advanced ML algorithms offer flexibility, they showed limited additional value compared to traditional logistic regression in this study. The current study confirmed the importance of taking early change scores in both psychopathology and well-being into account for predicting longer-term outcomes in symptomatic distress.

Amplifying research influence through the social network, open access publishing, and international collaboration: A mediation analysis on nursing studies literature

INTRODUCTION

Research impact and influence are commonly measured quantitatively by citation count received by research articles. Many institutes also use citation count as one of the factors in faculty performance appraisal and candidate selection of academic positions. Various strategies were recommended to amplify and accelerate research influence, particularly citation counts, by bringing research articles to a wider reach for potential readers. However, no prior empirical study was conducted to examine and valid effects of those strategies on nursing studies. This study examines and verifies the direct effects and mediation effects of some strategies, namely, the use of Twitter, international collaboration, the use of ResearchGate, and open access publishing, for amplifying the citation of research and review articles in nursing studies.

DESIGN

Cross-sectional study design.

METHODS

Articles published in top nursing journals in 2016 were identified in PUBMED and the citation metrics for individual articles until 2021 were extracted from Scopus. The primary outcome was the citation count of the article, while the tweet count on Twitter of the article was considered a mediator. The predictors included paper type, the total number of authors, the proportion of authors with a ResearchGate account in the article, funding support, open-accessed article, and the number of different countries stated in the authors' affiliation. A mediation analysis was conducted to examine the predictors' direct and indirect effects (i.e., via tweet count) on the citation count of the article.

RESULTS

A total of 2210 articles were included in this study, of which 223 (10.1%) were review articles. The median (IQR) number of Scopus citations, tweets, countries, and percentage of authors with ResearchGate accounts were 12 (6-21), 2 (0-6), 1 (1-1), and 75% (50%-100%) respectively. In the mediation analysis, tweet count, article type, number of countries, percentage of authors with a ResearchGate account, and journal impact factors in 2014 were positively associated with the Scopus citation count. The effects of article type, open access, and journals' impact factors in 2014 on Scopus citation count were mediated by the tweet count.

CONCLUSION

This study provides empirical support for some strategies researchers may employ to amplify the citation count of their research articles. The methodology of our study can be extended to compare research influence between entities (e.g., across countries or institutes).

CLINICAL RELEVANCE

The citation refers to the research work cited by peers and is one of the indicators for research impact. Higher citations implied the research work is read and used by others, therefore, understanding the associated factors with higher citations is critical.

Rationale for participation in venous leg ulcer clinical research: Patient interview study

Recruitment to wound care clinical trials is challenging and a better understanding of patient decisions to participate has the potential to influence recruitment success. We conducted 31 semi-structured telephone interviews of patients who participated in the Aspirin in Venous Leg Ulcer (ASPiVLU) randomised controlled trail (RCT) or ASPiVLU cohort study. Data were coded and analysed using thematic analysis. We identified four key themes: (a) "I participated to help others"; (b) "I participated in research to thank those who cared for me"; (c) "I participated to receive better care"; and (d) "I participated to have a say on what works." These themes became basic elements for the Rationale for Research Participation Framework that we have developed to improve the participant recruitment process for clinical trials in wound care.