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Background: Previous research has found that a "web-push" approach to data collection, which involves contacting people by mail to request an Internet survey response while withholding a paper response option until later in the contact process, consistently achieves lower response rates than a "paper-only" approach, whereby all respondents are contacted and requested to respond by mail.Method: An experiment was designed, as part of the Millennium Cohort Family Study, to compare response rates, sample representativeness, and cost between a web-push and a paper-only approach; each approach comprised 3 stages of mail contacts. The invited sample (n = 4,935) consisted of spouses married to U.S. Service members, who had been serving in the military between 2 and 5 years as of October, 2011.Results: The web-push methodology produced a significantly higher response rate, 32.8% compared to 27.8%. Each of the 3 stages of postal contact significantly contributed to response for both treatments with 87.1% of the web-push responses received over the Internet. The per-respondent cost of the paper-only treatment was almost 40% higher than the web-push treatment group. Analyses revealed no meaningfully significant differences between treatment groups in representation.Conclusion: These results provide evidence that a web-push methodology is more effective and less expensive than a paper-only approach among young military spouses, perhaps due to their heavy reliance on the internet, and we suggest that this approach may be more effective with the general population as they become more uniformly internet savvy. [ABSTRACT FROM AUTHOR]

B Correction: BMC Medical Research Methodology 23, 10 (2023) b B https://doi.org/10.1186/s12874-023-01833-8 b Following publication of the original article [[1]], the author requested to correct the word "primiparous" to "multiparous" in the Abstract results and under the paragraph "Representativeness of respondents" in the main results section. Impact of sampling and data collection methods on maternity survey response: a randomised controlled trial of paper and push-to-web surveys and a concurrent social media survey. [Extracted from the article]

Background: In this paper we discuss the emergence of many different methods for doing a literature review. Referring back to the early days, when there were essentially two types of review; a Cochrane systematic review and a narrative review, we identify how the term systematic review is now widely used to describe a variety of review types and how the number of available methods for doing a literature review has increased dramatically. This led us to undertake a review of current practice of those doing a literature review and the terms used to describe them.Method: We undertook a focused mapping review and synthesis. Literature reviews; defined as papers with the terms review or synthesis in the title, published in five nursing journals between January 2017-June 2018 were identified. We recorded the type of review and how these were undertaken.Results: We identified more than 35 terms used to describe a literature review. Some terms reflected established methods for doing a review whilst others could not be traced to established methods and/or the description of method in the paper was limited. We also found inconsistency in how the terms were used.Conclusion: We have identified a proliferation of terms used to describe doing a literature review; although it is not clear how many distinct methods are being used. Our review indicates a move from an era when the term narrative review was used to describe all 'non Cochrane' reviews; to a time of expansion when alternative systematic approaches were developed to enhance rigour of such narrative reviews; to the current situation in which these approaches have proliferated to the extent so that the academic discipline of doing a literature review has become muddled and confusing. We argue that an 'era of consolidation' is needed in which those undertaking reviews are explicit about the method used and ensure that their processes can be traced back to a well described, original primary source. [ABSTRACT FROM AUTHOR]

Background: There is a growing need for methods that acknowledge and successfully capture the dynamic interaction between context and implementation of complex interventions. Case study research has the potential to provide such understanding, enabling in-depth investigation of the particularities of phenomena. However, there is limited guidance on how and when to best use different case study research approaches when evaluating complex interventions. This study aimed to review and synthesise the literature on case study research across relevant disciplines, and determine relevance to the study of contextual influences on complex interventions in health systems and public health research.Methods: Systematic meta-narrative review of the literature comprising (i) a scoping review of seminal texts (n = 60) on case study methodology and on context, complexity and interventions, (ii) detailed review of empirical literature on case study, context and complex interventions (n = 71), and (iii) identifying and reviewing 'hybrid papers' (n = 8) focused on the merits and challenges of case study in the evaluation of complex interventions.Results: We identified four broad (and to some extent overlapping) research traditions, all using case study in a slightly different way and with different goals: 1) developing and testing complex interventions in healthcare; 2) analysing change in organisations; 3) undertaking realist evaluations; 4) studying complex change naturalistically. Each tradition conceptualised context differently-respectively as the backdrop to, or factors impacting on, the intervention; sets of interacting conditions and relationships; circumstances triggering intervention mechanisms; and socially structured practices. Overall, these traditions drew on a small number of case study methodologists and disciplines. Few studies problematised the nature and boundaries of 'the case' and 'context' or considered the implications of such conceptualisations for methods and knowledge production.Conclusions: Case study research on complex interventions in healthcare draws on a number of different research traditions, each with different epistemological and methodological preferences. The approach used and consequences for knowledge produced often remains implicit. This has implications for how researchers, practitioners and decision makers understand, implement and evaluate complex interventions in different settings. Deeper engagement with case study research as a methodology is strongly recommended. [ABSTRACT FROM AUTHOR]

Background: Central cancer registries are often used to survey population-based samples of cancer survivors. These surveys are typically administered via paper or telephone. In most populations, web surveys obtain much lower response rates than paper surveys. This study assessed the feasibility of web surveys for collecting patient-reported outcomes via a central cancer registry.Methods: Potential participants were sampled from Utah Cancer Registry records. Sample members were randomly assigned to receive a web or paper survey, and then randomized to either receive or not receive an informative brochure describing the cancer registry. We calculated adjusted risk ratios with 95% confidence intervals to compare response likelihood and the demographic profile of respondents across study arms.Results: The web survey response rate (43.2%) was lower than the paper survey (50.4%), but this difference was not statistically significant (adjusted risk ratio = 0.88, 95% confidence interval = 0.72, 1.07). The brochure also did not significantly influence the proportion responding (adjusted risk ratio = 1.03, 95% confidence interval = 0.85, 1.25). There were few differences in the demographic profiles of respondents across the survey modes. Older age increased likelihood of response to a paper questionnaire but not a web questionnaire.Conclusions: Web surveys of cancer survivors are feasible without significantly influencing response rates, but providing a paper response option may be advisable particularly when surveying older individuals. Further examination of the varying effects of brochure enclosures across different survey modes is warranted. [ABSTRACT FROM AUTHOR]

The letter responds to a paper that claims there was a risk of selection bias in the NINDS rt-PA for Acute Ischemic Stroke Study. The authors of the letter argue that the paper contains factual errors and misconceptions about the study. They clarify that there were errors in the randomization process, but these errors did not contribute to bias in the treatment effect. The authors also explain the difference between concealment and blinding in clinical trials and refute the suggestion that treatment assignment was manipulated. They emphasize the benefits of thrombolytic therapy for acute ischemic stroke and urge readers to review the literature on the topic. [Extracted from the article]

Background: The literature on qualitative data analysis mostly concerns analyses pertaining to an individual research question and the organization of data within that research question. Few authors have written about the entire qualitative dataset from which multiple and separate analyses could be conducted and reported. The concept of analytic direction is a strategy that can assist qualitative researchers in deciding which findings to highlight within a dataset. The objectives of this paper were to: 1) describe the importance of analytic direction in qualitative research, and 2) provide a working example of the concept of analytic direction.Methods: A qualitative dataset from one of the author's research programs was selected for review. Ten potential analytic directions were identified after the initial phenomenological analysis was conducted. Three analytic directions based on the same coding template but different content areas of the data were further developed using phenomenological analysis (n = 2) and qualitative description (n = 1) and are the focus of this paper. Development and selection of these three analytic directions was determined partially relying on methodological criteria to promote rigour including a comprehensive examination of the data, the use of multiple analysts, direct quotations to support claims, negative case analysis, and reflexivity.Results: The three analytic directions addressed topics within the scope of the overall research question. Each analytic direction had its own central point or story line and each highlighted a different perspective or voice. The use of an inductive and deductive approach to analysis and how the role of theory was integrated varied in each analytic direction.Conclusions: The concept of analytic direction enables researchers to organize their qualitative datasets in order to tell different and unique "stories". The concept relies upon, and promotes, the conduct of rigourous qualitative research. [ABSTRACT FROM AUTHOR]

Background: Rates of non-communicable diseases (NCDs) are rapidly rising in the Eastern Mediterranean Region (EMR). Systematic reviews satisfy the demand from practitioners and policy makers for prompt comprehensive evidence. The aim of this study is to review trends in NCD systematic reviews research output and quality by time and place, describe design and focus, and examine gaps in knowledge produced.Methods: Using the Montori et al. systematic reviews filter, MeSH and keywords were applied to search Medline Ovid, Cochrane Central and Epistemonikos for publications from 1996 until 2015 in the 22 countries of the EMR. The 'Measurement Tool to Assess Systematic Reviews', AMSTAR, was used to assess the methodological quality of the papers.Results: Our search yielded 2439 papers for abstract and title screening, and 89 papers for full text screening. A total of 39 (43.8%) studies included meta-analysis. Most of the papers were judged as being of low AMSTAR quality (83.2%), and only one paper was judged as being of high AMSTAR quality. Whilst annual number of papers increased over the years, the growth was mainly attributed to an increase in low-quality publications approaching in 2015 over four times the number of medium-quality publications. Reviews were significantly more likely to be characterized by higher AMSTAR scores (±SD) when meta-analysis was performed compared to when meta-analysis was not performed (3.4 ± 1.5 vs 2.6 ± 2.0; p-value = 0.034); and when critical appraisal of the included studies was conducted (4.3 ± 2.3 vs 2.5 ± 1.5; p-value = 0.004). Most of the reviews focused on cancer and diabetes as an outcome (25.8% and 24.7%, respectively), and on smoking, dietary habits and physical activity as exposures (15.7%, 12.4%, 9.0%, respectively). There was a blatant deficit in reviews examining associations between behaviors and physiologic factors, notably metabolic conditions.Conclusions: Systematic reviews research in the EMR region are overwhelmingly of low quality, with gaps in the literature for studies on cardiovascular disease and on associations between behavioral factors and intermediary physiologic parameters. This study raises awareness of the need for high-quality evidence guided by locally driven research agenda responsive to emerging needs in countries of the EMR. [ABSTRACT FROM AUTHOR]

Background: The relationship between ethnography and healthcare improvement has been the subject of methodological concern. We conducted a scoping review of ethnographic literature on healthcare improvement topics, with two aims: (1) to describe current ethnographic methods and practices in healthcare improvement research and (2) to consider how these may affect habit and skill formation in the service of healthcare improvement.Methods: We used a scoping review methodology drawing on Arksey and O'Malley's methods and more recent guidance. We systematically searched electronic databases including Medline, PsychINFO, EMBASE and CINAHL for papers published between April 2013 - April 2018, with an update in September 2019. Information about study aims, methodology and recommendations for improvement were extracted. We used a theoretical framework outlining the habits and skills required for healthcare improvement to consider how ethnographic research may foster improvement skills.Results: We included 283 studies covering a wide range of healthcare topics and methods. Ethnography was commonly used for healthcare improvement research about vulnerable populations, e.g. elderly, psychiatry. Focussed ethnography was a prominent method, using a rapid feedback loop into improvement through focus and insider status. Ethnographic approaches such as the use of theory and focus on every day practices can foster improvement skills and habits such as creativity, learning and systems thinking.Conclusions: We have identified that a variety of ethnographic approaches can be relevant to improvement. The skills and habits we identified may help ethnographers reflect on their approaches in planning healthcare improvement studies and guide peer-review in this field. An important area of future research will be to understand how ethnographic findings are received by decision-makers. [ABSTRACT FROM AUTHOR]

Background: Although the general statistical advice is to keep continuous exposure variables as continuous in statistical analyses, categorisation is still a common approach in medical research. In a recent paper from the Hyperglycaemia and Adverse Pregnancy Outcome (HAPO) Study, categorisation of body mass index (BMI) was used when analysing the effect of BMI on adverse pregnancy outcomes. The lowest category, labelled "underweight", was used as the reference category. Methods: The present paper gives a summary of reasons for categorisation and methodological drawbacks of this approach. We also discuss the choice of reference category and alternative analyses. We exemplify our arguments by a reanalysis of results from the HAPO paper. Results: Categorisation of continuous exposure data results in loss of power and other methodological challenges. An unfortunate choice of reference category can give additional lack of precision and obscure the interpretation of risk estimates. A highlighted odds ratio (OR) in the HAPO study is the OR for birth weight >90th percentile for women in the highest compared to the lowest BMI category ("obese class III" versus "underweight"). This estimate was OR = 4.55 and OR = 3.52, with two different multiple logistic regression models. When using the "normal weight" category as the reference, our corresponding estimates were OR = 2.03 and OR = 1.62, respectively. Moreover, our choice of reference category also gave narrower confidence intervals. Summary: Due to several methodological drawbacks, categorisation should be avoided. Modern statistical analyses should be used to analyse continuous exposure data, and to explore non-linear relations. If continuous data are categorised, special attention must be given to the choice of reference category. [ABSTRACT FROM AUTHOR]

Background: Self-interviews, where the respondent rather than the interviewer enters answers to questions, have been proposed as a way to reduce social desirability bias associated with interviewer-led interviews. Computer-assisted self-interviews (CASI) are commonly proposed since the computer programme can guide respondents; however they require both language and computer literacy. We evaluated the feasibility and acceptability of using electronic methods to administer quantitative sexual behaviour questionnaires in the Somkhele demographic surveillance area (DSA) in rural KwaZulu-Natal, South Africa.Methods: We conducted a four-arm randomized trial of paper-and-pen-interview, computer-assisted personal-interview (CAPI), CASI and audio-CASI with an age-sex-urbanicity stratified sample of 504 adults resident in the DSA in 2015. We compared respondents' answers to their responses to the same questions in previous surveillance rounds. We also conducted 48 cognitive interviews, dual-coding responses using the Framework approach.Results: Three hundred forty (67%) individuals were interviewed and covariates and participation rates were balanced across arms. CASI and audio-CASI were significantly slower than interviewer-led interviews. Item non-response rates were higher in self-interview arms. In single-paper meta-analysis, self-interviewed individuals reported more socially undesirable sexual behaviours. Cognitive interviews found high acceptance of both self-interviews and the use of electronic methods, with some concerns that self-interview methods required more participant effort and literacy.Conclusions: Electronic data collection methods, including self-interview methods, proved feasible and acceptable for completing quantitative sexual behaviour questionnaires in a poor, rural South African setting. However, each method had both benefits and costs, and the choice of method should be based on context-specific criteria. [ABSTRACT FROM AUTHOR]

Background: Current dose-finding designs for phase I clinical trials can correctly select the MTD in a range of 30-80% depending on various conditions based on a sample of 30 subjects. However, there is still an unmet need for efficiency and cost saving.Methods: We propose a novel dose-finding design based on Bayesian stochastic approximation. The design features utilization of dose level information through local adaptive modelling and free assumption of toxicity probabilities and hyper-parameters. It allows a flexible target toxicity rate and varying cohort size. And we extend it to accommodate historical information via prior effective sample size. We compare the proposed design to some commonly used methods in terms of accuracy and safety by simulation.Results: On average, our design can improve the percentage of correct selection to about 60% when the MTD resides at a early or middle position in the search domain and perform comparably to other competitive methods otherwise. A free online software package is provided to facilitate the application, where a simple decision tree for the design can be pre-printed beforehand.Conclusion: The paper proposes a novel dose-finding design for phase I clinical trials. Applying the design to future cancer trials can greatly improve the efficiency, consequently save cost and shorten the development period. [ABSTRACT FROM AUTHOR]

Background: This paper reports the construction and use of a specific method for qualitative medical research: The Inductive Process to Analyze the Structure of lived Experience (IPSE), an inductive and phenomenological approach designed to gain the closest access possible to the patients' experience and to produce concrete recommendations for improving care. This paper describes this innovative method.Methods: IPSE has five steps: 1) set up a research group, 2) ensure the originality of the research, 3) organize recruitment and sampling intended to optimize exemplarity, 4) collect data that enable entry into the subjects' experience, and 5) analyze the data. This final stage is composed of one individual descriptive phase, followed by two group phases: i) structure the experience, and ii) translate the findings into concrete proposals that make a difference in care.Results: This innovative method has provided original findings that have opened up new avenues of research and have important practical implications, including (1) the development of patient-reported outcomes, (2) clinical recommendations concerning assessment and treatment, (3) innovative ways to improve communication between patients and doctors, and (4) new insights for medical pedagogy.Conclusions: IPSE is a qualitative method specifically developed for clinical medical research to reach concrete proposals, easily combined with quantitative research within a mixed-method study design and then directly integrated within evidence-based medicine. [ABSTRACT FROM AUTHOR]

Background: Publication and related biases (including publication bias, time-lag bias, outcome reporting bias and p-hacking) have been well documented in clinical research, but relatively little is known about their presence and extent in health services research (HSR). This paper aims to systematically review evidence concerning publication and related bias in quantitative HSR.Methods: Databases including MEDLINE, EMBASE, HMIC, CINAHL, Web of Science, Health Systems Evidence, Cochrane EPOC Review Group and several websites were searched to July 2018. Information was obtained from: (1) Methodological studies that set out to investigate publication and related biases in HSR; (2) Systematic reviews of HSR topics which examined such biases as part of the review process. Relevant information was extracted from included studies by one reviewer and checked by another. Studies were appraised according to commonly accepted scientific principles due to lack of suitable checklists. Data were synthesised narratively.Results: After screening 6155 citations, four methodological studies investigating publication bias in HSR and 184 systematic reviews of HSR topics (including three comparing published with unpublished evidence) were examined. Evidence suggestive of publication bias was reported in some of the methodological studies, but evidence presented was very weak, limited in both quality and scope. Reliable data on outcome reporting bias and p-hacking were scant. HSR systematic reviews in which published literature was compared with unpublished evidence found significant differences in the estimated intervention effects or association in some but not all cases.Conclusions: Methodological research on publication and related biases in HSR is sparse. Evidence from available literature suggests that such biases may exist in HSR but their scale and impact are difficult to estimate for various reasons discussed in this paper.Systematic Review Registration: PROSPERO 2016 CRD42016052333. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via the original article. [ABSTRACT FROM AUTHOR]

Background: Mendelian randomization (MR) studies using Genetic risk scores (GRS) as an instrumental variable (IV) have increasingly been used to control for unmeasured confounding in observational healthcare databases. However, proper reporting of methodological issues is sparse in these studies. We aimed to review published papers related to MR studies and identify reporting problems.Methods: We conducted a systematic review using the clinical articles published between 2009 and 2019. We searched PubMed, Scopus, and Embase databases. We retrieved information from every MR study, including the tests performed to evaluate assumptions and the modelling approach used for estimation. Using our inclusion/exclusion criteria, finally, we identified 97 studies to conduct the review according to the PRISMA statement.Results: Only 66 (68%) of the studies empirically verified the first assumption (Relevance assumption), and 40 (41.2%) studies reported the appropriate tests (e.g., R2, F-test) to investigate the association. A total of 35.1% clearly stated and discussed theoretical justifications for the second and third assumptions. 30.9% of the studies used a two-stage least square, and 11.3% used the Wald estimator method for estimating IV. Also, 44.3% of the studies conducted a sensitivity analysis to illuminate the robustness of estimates for violations of the untestable assumptions.Conclusions: We found that incompleteness of the justification of the assumptions for the instrumental variable in MR studies was a common problem in our selected studies. This may misdirect the findings of the studies. [ABSTRACT FROM AUTHOR]

Background: Ensuring fair comparisons of cancer survival statistics across population groups requires careful consideration of differential competing mortality due to other causes, and adjusting for imbalances over groups in other prognostic covariates (e.g. age). This has typically been achieved using comparisons of age-standardised net survival, with age standardisation addressing covariate imbalance, and the net estimates removing differences in competing mortality from other causes. However, these estimates lack ease of interpretability. In this paper, we motivate an alternative non-parametric approach that uses a common rate of other cause mortality across groups to give reference-adjusted estimates of the all-cause and cause-specific crude probability of death in contrast to solely reporting net survival estimates.Methods: We develop the methodology for a non-parametric equivalent of standardised and reference adjusted crude probabilities of death, building on the estimation of non-parametric crude probabilities of death. We illustrate the approach using regional comparisons of survival following a diagnosis of rectal cancer for men in England. We standardise to the covariate distribution and other cause mortality of England as a whole to offer comparability, but with close approximation to the observed all-cause region-specific mortality.Results: The approach gives comparable estimates to observed crude probabilities of death, but allows direct comparison across population groups with different covariate profiles and competing mortality patterns. In our illustrative example, we show that regional variations in survival following a diagnosis of rectal cancer persist even after accounting for the variation in deprivation, age at diagnosis and other cause mortality.Conclusions: The methodological approach of using standardised and reference adjusted metrics offers an appealing approach for future cancer survival comparison studies and routinely published cancer statistics. Our non-parametric estimation approach through the use of weighting offers the ability to estimate comparable survival estimates without the need for statistical modelling. [ABSTRACT FROM AUTHOR]

Background: Th EQUATOR Network improves the quality and transparency in health research, primarily by promoting awareness and use of reporting guidelines. In 2018, the UK EQUATOR Centre launched GoodReports.org , a website that helps authors find and use reporting guidelines. This paper describes the tool's development so far. We describe user experience and behaviour of using GoodReports.org both inside and outside a journal manuscript submission process. We intend to use our findings to inform future development and testing of the tool.Methods: We conducted a survey to collect data on user experience of the GoodReports website. We cross-checked a random sample of 100 manuscripts submitted to a partner journal to describe the level of agreement between the tool's checklist recommendation and what we would have recommended. We compared the proportion of authors submitting a completed reporting checklist alongside their manuscripts between groups exposed or not exposed to the GoodReports tool. We also conducted a study comparing completeness of reporting of manuscript text before an author received a reporting guideline recommendation from GoodReports.org with the completeness of the text subsequently submitted to a partner journal.Results: Seventy percent (423/599) of survey respondents rated GoodReports 8 or more out of 10 for usefulness, and 74% (198/267) said they had made changes to their manuscript after using the website. We agreed with the GoodReports reporting guideline recommendation in 84% (72/86) of cases. Of authors who completed the guideline finder questionnaire, 14% (10/69) failed to submit a completed checklist compared to 30% (41/136) who did not use the tool. Of the 69 authors who received a GoodReports reporting guideline recommendation, 20 manuscript pairs could be reviewed before and after use of GoodReports. Five included more information in their methods section after exposure to GoodReports. On average, authors reported 57% of necessary reporting items before completing a checklist on GoodReports.org and 60% after.Conclusion: The data suggest that reporting guidance is needed early in the writing process, not at submission stage. We are developing GoodReports by adding more reporting guidelines and by creating editable article templates. We will test whether GoodReports users write more complete study reports in a randomised trial targeting researchers starting to write health research articles. [ABSTRACT FROM AUTHOR]

Background: Several indirect comparison methods, including network meta-analyses (NMAs), using individual patient data (IPD) have been developed to synthesize evidence from a network of trials. Although IPD indirect comparisons are published with increasing frequency in health care literature, there is no guidance on selecting the appropriate methodology and on reporting the methods and results.Methods: In this paper we examine the methods and reporting of indirect comparison methods using IPD. We searched MEDLINE, Embase, the Cochrane Library, and CINAHL from inception until October 2014. We included published and unpublished studies reporting a method, application, or review of indirect comparisons using IPD and at least three interventions.Results: We identified 37 papers, including a total of 33 empirical networks. Of these, only 9 (27 %) IPD-NMAs reported the existence of a study protocol, whereas 3 (9 %) studies mentioned that protocols existed without providing a reference. The 33 empirical networks included 24 (73 %) IPD-NMAs and 9 (27 %) matching adjusted indirect comparisons (MAICs). Of the 21 (64 %) networks with at least one closed loop, 19 (90 %) were IPD-NMAs, 13 (68 %) of which evaluated the prerequisite consistency assumption, and only 5 (38 %) of the 13 IPD-NMAs used statistical approaches. The median number of trials included per network was 10 (IQR 4-19) (IPD-NMA: 15 [IQR 8-20]; MAIC: 2 [IQR 3-5]), and the median number of IPD trials included in a network was 3 (IQR 1-9) (IPD-NMA: 6 [IQR 2-11]; MAIC: 2 [IQR 1-2]). Half of the networks (17; 52 %) applied Bayesian hierarchical models (14 one-stage, 1 two-stage, 1 used IPD as an informative prior, 1 unclear-stage), including either IPD alone or with aggregated data (AD). Models for dichotomous and continuous outcomes were available (IPD alone or combined with AD), as were models for time-to-event data (IPD combined with AD).Conclusions: One in three indirect comparison methods modeling IPD adjusted results from different trials to estimate effects as if they had come from the same, randomized, population. Key methodological and reporting elements (e.g., evaluation of consistency, existence of study protocol) were often missing from an indirect comparison paper. [ABSTRACT FROM AUTHOR]

Background: Frailty, neurodegeneration and geriatric syndromes cause a significant impact at the clinical, social, and economic level, mainly in the context of the aging world. Recently, Information and Communication Technologies (ICTs), virtual reality tools, and machine learning models have been increasingly applied to the care of older patients to improve diagnosis, prognosis, and interventions. However, so far, the methodological limitations of studies in this field have prevented to generalize data to real-word. This review systematically overviews the research designs used by studies applying technologies for the assessment and treatment of aging-related syndromes in older people. Methods: Following the PRISMA guidelines, records from PubMed, EMBASE, and Web of Science were systematically screened to select original articles in which interventional or observational designs were used to study technologies' applications in samples of frail, comorbid, or multimorbid patients. Results: Thirty-four articles met the inclusion criteria. Most of the studies used diagnostic accuracy designs to test assessment procedures or retrospective cohort designs to build predictive models. A minority were randomized or non-randomized interventional studies. Quality evaluation revealed a high risk of bias for observational studies, while a low risk of bias for interventional studies. Conclusions: The majority of the reviewed articles use an observational design mainly to study diagnostic procedures and suffer from a high risk of bias. The scarce presence of methodologically robust interventional studies may suggest that the field is in its infancy. Methodological considerations will be presented on how to standardize procedures and research quality in this field. [ABSTRACT FROM AUTHOR]

Background: Confounding bias is a common concern in epidemiological research. Its presence is often determined by comparing exposure effects between univariable- and multivariable regression models, using an arbitrary threshold of a 10% difference to indicate confounding bias. However, many clinical researchers are not aware that the use of this change-in-estimate criterion may lead to wrong conclusions when applied to logistic regression coefficients. This is due to a statistical phenomenon called noncollapsibility, which manifests itself in logistic regression models. This paper aims to clarify the role of noncollapsibility in logistic regression and to provide guidance in determining the presence of confounding bias.Methods: A Monte Carlo simulation study was designed to uncover patterns of confounding bias and noncollapsibility effects in logistic regression. An empirical data example was used to illustrate the inability of the change-in-estimate criterion to distinguish confounding bias from noncollapsibility effects.Results: The simulation study showed that, depending on the sign and magnitude of the confounding bias and the noncollapsibility effect, the difference between the effect estimates from univariable- and multivariable regression models may underestimate or overestimate the magnitude of the confounding bias. Because of the noncollapsibility effect, multivariable regression analysis and inverse probability weighting provided different but valid estimates of the confounder-adjusted exposure effect. In our data example, confounding bias was underestimated by the change in estimate due to the presence of a noncollapsibility effect.Conclusion: In logistic regression, the difference between the univariable- and multivariable effect estimate might not only reflect confounding bias but also a noncollapsibility effect. Ideally, the set of confounders is determined at the study design phase and based on subject matter knowledge. To quantify confounding bias, one could compare the unadjusted exposure effect estimate and the estimate from an inverse probability weighted model. [ABSTRACT FROM AUTHOR]

Background: Even though investigating predictors of intervention success (e.g Cognitive Training, CT) is gaining more and more interest in the light of an individualized medicine, results on specific predictors of intervention success in the overall field are mixed and inconsistent due to different and sometimes inappropriate statistical methods used. Therefore, the present paper gives a guidance on the appropriate use of multiple regression analyses to identify predictors of CT and similar non-pharmacological interventions.Methods: We simulated data based on a predefined true model and ran a series of different analyses to evaluate their performance in retrieving the true model coefficients. The true model consisted of a 2 (between: experimental vs. control group) × 2 (within: pre- vs. post-treatment) design with two continuous predictors, one of which predicted the success in the intervention group and the other did not. In analyzing the data, we considered four commonly used dependent variables (post-test score, absolute change score, relative change score, residual score), five regression models, eight sample sizes, and four levels of reliability.Results: Our results indicated that a regression model including the investigated predictor, Group (experimental vs. control), pre-test score, and the interaction between the investigated predictor and the Group as predictors, and the absolute change score as the dependent variable seemed most convenient for the given experimental design. Although the pre-test score should be included as a predictor in the regression model for reasons of statistical power, its coefficient should not be interpreted because even if there is no true relationship, a negative and statistically significant regression coefficient commonly emerges.Conclusion: Employing simulation methods, theoretical reasoning, and mathematical derivations, we were able to derive recommendations regarding the analysis of data in one of the most prevalent experimental designs in research on CT and external predictors of CT success. These insights can contribute to the application of considered data analyses in future studies and facilitate cumulative knowledge gain. [ABSTRACT FROM AUTHOR]

Background: Paramedics make important and increasingly complex decisions at scene about patient care. Patient safety implications of influences on decision making in the pre-hospital setting were previously under-researched. Cutting edge perspectives advocate exploring the whole system rather than individual influences on patient safety. Ethnography (the study of people and cultures) has been acknowledged as a suitable method for identifying health care issues as they occur within the natural context. In this paper we compare multiple methods used in a multi-site, qualitative study that aimed to identify system influences on decision making.Methods: The study was conducted in three NHS Ambulance Trusts in England and involved researchers from each Trust working alongside academic researchers. Exploratory interviews with key informants e.g. managers (n = 16) and document review provided contextual information. Between October 2012 and July 2013 researchers observed 34 paramedic shifts and ten paramedics provided additional accounts via audio-recorded 'digital diaries' (155 events). Three staff focus groups (total n = 21) and three service user focus groups (total n = 23) explored a range of experiences and perceptions. Data collection and analysis was carried out by academic and ambulance service researchers as well as service users. Workshops were held at each site to elicit feedback on the findings and facilitate prioritisation of issues identified.Results: The use of a multi-method qualitative approach allowed cross-validation of important issues for ambulance service staff and service users. A key factor in successful implementation of the study was establishing good working relationships with academic and ambulance service teams. Enrolling at least one research lead at each site facilitated the recruitment process as well as study progress. Active involvement with the study allowed ambulance service researchers and service users to gain a better understanding of the research process. Feedback workshops allowed stakeholders to discuss and prioritise findings as well as identify new research areas.Conclusion: Combining multiple qualitative methods with a collaborative research approach can facilitate exploration of system influences on patient safety in under-researched settings. The paper highlights empirical issues, strengths and limitations for this approach. Feedback workshops were effective for verifying findings and prioritising areas for future intervention and research. [ABSTRACT FROM AUTHOR]

Background: Machine learning (ML) has made a significant impact in medicine and cancer research; however, its impact in these areas has been undeniably slower and more limited than in other application domains. A major reason for this has been the lack of availability of patient data to the broader ML research community, in large part due to patient privacy protection concerns. High-quality, realistic, synthetic datasets can be leveraged to accelerate methodological developments in medicine. By and large, medical data is high dimensional and often categorical. These characteristics pose multiple modeling challenges.Methods: In this paper, we evaluate three classes of synthetic data generation approaches; probabilistic models, classification-based imputation models, and generative adversarial neural networks. Metrics for evaluating the quality of the generated synthetic datasets are presented and discussed.Results: While the results and discussions are broadly applicable to medical data, for demonstration purposes we generate synthetic datasets for cancer based on the publicly available cancer registry data from the Surveillance Epidemiology and End Results (SEER) program. Specifically, our cohort consists of breast, respiratory, and non-solid cancer cases diagnosed between 2010 and 2015, which includes over 360,000 individual cases.Conclusions: We discuss the trade-offs of the different methods and metrics, providing guidance on considerations for the generation and usage of medical synthetic data. [ABSTRACT FROM AUTHOR]

Background: One of the frequently used methods for assessing research trends and the impact of published scientific literature in a particular discipline is citation analysis. Journals may strive to improve their metrics by choosing manuscripts and study designs that are more likely to be cited. The aim of this study was to identify the 50 most-cited articles in the field of pediatrics, analyze their study design and other characteristics of those articles, and assess the prevalence of systematic reviews among them.Methods: In December 2017, we searched Web of Science (WoS) for all articles published in the field of pediatrics. Two authors screened articles independently and in the further analysis included 50 articles with the highest number of citations. To avoid bias for scientific papers published earlier, the citation density was calculated. We also analyzed Journal Impact Factor (JIF) of journals where citation classics were published.Results: The citation density in top 50 cited articles in the field of pediatrics ranged from 33.16 to 432.8, with the average of 119.95. Most of the articles reported clinical science. Median 2016 JIF for journals that published them was 6.226 (range: 2.778 to 72.406). Half of the top 10 highly cited articles in pediatrics were published in a journal with JIF below 5. Most of the studies among the citation classics in pediatrics were cross-sectional studies (N = 22), followed by non-systematic narrative reviews (N = 10), randomized controlled trials (N = 5), cohort studies (N = 5), systematic reviews (N = 2), case-control studies (N = 2), case reports (N = 2), and there was one study protocol and one expert opinion.Conclusion: Few randomized controlled trials and systematic reviews were among citation classics in the field of pediatrics. Articles that use observational research methodology, and are published in journals with lower impact factors, can become citation classics. [ABSTRACT FROM AUTHOR]

Background: Patients infected with the Human Immunodeficiency Virus (HIV) are susceptible to many diseases. In these patients, the occurrence of one disease alters the chance of contracting another. Under such circumstances, methods for competing risks are required. Recently, competing risks analyses in the scope of flexible parametric models have risen to address this requirement. These lesser-known analyses have considerable advantages over conventional methods.Methods: Using data from Multi Centre AIDS Cohort Study (MACS), this paper reviews and applies methods of competing risks flexible parametric models to analyze the risk of the first disease (AIDS or non-AIDS) among HIV-infected patients. We compared two alternative subdistribution hazard flexible parametric models (SDHFPM1 and SDHFPM2) with the Fine & Gray model. To make a complete inference, we performed cause-specific hazard flexible parametric models for each event separately as well.Results: Both SDHFPM1 and SDHFPM2 provided consistent results regarding the magnitude of coefficients and risk estimations compared with estimations obtained from the Fine & Gray model, However, competing risks flexible parametric models provided more efficient and smoother estimations for the baseline risks of the first disease. We found that age at HIV diagnosis indirectly affected the risk of AIDS as the first event by increasing the number of patients who experience a non-AIDS disease prior to AIDS among > 40 years. Other significant covariates had direct effects on the risks of AIDS and non-AIDS.Discussion: The choice of an appropriate model depends on the research goals and computational challenges. The SDHFPM1 models each event separately and requires calculating censoring weights which is time-consuming. In contrast, SDHFPM2 models all events simultaneously and is more appropriate for large datasets, however, when the focus is on one particular event SDHFPM1 is more preferable. [ABSTRACT FROM AUTHOR]

Background: Researchers and clinicians use text messages to collect data with the advantage of real time capture when compared with standard data collection methods. This article reviews project setup and management for successfully collecting patient-reported data through text messages.Methods: We review our experience enrolling over 2600 participants in six clinical trials that used text messages to relay information or collect data. We also reviewed the literature on text messages used for repeated data collection. We classify recommendations according to common themes: the text message, the data submitted and the phone used.Results: We present lessons learned and discuss how to create text message content, select a data collection platform with practical features, manage the data thoughtfully and consistently, and work with patients, participants and their phones to protect privacy. Researchers and clinicians should design text messages to include short, simple prompts and answer choices. They should decide whether and when to send reminders if participants do not respond and set parameters regarding when and how often to contact patients for missing data. Data collection platforms send, receive, and store messages. They can validate responses and send error messages. Researchers should develop a protocol to append and correct data in order to improve consistency with data handling. At the time of enrollment, researchers should ensure that participants can receive and respond to messages. Researchers should address privacy concerns and plan for service interruptions by obtaining alternate participant contact information and providing participants with a backup data collection method.Conclusions: Careful planning and execution can reward clinicians and investigators with complete, timely and accurate data sets. [ABSTRACT FROM AUTHOR]

Background: The story completion method provides a different way of doing qualitative research. We note the emergent popularity of this method in health-related research, while much remains to be negotiated in terms of best practices for such studies. This scoping review aims to provide a synthesis on how researchers have used the story completion method in health services research. We offer implications for research and practice for further discussion by the scholarly community. Methods: We used the JBI methodology for scoping reviews. Six databases were searched for published literature till March 1, 2023: Medline, Embase, CINAHL, PsycINFO, SAGE Journals Online databases, and SAGE Research Methods. We included primary studies of any study design using the story completion method in health services research. Results: A total of 17 studies were included. Findings suggest that the story completion method is useful for research on sensitive topics, and affords the use of comparative study designs and large sample sizes which may be difficult with conventional qualitative research methods. More than 80% of included studies used story completion as the sole method. However, the data collected from this method were limited in terms of the inferences that can be drawn; and richness of participant responses may vary widely. Less than 30% of included studies reported piloting of the story stems. Most studies were conducted online and analyzed qualitatively, though the story stem design and sample size varied widely. Conclusion: The story completion method, with its attendant affordances for larger sample sizes, comparative study designs, and streamlined data collection is an innovative and useful stand-alone or adjunct qualitative method for health services research. [ABSTRACT FROM AUTHOR]

Background: Several papers report deficiencies in the reporting of information about the implementation of interventions in clinical trials. Information about implementation is also required in systematic reviews of complex interventions to facilitate the translation and uptake of evidence of provider-based prevention and treatment programs. To capture whether and how implementation is assessed within systematic effectiveness reviews, we developed a checklist for implementation (Ch-IMP) and piloted it in a cohort of reviews on provider-based prevention and treatment interventions for children and young people. This paper reports on the inter-rater reliability, feasibility and reasons for discrepant ratings. Methods: Checklist domains were informed by a framework for program theory; items within domains were generated from a literature review. The checklist was pilot-tested on a cohort of 27 effectiveness reviews targeting children and youth. Two raters independently extracted information on 47 items. Inter-rater reliability was evaluated using percentage agreement and unweighted kappa coefficients. Reasons for discrepant ratings were content analysed. Results: Kappa coefficients ranged from 0.37 to 1.00 and were not influenced by one-sided bias. Most kappa values were classified as excellent (n = 20) or good (n = 17) with a few items categorised as fair (n = 7) or poor (n = 1). Prevalence-adjusted kappa coefficients indicate good or excellent agreement for all but one item. Four areas contributed to scoring discrepancies: 1) clarity or sufficiency of information provided in the review; 2) information missed in the review; 3) issues encountered with the tool; and 4) issues encountered at the review level. Use of the tool demands time investment and it requires adjustment to improve its feasibility for wider use. Conclusions: The case of provider-based prevention and treatment interventions showed relevancy in developing and piloting the Ch-IMP as a useful tool for assessing the extent to which systematic reviews assess the quality of implementation. The checklist could be used by authors and editors to improve the quality of systematic reviews, and shows promise as a pedagogical tool to facilitate the extraction and reporting of implementation characteristics. [ABSTRACT FROM AUTHOR]

Background: Electronic diaries are increasingly used in diverse disciplines to collect momentary data on experienced feelings, cognitions, behavior and social context in real life situations. Choices to be made for an effective and feasible design are however a challenge. Careful and detailed documentation of argumentation of choosing a particular design, as well as general guidelines on how to design such studies are largely lacking in scientific papers. This qualitative study provides a systematic overview of arguments for choosing a specific diary study design (e.g. time frame) in order to optimize future design decisions.Methods: During the first data assessment round, 47 researchers experienced in diary research from twelve different countries participated. They gave a description of and arguments for choosing their diary design (i.e., study duration, measurement frequency, random or fixed assessment, momentary or retrospective assessment, allowed delay to respond to the beep). During the second round, 38 participants (81%) rated the importance of the different themes identified during the first assessment round for the different diary design topics.Results: The rationales for diary design choices reported during the first round were mostly strongly related to the research question. The rationales were categorized into four overarching themes: nature of the variables, reliability, feasibility, and statistics. During the second round, all overarching themes were considered important for all diary design topics.Conclusions: We conclude that no golden standard for the optimal design of a diary study exists since the design depends heavily upon the research question of the study. The findings of the current study are helpful to explicate and guide the specific choices that have to be made when designing a diary study. [ABSTRACT FROM AUTHOR]

Background Maternal death auditing is widely used to ascertain in-depth information on the clinical, social, cultural, and other contributing factors that result in a maternal death. As the 2015 deadline for Millennium Development Goal 5 of reducing maternal mortality by three quarters between 1990 and 2015 draws near, this information becomes even more critical for informing intensified maternal mortality reduction strategies. Studies using maternal death audit methodologies are widely available, but few discuss the challenges in their implementation. The purpose of this paper is to discuss the methodological issues that arose while conducting maternal death review research in Lilongwe, Malawi. Methods Critical reflections were based on a recently conducted maternal mortality study in Lilongwe, Malawi in which a facility-based maternal death review approach was used. The five-step maternal mortality surveillance cycle provided the framework for discussion. The steps included: 1) identification of cases, 2) data collection, 3) data analysis, 4) recommendations, and 5) evaluation. Results Challenges experienced were related to the first three steps of the surveillance cycle. They included: 1) identification of cases: conflicting maternal death numbers, and missing medical charts, 2) data collection: poor record keeping, poor quality of documentation, difficulties in identifying and locating appropriate healthcare workers for interviews, the potential introduction of bias through the use of an interpreter, and difficulties with locating family and community members and recall bias; and 3) data analysis: determining the causes of death and clinical diagnoses. Conclusion Conducting facility-based maternal death reviews for the purpose of research has several challenges. This paper illustrated that performing such an activity, particularly the data collection phase, was not as easy as conveyed in international guidelines and in published studies. However, these challenges are not insurmountable. If they are anticipated and proper steps are taken in advance, they can be avoided or their effects minimized. [ABSTRACT FROM AUTHOR]

Background: Methodological studies - studies that evaluate the design, analysis or reporting of other research-related reports - play an important role in health research. They help to highlight issues in the conduct of research with the aim of improving health research methodology, and ultimately reducing research waste.Main Body: We provide an overview of some of the key aspects of methodological studies such as what they are, and when, how and why they are done. We adopt a "frequently asked questions" format to facilitate reading this paper and provide multiple examples to help guide researchers interested in conducting methodological studies. Some of the topics addressed include: is it necessary to publish a study protocol? How to select relevant research reports and databases for a methodological study? What approaches to data extraction and statistical analysis should be considered when conducting a methodological study? What are potential threats to validity and is there a way to appraise the quality of methodological studies?Conclusion: Appropriate reflection and application of basic principles of epidemiology and biostatistics are required in the design and analysis of methodological studies. This paper provides an introduction for further discussion about the conduct of methodological studies. [ABSTRACT FROM AUTHOR]

Background: Missing outcome data can lead to bias in the results of systematic reviews. One way to address missing outcome data is by requesting the data from the trial authors, but non-response is common. One way to potentially improve response rates is by sending study participants advance communication. During the update of a systematic review examining the effect of pre-notification on response rates, study authors needed to be contacted for further information. This study was nested within the systematic review by randomising authors to receive a notification of the upcoming request for information. The objective was to test if pre-notification increased response rates.Methods: The participants were study authors included in the systematic review, whose studies were at unclear risk of bias. The intervention was a pre-notification of the request for further information, sent 1 day before the request. The outcome was defined as the proportion of authors who responded to the request for information. Authors were randomised by simple randomisation. Thirty three authors were randomised to the pre-notification arm, and 42 were randomised to the control arm. Authors were blinded to the possibility of an alternative condition.Results: All authors randomised were analysed. 14/33 (42.4%) authors in the pre-notification arm had returned responses to the questionnaire, and 18/42 (42.9%) in the control arm. There was no evidence of a difference between these groups (absolute difference = - 0.5, 95% CI (- 23.4 to 22.5%), p = 1). We received no complaints about receiving the pre-notification.Conclusions: This study's results do not support the hypothesis that pre-notification increases response from study authors being contacted for a request for more information. However, the study has a low power, and the results may not generalise to other contexts, methods of administering a pre-notification, or study populations.Trial Registration: Registration and protocol: This trial is not registered with any trial registry. However, the protocol was posted in advance on the Open Science Framework website and is available on the Open Science Framework website: DOI: https://doi.org/10.17605/OSF.IO/MSV2W or https://osf.io/msv2w/. [ABSTRACT FROM AUTHOR]

Background: The Matchpoint trial aims to identify the optimal dose of ponatinib to give with conventional chemotherapy consisting of fludarabine, cytarabine and idarubicin to chronic myeloid leukaemia patients in blastic transformation phase. The dose should be both tolerable and efficacious. This paper describes our experience implementing EffTox in the Matchpoint trial.Methods: EffTox is a Bayesian adaptive dose-finding trial design that jointly scrutinises binary efficacy and toxicity outcomes. We describe a nomenclature for succinctly describing outcomes in phase I/II dose-finding trials. We use dose-transition pathways, where doses are calculated for each feasible set of outcomes in future cohorts. We introduce the phenomenon of dose ambivalence, where EffTox can recommend different doses after observing the same outcomes. We also describe our experiences with outcome ambiguity, where the categorical evaluation of some primary outcomes is temporarily delayed.Results: We arrived at an EffTox parameterisation that is simulated to perform well over a range of scenarios. In scenarios where dose ambivalence manifested, we were guided by the dose-transition pathways. This technique facilitates planning, and also helped us overcome short-term outcome ambiguity.Conclusions: EffTox is an efficient and powerful design, but not without its challenges. Joint phase I/II clinical trial designs will likely become increasingly important in coming years as we further investigate non-cytotoxic treatments and streamline the drug approval process. We hope this account of the problems we faced and the solutions we used will help others implement this dose-finding clinical trial design.Trial Registration: Matchpoint was added to the European Clinical Trials Database ( https://www.clinicaltrialsregister.eu/ctr-search/trial/2012-005629-65/GB ) on 2013-12-30. [ABSTRACT FROM AUTHOR]

Background: The sample size required to power a study to a nominal level in a paired comparative diagnostic accuracy study, i.e. studies in which the diagnostic accuracy of two testing procedures is compared relative to a gold standard, depends on the conditional dependence between the two tests - the lower the dependence the greater the sample size required. A priori, we usually do not know the dependence between the two tests and thus cannot determine the exact sample size required. One option is to use the implied sample size for the maximal negative dependence, giving the largest possible sample size. However, this is potentially wasteful of resources and unnecessarily burdensome on study participants as the study is likely to be overpowered. A more accurate estimate of the sample size can be determined at a planned interim analysis point where the sample size is re-estimated.Methods: This paper discusses a sample size estimation and re-estimation method based on the maximum likelihood estimates, under an implied multinomial model, of the observed values of conditional dependence between the two tests and, if required, prevalence, at a planned interim. The method is illustrated by comparing the accuracy of two procedures for the detection of pancreatic cancer, one procedure using the standard battery of tests, and the other using the standard battery with the addition of a PET/CT scan all relative to the gold standard of a cell biopsy. Simulation of the proposed method illustrates its robustness under various conditions.Results: The results show that the type I error rate of the overall experiment is stable using our suggested method and that the type II error rate is close to or above nominal. Furthermore, the instances in which the type II error rate is above nominal are in the situations where the lowest sample size is required, meaning a lower impact on the actual number of participants recruited.Conclusion: We recommend multinomial model maximum likelihood estimation of the conditional dependence between paired diagnostic accuracy tests at an interim to reduce the number of participants required to power the study to at least the nominal level.Trial Registration: ISRCTN ISRCTN73852054 . Registered 9th of January 2015. Retrospectively registered. [ABSTRACT FROM AUTHOR]

Background: Relative survival analysis is a subfield of survival analysis where competing risks data are observed, but the causes of death are unknown. A first step in the analysis of such data is usually the estimation of a net survival curve, possibly followed by regression modelling. Recently, a log-rank type test for comparison of net survival curves has been introduced and the goal of this paper is to explore its properties and put this methodological advance into the context of the field.Methods: We build on the association between the log-rank test and the univariate or stratified Cox model and show the analogy in the relative survival setting. We study the properties of the methods using both the theoretical arguments as well as simulations. We provide an R function to enable practical usage of the log-rank type test.Results: Both the log-rank type test and its model alternatives perform satisfactory under the null, even if the correlation between their p-values is rather low, implying that both approaches cannot be used simultaneously. The stratified version has a higher power in case of non-homogeneous hazards, but also carries a different interpretation.Conclusions: The log-rank type test and its stratified version can be interpreted in the same way as the results of an analogous semi-parametric additive regression model despite the fact that no direct theoretical link can be established between the test statistics. [ABSTRACT FROM AUTHOR]

Background: When developing a prediction model for survival data it is essential to validate its performance in external validation settings using appropriate performance measures. Although a number of such measures have been proposed, there is only limited guidance regarding their use in the context of model validation. This paper reviewed and evaluated a wide range of performance measures to provide some guidelines for their use in practice.Methods: An extensive simulation study based on two clinical datasets was conducted to investigate the performance of the measures in external validation settings. Measures were selected from categories that assess the overall performance, discrimination and calibration of a survival prediction model. Some of these have been modified to allow their use with validation data, and a case study is provided to describe how these measures can be estimated in practice. The measures were evaluated with respect to their robustness to censoring and ease of interpretation. All measures are implemented, or are straightforward to implement, in statistical software.Results: Most of the performance measures were reasonably robust to moderate levels of censoring. One exception was Harrell's concordance measure which tended to increase as censoring increased.Conclusions: We recommend that Uno's concordance measure is used to quantify concordance when there are moderate levels of censoring. Alternatively, Gönen and Heller's measure could be considered, especially if censoring is very high, but we suggest that the prediction model is re-calibrated first. We also recommend that Royston's D is routinely reported to assess discrimination since it has an appealing interpretation. The calibration slope is useful for both internal and external validation settings and recommended to report routinely. Our recommendation would be to use any of the predictive accuracy measures and provide the corresponding predictive accuracy curves. In addition, we recommend to investigate the characteristics of the validation data such as the level of censoring and the distribution of the prognostic index derived in the validation setting before choosing the performance measures. [ABSTRACT FROM AUTHOR]

Background: Proliferation of terms describing the science of effectively promoting and supporting the use of research evidence in healthcare policy and practice has hampered understanding and development of the field. To address this, an international Terminology Working Group developed and published a simplified framework of interventions to promote and integrate evidence into health practices, systems, and policies. This paper presents results of validation work and a second international workgroup meeting, culminating in the updated AIMD framework [Aims, Ingredients, Mechanism, Delivery].Methods: Framework validity was evaluated against terminology schemas (n = 51); primary studies (n = 37); and reporting guidelines (n = 10). Framework components were independently categorized as fully represented, partly represented, or absent by two researchers. Opportunities to refine the framework were systematically recorded. A meeting of the expanded international Terminology Working Group updated the framework by reviewing and deliberating upon validation findings and refinement proposals.Results: There was variation in representativeness of the components across the three types of literature, in particular for the component 'causal mechanisms'. Analysis of primary studies revealed that representativeness of this concept lowered from 92 to 68% if only explicit, rather than explicit and non-explicit references to causal mechanisms were included. All components were very well represented in reporting guidelines, however the level of description of these was lower than in other types of literature. Twelve opportunities were identified to improve the framework, 9 of which were operationalized at the meeting. The updated AIMD framework comprises four components: (1) Aims: what do you want your intervention to achieve and for whom? (2) Ingredients: what comprises the intervention? (3) Mechanisms: how do you propose the intervention will work? and (4) Delivery: how will you deliver the intervention?Conclusions: The draft simplified framework was validated with reference to a wide range of relevant literature and improvements have enhanced useability. The AIMD framework could aid in the promotion of evidence into practice, remove barriers to understanding how interventions work, enhance communication of interventions and support knowledge synthesis. Future work needs to focus on developing and testing resources and educational initiatives to optimize use of the AIMD framework in collaboration with relevant end-user groups. [ABSTRACT FROM AUTHOR]

Background: While web conferencing technologies are being widely used in communication and collaboration, their uptake in conducting research field work has been relatively slow. The benefits that these technologies offer researchers for engaging with hard-to-reach populations are beginning to be recognised, however, the acceptability and feasibility of using web conferencing technology to engage Aboriginal and Torres Strait Islander young people in research is unknown.Objective: This study aims to evaluate whether the use of web conferencing to engage Aboriginal and Torres Strait Islander young people in research is an acceptable and feasible alternative to conventional face-to-face methods.Methods: Aboriginal and Torres Strait Islander young people aged between 18 and 24 years were recruited via emails, flyers and snowballing to participate in an Online Yarning Circle (OYC) about wellbeing conducted via web conferencing. Five young Aboriginal and Torres Strait Islander Australians were trained as peer facilitators and each conducted one or more OYCs with support from an experienced Aboriginal and Torres Strait Islander researcher. The OYCs were recorded and the researchers conducted post-OYC interviews with the facilitators. OYC recordings, facilitator interviews and researchers' reflections about the method were analysed to assess acceptability and feasibility for use with this population.Results: 11 OYCs were conducted with 21 participants. The evaluation focused on (a) acceptability of the method for participants and facilitators and (b) feasibility of data collection method and procedures for use in research. Our evaluation revealed good acceptability and feasibility of the method, with only minor challenges experienced, which were predominantly logistical in nature and related to scheduling, obtaining documentation of consent, and technical issues. These challenges were offset by the greater control over the level of engagement that was comfortable for individual participants and the greater ease with which they felt they could withdraw from participating. This shift in the traditional researcher-participant power dynamic was recognised by both participants and peer facilitators and was regarded as a support for Aboriginal and Torres Strait Islander young people's participation in research.Conclusions: The use of web conferencing to engage Aboriginal and Torres Strait Islander young people in research offers an acceptable and feasible alternative to face-to-face research methods. The benefits conferred by these technologies associated with yielding greater control and power to the research participant has broad relevance to research with marginalised populations. [ABSTRACT FROM AUTHOR]

Background: When patients are admitted to an intensive care unit (ICU) their risk of getting an infection will be highly depend on the length of stay at-risk in the ICU. In addition, risk of infection is likely to vary over calendar time as a result of fluctuations in the prevalence of the pathogen on the ward. Hence risk of infection is expected to depend on two time scales (time in ICU and calendar time) as well as competing events (discharge or death) and their spatial location. The purpose of this paper is to develop and apply appropriate statistical models for the risk of ICU-acquired infection accounting for multiple time scales, competing risks and the spatial clustering of the data.Methods: A multi-center data base from a Spanish surveillance network was used to study the occurrence of an infection due to Methicillin-resistant Staphylococcus aureus (MRSA). The analysis included 84,843 patient admissions between January 2006 and December 2011 from 81 ICUs. Stratified Cox models were used to study multiple time scales while accounting for spatial clustering of the data (patients within ICUs) and for death or discharge as competing events for MRSA infection.Results: Both time scales, time in ICU and calendar time, are highly associated with the MRSA hazard rate and cumulative risk. When using only one basic time scale, the interpretation and magnitude of several patient-individual risk factors differed. Risk factors concerning the severity of illness were more pronounced when using only calendar time. These differences disappeared when using both time scales simultaneously.Conclusions: The time-dependent dynamics of infections is complex and should be studied with models allowing for multiple time scales. For patient individual risk-factors we recommend stratified Cox regression models for competing events with ICU time as the basic time scale and calendar time as a covariate. The inclusion of calendar time and stratification by ICU allow to indirectly account for ICU-level effects such as local outbreaks or prevention interventions. [ABSTRACT FROM AUTHOR]

Background: A systematic review on the use of incentives to promote questionnaire return in clinical trials suggest they are effective, but not all studies have sufficient funds to use them. Promising an incentive once data are returned can reduce the cost-burden of this approach, with possible further cost-savings if the offer were restricted to reminder letters only. This study aimed to evaluate the effect of promising a monetary incentive at first mailout versus a promise on reminder letters only.Methods: This was a randomised Study Within A Trial (SWAT) nested within BUMPES, a multicentre randomised controlled trial of maternal position in the late stage of labour in women with an epidural. The follow-up questionnaire asked for information on the women's health, wellbeing and health service use one year following the birth of their baby. Women who consented to be contacted were randomised to a promise of a monetary incentive at first mailout or a promise on reminder letters only. Women were given an option of completing the questionnaire on paper or on online. The incentive was posted out on receipt of a completed questionnaire. The primary outcome was the overall return rate, and secondary outcomes were the return rate without any chasing from the study office, and the total cost of the vouchers.Results: A total of 1,029 women were randomised, 508 to the first mailout group and 518 to the reminder group. There was no evidence to suggest a difference between groups in the overall return rate (adjusted RR 1.03 (95 % CI 0.96 to 1.11), however the proportion returned without chasing was higher in the first mailout group (adjusted RR 1.22, 95 % CI 1.07 to 1.39). The total cost of the vouchers per participant was higher in the first mailout group (mean difference £4.56, 95 % CI £4.02 to £5.11).Conclusions: Offering a monetary incentive when a reminder is required could be cost-effective depending on the sample size of the study and the resources available to administer the reminder letters.Trial Registration: The BUMPES Trial is registered with Current Controlled Trials: ISRCTN35706297 , 26(th) August 2009. [ABSTRACT FROM AUTHOR]

Background: Recent developments in psychometric modeling and technology allow pooling well-validated items from existing instruments into larger item banks and their deployment through methods of computerized adaptive testing (CAT). Use of item response theory-based bifactor methods and integrative data analysis overcomes barriers in cross-instrument comparison. This paper presents the joint calibration of an item bank for researchers keen to investigate population variations in general psychological distress (GPD).Methods: Multidimensional item response theory was used on existing health survey data from the Scottish Health Education Population Survey (n = 766) to calibrate an item bank consisting of pooled items from the short common mental disorder screen (GHQ-12) and the Affectometer-2 (a measure of "general happiness"). Computer simulation was used to evaluate usefulness and efficacy of its adaptive administration.Results: A bifactor model capturing variation across a continuum of population distress (while controlling for artefacts due to item wording) was supported. The numbers of items for different required reliabilities in adaptive administration demonstrated promising efficacy of the proposed item bank.Conclusions: Psychometric modeling of the common dimension captured by more than one instrument offers the potential of adaptive testing for GPD using individually sequenced combinations of existing survey items. The potential for linking other item sets with alternative candidate measures of positive mental health is discussed since an optimal item bank may require even more items than these. [ABSTRACT FROM AUTHOR]

Background: Electronic administrative data exist in several domains which, if linked, are potentially useful for research. However, benefits from data linkage should be considered alongside risks such as the threat to privacy. Avon Longitudinal Study of Parents and Children (ALSPAC) is a birth cohort study. The Project to Enhance ALSPAC through Record Linkage (PEARL) was established to enrich the ALSPAC resource through linkage between ALSPAC participants and routine sources of health and social data. Qualitative research was incorporated in the PEARL study to examine participants' views about data linkage and inform approaches to information sharing. This paper focusses on issues of consent.Methods: Digitally recorded interviews were conducted with 55 participants aged 17-19 years. Terms and processes relating to consent, anonymization and data linkage were explained to interviewees. Scenarios were used to prompt consideration of linking different sources of data, and whether consent should be requested. Interview recordings were fully transcribed. Thematic analysis was undertaken using the Framework approach.Results: Participant views on data linkage appeared to be most influenced by: considerations around the social sensitivity of the research question, and; the possibility of tangible health benefits in the public interest. Some participants appeared unsure about the effectiveness of anonymization, or did not always view effective anonymization as making consent unnecessary. This was related to notions of ownership of personal information and etiquette around asking permission for secondary use. Despite different consent procedures being explained, participants tended to equate consent with 'opt-in' consent through which participants are 'asked' if their data can be used for a specific study. Participants raising similar concerns came to differing conclusions about whether consent was needed. Views changed when presented with different scenarios, and were sometimes inconsistent.Conclusions: Findings from this study question the validity of 'informed consent' as a cornerstone of good governance, and the extent to which potential research participants understand different types of consent and what they are consenting, or not consenting, to. Pragmatic, imaginative and flexible approaches are needed if research using data linkage is to successfully realise its potential for public good without undermining public trust in the research process. [ABSTRACT FROM AUTHOR]

Background: Although some nonparametric methods have been proposed in the literature to test for the equality of median survival times for censored data in medical research, in general they have inflated type I error rates, which make their use limited in practice, especially when the sample sizes are small.Methods: In this paper, we propose a new nonparametric test with a simple test statistic to compare median survival times.Results: The results from our comprehensive simulation study show the new test controls type I error rate very well under all situations considered, even for small sample sizes. In addition, it has comparable detecting power compared to existing methods. Another advantage of the proposed test is its relatively simple formula that requires less computation.Conclusions: We propose a new statistical method for comparing survival curves based on their medians. The new method can be easily implemented and applied to censored event time data. [ABSTRACT FROM AUTHOR]

Background: This paper explores the importance of electronic medical records (EMR) for predicting 30-day all-cause non-elective readmission risk of patients and presents a comparison of prediction performance of commonly used methods.Methods: The data are extracted from eight Advocate Health Care hospitals. Index admissions are excluded from the cohort if they are observation, inpatient admissions for psychiatry, skilled nursing, hospice, rehabilitation, maternal and newborn visits, or if the patient expires during the index admission. Data are randomly and repeatedly divided into fitting and validating sets for cross validations. Approaches including LACE, STEPWISE logistic, LASSO logistic, and AdaBoost, are compared with sample sizes varying from 2,500 to 80,000.Results: Our results confirm that LACE has moderate discrimination power with the area under receiver operating characteristic curve (AUC) around 0.65-0.66, which can be improved to 0.73-0.74 when additional variables from EMR are considered. These variables include Inpatient in the last six months, Number of emergency room visits or inpatients in the last year, Braden score, Polypharmacy, Employment status, Discharge disposition, Albumin level, and medical condition variables such as Leukemia, Malignancy, Renal failure with hemodialysis, History of alcohol substance abuse, Dementia and Trauma. When sample size is small (≤5000), LASSO is the best; when sample size is large (≥20,000), the predictive performance is similar. The STEPWISE method has a slightly lower AUC (0.734) comparing to LASSO (0.737) and AdaBoost (0.737). More than one half of the selected predictors can be false positives when using a single method and a single division of fitting/validating data.Conclusions: True predictors can be identified by repeatedly dividing data into fitting/validating subsets and referring the final model based on summarizing results. LASSO is a better alternative to the STEPWISE logistic regression, especially when sample size is not large. The evidence for adequate sample size can be explored by fitting models on gradually reduced samples. Our model comparison strategy is not only good for 30-day all-cause non-elective readmission risk predictions, but also applicable to other types of predictive models in clinical studies. [ABSTRACT FROM AUTHOR]

Background: As a part of a larger Health Technology Assessment (HTA), the measurement error of a device used to monitor the hemoglobin concentration of a patient undergoing surgery, as well as its decision consequences, were to be estimated from published data.Methods: A Bayesian hierarchical model of measurement error, allowing the meta-analytic estimation of both central and dispersion parameters (under the assumption of normality of measurement errors) is proposed and applied to published data; the resulting potential decision errors are deduced from this estimation. The same method is used to assess the impact of an initial calibration.Results: The posterior distributions are summarized as mean ± sd (credible interval). The fitted model exhibits a modest mean expected error (0.24 ± 0.73 (-1.23 1.59) g/dL) and a large variability (mean absolute expected error 1.18 ± 0.92 (0.05 3.36) g/dL). The initial calibration modifies the bias (-0.20 ± 0.87 (-1.99 1.49) g/dL), but the variability remains almost as large (mean absolute expected error 1.05 ± 0.87 (0.04 3.21) g/dL). This entails a potential decision error ("false positive" or "false negative") for about one patient out of seven.Conclusions: The proposed hierarchical model allows the estimation of the variability from published aggregates, and allows the modeling of the consequences of this variability in terms of decision errors. For the device under assessment, these potential decision errors are clinically problematic. [ABSTRACT FROM AUTHOR]

Background: There are inherent methodological challenges in the measurement of mental health problems in longitudinal research. There is constant development in definitions, taxonomies and demands concerning the properties of mental health measurements. The aim of this paper was to construct composite measures of mental health problems (according to today's standard) from single questionnaire items devised in the early 1980s, and to evaluate their internal consistency and factorial invariance across the life course using the Northern Swedish Cohort.Methods: All pupils in the last year of compulsory school in Luleå in 1981 (n = 1083) form a prospective cohort study where the participants have been followed with questionnaires from the age of 16 (in 1981) until the age of 43 (in 2008). We created and tested the following composite measures from self-reports at each follow-up: depressive symptoms, anxiety symptoms, functional somatic symptoms, modified GHQ and positive health. Validity and internal consistency were tested by confirmatory factor analysis, including tests of factorial invariance over time.Results: As an overall assessment, the results showed that the composite measures (based on more than 30-year-old single item questions) are likely to have acceptable factorial invariance as well as internal consistency over time.Conclusions: Testing the properties of the mental health measures used in older studies according to the standards of today is of great importance in longitudinal research. Our study demonstrates that composite measures of mental health problems can be constructed from single items which are more than 30 years old and that these measures seem to have the same factorial structure and internal consistency across a significant part of the life course. Thus, it can be possible to overcome some specific inherent methodological challenges in using historical data in longitudinal research. [ABSTRACT FROM AUTHOR]

Background: Surveys are commonly used in health research to assess patient satisfaction with hospital care. Achieving an adequate response rate, in the face of declining trends over time, threatens the quality and reliability of survey results. This paper evaluates a strategy to increase the response rate in a postal satisfaction survey with women who had recently given birth.Methods: A sample of 2048 Australian women who had recently given birth at seven maternity units in New South Wales were invited to participate in a postal survey about their recent experiences with maternity care. The study design included a randomised controlled trial that tested two types of pre-notification letter (with or without the option of opting out of the survey). The study also explored the acceptability of a request for consent to link survey data with existing routinely collected health data (omitting the latter data items from the survey reduced survey length and participant burden). This consent was requested of all women.Results: The survey had an overall response rate of 46% (913 completed surveys returned, total sample 1989). Women receiving the pre-notification letter with the option of opting out of the survey were more likely to actively decline to participate than women receiving the letter without this option, although the overall numbers of women declining were small (27 versus 12). Letter type was not significantly associated with the return of a completed survey. Among women who completed the survey, 97% gave consent to link their survey data with existing health data.Conclusions: The two types of pre-notification letters used in our study did not influence the survey response rate. However, seeking consent for record linkage was highly acceptable to women who completed the survey, and represents an important strategy to add to the arsenal for designing and implementing effective surveys. In addition to aspects of survey design, future research should explore how to more effectively influence personal constructs that contribute to the decision to participate in surveys. [ABSTRACT FROM AUTHOR]

Background: Using patient-reported outcome measures (PROMs) to assess Quality of Life (QoL) is well established, but commonly-used PROM item-sets do not necessarily capture what all respondents consider important. Measuring complex constructs is particularly difficult in randomised controlled trials (RCTs). The Mother-Generated Index (MGI) is a validated antenatal and postnatal QoL instrument in which the variables and scores are completely respondent-driven. This paper reports on the feasibility and acceptability of the MGI in an RCT, and compares the resulting variables and QoL scores with more commonly used instruments.Methods: The single-page MGI was included at the end of a ten page questionnaire pack and posted to the RCT participants at baseline (28-32 weeks' gestation) and follow-up (six weeks postnatal). Feasibility and acceptability were assessed by ease of administration, data entry and completion rates. Variables cited by women were analysed thematically. MGI QoL scores were compared with outcomes from the EQ-5D-3 L; Edinburgh Postnatal Depression Scale; Satisfaction With Life Scale; and State Trait Anxiety Inventory.Results: Six hundred and seventy eight pregnant women returned the pack at baseline; 668 completed the MGI (98.5%); 383/400 returns at follow up included a completed MGI (95.7%). Quantitative data were scanned into SPSS using a standard data scanning system, and were largely error-free; qualitative data were entered manually. The variables recorded by participants on the MGI forms incorporated many of those in the comparison instruments, and other outcomes commonly used in intrapartum trials, but they also revealed a wider range of issues affecting their quality of life. These included financial and work-related worries; moving house; and concerns over family illness and pets. The MGI scores demonstrated low-to-moderate correlation with other tools (all r values p < .01).Conclusions: Without face-to-face explanation and at the end of a long questionnaire, the MGI was feasible to use, and acceptable to RCT participants. It allowed individual participants to include issues that were important to them, but which are not well captured by existing tools. The MGI unites the explanatory power of qualitative research with the comparative power of quantitative designs, is inexpensive to administer, and requires minimal linguistic and conceptual translation.Trial Registration: ISRCTN27575146 (date assigned 23 March 2011). [ABSTRACT FROM AUTHOR]