Sample size calculation for comparing two ROC curves.

Biomarkers are key components of personalized medicine. In this paper, we consider biomarkers taking continuous values that are associated with disease status, called case and control. The performance of such a biomarker is evaluated by the area under the curve (AUC) of its receiver operating characteristic curve. Oftentimes, two biomarkers are collected from each subject to test if one has a larger AUC than the other. We propose a simple non-parametric statistical test for comparing the performance of two biomarkers. We also present a simple sample size calculation method for this test statistic. Our sample size formula requires specification of AUC values (or the standardized effect size of each biomarker between cases and controls together with the correlation coefficient between two biomarkers), prevalence of cases in the study population, type I error rate, and power. Through simulations, we show that the testing on two biomarkers controls type I error rate accurately and the proposed sample size closely maintains specified statistical power.

Sampling weighting strategies in causal mediation analysis.

BACKGROUND: Causal mediation analysis plays a crucial role in examining causal effects and causal mechanisms. Yet, limited work has taken into consideration the use of sampling weights in causal mediation analysis. In this study, we compared different strategies of incorporating sampling weights into causal mediation analysis. METHODS: We conducted a simulation study to assess 4 different sampling weighting strategies-1) not using sampling weights, 2) incorporating sampling weights into mediation "cross-world" weights, 3) using sampling weights when estimating the outcome model, and 4) using sampling weights in both stages. We generated 8 simulated population scenarios comprising an exposure (A), an outcome (Y), a mediator (M), and six covariates (C), all of which were binary. The data were generated so that the true model of A given C and the true model of A given M and C were both logit models. We crossed these 8 population scenarios with 4 different sampling methods to obtain 32 total simulation conditions. For each simulation condition, we assessed the performance of 4 sampling weighting strategies when calculating sample-based estimates of the total, direct, and indirect effects. We also applied the four sampling weighting strategies to a case study using data from the National Survey on Drug Use and Health (NSDUH). RESULTS: Using sampling weights in both stages (mediation weight estimation and outcome models) had the lowest bias under most simulation conditions examined. Using sampling weights in only one stage led to greater bias for multiple simulation conditions. DISCUSSION: Using sampling weights in both stages is an effective approach to reduce bias in causal mediation analyses under a variety of conditions regarding the structure of the population data and sampling methods.

Evolving Improved Sampling Protocols for Dose-Response Modelling Using Genetic Algorithms with a Profile-Likelihood Metric.

Practical limitations of quality and quantity of data can limit the precision of parameter identification in mathematical models. Model-based experimental design approaches have been developed to minimise parameter uncertainty, but the majority of these approaches have relied on first-order approximations of model sensitivity at a local point in parameter space. Practical identifiability approaches such as profile-likelihood have shown potential for quantifying parameter uncertainty beyond linear approximations. This research presents a genetic algorithm approach to optimise sample timing across various parameterisations of a demonstrative PK-PD model with the goal of aiding experimental design. The optimisation relies on a chosen metric of parameter uncertainty that is based on the profile-likelihood method. Additionally, the approach considers cases where multiple parameter scenarios may require simultaneous optimisation. The genetic algorithm approach was able to locate near-optimal sampling protocols for a wide range of sample number (n = 3-20), and it reduced the parameter variance metric by 33-37% on average. The profile-likelihood metric also correlated well with an existing Monte Carlo-based metric (with a worst-case r > 0.89), while reducing computational cost by an order of magnitude. The combination of the new profile-likelihood metric and the genetic algorithm demonstrate the feasibility of considering the nonlinear nature of models in optimal experimental design at a reasonable computational cost. The outputs of such a process could allow for experimenters to either improve parameter certainty given a fixed number of samples, or reduce sample quantity while retaining the same level of parameter certainty.

How to Use and Report on p-values.

The use of the p-value in quantitative research, particularly its threshold of "P < 0.05" for determining "statistical significance," has long been a cornerstone of statistical analysis in research. However, this standard has been increasingly scrutinized for its potential to mislead findings, especially when the practical significance, the number of comparisons, or the suitability of statistical tests are not properly considered. In response to controversy around use of p-values, the American Statistical Association published a statement in 2016 that challenged the research community to abandon the term "statistically significant". This stance has been echoed by leading scientific journals to urge a significant reduction or complete elimination in the reliance on p-values when reporting results. To provide guidance to researchers in health professions education, this paper provides a succinct overview of the ongoing debate regarding the use of p-values and the definition of p-values. It reflects on the controversy by highlighting the common pitfalls associated with p-value interpretation and usage, such as misinterpretation, overemphasis, and false dichotomization between "significant" and "non-significant" results. This paper also outlines specific recommendations for the effective use of p-values in statistical reporting including the importance of reporting effect sizes, confidence intervals, the null hypothesis, and conducting sensitivity analyses for appropriate interpretation. These considerations aim to guide researchers toward a more nuanced and informative use of p-values.

A brief overview of pilot studies and their sample size justification.

Pilot studies, when properly designed and implemented, are an important tool that provide critical information for the development and potential success of a subsequent, larger trial. In fact, these small-scale studies are commonly used to assess the feasibility of whether a larger trial should be initiated. A popular investigator question is whether a pilot study requires a formal statistical power calculation. In general, the answer is "no"; however, the sample size needs to be justified on the basis of the goal of the pilot study.

Cautionary note on regional consistency evaluation in multiregional clinical trials with binary outcomes.

Multiregional clinical trials (MRCTs) have become increasingly common during the development of new drugs to obtain simultaneous drug approvals worldwide. When planning MRCTs, a major statistical challenge is determination of the regional sample size. In general, the regional sample size must be determined as the sample size such that the regional consistency probability, defined as the probability of meeting the regional consistency criterion, is greater than a prespecified value. The Japanese Ministry of Health, Labour and Welfare proposed two criteria for regional consistency. Moreover, many researchers have proposed corresponding closed-form formulas for calculating regional consistency probabilities when the primary outcome is continuous. Although some researchers have argued that those formulas are also applicable to cases with binary outcomes, it remains questionable whether such an argument can be true. Based on simulation results, we demonstrate that the existing formulas are inappropriate for binary cases, even when the regional sample size is sufficiently large. To address this issue, we develop alternative formulas and use simulation to show that they provide accurate regional consistency probabilities. Furthermore, we present an application of our proposed formulas for an MRCT of advanced or metastatic clear-cell renal cell carcinoma.

Sample size calculation for mixture model based on geometric average hazard ratio and its applications to nonproportional hazard.

With the advent of cancer immunotherapy, some special features including delayed treatment effect, cure rate, diminishing treatment effect and crossing survival are often observed in survival analysis. They violate the proportional hazard model assumption and pose a unique challenge for the conventional trial design and analysis strategies. Many methods like cure rate model have been developed based on mixture model to incorporate some of these features. In this work, we extend the mixture model to deal with multiple non-proportional patterns and develop its geometric average hazard ratio (gAHR) to quantify the treatment effect. We further derive a sample size and power formula based on the non-centrality parameter of the log-rank test and conduct a thorough analysis of the impact of each parameter on performance. Simulation studies showed a clear advantage of our new method over the proportional hazard based calculation across different non-proportional hazard scenarios. Moreover, the mixture modeling of two real trials demonstrates how to use the prior information on the survival distribution among patients with different biomarker and early efficacy results in practice. By comparison with a simulation-based design, the new method provided a more efficient way to compute the power and sample size with high accuracy of estimation. Overall, both theoretical derivation and empirical studies demonstrate the promise of the proposed method in powering future innovative trial designs.

Sesgos en investigación quirúrgica / Biases in surgical research

Resumen Los resultados de diversos hallazgos de investigación han sido objeto de crítica, en especial en los últimos años, debido a presencia de errores sistemáticos (sesgos), los que ponen en duda la validez interna de los resultados obtenidos. Estos sesgos pueden ocurrir en cualquier etapa del curso de una investigación, es decir, desde la planificación del estudio hasta la presentación y publicación de sus resultados. Los sesgos se han clasificado de diferentes formas, intentado agruparlos bajo dimensiones conceptuales, objeto de organizar de mejor forma la información existente, que además es considerable. Los sesgos pueden ocurrir por diversos motivos, pero en general, los más frecuentes son aquellos originados por el observador (él o los que miden), por lo que es observado (sujeto en estudio); y aquello con lo que se observa (instrumento de medición). Por otra parte, varios de los múltiples sesgos existentes, se pueden agrupar en: sesgos de selección, de medición o información, y de confusión. El objetivo de este manuscrito fue comentar la importancia de los sesgos más comunes en la investigación quirúrgica, y su relación con algunos diseños de investigación; así como, conocer las estrategias existentes para minimizar su ocurrencia.

The results of many research findings have come under scrutiny in recent years due to the introduction of systematic errors (biases), which can occur at any stage during an investigation, from planning to presentation of results and their presentation and further publication. Biases have been classified in different ways, trying to group them under conceptual dimensions to better organize the existing information, which is considerable. Biases can occur for various reasons, but in general, the most frequent are those originated by the observer, what is observed; and what is observed with. I.e., the subject that is measured, who measures it and with what it measures it. On the other hand, several of the multiple biases can be grouped into selection, measurement or information, and confounding biases. The aim of this manuscript was to comment on the importance of the most common biases in surgical research, and their relationship with some research designs; as well as know the existing strategies to reduce its occurrence.

Cancer patient survival can be parametrized to improve trial precision and reveal time-dependent therapeutic effects.

Individual participant data (IPD) from oncology clinical trials is invaluable for identifying factors that influence trial success and failure, improving trial design and interpretation, and comparing pre-clinical studies to clinical outcomes. However, the IPD used to generate published survival curves are not generally publicly available. We impute survival IPD from ~500 arms of Phase 3 oncology trials (representing ~220,000 events) and find that they are well fit by a two-parameter Weibull distribution. Use of Weibull functions with overall survival significantly increases the precision of small arms typical of early phase trials: analysis of a 50-patient trial arm using parametric forms is as precise as traditional, non-parametric analysis of a 90-patient arm. We also show that frequent deviations from the Cox proportional hazards assumption, particularly in trials of immune checkpoint inhibitors, arise from time-dependent therapeutic effects. Trial duration therefore has an underappreciated impact on the likelihood of success.

Hospital Characteristics and Early Enrollment Trends in the American College of Cardiology Voluntary Public Reporting Program.

Importance: Limited data exist regarding the characteristics of hospitals that do and do not participate in voluntary public reporting programs. Objective: To describe hospital characteristics and trends associated with early participation in the American College of Cardiology (ACC) voluntary reporting program for cardiac catheterization-percutaneous coronary intervention (CathPCI) and implantable cardioverter-defibrillator (ICD) registries. Design, Setting, and Participants: This cross-sectional study analyzed enrollment trends and characteristics of hospitals that did and did not participate in the ACC voluntary public reporting program. All hospitals reporting procedure data to the National Cardiovascular Data Registry (NCDR) CathPCI or ICD registries that were eligible for the public reporting program from July 2014 (ie, program launch date) to May 2017 were included. Stepwise logistic regression was used to identify hospital characteristics associated with voluntary participation. Enrollment trends were evaluated considering the date US News & World Report (USNWR) announced that it would credit participating hospitals. Data analysis was performed from March 2017 to January 2018. Main Outcomes and Measures: Hospital characteristics and participation in the public reporting program. Results: By May 2017, 561 of 1747 eligible hospitals (32.1%) had opted to participate in the program. Enrollment increased from 240 to 376 hospitals (56.7%) 1 month after the USNWR announcement that program participation would be considered as a component of national hospital rankings. Compared with hospitals that did not enroll, program participants had increased median (IQR) procedural volumes for PCI (481 [280-764] procedures vs 332 [186-569] procedures; P < .001) and ICD (114 [56-220] procedures vs 62 [25-124] procedures; P < .001). Compared with nonparticipating hospitals, an increased mean (SD) proportion of participating hospitals adhered to composite discharge medications after PCI (0.96 [0.03] vs 0.92 [0.07]; P < .001) and ICD (0.88 [0.10] vs 0.81 [0.12]; P < .001). Hospital factors associated with enrollment included participation in 5 or more NCDR registries (odds ratio [OR],1.98; 95% CI, 1.24-3.19; P = .005), membership in a larger hospital system (ie, 3-20 hospitals vs ≤2 hospitals in the system: OR, 2.29; 95% CI, 1.65-3.17; P = .001), participation in an NCDR pilot public reporting program of PCI 30-day readmissions (OR, 2.93; 95% CI, 2.19-3.91; P < .001), university affiliation (vs government affiliation: OR, 3.85, 95% CI, 1.03-14.29; P = .045; vs private affiliation: OR, 2.22; 95% CI, 1.35-3.57; P < .001), Midwest location (vs South: OR, 1.47; 95% CI, 1.06-2.08; P = .02), and increased comprehensive quality ranking (4 vs 1-2 performance stars in CathPCI: OR, 8.08; 95% CI, 5.07-12.87; P < .001; 4 vs 1 performance star in ICD: OR, 2.26; 95% CI, 1.48-3.44; P < .001) (C statistic = 0.829). Conclusions and Relevance: This study found that one-third of eligible hospitals participated in the ACC voluntary public reporting program and that enrollment increased after the announcement that program participation would be considered by USNWR for hospital rankings. Several hospital characteristics, experience with public reporting, and quality of care were associated with increased odds of participation.

Reporting bias in clinical trials: Progress toward transparency and next steps.

Mayookha Mitra-Majumdar and Aaron Kesselheim reflect on steps taken to combat reporting bias in clinical trials over the last two decades.

How informative were early SARS-CoV-2 treatment and prevention trials? a longitudinal cohort analysis of trials registered on ClinicalTrials.gov.

BACKGROUND: Early in the SARS-CoV-2 pandemic, commentators warned that some COVID trials were inadequately conceived, designed and reported. Here, we retrospectively assess the prevalence of informative COVID trials launched in the first 6 months of the pandemic. METHODS: Based on prespecified eligibility criteria, we created a cohort of Phase 1/2, Phase 2, Phase 2/3 and Phase 3 SARS-CoV-2 treatment and prevention efficacy trials that were initiated from 2020-01-01 to 2020-06-30 using ClinicalTrials.gov registration records. We excluded trials evaluating behavioural interventions and natural products, which are not regulated by the U.S. Food and Drug Administration (FDA). We evaluated trials on 3 criteria of informativeness: potential redundancy (comparing trial phase, type, patient-participant characteristics, treatment regimen, comparator arms and primary outcome), trials design (according to the recommendations set-out in the May 2020 FDA guidance document on SARS-CoV-2 treatment and prevention trials) and feasibility of patient-participant recruitment (based on timeliness and success of recruitment). RESULTS: We included all 500 eligible trials in our cohort, 58% of which were Phase 2 and 84.8% were directed towards the treatment of SARS-CoV-2. Close to one third of trials met all three criteria and were deemed informative (29.9% (95% Confidence Interval 23.7-36.9)). The proportion of potentially redundant trials in our cohort was 4.1%. Over half of the trials in our cohort (56.2%) did not meet our criteria for high quality trial design. The proportion of trials with infeasible patient-participant recruitment was 22.6%. CONCLUSIONS: Less than one third of COVID-19 trials registered on ClinicalTrials.gov during the first six months met all three criteria for informativeness. Shortcomings in trial design, recruitment feasibility and redundancy reflect longstanding weaknesses in the clinical research enterprise that were likely amplified by the exceptional circumstances of a pandemic.

A systematic review and meta-analysis uncovering the relationship between alcohol consumption and sickness absence. When type of design, data, and sickness absence make a difference.

AIM: Earlier research has revealed a strong relationship between alcohol use and sickness absence. The aim of this review was to explore and uncover this relationship by looking at differences in type of design (cross-sectional vs. longitudinal), type of data (self-reported vs. registered data), and type of sickness absence (long-term vs. short term). METHOD: Six databases were searched through June 2020. Observational and experimental studies from 1980 to 2020, in English or Scandinavian languages reporting the results of the association between alcohol consumption and sickness absence among working population were included. Quality assessment, and statistical analysis focusing on differences in the likelihood of sickness absence on subgroup levels were performed on each association, not on each study. Differences in the likelihood of sickness absence were analyzed by means of meta-analysis. PROSPERO registration number: CRD42018112078. RESULTS: Fifty-nine studies (58% longitudinal) including 439,209 employees (min. 43, max. 77,746) from 15 countries were included. Most associations indicating positive and statistically significant results were based on longitudinal data (70%) and confirmed the strong/causal relationship between alcohol use and sickness absence. The meta-analysis included eight studies (ten samples). The increased risk for sickness absence was likely to be found in cross-sectional studies (OR: 8.28, 95% CI: 6.33-10.81), studies using self-reported absence data (OR: 5.16, 95% CI: 3.16-8.45), and those reporting short-term sickness absence (OR: 4.84, 95% CI: 2.73-8.60). CONCLUSION: This review supports, but also challenges earlier evidence on the association between alcohol use and sickness absence. Certain types of design, data, and types of sickness absence may produce large effects. Hence, to investigate the actual association between alcohol and sickness absence, research should produce and review longitudinal designed studies using registry data and do subgroup analyses that cover and explain variability of this association.

NUQUEST-NUtrition QUality Evaluation Strengthening Tools: development of tools for the evaluation of risk of bias in nutrition studies.

BACKGROUND: Dietary exposure assessments are a critical issue in evaluating human nutrition studies; however, nutrition-specific criteria are not consistently included in existing bias assessment tools. OBJECTIVES: Our objective was to develop a set of risk of bias (RoB) tools that integrated nutrition-specific criteria into validated generic assessment tools to address RoB issues, including those specific to dietary exposure assessment. METHODS: The Nutrition QUality Evaluation Strengthening Tools (NUQUEST) development and validation process included 8 steps. The first steps identified 1) a development strategy; 2) generic assessment tools with demonstrated validity; and 3) nutrition-specific appraisal issues. This was followed by 4) generation of nutrition-specific items and 5) development of guidance to aid users of NUQUEST. The final steps used established ratings of selected studies and feedback from independent raters to 6) assess reliability and validity; 7) assess formatting and usability; and 8) finalize NUQUEST. RESULTS: NUQUEST is based on the Scottish Intercollegiate Guidelines Network checklists for randomized controlled trials, cohort studies, and case-control studies. Using a purposive sample of 45 studies representing the 3 study designs, interrater reliability was high (Cohen's κ: 0.73; 95% CI: 0.52, 0.93) across all tools and at least moderate for individual tools (range: 0.57-1.00). The use of a worksheet improved usability and consistency of overall interrater agreement across all study designs (40% without worksheet, 80%-100% with worksheet). When compared to published ratings, NUQUEST ratings for evaluated studies demonstrated high concurrent validity (93% perfect or near-perfect agreement). Where there was disagreement, the nutrition-specific component was a contributing factor in discerning exposure methodological issues. CONCLUSIONS: NUQUEST integrates nutrition-specific criteria with generic criteria from assessment tools with demonstrated reliability and validity. NUQUEST represents a consistent and transparent approach for evaluating RoB issues related to dietary exposure assessment commonly encountered in human nutrition studies.

A simple clinical score to promote and enhance ferroportin disease screening.

BACKGROUND & AIMS: Ferroportin disease is a rare genetic iron overload disorder which may be underdiagnosed, with recent data suggesting it occurs at a higher prevalence than suspected. Costs and the lack of defined criteria to prompt genetic testing preclude large-scale molecular screening. Hence, we aimed to develop a readily available scoring system to promote and enhance ferroportin disease screening. METHODS: Our derivation cohort included probands tested for ferroportin disease from 2008 to 2016 in our rare disease network. Data were prospectively recorded. Univariate and multivariate logistic regression were used to determine significant criteria, and odds ratios were used to build a weighted score. A cut-off value was defined using a ROC curve with a predefined aim of 90% sensitivity. An independent cohort was used for cross validation. RESULTS: Our derivation cohort included 1,306 patients. Mean age was 55±14 years, ferritin 1,351±1,357 µg/L, and liver iron concentration (LIC) 166±77 µmol/g. Pathogenic variants (n = 32) were identified in 71 patients. In multivariate analysis: female sex, younger age, higher ferritin, higher LIC and the absence of hypertension or diabetes were significantly associated with the diagnosis of ferroportin disease (AUROC in whole derivation cohort 0.83 [0.78-0.88]). The weighted score was based on sex, age, the presence of hypertension or diabetes, ferritin level and LIC. An AUROC of 0.83 (0.77-0.88) was obtained in the derivation cohort without missing values. Using 9.5 as a cut-off, sensitivity was 93.6 (91.7-98.3) %, specificity 49.5 (45.5-53.6) %, positive likelihood ratio 1.8 (1.6-2.0) and negative likelihood ratio 0.17 (0.04-0.37). CONCLUSION: We describe a readily available score with simple criteria and good diagnostic performance that could be used to screen patients for ferroportin disease in routine clinical practice. LAY SUMMARY: Increased iron burden associated with metabolic syndrome is a very common condition. Ferroportin disease is a dominant genetic iron overload disorder whose prevalence is higher than initially thought. They can be difficult to distinguish from each other, but the limited availability of genetic testing and the lack of definitive guidelines prevent adequate screening. We herein describe a simple and definitive clinical score to help clinicians decide whether to perform genetic testing.

Guidelines for Designing and Evaluating Feasibility Pilot Studies.

BACKGROUND: Pilot studies test the feasibility of methods and procedures to be used in larger-scale studies. Although numerous articles describe guidelines for the conduct of pilot studies, few have included specific feasibility indicators or strategies for evaluating multiple aspects of feasibility. In addition, using pilot studies to estimate effect sizes to plan sample sizes for subsequent randomized controlled trials has been challenged; however, there has been little consensus on alternative strategies. METHODS: In Section 1, specific indicators (recruitment, retention, intervention fidelity, acceptability, adherence, and engagement) are presented for feasibility assessment of data collection methods and intervention implementation. Section 1 also highlights the importance of examining feasibility when adapting an intervention tested in mainstream populations to a new more diverse group. In Section 2, statistical and design issues are presented, including sample sizes for pilot studies, estimates of minimally important differences, design effects, confidence intervals (CI) and nonparametric statistics. An in-depth treatment of the limits of effect size estimation as well as process variables is presented. Tables showing CI around parameters are provided. With small samples, effect size, completion and adherence rate estimates will have large CI. CONCLUSION: This commentary offers examples of indicators for evaluating feasibility, and of the limits of effect size estimation in pilot studies. As demonstrated, most pilot studies should not be used to estimate effect sizes, provide power calculations for statistical tests or perform exploratory analyses of efficacy. It is hoped that these guidelines will be useful to those planning pilot/feasibility studies before a larger-scale study.

Analysis of spin in vascular surgery randomized controlled trials with nonsignificant outcomes.

OBJECTIVE: Spin is the manipulation of language that distorts the interpretation of objective findings. The purpose of this study is to describe the characteristics of spin found in statistically nonsignificant randomized controlled trials (RCT) comparing carotid endarterectomy with carotid artery stenting for carotid artery stenosis (CS), and endovascular repair with open repair (OR) for abdominal aortic aneurysms (AAA). METHODS: A search of MEDLINE, EMBASE, and the Cochrane Controlled Register of Trials was performed in June 2020 for studies published describing AAA or CS. All phase III RCTs with nonsignificant primary outcomes comparing open repair with endovascular repair or carotid endarterectomy to carotid artery stenting were included. Studies were appraised for the characteristics and severity of spin using a validated tool. Binary logistic regression was performed to assess the association of spin grade to (1) funding source (commercial vs noncommercial) and (2) the publishing journal's impact factor. RESULTS: Thirty-one of 355 articles captured were included for analysis. Spin was identified in 9 abstracts (9/18) and 13 main texts (13/18) of AAA articles and 7 abstracts (7/13) and 10 main texts (10/13) of CS articles. For both AAA and CS articles, spin was most commonly found in the discussion section, with the most commonly used strategy being the interpretation of statistically nonsignificant primary results to show treatment equivalence or rule out adverse treatment effects. Increasing journal impact factor was associated with a statistically significant lower likelihood of spin in the study title or abstract conclusion (ß odds ratio, 0.96; 95% confidence interval, 0.94-0.98; P < .01); no significant association could be found with funding source (ß odds ratio, 1.33; 95% confidence interval, 0.30-5.92; P = .71). CONCLUSIONS: A large proportion of statistically nonsignificant RCTs contain interpretations that are inconsistent with their results. These findings should prompt authors and readers to appraise study findings independently and to limit the use of spin in study interpretations.

Panorama de la investigación en fonoaudiología: tendencias en Colombia / Overview of research in speech therapy: trends in Colombia

El objetivo de este artículo es analizar el panorama investigativo disciplinar desde la producción académica nacional, para identificar poblaciones, áreas, enfoques de investigación, de esta manera reconocer necesidades y retos para la investigación formativa de la fonoaudiología en Colombia. Se analizó información proveniente de proyectos de investigación de pregrado y posgrado de 13 programas de formación en Fonoaudiología del país, entre 2010 y 2019, reportados por las instituciones de educación superior. Se recolectó información de informes y documentos de investigación (N=638). En primer lugar, se observa un incremento en la producción investigativa entre 2010 (4,9%) y 2019 (18,3%). El 85% de la investigación se identificó en pregrado y el 15% en postgrado. El enfoque predominante fue cualitativo (55,3%), el que, junto con el alcance descriptivo (77,8%) que representan la mayoría de productos de investigación. El escenario relacionado con el ámbito de salud clínico asistencial lidera la investigación (52,9%). Las áreas predominantes en este escenario investigativo son Lenguaje (29,1%) y Audición (27,4%) en poblaciones de adultos (56,7%) e infantil (28,52%). Se concluye que la producción investigativa desde la academia, específicamente desde los programas de formación de fonoaudiólogos en Colombia, evidencian la necesidad de fomentar la investigación de tipo experimental y analítica, con poblaciones de neonatos y lactantes, en áreas relacionadas con la función oralfaríngea y en escenarios inéditos. Además, es necesario incluir el uso de tecnologías y nuevos enfoques de intervención para aportar al abordaje basado en la evidencia. Futuras investigaciones deben permitir comparar la investigación antes y después de la pandemia, dada la virtualización y la inclusión de la fonoaudiología en escenarios como las unidades de cuidado crítico y cuidado paliativo.

The objective of this article is to analyze the disciplinary research landscape from the national academic production, to identify populations, areas, research approaches, in this way identify needs and challenges for formative research of speech therapy in Colombia. Information from undergraduate and graduate research projects of 13 training programs in Speech Therapy in the country, between 2010 and 2019, reported by higher education institutions, was analyzed. Information was collected from reports and research papers (N=638). First, there was an increase in research production between 2010 (4.9%) and 2019 (18.3%). 85% of the researchwas identified in undergraduate and 15% in postgraduate. The predominant approach was qualitative (55.3%), which, together with the descriptive scope (77.8%) that represent the majority of research products. The scenario related to the field of clinical health care leads the research (52.9%). The predominant areas in this research scenario are Language (29.1%) and Hearing (27.4%)in adult (56.7%) and child (28.52%) populations. It is concluded that the research production from the academy, specifically from the trainingprograms of speech therapists in Colombia, evidences the need to promote experimental and analytical research, with populations of neonates and infants, in areas related to oral function and in unprecedented scenarios. Besides, it is necessary to include the use oftechnologies and new intervention approaches to contribute to the evidence-based approach. Future research should allow comparison of research before and after the pandemic, given the virtualization and the inclusion of speech therapy in settings such as critical careand palliative care units.

Conduct and reporting of formula milk trials: systematic review.

OBJECTIVE: To systematically review the conduct and reporting of formula trials. DESIGN: Systematic review. DATA SOURCES: Medline, Embase, and Cochrane Central Register of Controlled Trials (CENTRAL) were searched from 1 January 2006 to 31 December 2020. REVIEW METHODS: Intervention trials comparing at least two formula products in children less than three years of age were included, but not trials of human breast milk or fortifiers of breast milk. Data were extracted in duplicate and primary outcome data were synthesised for meta-analysis with a random effects model weighted by the inverse variance method. Risk of bias was evaluated with Cochrane risk of bias version 2.0, and risk of undermining breastfeeding was evaluated according to published consensus guidance. Primary outcomes of the trials included in the systematic review were identified from clinical trial registries, protocols, or trial publications. RESULTS: 22 201 titles were screened and 307 trials were identified that were published between 2006 and 2020, of which 73 (24%) trials in 13 197 children were prospectively registered. Another 111 unpublished but registered trials in 17 411 children were identified. Detailed analysis was undertaken for 125 trials (23 757 children) published since 2015. Seventeen (14%) of these recently published trials were conducted independently of formula companies, 26 (21%) were prospectively registered with a clear aim and primary outcome, and authors or sponsors shared prospective protocols for 11 (9%) trials. Risk of bias was low in five (4%) and high in 100 (80%) recently published trials, mainly because of inappropriate exclusions from analysis and selective reporting. For 68 recently published superiority trials, a pooled standardised mean difference of 0.51 (range -0.43 to 3.29) was calculated with an asymmetrical funnel plot (Egger's test P<0.001), which reduced to 0.19 after correction for asymmetry. Primary outcomes were reported by authors as favourable in 86 (69%) trials, and 115 (92%) abstract conclusions were favourable. One of 38 (3%) trials in partially breastfed infants reported adequate support for breastfeeding and 14 of 87 (16%) trials in non-breastfed infants confirmed the decision not to breastfeed was firmly established before enrolment in the trial. CONCLUSIONS: The results show that formula trials lack independence or transparency, and published outcomes are biased by selective reporting. SYSTEMATIC REVIEW REGISTRATION: PROSPERO 2018 CRD42018091928.