scholarly journals Overcoming challenges to data quality in the ASPREE clinical trial

Trials ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Jessica E. Lockery ◽  
◽  
Taya A. Collyer ◽  
Christopher M. Reid ◽  
Michael E. Ernst ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Missing Data ◽  
Data Quality ◽  
Large Scale ◽  
Controlled Trial ◽  
Data Entry ◽  
Quality Data ◽  
Community Based ◽  
Data Set ◽  
The Impact

Abstract Background Large-scale studies risk generating inaccurate and missing data due to the complexity of data collection. Technology has the potential to improve data quality by providing operational support to data collectors. However, this potential is under-explored in community-based trials. The Aspirin in reducing events in the elderly (ASPREE) trial developed a data suite that was specifically designed to support data collectors: the ASPREE Web Accessible Relational Database (AWARD). This paper describes AWARD and the impact of system design on data quality. Methods AWARD’s operational requirements, conceptual design, key challenges and design solutions for data quality are presented. Impact of design features is assessed through comparison of baseline data collected prior to implementation of key functionality (n = 1000) with data collected post implementation (n = 18,114). Overall data quality is assessed according to data category. Results At baseline, implementation of user-driven functionality reduced staff error (from 0.3% to 0.01%), out-of-range data entry (from 0.14% to 0.04%) and protocol deviations (from 0.4% to 0.08%). In the longitudinal data set, which contained more than 39 million data values collected within AWARD, 96.6% of data values were entered within specified query range or found to be accurate upon querying. The remaining data were missing (3.4%). Participant non-attendance at scheduled study activity was the most common cause of missing data. Costs associated with cleaning data in ASPREE were lower than expected compared with reports from other trials. Conclusions Clinical trials undertake complex operational activity in order to collect data, but technology rarely provides sufficient support. We find the AWARD suite provides proof of principle that designing technology to support data collectors can mitigate known causes of poor data quality and produce higher-quality data. Health information technology (IT) products that support the conduct of scheduled activity in addition to traditional data entry will enhance community-based clinical trials. A standardised framework for reporting data quality would aid comparisons across clinical trials. Trial registration International Standard Randomized Controlled Trial Number Register, ISRCTN83772183. Registered on 3 March 2005.

scholarly journals Psychometric Report for the 2019 Knowledge for Teaching Early Elementary Mathematics (K-TEEM) Test

2021 ◽  
Author(s):  
Robert Schoen ◽  
Xiaotong Yang ◽  
Gizem Solmaz
Keyword(s):  
Large Scale ◽  
Elementary Mathematics ◽  
Controlled Trial ◽  
Data Entry ◽  
Mathematical Knowledge For Teaching ◽  
Development Program ◽  
Early Elementary ◽  
Knowledge For Teaching ◽  
Large Scale Field ◽  
The Impact

The 2019 Knowledge for Teaching Early Elementary Mathematics (2019 K-TEEM) test measures teachers’ mathematical knowledge for teaching early elementary mathematics. This report presents information about a large-scale field test of the 2019 K-TEEM test with 649 practicing educators. The report contains information about the development process used for the test; a description of the sample; descriptions of the procedures used for data entry, scoring of responses, and analysis of data; recommended scoring procedures; and findings regarding the distribution of test scores, standard error of measurement, and marginal reliability. The intended use of the data from the 2019 K-TEEM test is to serve as a measure of teacher knowledge that will be used in a randomized controlled trial to investigate the impact—and variation in impact—of a teacher professional-development program for early elementary teachers.

scholarly journals A Systematic Framework for Analyzing Observation Data in Patient-Centered Registries: Case Study for Patients With Depression (Preprint)

2020 ◽  
Author(s):  
Maryam Zolnoori ◽  
Mark D Williams ◽  
William B Leasure ◽  
Kurt B Angstman ◽  
Che Ngufor
Keyword(s):  
Data Quality ◽  
Quality Data ◽  
Patient Centered ◽  
High Quality ◽  
Data Set ◽  
High Quality Data ◽  
Research Questions ◽  
Quality Issues ◽  
The Impact ◽  
Systematic Framework

BACKGROUND Patient-centered registries are essential in population-based clinical care for patient identification and monitoring of outcomes. Although registry data may be used in real time for patient care, the same data may further be used for secondary analysis to assess disease burden, evaluation of disease management and health care services, and research. The design of a registry has major implications for the ability to effectively use these clinical data in research. OBJECTIVE This study aims to develop a systematic framework to address the data and methodological issues involved in analyzing data in clinically designed patient-centered registries. METHODS The systematic framework was composed of 3 major components: visualizing the multifaceted and heterogeneous patient-centered registries using a data flow diagram, assessing and managing data quality issues, and identifying patient cohorts for addressing specific research questions. RESULTS Using a clinical registry designed as a part of a collaborative care program for adults with depression at Mayo Clinic, we were able to demonstrate the impact of the proposed framework on data integrity. By following the data cleaning and refining procedures of the framework, we were able to generate high-quality data that were available for research questions about the coordination and management of depression in a primary care setting. We describe the steps involved in converting clinically collected data into a viable research data set using registry cohorts of depressed adults to assess the impact on high-cost service use. CONCLUSIONS The systematic framework discussed in this study sheds light on the existing inconsistency and data quality issues in patient-centered registries. This study provided a step-by-step procedure for addressing these challenges and for generating high-quality data for both quality improvement and research that may enhance care and outcomes for patients. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/18366

scholarly journals A Systematic Framework for Analyzing Observation Data in Patient-Centered Registries: Case Study for Patients With Depression

10.2196/18366 ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. e18366
Author(s):  
Maryam Zolnoori ◽  
Mark D Williams ◽  
William B Leasure ◽  
Kurt B Angstman ◽  
Che Ngufor
Keyword(s):  
Data Quality ◽  
Quality Data ◽  
Patient Centered ◽  
High Quality ◽  
Data Set ◽  
High Quality Data ◽  
Research Questions ◽  
Quality Issues ◽  
The Impact ◽  
Systematic Framework

Background Patient-centered registries are essential in population-based clinical care for patient identification and monitoring of outcomes. Although registry data may be used in real time for patient care, the same data may further be used for secondary analysis to assess disease burden, evaluation of disease management and health care services, and research. The design of a registry has major implications for the ability to effectively use these clinical data in research. Objective This study aims to develop a systematic framework to address the data and methodological issues involved in analyzing data in clinically designed patient-centered registries. Methods The systematic framework was composed of 3 major components: visualizing the multifaceted and heterogeneous patient-centered registries using a data flow diagram, assessing and managing data quality issues, and identifying patient cohorts for addressing specific research questions. Results Using a clinical registry designed as a part of a collaborative care program for adults with depression at Mayo Clinic, we were able to demonstrate the impact of the proposed framework on data integrity. By following the data cleaning and refining procedures of the framework, we were able to generate high-quality data that were available for research questions about the coordination and management of depression in a primary care setting. We describe the steps involved in converting clinically collected data into a viable research data set using registry cohorts of depressed adults to assess the impact on high-cost service use. Conclusions The systematic framework discussed in this study sheds light on the existing inconsistency and data quality issues in patient-centered registries. This study provided a step-by-step procedure for addressing these challenges and for generating high-quality data for both quality improvement and research that may enhance care and outcomes for patients. International Registered Report Identifier (IRRID) DERR1-10.2196/18366

PAPRASTŲJŲ JONAŽOLIŲ NAUDOJIMAS GYDANT SUNKIĄ DEPRESIJĄ

2016 ◽  
Vol 22 (2) ◽  
pp. 173-176
Author(s):  
Marija Sabaliauskaitė ◽  
Gediminas Brazaitis
Keyword(s):  
Clinical Trials ◽  
Major Depressive Disorder ◽  
Missing Data ◽  
Depressive Disorder ◽  
Hypericum Perforatum ◽  
Controlled Trial ◽  
Major Depressive ◽  
St John’S Wort ◽  
St John's Wort ◽  
The Impact

Reikšminiai žodžiai: jonažolė, depresija. Paprastosios jonažolės, remiantis moksliniais tyrimais, šiuo metu gali būti skiriamos lengvai ir vidutinio sunkumo depresijai gydyti [6, 7]. Tačiau sergantieji sunkia depresija taip pat linkę jas vartoti, bet nauda yra abejotina [8]. Šio tyrimo tikslas yra išanalizuoti pastarųjų penkerių metų tyrimus apie jonažolių poveikį gydant sunkią depresiją, jį lyginant su sintetiniais antidepresantais ir placebu. Tyrimų, analizuojančių jonažolių poveikį gydant sunkią depresiją, paieška atlikta „PubMed“, „Embase“ ir „Cochrane“ duomenų bazėse. Atrinkti moksliniai darbai buvo ne senesni kaip penkerių metų. Tyrimai turėjo būti dvigubai akli, placebo – kontroliuojami, užtikrinta tiriamųjų randomizacija. Analizei atrinkti du tiriamieji darbai – Grobler A. J. (2014 m.) „The impact of missing data on clinical trials: A re-analysis of a placebo controlled trial of hypericum perforatum (St Johns Wort) and sertraline in Major Depressive Disorder“ ir Sarris J. (2012 m.) „St John’s wort (Hypericum perforatum) versus sertraline and placebo in major depressive disorder: continuation data from a 26-week RCT. Pharmacopsychiatry“ [20, 21]. Analizuotų tyrimų metu nustatyta, jog monoterapija jonažolių ekstraktu reikšmingai nesiskiria nuo placebo poveikio gydant sunkią depresiją. Todėl ši vaistažolė gydyti sunkią depresiją neturėtų būti skiriama viena, o pacientai, sergantys šia liga, perspėjami nevartoti jonažolių monoterapijai. Ateityje jonažolių poveikis gydant sunkią depresiją turėtų būti tiriamas labiau atsižvelgiant į šios vaistažolės ir sintetinių vaistų derinius.

Dietary Patterns: A New Therapeutic Approach for Depression?

2019 ◽  
Vol 20 (2) ◽  
pp. 123-129 ◽  
Author(s):  
Mariana Jesus ◽  
Tânia Silva ◽  
César Cagigal ◽  
Vera Martins ◽  
Carla Silva
Keyword(s):  
Depressive Symptoms ◽  
Dietary Patterns ◽  
Large Scale ◽  
Randomized Clinical Trials ◽  
Dietary Intervention ◽  
Common Mental Disorder ◽  
Controlled Trial ◽  
Statistical Significance ◽  
Inverse Association ◽  
The Impact

Introduction: The field of nutritional psychiatry is a fast-growing one. Although initially, it focused on the effects of vitamins and micronutrients in mental health, in the last decade, its focus also extended to the dietary patterns. The possibility of a dietary cost-effective intervention in the most common mental disorder, depression, cannot be overlooked due to its potential large-scale impact. Method: A classic review of the literature was conducted, and studies published between 2010 and 2018 focusing on the impact of dietary patterns in depression and depressive symptoms were included. Results: We found 10 studies that matched our criteria. Most studies showed an inverse association between healthy dietary patterns, rich in fruits, vegetables, lean meats, nuts and whole grains, and with low intake of processed and sugary foods, and depression and depressive symptoms throughout an array of age groups, although some authors reported statistical significance only in women. While most studies were of cross-sectional design, making it difficult to infer causality, a randomized controlled trial presented similar results. Discussion: he association between dietary patterns and depression is now well-established, although the exact etiological pathways are still unknown. Dietary intervention, with the implementation of healthier dietary patterns, closer to the traditional ones, can play an important role in the prevention and adjunctive therapy of depression and depressive symptoms. Conclusion: More large-scale randomized clinical trials need to be conducted, in order to confirm the association between high-quality dietary patterns and lower risk of depression and depressive symptoms.

scholarly journals Effectiveness of Yoga Lifestyle on Lipid Metabolism in a Vulnerable Population—A Community Based Multicenter Randomized Controlled Trial

Medicines ◽  
2021 ◽  
Vol 8 (7) ◽  
pp. 37
Author(s):  
Raghuram Nagarathna ◽  
Saurabh Kumar ◽  
Akshay Anand ◽  
Ishwara N. Acharya ◽  
Amit Kumar Singh ◽  
...  
Keyword(s):  
Randomized Controlled Trial ◽  
High Risk ◽  
Controlled Trial ◽  
Density Lipoprotein ◽  
Lipid Profiles ◽  
Community Based ◽  
Randomized Controlled ◽  
Rural And Urban ◽  
Yoga Group ◽  
The Impact

Background: Dyslipidemia poses a high risk for cardiovascular disease and stroke in Type 2 diabetes (T2DM). There are no studies on the impact of a validated integrated yoga lifestyle protocol on lipid profiles in a high-risk diabetes population. Methods: Here, we report the results of lipid profile values of 11,254 (yoga 5932 and control 5322) adults (20–70 years) of both genders with high risk (≥60 on Indian diabetes risk score) for diabetes from a nationwide rural and urban community-based two group (yoga and conventional management) cluster randomized controlled trial. The yoga group practiced a validated integrated yoga lifestyle protocol (DYP) in nine day camps followed by daily one-hour practice. Biochemical profiling included glycated hemoglobin and lipid profiles before and after three months. Results: There was a significant difference between groups (p < 0.001 ANCOVA) with improved serum total cholesterol, triglycerides, low-density lipoprotein, and high-density lipoprotein in the yoga group compared to the control group. Further, the regulatory effect of yoga was noted with a significant decrease or increase in those with high or low values of lipids, respectively, with marginal or no change in those within the normal range. Conclusion: Yoga lifestyle improves and regulates (lowered if high, increased if low) the blood lipid levels in both genders of prediabetic and diabetic individuals in both rural and urban Indian communities.

scholarly journals Handling Complex Missing Data Using Random Forest Approach for an Air Quality Monitoring Dataset: A Case Study of Kuwait Environmental Data (2012 to 2018)

2021 ◽  
Vol 18 (3) ◽  
pp. 1333
Author(s):  
Ahmad R. Alsaber ◽  
Jiazhu Pan ◽  
Adeeba Al-Hurban 
Keyword(s):  
Air Quality ◽  
Missing Data ◽  
Random Forest ◽  
Missing Values ◽  
Imputation Method ◽  
Environmental Data ◽  
Environmental Research ◽  
Quality Data ◽  
Data Set ◽  
Air Quality Data

In environmental research, missing data are often a challenge for statistical modeling. This paper addressed some advanced techniques to deal with missing values in a data set measuring air quality using a multiple imputation (MI) approach. MCAR, MAR, and NMAR missing data techniques are applied to the data set. Five missing data levels are considered: 5%, 10%, 20%, 30%, and 40%. The imputation method used in this paper is an iterative imputation method, missForest, which is related to the random forest approach. Air quality data sets were gathered from five monitoring stations in Kuwait, aggregated to a daily basis. Logarithm transformation was carried out for all pollutant data, in order to normalize their distributions and to minimize skewness. We found high levels of missing values for NO2 (18.4%), CO (18.5%), PM10 (57.4%), SO2 (19.0%), and O3 (18.2%) data. Climatological data (i.e., air temperature, relative humidity, wind direction, and wind speed) were used as control variables for better estimation. The results show that the MAR technique had the lowest RMSE and MAE. We conclude that MI using the missForest approach has a high level of accuracy in estimating missing values. MissForest had the lowest imputation error (RMSE and MAE) among the other imputation methods and, thus, can be considered to be appropriate for analyzing air quality data.

scholarly journals UTILIZING NUTRITION SERVICES PROVIDERS TO PREVENT LATE-LIFE SUICIDE: IMPLEMENTING A RANDOMIZED, CONTROLLED TRIAL

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S824-S825
Author(s):  
Matthew C Fullen ◽  
Mary Chase Mize ◽  
Laura R Shannonhouse
Keyword(s):  
Randomized Controlled Trial ◽  
Suicide Prevention ◽  
Controlled Trial ◽  
Late Life ◽  
Warning Signs ◽  
Community Based ◽  
Randomized Controlled ◽  
Nutrition Services ◽  
Suicide Prevention Training ◽  
The Impact

Abstract A challenge in preventing late-life suicide is identifying and responding to persons-at-risk prior to a suicide attempt. Distressed older adults are less likely to turn to a mental health professional, meaning that community-based prevention strategies are vitally important to comprehensive prevention frameworks. Due to their “natural helper” role, nutrition services (NS) volunteers may be well-positioned to identify suicide warning signs and respond accordingly. Unfortunately, there is a lack of systematic, empirically-tested evaluations of the effectiveness of community-based strategies to prevent older adult suicide, including the use of NS volunteers. To remedy this, the authors partnered with several home- and community-based service organizations to measure the impact of training nutrition services volunteers in suicide prevention skills. The authors will present preliminary findings from this federally-funded randomized, controlled trial of suicide prevention training (i.e., ASIST; safeTALK) on late-life suicidality and its correlates.

Sign in / Sign up

Export Citation Format

Share Document