A review of randomized controlled trials comparing the effectiveness of hand held computers with paper methods for data collection

Background: The 2019 Science for Dialysis Meeting at Bellvitge University Hospital was devoted to the challenges and opportunities posed by the use of data science to facilitate precision and personalized medicine in nephrology, and to describe new approaches and technologies. The meeting included separate sections for issues in data collection and data analysis. As part of data collection, we presented the institutional ARGOS e-health project, which provides a common model for the standardization of clinical practice. We also pay specific attention to the way in which randomized controlled trials offer data that may be critical to decision-making in the real world. The opportunities of open source software (OSS) for data science in clinical practice were also discussed. Summary: Precision medicine aims to provide the right treatment for the right patients at the right time and is deeply connected to data science. Dialysis patients are highly dependent on technology to live, and their treatment generates a huge volume of data that has to be analysed. Data science has emerged as a tool to provide an integrated approach to data collection, storage, cleaning, processing, analysis, and interpretation from potentially large volumes of information. This is meant to be a perspective article about data science based on the experience of the experts invited to the Science for Dialysis Meeting and provides an up-to-date perspective of the potential of data science in kidney disease and dialysis. Key messages: Healthcare is quickly becoming data-dependent, and data science is a discipline that holds the promise of contributing to the development of personalized medicine, although nephrology still lags behind in this process. The key idea is to ensure that data will guide medical decisions based on individual patient characteristics rather than on averages over a whole population usually based on randomized controlled trials that excluded kidney disease patients. Furthermore, there is increasing interest in obtaining data about the effectiveness of available treatments in current patient care based on pragmatic clinical trials. The use of data science in this context is becoming increasingly feasible in part thanks to the swift developments in OSS.

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Commencement of and Retention in Web-Based Interventions and Response to Prompts and Reminders: Longitudinal Observational Study Based on Two Randomized Controlled Trials

Journal of Medical Internet Research ◽

10.2196/24590 ◽

2021 ◽

Vol 23 (3) ◽

pp. e24590

Author(s):

Athanasios Andriopoulos ◽

Erik M G Olsson ◽

Ylva Hägg Sylvén ◽

Jonas Sjöström ◽

Birgitta Johansson ◽

...

Keyword(s):

Data Collection ◽

Randomized Controlled Trials ◽

Median Duration ◽

Controlled Trials ◽

Web Based ◽

Baseline Data Collection ◽

Use Patterns ◽

Randomized Controlled ◽

Comparison Groups

Background Web-based interventions are effective for several psychological problems. However, recruitment, adherence, and missing data are challenges when evaluating these interventions. Objective This study aimed to describe the use patterns during the commencement phase, possible retention patterns (continuation of data provision), and responses to prompts and reminders among participants in 2 randomized controlled trials (RCTs) evaluating web-based interventions. Methods Data on use patterns logged in 2 RCTs aiming to reduce symptoms of anxiety and depression among adult patients recently diagnosed with cancer (AdultCan RCT) and patients with a recent myocardial infarction (Heart RCT) were analyzed. The web-based intervention in the AdultCan trial consisted of unguided self-help and psychoeducation and that in the Heart trial consisted of therapist-supported cognitive behavioral therapy. In total, 2360 participants’ use patterns at first log-in, including data collection at baseline (ie, commencement) and at 2 follow-ups, were analyzed. Both the intervention and comparison groups were analyzed. Results At commencement, 70.85% (909/1283) and 86.82% (935/1077) of the participants in AdultCan and Heart RCTs, respectively, logged in and completed baseline data collection after receiving a welcome email with log-in credentials. The median duration of the first log-in was 44 minutes and 38 minutes in AdultCan and Heart RCTs, respectively. Slightly less than half of the participants’ first log-ins were completed outside standard office hours. More than 80% (92/114 and 103/111) of the participants in both trials explored the intervention within 2 weeks of being randomized to the treatment group, with a median duration of 7 minutes and 47 minutes in AdultCan and Heart RCTs, respectively. There was a significant association between intervention exploration time during the first 2 weeks and retention in the Heart trial but not in the AdultCan trial. However, the control group was most likely to retain and provide complete follow-up data. Across the 3 time points of data collection explored in this study, the proportion of participants responding to all questionnaires within 1 week from the prompt, without a reminder, varied between 35.45% (413/1165) and 66.3% (112/169). After 2 reminders, up to 97.6% (165/169) of the participants responded. Conclusions Most participants in both RCTs completed the baseline questionnaires within 1 week of receiving the welcome email. Approximately half of them answered questions at baseline data collection outside office hours, suggesting that the time flexibility inherent in web-based interventions contributes to commencement and use. In contrast to what was expected, the intervention groups generally had lower completion rates than the comparison groups. About half of the participants completed the questionnaires without a reminder, but thereafter, reminders contributed to both baseline and follow-up retention, suggesting they were effective. Strategies to increase commencement of and retention in eHealth interventions are important for the future development of effective interventions and relevant research.

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Commencement of and Retention in Web-Based Interventions and Response to Prompts and Reminders: Longitudinal Observational Study Based on Two Randomized Controlled Trials (Preprint)

10.2196/preprints.24590 ◽

2020 ◽

Author(s):

Athanasios Andriopoulos ◽

Erik M G Olsson ◽

Ylva Hägg Sylvén ◽

Jonas Sjöström ◽

Birgitta Johansson ◽

...

Keyword(s):

Data Collection ◽

Randomized Controlled Trials ◽

Median Duration ◽

Controlled Trials ◽

Web Based ◽

Baseline Data Collection ◽

Use Patterns ◽

Randomized Controlled ◽

Comparison Groups

BACKGROUND Web-based interventions are effective for several psychological problems. However, recruitment, adherence, and missing data are challenges when evaluating these interventions. OBJECTIVE This study aimed to describe the use patterns during the commencement phase, possible retention patterns (continuation of data provision), and responses to prompts and reminders among participants in 2 randomized controlled trials (RCTs) evaluating web-based interventions. METHODS Data on use patterns logged in 2 RCTs aiming to reduce symptoms of anxiety and depression among adult patients recently diagnosed with cancer (AdultCan RCT) and patients with a recent myocardial infarction (Heart RCT) were analyzed. The web-based intervention in the AdultCan trial consisted of unguided self-help and psychoeducation and that in the Heart trial consisted of therapist-supported cognitive behavioral therapy. In total, 2360 participants’ use patterns at first log-in, including data collection at baseline (ie, commencement) and at 2 follow-ups, were analyzed. Both the intervention and comparison groups were analyzed. RESULTS At commencement, 70.85% (909/1283) and 86.82% (935/1077) of the participants in AdultCan and Heart RCTs, respectively, logged in and completed baseline data collection after receiving a welcome email with log-in credentials. The median duration of the first log-in was 44 minutes and 38 minutes in AdultCan and Heart RCTs, respectively. Slightly less than half of the participants’ first log-ins were completed outside standard office hours. More than 80% (92/114 and 103/111) of the participants in both trials explored the intervention within 2 weeks of being randomized to the treatment group, with a median duration of 7 minutes and 47 minutes in AdultCan and Heart RCTs, respectively. There was a significant association between intervention exploration time during the first 2 weeks and retention in the Heart trial but not in the AdultCan trial. However, the control group was most likely to retain and provide complete follow-up data. Across the 3 time points of data collection explored in this study, the proportion of participants responding to all questionnaires within 1 week from the prompt, without a reminder, varied between 35.45% (413/1165) and 66.3% (112/169). After 2 reminders, up to 97.6% (165/169) of the participants responded. CONCLUSIONS Most participants in both RCTs completed the baseline questionnaires within 1 week of receiving the welcome email. Approximately half of them answered questions at baseline data collection outside office hours, suggesting that the time flexibility inherent in web-based interventions contributes to commencement and use. In contrast to what was expected, the intervention groups generally had lower completion rates than the comparison groups. About half of the participants completed the questionnaires without a reminder, but thereafter, reminders contributed to both baseline and follow-up retention, suggesting they were effective. Strategies to increase commencement of and retention in eHealth interventions are important for the future development of effective interventions and relevant research.

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Perspective: Laboratory Considerations and Clinical Data Management for Human Nutrition Randomized Controlled Trials: Guidance for Ensuring Quality and Integrity

Advances in Nutrition ◽

10.1093/advances/nmaa088 ◽

2020 ◽

Cited By ~ 2

Author(s):

Kevin C Maki ◽

Joshua W Miller ◽

George P McCabe ◽

Gowri Raman ◽

Penny M Kris-Etherton

Keyword(s):

Data Collection ◽

Data Management ◽

Randomized Controlled Trials ◽

Clinical Data ◽

Human Nutrition ◽

Controlled Trials ◽

Quality Of Data ◽

Randomized Controlled ◽

Nutrition Research ◽

Human Nutrition Research

ABSTRACT In human nutrition randomized controlled trials (RCTs), planning, and careful execution of clinical data collection and management are vital for producing valid and reliable results. In this article, we provide an overview of best practices for biospecimen collection and analyses, and for the fundamentals of clinical data management, including preparation and study startup; data collection, entry, cleaning, and authentication; and database lock. The reader is also referred to additional resources for information to assist in the planning and conduct of human RCTs. The tools and strategies described are expected to improve the quality of data produced in human nutrition research that can, therefore, be used to support food and nutrition policies.

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Abstracts #300082: Current Therapies in Nonalcoholic Fatty Liver Disease (NAFLD). A Meta-analysis of Randomized Controlled Trials

Endocrine Practice ◽

10.1016/s1530-891x(20)48249-7 ◽

2020 ◽

Vol 26 ◽

pp. 5

Author(s):

Emily Wood

Keyword(s):

Liver Disease ◽

Fatty Liver ◽

Randomized Controlled Trials ◽

Nonalcoholic Fatty Liver Disease ◽

Fatty Liver Disease ◽

Meta Analysis ◽

Controlled Trials ◽

Nonalcoholic Fatty Liver ◽

Randomized Controlled ◽

Current Therapies

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258 Ambulatory monitoring for heart failure patients: it is possible to improve survival and quality of life: a metanalysis of randomized controlled trials

European Journal of Heart Failure Supplements ◽

10.1016/s1567-4215(07)60137-4 ◽

2007 ◽

Vol 6 (1) ◽

pp. 51-51

Author(s):

F CANARIOALMEIDA ◽

L AZEVEDO ◽

A COSTAPEREIRA

Keyword(s):

Quality Of Life ◽

Heart Failure ◽

Randomized Controlled Trials ◽

Ambulatory Monitoring ◽

Controlled Trials ◽

Heart Failure Patients ◽

Randomized Controlled

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The Effect of Partly Missing Covariates on Statistical Power in Randomized Controlled Trials With Discrete-Time Survival Endpoints

Methodology ◽

10.1027/1614-2241/a000121 ◽

2017 ◽

Vol 13 (2) ◽

pp. 41-60

Author(s):

Shahab Jolani ◽

Maryam Safarkhani

Keyword(s):

Randomized Controlled Trials ◽

Discrete Time ◽

Treatment Effect ◽

Survival Data ◽

Controlled Trials ◽

Missing Covariates ◽

Indicator Method ◽

Imputation Methods ◽

Randomized Controlled ◽

Baseline Covariates

Abstract. In randomized controlled trials (RCTs), a common strategy to increase power to detect a treatment effect is adjustment for baseline covariates. However, adjustment with partly missing covariates, where complete cases are only used, is inefficient. We consider different alternatives in trials with discrete-time survival data, where subjects are measured in discrete-time intervals while they may experience an event at any point in time. The results of a Monte Carlo simulation study, as well as a case study of randomized trials in smokers with attention deficit hyperactivity disorder (ADHD), indicated that single and multiple imputation methods outperform the other methods and increase precision in estimating the treatment effect. Missing indicator method, which uses a dummy variable in the statistical model to indicate whether the value for that variable is missing and sets the same value to all missing values, is comparable to imputation methods. Nevertheless, the power level to detect the treatment effect based on missing indicator method is marginally lower than the imputation methods, particularly when the missingness depends on the outcome. In conclusion, it appears that imputation of partly missing (baseline) covariates should be preferred in the analysis of discrete-time survival data.

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