Randomized Controlled Trials 4: Biomarkers and Surrogate Outcomes

2015 ◽  
pp. 207-224 ◽  
Author(s):  
Claudio Rigatto ◽  
Brendan J. Barrett
Keyword(s):  
Randomized Controlled Trials ◽  
Controlled Trials ◽  
Randomized Controlled ◽  
Surrogate Outcomes

scholarly journals How Often Are Patient-Important Outcomes Represented in Neonatal Randomized Controlled Trials? An Analysis of Cochrane Neonatal Reviews

Neonatology ◽  
2020 ◽  
Vol 117 (4) ◽  
pp. 428-435
Author(s):  
Nai Ming Lai ◽  
Denise Yin Xian Leom ◽  
Wen Li Chow ◽  
Kee-Hsin Chen ◽  
Pu-Hong Lin ◽  
...  
Keyword(s):  
Randomized Controlled Trials ◽  
Small Proportion ◽  
Controlled Trials ◽  
Randomized Controlled ◽  
Surrogate Outcomes ◽  
Clinical Events ◽  
Independent Determination ◽  
Research Findings ◽  
Objective Outcomes

<b><i>Background:</i></b> Research findings based on patient-important outcomes (PIOs) provide more useful conclusions than those that are based on surrogate outcomes. It is unclear to what extent PIOs are represented in neonatal randomized controlled trials (RCTs). <b><i>Objectives:</i></b> We determined the proportion of PIOs in neonatal RCTs included in Cochrane Neonatal reviews. <b><i>Methods:</i></b> We extracted up to 5 outcomes from each RCT included in Cochrane Neonatal reviews published until January 2018, with independent determination of PIOs among authors followed by a discussion leading to a consensus. We defined PIOs as outcomes that matter to patient care, such as clinical events or physiological or laboratory parameters that are widely used to guide management. <b><i>Results:</i></b> Among 6,832 outcomes extracted from 1,874 RCTs included in 276 reviews, 5,349 (78.3%) were considered PIOs; 461 studies (24.5%) included 5 or more PIOs, 1,278 (68.2%) included 1–4 PIOs, while 135 (7.2%) had no PIO included. PIOs were observed more often among dichotomous than among continuous outcomes (94.9 vs. 61.5%; RR: 1.54; 95% CI: 1.50–1.58), and more among subjective than among objective outcomes (95.9 vs. 76.8%; RR: 1.25; 95% CI: 1.22–1.28). Newer studies were more likely to have a greater number of PIOs (adjusted OR: 1.033 [95% CI: 1.025–1.041] with each publication year). <b><i>Conclusions:</i></b> The large and increasing representation of PIOs over the years suggests an improving awareness by neonatal trialists of the need to incorporate important outcomes in order to justify the utilization of resources. Further research should explore the reasons for non-inclusion or non-reporting of PIOs in a small proportion of RCTs.

scholarly journals Patient-important outcomes reported in randomized controlled trials of pharmacologic treatments for COVID-19: a protocol of a META-epidemiological study

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Mario A. Jimenez-Mora ◽  
Andrea Ramírez Varela ◽  
Jose F. Meneses-Echavez ◽  
Julia Bidonde ◽  
Adriana Angarita-Fonseca ◽  
...  
Keyword(s):  
Randomized Controlled Trials ◽  
Epidemiological Study ◽  
Health Care Professionals ◽  
Multivariable Analysis ◽  
Open Science ◽  
Controlled Trials ◽  
Randomized Controlled ◽  
Surrogate Outcomes ◽  
Pharmacologic Treatments ◽  
Science Framework

Abstract Background The coronavirus disease 19 (covid-19) pandemic has underscored the need to expedite clinical research, which may lead investigators to shift away from measuring patient-important outcomes (PIO), limiting research applicability. We aim to investigate if randomized controlled trials (RCTs) of covid-19 pharmacological therapies include PIOs. Methods We will perform a meta-epidemiological study of RCTs that included people at risk for, or with suspected, probable, or confirmed covid-19, examining any pharmacological treatment or blood product aimed at prophylaxis or treatment. We will obtain data from all RCTs identified in a living network metanalysis (NMA). The main data sources are the living WHO covid-19 database up to 1 March 2021 and six additional Chinese databases up to 20 February 2021. Two reviewers independently will review each citation, full-text article, and abstract data. To categorize the outcomes according to their importance to patients, we will adapt a previously defined hierarchy: a) mortality, b) quality of life/ functional status/symptoms, c) morbidity, and d) surrogate outcomes. Outcomes within the category a) and b) will be considered critically important to patients, and outcomes within the category c) will be regarded as important. We will use descriptive statistics to assess the proportion of studies that report each category of outcomes. We will perform univariable and multivariable analysis to explore associations between trial characteristics and the likelihood of reporting PIOs. Discussion The findings from this meta-epidemiological study will help health care professionals and researchers understand if the current covid-19 trials are effectively assessing and reporting the outcomes that are important to patients. If a deficiency in capturing PIOs is identified, this information may help inform the development of future RCTs in covid-19. Systematic review registrations Open Science Framework registration: osf.io/6xgjz.

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

Methodology ◽  
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.

Interventions for suicide and self-injury: A meta-analysis of randomized controlled trials across nearly 50 years of research.

2020 ◽  
Vol 146 (12) ◽  
pp. 1117-1145
Author(s):  
Kathryn R. Fox ◽  
Xieyining Huang ◽  
Eleonora M. Guzmán ◽  
Kensie M. Funsch ◽  
Christine B. Cha ◽  
...  
Keyword(s):  
Randomized Controlled Trials ◽  
Meta Analysis ◽  
Controlled Trials ◽  
Randomized Controlled ◽  
Self Injury
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