scholarly journals Treatment effect modifiers in a randomized trial of the good behavior game during middle childhood.

2021 ◽  
Vol 89 (8) ◽  
pp. 668-681
Author(s):  
Neil Humphrey ◽  
Margarita Panayiotou ◽  
Alexandra Hennessey ◽  
Emma Ashworth
Keyword(s):  
Randomized Trial ◽  
Middle Childhood ◽  
Treatment Effect ◽  
Good Behavior Game

scholarly journals Generalizing Treatment Effect Estimates From Sample to Population: A Case Study in the Difficulties of Finding Sufficient Data

2016 ◽  
Vol 41 (4) ◽  
pp. 357-388 ◽  
Author(s):  
Elizabeth A. Stuart ◽  
Anna Rhodes
Keyword(s):  
Head Start ◽  
Randomized Trial ◽  
Treatment Effect ◽  
External Validity ◽  
Randomized Trials ◽  
Target Population ◽  
Data Sets ◽  
Common Data Elements ◽  
Ex Post

Background: Given increasing concerns about the relevance of research to policy and practice, there is growing interest in assessing and enhancing the external validity of randomized trials: determining how useful a given randomized trial is for informing a policy question for a specific target population. Objectives: This article highlights recent advances in assessing and enhancing external validity, with a focus on the data needed to make ex post statistical adjustments to enhance the applicability of experimental findings to populations potentially different from their study sample. Research design: We use a case study to illustrate how to generalize treatment effect estimates from a randomized trial sample to a target population, in particular comparing the sample of children in a randomized trial of a supplemental program for Head Start centers (the Research-Based, Developmentally Informed study) to the national population of children eligible for Head Start, as represented in the Head Start Impact Study. Results: For this case study, common data elements between the trial sample and population were limited, making reliable generalization from the trial sample to the population challenging. Conclusions: To answer important questions about external validity, more publicly available data are needed. In addition, future studies should make an effort to collect measures similar to those in other data sets. Measure comparability between population data sets and randomized trials that use samples of convenience will greatly enhance the range of research and policy relevant questions that can be answered.

scholarly journals Safety and Efficacy of Erythropoietin in Traumatic Brain Injury Patients: A Pilot Randomized Trial

2010 ◽  
Vol 2010 ◽  
pp. 1-5 ◽  
Author(s):  
R. Nirula ◽  
R. Diaz-Arrastia ◽  
K. Brasel ◽  
J. A. Weigelt ◽  
K. Waxman
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Randomized Trial ◽  
Treatment Effect ◽  
Neuronal Cell Death ◽  
Neuron Specific Enolase ◽  
Neuronal Cell ◽  
Trauma Patients ◽  
Stroke Patients ◽  
Rule Out

Background. Erythropoietin (EPO) is a neuroprotective agent utilized in stroke patients. This pilot study represents the first randomized trial of EPO in traumatic brain injury (TBI) patients.Methods. Adult, blunt trauma patients with evidence of TBI were randomized to EPO or placebo within 6 hours of injury. Baseline and daily serum S-100B and Neuron Specific Enolase (NSE) levels were measured.Results. TBI was worse in the EPO (n=11) group compared to placebo patients (n=5). The use of EPO did not impact NSE (P=.89) or S100 B (P=.53) levels compared to placebo.Conclusions. At the dose used, EPO did not reduce neuronal cell death compared to placebo; however, TBI severity was worse in the EPO group while levels of NSE and S100-B were similar to the less injured placebo group making it difficult to rule out a treatment effect. A larger, balanced study is necessary to confirm a potential treatment effect.

scholarly journals Extending the I-squared statistic to describe treatment effect heterogeneity in cluster, multi-centre randomized trials and individual patient data meta-analysis

2020 ◽  
pp. 096228022094855
Author(s):  
Karla Hemming ◽  
James P Hughes ◽  
Joanne E McKenzie ◽  
Andrew B Forbes
Keyword(s):  
Randomized Trial ◽  
Treatment Effect ◽  
Treatment Effects ◽  
Randomized Trials ◽  
Individual Patient Data ◽  
Meta Analysis ◽  
Cluster Randomized Trial ◽  
Patient Data ◽  
Treatment Effect Heterogeneity ◽  
Effect Heterogeneity

Treatment effect heterogeneity is commonly investigated in meta-analyses to identify if treatment effects vary across studies. When conducting an aggregate level data meta-analysis it is common to describe the magnitude of any treatment effect heterogeneity using the I-squared statistic, which is an intuitive and easily understood concept. The effect of a treatment might also vary across clusters in a cluster randomized trial, or across centres in multi-centre randomized trial, and it can be of interest to explore this at the analysis stage. In cross-over trials and other randomized designs, in which clusters or centres are exposed to both treatment and control conditions, this treatment effect heterogeneity can be identified. Here we derive and evaluate a comparable I-squared measure to describe the magnitude of heterogeneity in treatment effects across clusters or centres in randomized trials. We further show how this methodology can be used to estimate treatment effect heterogeneity in an individual patient data meta-analysis.

scholarly journals Generalizability of Randomized Trial Results to Target Populations

2017 ◽  
Vol 28 (5) ◽  
pp. 532-537 ◽  
Author(s):  
Elizabeth A. Stuart ◽  
Benjamin Ackerman ◽  
Daniel Westreich
Keyword(s):  
Social Work ◽  
Randomized Trial ◽  
Treatment Effect ◽  
Randomized Trials ◽  
Internal Validity ◽  
Target Population ◽  
Average Treatment Effect ◽  
Social Work Research ◽  
Average Treatment ◽  
Social Work Program

Randomized trials play an important role in estimating the effect of a policy or social work program in a given population. While most trial designs benefit from strong internal validity, they often lack external validity, or generalizability, to the target population of interest. In other words, one can obtain an unbiased estimate of the study sample average treatment effect from a randomized trial; however, this estimate may not equal the target population average treatment effect if the study sample is not fully representative of the target population. This article provides an overview of existing strategies to assess and improve upon the generalizability of randomized trials, both through statistical methods and study design, as well as recommendations on how to implement these ideas in social work research.

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