Randomized Trials

2019 ◽  
pp. 107-138
Author(s):  
Daniel Westreich
Keyword(s):  
Randomized Trial ◽  
Subgroup Analysis ◽  
Randomized Trials ◽  
Trial Data ◽  
Causal Interaction ◽  
Effect Measure ◽  
Effect Measure Modification ◽  
Broad Overview ◽  
Quantitative Approaches

In Chapter 5, the author describes randomized trials. The chapter gives a broad overview of types of trials and the steps in conducting a trial and also describes how trials meet (and fail to meet) core causal identification conditions. The author provides a brief introduction to the analysis of randomized trial data. As well, the chapter introduces factorial trials as well as subgroup analysis of trials as a way of explaining differences between causal interaction and effect measure modification. Finally, the author describes issues in the generalizability and transportability of trials and quantitative approaches to these issues.

Observational Cohort Studies

2019 ◽  
pp. 139-168
Author(s):  
Daniel Westreich
Keyword(s):  
Cohort Study ◽  
Cohort Studies ◽  
Randomized Trial ◽  
Randomized Trials ◽  
Observational Cohort Study ◽  
Effect Measure ◽  
Observational Cohort ◽  
Effect Measure Modification

In contrast to a randomized trial, an observational cohort study is one in which the investigator observes a group of participants with varying levels of an exposure and then follows-up those participants for a period of time to examine the incidence of one or more specified outcomes. This chapter addresses observational cohort studies in much the same way as the previous chapter addressed randomized trials: discussed are the types of cohort studies, the steps in conducting such a study, and the ways in which such studies meet or do not meet causal identification conditions. Also presented is a brief introduction to analysis. The author expands his discussion of interaction and effect measure modification, as well as generalizability, in this setting.

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 Effects of diabetes on small aortic aneurysms under surveillance according to a subgroup analysis from a randomized trial

2012 ◽  
Vol 56 (6) ◽  
pp. 1555-1563 ◽  
Author(s):  
Paola De Rango ◽  
Piergiorgio Cao ◽  
Enrico Cieri ◽  
Gianbattista Parlani ◽  
Massimo Lenti ◽  
...  
Keyword(s):  
Randomized Trial ◽  
Subgroup Analysis ◽  
Aortic Aneurysms

scholarly journals Comparing cluster-level dynamic treatment regimens using sequential, multiple assignment, randomized trials: Regression estimation and sample size considerations

2017 ◽  
Vol 26 (4) ◽  
pp. 1572-1589 ◽  
Author(s):  
Timothy NeCamp ◽  
Amy Kilbourne ◽  
Daniel Almirall
Keyword(s):  
Randomized Trial ◽  
Randomized Trials ◽  
Treatment Regimens ◽  
Patient Level ◽  
Multiple Assignment ◽  
Dynamic Treatment ◽  
The Mean ◽  
Cluster Randomized ◽  
The Individual ◽  
Cluster Level

Cluster-level dynamic treatment regimens can be used to guide sequential treatment decision-making at the cluster level in order to improve outcomes at the individual or patient-level. In a cluster-level dynamic treatment regimen, the treatment is potentially adapted and re-adapted over time based on changes in the cluster that could be impacted by prior intervention, including aggregate measures of the individuals or patients that compose it. Cluster-randomized sequential multiple assignment randomized trials can be used to answer multiple open questions preventing scientists from developing high-quality cluster-level dynamic treatment regimens. In a cluster-randomized sequential multiple assignment randomized trial, sequential randomizations occur at the cluster level and outcomes are observed at the individual level. This manuscript makes two contributions to the design and analysis of cluster-randomized sequential multiple assignment randomized trials. First, a weighted least squares regression approach is proposed for comparing the mean of a patient-level outcome between the cluster-level dynamic treatment regimens embedded in a sequential multiple assignment randomized trial. The regression approach facilitates the use of baseline covariates which is often critical in the analysis of cluster-level trials. Second, sample size calculators are derived for two common cluster-randomized sequential multiple assignment randomized trial designs for use when the primary aim is a between-dynamic treatment regimen comparison of the mean of a continuous patient-level outcome. The methods are motivated by the Adaptive Implementation of Effective Programs Trial which is, to our knowledge, the first-ever cluster-randomized sequential multiple assignment randomized trial in psychiatry.

scholarly journals Nintedanib in Japanese patients with idiopathic pulmonary fibrosis: A subgroup analysis of the INPULSIS® randomized trials

Respirology ◽  
2016 ◽  
Vol 22 (4) ◽  
pp. 750-757 ◽  
Author(s):  
Arata Azuma ◽  
Hiroyuki Taniguchi ◽  
Yoshikazu Inoue ◽  
Yasuhiro Kondoh ◽  
Takashi Ogura ◽  
...  
Keyword(s):  
Idiopathic Pulmonary Fibrosis ◽  
Pulmonary Fibrosis ◽  
Subgroup Analysis ◽  
Randomized Trials ◽  
Japanese Patients

scholarly journals Cardiovascular and renal protective role of angiotensin blockade in hypertension with advanced CKD: a subgroup analysis of ATTEMPT-CVD randomized trial

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Shokei Kim-Mitsuyama ◽  
Hirofumi Soejima ◽  
Osamu Yasuda ◽  
Koichi Node ◽  
Hideaki Jinnouchi ◽  
...  
Keyword(s):  
Randomized Trial ◽  
Subgroup Analysis ◽  
Protective Role ◽  
Angiotensin Blockade
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