Dataset Information

Individual differences methods for randomized experiments.

ABSTRACT: Experiments allow researchers to randomly vary the key manipulation, the instruments of measurement, and the sequences of the measurements and manipulations across participants. To date, however, the advantages of randomized experiments to manipulate both the aspects of interest and the aspects that threaten internal validity have been primarily used to make inferences about the average causal effect of the experimental manipulation. This article introduces a general framework for analyzing experimental data to make inferences about individual differences in causal effects. Approaches to analyzing the data produced by a number of classical designs and 2 more novel designs are discussed. Simulations highlight the strengths and weaknesses of the data produced by each design with respect to internal validity. Results indicate that, although the data produced by standard designs can be used to produce accurate estimates of average causal effects of experimental manipulations, more elaborate designs are often necessary for accurate inferences with respect to individual differences in causal effects. The methods described here can be diversely applied by researchers interested in determining the extent to which individuals respond differentially to an experimental manipulation or treatment and how differential responsiveness relates to individual participant characteristics.

SUBMITTER: Tucker-Drob EM

PROVIDER: S-EPMC3168713 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Individual differences methods for randomized experiments.

Tucker-Drob Elliot M EM

Psychological methods 20110901 3

Experiments allow researchers to randomly vary the key manipulation, the instruments of measurement, and the sequences of the measurements and manipulations across participants. To date, however, the advantages of randomized experiments to manipulate both the aspects of interest and the aspects that threaten internal validity have been primarily used to make inferences about the average causal effect of the experimental manipulation. This article introduces a general framework for analyzing expe ...[more]

PMID: 21744970

Similar Datasets

Project description:ImportanceAntidepressants are commonly used worldwide to treat major depressive disorder. Symptomatic response to antidepressants can vary depending on differences between individuals; however, this variability may reflect nonspecific or random factors.ObjectivesTo investigate the assumption of systematic variability in symptomatic response to antidepressants and to assess whether this variability is associated with severity of major depressive disorder, antidepressant class, or year of study publication.Data sourcesData used were from a recent network meta-analysis of acute treatment with licensed antidepressants in adults with major depressive disorder. The following databases were searched from inception to January 8, 2016: the Cochrane Central Register of Controlled Trials, CINAHL, Embase, LILACS database, MEDLINE, MEDLINE In-Process, and PsycINFO. Additional sources were international trial registries, drug approval agency websites, and key scientific journals.Study selectionAnalysis was restricted to double-blind, randomized placebo-controlled trials with available data at the study's end point.Data extraction and synthesisBaseline and end point means, SDs, number of participants in each group, antidepressant class, and publication year were extracted. The data were analyzed between August 14 and November 18, 2019.Main outcomes and measuresWith the use of validated methods, coefficients of variation were derived for antidepressants and placebo, and their ratios were calculated to compare outcome variability between antidepressant and placebo. Ratios were entered into a random-effects model, with the expectation that response to antidepressants would be more variable than response to placebo. Analysis was repeated after stratifying by baseline severity of depression, antidepressant class (selective serotonin reuptake inhibitors: citalopram, escitalopram, fluoxetine, fluvoxamine, paroxetine, sertraline, and vilazodone; serotonin and norepinephrine reuptake inhibitors: desvenlafaxine and venlafaxine; norepinephrine-dopamine reuptake inhibitor: bupropion; noradrenergic agents: amitriptyline and reboxetine; and other antidepressants: agomelatine, mirtazapine, and trazodone), and publication year.ResultsIn the 87 eligible randomized placebo-controlled trials (17 540 unique participants), there was significantly more variability in response to antidepressants than to placebo (coefficients of variation ratio, 1.14; 95% CI, 1.11-1.17; P < .001). Baseline severity of depression did not moderate variability in response to antidepressants. Variability in response to selective serotonin reuptake inhibitors was lower than variability in response to noradrenergic agents (coefficients of variation ratio, 0.88; 95% CI, 0.80-0.97; P = .01), as was the variability in response to other antidepressants compared with noradrenergic agents (coefficients of variation ratio, 0.87; 95% CI, 0.79-0.97; P = .001). Variability also tended to be lower in studies that were published more recently, with coefficients of variation changing by a value of 0.005 (95% CI, 0.002-0.008; P = .003) for every year a study is more recent.Conclusions and relevanceIndividual differences may be systematically associated with responses to antidepressants in major depressive disorder beyond placebo effects or statistical factors. This study provides empirical support for identifying moderators and personalizing antidepressant treatment.

Dataset Information

Individual differences methods for randomized experiments.

Publications

Individual differences methods for randomized experiments.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets