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Biomedical subjects

William R Shadish

Publications and source records attributed to William R Shadish.

10 recordsLinked to original sources
PubMed2006

On blowing trumpets to the tulips: to prove or not to prove the null hypothesis--comment on Bösch, Steinkamp, and Boller (2006).

The H. Bösch, F. Steinkamp, and E. Boller meta-analysis reaches mixed and cautious conclusions about the possibility of psychokinesis. The authors argue that, for both methodological and philosophical reasons, it is nearly impossible to draw any conclusions from this body of research. The authors do not agree that any significant effect at all, no matter how small, is fundamentally important (Bösch et al., 2006, p. 517), and they suggest that psychokinesis researchers focus either on producing larger effects or on specifying the conditions under which they would be willing to accept the null hypothesis.

Humans↗
PubMed2005

Effects of behavioral marital therapy: a meta-analysis of randomized controlled trials.

This meta-analysis summarizes results from 30 randomized experiments that compare behavioral marital therapy with no-treatment control with distressed couples. Results showed that behavioral marital therapy is significantly more effective than no treatment (d=.585). Although behavioral marital therapy research studies tend to be conducted under conditions that are less clinically representative than other samples of studies, representativeness was not significantly related to outcome. However, evidence also suggested that publication bias may exist in this literature whereby small sample studies with small effects are systematically missing compared with other studies. This bias may inflate the effects of behavioral marital therapies reported in previous meta-analyses, though we also explore a number of alternative explanations for this small sample bias.

Behavior Therapy↗
PubMed2005

Empirically supported treatments or type I errors? Problems with the analysis of data from group-administered treatments.

When treatments are administered in groups, clients interact in ways that lead to violations of a key assumption of most statistical analyses-the assumption of independence of observations. The resulting dependencies, when not properly accounted for, can increase Type I errors dramatically. Of the 33 studies of group-administered treatment on the empirically supported treatments list, none appropriately analyzed their data. The current authors provide corrections that can be applied to improper analyses. After the corrections, only 12.4% to 68.2% of tests that were originally reported as significant remained significant, depending on what assumptions were made about how large the dependencies among observations really are. Of the 33 studies, 6-19 studies no longer had any significant results after correction. The authors end by providing recommendations for researchers planning group-administered treatment research.

Analysis of Variance↗
PubMed2005

Increasing the degrees of freedom in existing group randomized trials: the df* approach.

This study describes a method for incorporating external estimates of intraclass correlation to improve the precision for the analysis of an existing group-randomized trial. The authors use a random-effects meta-analytic approach to pool the information across studies, which takes into account any interstudy heterogeneity that may exist. This approach can be used in several different situations to estimate the degrees of freedom available for an adjusted test of the intervention effect in a study where the challenges of group-randomized trials were not fully considered when the study was planned. The authors discuss the limitations of this approach and the circumstances in which it is likely to be helpful.

Humans↗
PubMed2005

Increasing the degrees of freedom in future group randomized trials: the df* approach.

This article builds on the previous article by Blitstein et al. (2005), which showed how external estimates of intraclass correlation can be used to improve the precision for the analysis of an existing group randomized trial. The authors extend that work to sample size estimation and power analysis for future group-randomized trials. Often this approach will allow a smaller study than would otherwise be possible without sacrificing statistical power. Such studies are needed, for example, as pilot studies to help plan for a full-scale efficacy trial, as replication studies, or in situations in which resource constraints prohibit a larger trial. The authors discuss the circumstances under which this strategy will be most helpful and the risks associated with conducting smaller studies.

Humans↗
PubMed2005

Propensity scores: an introduction and experimental test.

Propensity score analysis is a relatively recent statistical innovation that is useful in the analysis of data from quasi-experiments. The goal of propensity score analysis is to balance two non-equivalent groups on observed covariates to get more accurate estimates of the effects of a treatment on which the two groups differ. This article presents a general introduction to propensity score analysis, provides an example using data from a quasi-experiment compared to a benchmark randomized experiment, offers practical advice about how to do such analyses, and discusses some limitations of the approach. It also presents the first detailed instructions to appear in the literature on how to use classification tree analysis and bagging for classification trees in the construction of propensity scores. The latter two examples serve as an introduction for researchers interested in computing propensity scores using more complex classification algorithms known as ensemble methods.

Algorithms↗
PubMed2003

Meta-analysis of MFT interventions.

This article briefly reviews 20 meta-analyses of marital and family interventions. These meta-analyses support the efficacy of both MFT for distressed couples, and martial and family enrichment. Those effects are slightly reduced at follow-up, but still significant. Differences among kinds of marital and family interventions tend to be small. MFT produce clinically significant results in 40-50% of those treated, but the effects of MFT in clinically representative settings have not been much studied. The article also introduces the concept of meta-analytically supported treatments (MASTs), which are treatments that meet certain criteria for efficacy in meta-analysis, and which remedy certain problems in the empirically supported treatment (EST) literature. The article concludes with recommendations for doing better meta-analyses.

Clinical Trials as Topic↗
PubMed2002

Revisiting field experimentation: field notes for the future.

Field experiments in the social sciences were increasingly used in the 20th century. This article briefly reviews some important lessons in design, analysis, and theory of field experiments emerging from that experience. Topics include the importance of ensuring that selection into experiments and assignment to conditions occurs properly, how to prevent and analyze attrition, the need to attend to power and effect size, how to measure and take partial treatment implementation into account in analyses, modern analyses of quasi-experimental and multilevel data, Rubin's model, and the role of internal and external validity. The article ends with observations on the computer revolution in methodology and statistics, convergences in theory and methods across disciplines, the need for an empirical program of methodological research, the key problem of selection bias, and the inevitability of increased specialization in field experimentation in the years to come.

Forecasting↗