Repeated challenge studies: A comparison of union-intersection testing with linear modeling

Levine, Richard A.; Ohman, Pamela A.

doi:10.1007/bf02294560

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…Hence, because of their very nature, repeated measures designs do not meet the independent observation assumption of traditional parametric statistics (e.g., F tests in ANOVA and MANOVA). Designs with repeated measures require special statistical procedures for proper analysis (e.g., dependent samples statistics, such as repeated measures ANOVA or MANOVA, and Union-Intersection Tests) (Levine & Ohman, 1997). In addition, many repeated measures statistical procedures (e.g., ANOVA and MANOVA) require special follow-up or post hoc tests (e.g., Keselman, 1982;Lix & Keselman, 1995;Maxwell, 1980).…”

Section: Issues In the Analysis Of Repeated Measures Datamentioning

confidence: 99%

Repeated Measures Designs

Ellis

1999

The Counseling Psychologist

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To facilitate innovation in applied psychology research, investigators need to be wellinformed about available research designs. The purpose of this article is to provide an overview of repeated measures research designs (e.g., participants exposed to more than one treatment or measured on more than one occasion). My intent is twofold. First, I underscore the wide range of repeated measures research designs available to researchers in applied psychology. Second, I argue that the differentiation and polarity of group and single-subject research designs is largely arbitrary. I use examples to illustrate each repeated measures design and present its strengths and limitations.

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Section: Issues In the Analysis Of Repeated Measures Datamentioning

confidence: 99%

Repeated Measures Designs

Ellis

1999

The Counseling Psychologist

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“…In this paper we propose methods that aim to detect if any individual at all has a signiÿcant e ect. Levine and Ohman [9] used familiar multiple test procedures under this paradigm. But this approach is only applicable when each subject has multiple observations to construct individual-based tests.…”

Section: Introductionmentioning

confidence: 99%

Analysis of pilot and early phase studies with small sample sizes

Shih

Ohman‐Strickland

Lin

2004

Statistics in Medicine

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The need for statistical methodologies for analysing a small size study, such as a pilot or so-called 'proof of concept' study, has not been paid much attention in the past. Recently the Institute of Medicine (IOM) formed a committee and held a workshop to discuss methodologies for conducting clinical trials with small number participants. In this paper we argue that the hypothesis of treatment effect in a small pilot study should be set up to test whether any individual subject has an effect rather than whether the group mean or median has shifted as often done for large, confirmatory clinical trials. Based on this paradigm we propose multiple test procedures as one option when individuals have enough observations, and a mixture-distribution approach when individuals have one or more observations. The latter approach may be used in either a one- or two-group setting, and is our focus in this paper. We present the likelihood ratio tests for the mixture models. Examples are given to demonstrate the methods.

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Repeated challenge studies: A comparison of union-intersection testing with linear modeling

Cited by 2 publications

References 21 publications

Repeated Measures Designs

Repeated Measures Designs

Analysis of pilot and early phase studies with small sample sizes

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