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Lenzenweger MF. Schizotypy, Schizotypic Psychopathology, and Schizophrenia: Hearing Echoes, Leveraging Prior Advances, and Probing New Angles. Schizophr Bull 2018; 44:S564-S569. [PMID: 29897550 PMCID: PMC6188523 DOI: 10.1093/schbul/sby083] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
The nature and definition of schizotypy, as the latent liability for schizophrenia capable of generating various phenotypic and endophenotypic outcomes, is reviewed. The proceedings of the 2017 meeting of the International Consortium on Schizotypy Research are included in this Special Section and they are presented as illustrations of current research work on schizotypy. The potential leverage of the schizotypy framework for schizophrenia research continues to be realized and these articles present current research efforts that explore new angles of inquiry while building upon past advances. Methodological and substantive areas of concern are highlighted and suggestions for improvement of future schizotypy research are made.
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Affiliation(s)
- Mark F Lenzenweger
- Department of Psychology, The State University of New York at Binghamton, Binghamton, and Department of Psychiatry, Weill Cornell Medical College, New York, NY,To whom correspondence should be addressed; Department of Psychology, The State University of New York at Binghamton, Science IV (G-08), Binghamton, NY 13902-6000, USA; tel: (607)-777-7148, fax: (607)-777-4890, e-mail:
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Morgan CJ, Coleman MJ, Ulgen A, Boling L, Cole JO, Johnson FV, Lerbinger J, Bodkin JA, Holzman PS, Levy DL. Thought Disorder in Schizophrenia and Bipolar Disorder Probands, Their Relatives, and Nonpsychiatric Controls. Schizophr Bull 2017; 43:523-535. [PMID: 28338967 PMCID: PMC5463905 DOI: 10.1093/schbul/sbx016] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Thought disorder (TD) has long been associated with schizophrenia (SZ) and is now widely recognized as a symptom of mania and other psychotic disorders as well. Previous studies have suggested that the TD found in the clinically unaffected relatives of SZ, schizoaffective and bipolar probands is qualitatively similar to that found in the probands themselves. Here, we examine which quantitative measures of TD optimize the distinction between patients with diagnoses of SZ and bipolar disorder with psychotic features (BP) from nonpsychiatric controls (NC) and from each other. In addition, we investigate whether these same TD measures also distinguish their respective clinically unaffected relatives (RelSZ, RelBP) from controls as well as from each other. We find that deviant verbalizations are significantly associated with SZ and are co-familial in clinically unaffected RelSZ, but are dissociated from, and are not co-familial for, BP disorder. In contrast, combinatory thinking was nonspecifically associated with psychosis, but did not aggregate in either group of relatives. These results provide further support for the usefulness of TD for identifying potential non-penetrant carriers of SZ-risk genes, in turn enhancing the power of genetic analyses. These findings also suggest that further refinement of the TD phenotype may be needed in order to be suitable for use in genetic studies of bipolar disorder.
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Affiliation(s)
- Charity J Morgan
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL
| | | | - Ayse Ulgen
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY
| | - Lenore Boling
- Psychology Research Laboratory, McLean Hospital, Belmont, MA
| | - Jonathan O Cole
- Psychology Research Laboratory, McLean Hospital, Belmont, MA
- Department of Psychiatry, Harvard Medical School, Boston, MA
| | | | - Jan Lerbinger
- Psychology Research Laboratory, McLean Hospital, Belmont, MA
| | - J Alexander Bodkin
- Psychology Research Laboratory, McLean Hospital, Belmont, MA
- Department of Psychiatry, Harvard Medical School, Boston, MA
| | - Philip S Holzman
- Psychology Research Laboratory, McLean Hospital, Belmont, MA
- Department of Psychiatry, Harvard Medical School, Boston, MA
| | - Deborah L Levy
- Psychology Research Laboratory, McLean Hospital, Belmont, MA
- Department of Psychiatry, Harvard Medical School, Boston, MA
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Benecha HK, Neelon B, Divaris K, Preisser JS. Marginalized mixture models for count data from multiple source populations. JOURNAL OF STATISTICAL DISTRIBUTIONS AND APPLICATIONS 2017; 4:3. [PMID: 28446995 PMCID: PMC5384970 DOI: 10.1186/s40488-017-0057-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2016] [Accepted: 03/20/2017] [Indexed: 11/18/2022]
Abstract
Mixture distributions provide flexibility in modeling data collected from populations having unexplained heterogeneity. While interpretations of regression parameters from traditional finite mixture models are specific to unobserved subpopulations or latent classes, investigators are often interested in making inferences about the marginal mean of a count variable in the overall population. Recently, marginal mean regression modeling procedures for zero-inflated count outcomes have been introduced within the framework of maximum likelihood estimation of zero-inflated Poisson and negative binomial regression models. In this article, we propose marginalized mixture regression models based on two-component mixtures of non-degenerate count data distributions that provide directly interpretable estimates of exposure effects on the overall population mean of a count outcome. The models are examined using simulations and applied to two datasets, one from a double-blind dental caries incidence trial, and the other from a horticultural experiment. The finite sample performance of the proposed models are compared with each other and with marginalized zero-inflated count models, as well as ordinary Poisson and negative binomial regression.
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Affiliation(s)
- Habtamu K Benecha
- National Agricultural Statistics Service, USDA, Washington, 20250 DC USA
| | - Brian Neelon
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, 29425 SC USA
| | - Kimon Divaris
- Departments of Epidemiology and Pediatric Dentistry, University of North Carolina, Chapel Hill, 27599-7450 NC USA
| | - John S Preisser
- Department of Biostatistics, University of North Carolina, Chapel Hill, 27599-7420 NC USA
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Yiu S, Tom BDM, Farewell VT. Trivariate mover-stayer counting process models for investigating joint damage in psoriatic arthritis. Stat Med 2016; 35:5701-5716. [PMID: 27501256 PMCID: PMC5157786 DOI: 10.1002/sim.7074] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2015] [Revised: 04/28/2016] [Accepted: 07/13/2016] [Indexed: 11/29/2022]
Abstract
In psoriatic arthritis, many patients do not develop permanent joint damage even after a prolonged follow‐up. This has led several authors to consider the possibility of a subpopulation of stayers (those who do not have the propensity to experience the event of interest), as opposed to assuming the entire population consist of movers (those who have the propensity to experience the event of interest). In addition, it is recognised that the damaged joints process may act very differently across different joint areas, particularly the hands, feet and large joints. From a clinical perspective, interest lies in identifying possible relationships between the damaged joints processes in these joint areas for the movers and estimating the proportion of stayers in these joint areas, if they exist. For this purpose, this paper proposes a novel trivariate mover‐stayer model consisting of mover‐stayer truncated negative binomial margins, and patient‐level dynamic covariates and random effects in the models for the movers and stayers, respectively. The model is then extended to have a two‐level mover‐stayer structure for its margins so that the nature of the stayer property can be investigated. A particularly attractive feature of the proposed models is that only an optimisation routine is required in their model fitting procedures. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
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Affiliation(s)
- Sean Yiu
- MRC Biostatistics Unit, Cambridge, CB2 0SR, U.K
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Witkiewitz K, Finney JW, Harris AH, Kivlahan DR, Kranzler HR. Recommendations for the Design and Analysis of Treatment Trials for Alcohol Use Disorders. Alcohol Clin Exp Res 2015; 39:1557-70. [PMID: 26250333 PMCID: PMC4558228 DOI: 10.1111/acer.12800] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2015] [Accepted: 05/30/2015] [Indexed: 12/14/2022]
Abstract
BACKGROUND Over the past 60 years, the view that "alcoholism" is a disease for which the only acceptable goal of treatment is abstinence has given way to the recognition that alcohol use disorders (AUDs) occur on a continuum of severity, for which a variety of treatment options are appropriate. However, because the available treatments for AUDs are not effective for everyone, more research is needed to develop novel and more efficacious treatments to address the range of AUD severity in diverse populations. Here we offer recommendations for the design and analysis of alcohol treatment trials, with a specific focus on the careful conduct of randomized clinical trials of medications and nonpharmacological interventions for AUDs. METHODS This paper provides a narrative review of the quality of published clinical trials and recommendations for the optimal design and analysis of treatment trials for AUDs. RESULTS Despite considerable improvements in the design of alcohol clinical trials over the past 2 decades, many studies of AUD treatments have used faulty design features and statistical methods that are known to produce biased estimates of treatment efficacy. CONCLUSIONS The published statistical and methodological literatures provide clear guidance on methods to improve clinical trial design and analysis. Consistent use of state-of-the-art design features and analytic approaches will enhance the internal and external validity of treatment trials for AUDs across the spectrum of severity. The ultimate result of this attention to methodological rigor is that better treatment options will be identified for patients with an AUD.
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Affiliation(s)
- Katie Witkiewitz
- Department of Psychology and Center on Alcoholism, Substance Abuse, and Addictions, University of New Mexico
| | - John W. Finney
- Center for Innovation to Implementation, VA Palo Alto Health Care System, Menlo Park, CA
| | - Alex H.S Harris
- VA Substance Use Disorder Quality Enhancement Research Initiative, VA Palo Alto Health Care System, Menlo Park, CA
| | - Daniel R. Kivlahan
- Veterans Health Administration, Washington, DC and Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle
| | - Henry R. Kranzler
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine and VISN4 MIRECC, Philadelphia VAMC, Philadelphia, PA
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Cairns J, Lynch AG, Tavaré S. Quantifying the impact of inter-site heterogeneity on the distribution of ChIP-seq data. Front Genet 2014; 5:399. [PMID: 25452765 PMCID: PMC4231950 DOI: 10.3389/fgene.2014.00399] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2014] [Accepted: 10/29/2014] [Indexed: 12/13/2022] Open
Abstract
Chromatin Immunoprecipitation followed by sequencing (ChIP-seq) is a valuable tool for epigenetic studies. Analysis of the data arising from ChIP-seq experiments often requires implicit or explicit statistical modeling of the read counts. The simple Poisson model is attractive, but does not provide a good fit to observed ChIP-seq data. Researchers therefore often either extend to a more general model (e.g., the Negative Binomial), and/or exclude regions of the genome that do not conform to the model. Since many modeling strategies employed for ChIP-seq data reduce to fitting a mixture of Poisson distributions, we explore the problem of inferring the optimal mixing distribution. We apply the Constrained Newton Method (CNM), which suggests the Negative Binomial - Negative Binomial (NB-NB) mixture model as a candidate for modeling ChIP-seq data. We illustrate fitting the NB-NB model with an accelerated EM algorithm on four data sets from three species. Zero-inflated models have been suggested as an approach to improve model fit for ChIP-seq data. We show that the NB-NB mixture model requires no zero-inflation and suggest that in some cases the need for zero inflation is driven by the model's inability to cope with both artifactual large read counts and the frequently observed very low read counts. We see that the CNM-based approach is a useful diagnostic for the assessment of model fit and inference in ChIP-seq data and beyond. Use of the suggested NB-NB mixture model will be of value not only when calling peaks or otherwise modeling ChIP-seq data, but also when simulating data or constructing blacklists de novo.
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Affiliation(s)
- Jonathan Cairns
- Nuclear Dynamics Group, The Babraham Institute Cambridge, UK ; Cancer Research UK Cambridge Institute, University of Cambridge Cambridge, UK
| | - Andy G Lynch
- Cancer Research UK Cambridge Institute, University of Cambridge Cambridge, UK
| | - Simon Tavaré
- Cancer Research UK Cambridge Institute, University of Cambridge Cambridge, UK
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