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DAVID ALLISON to Data Interpretation, Statistical

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This is a "connection" page, showing publications DAVID ALLISON has written about Data Interpretation, Statistical.
DAVID ALLISON
Connection Strength
5.118
Data Interpretation, Statistical
  1. Persistent confusion in nutrition and obesity research about the validity of classic nonparametric tests in the presence of heteroscedasticity: evidence of the problem and valid alternatives. Am J Clin Nutr. 2021 03 11; 113(3):517-524.
    View in: PubMed
    Score: 0.627
  2. Murine genetic models of obesity: type I error rates and the power of commonly used analyses as assessed by plasmode-based simulation. Int J Obes (Lond). 2020 06; 44(6):1440-1449.
    View in: PubMed
    Score: 0.583
  3. The claim that effectiveness has been demonstrated in the Parenting, Eating and Activity for Child Health (PEACH) childhood obesity intervention is unsubstantiated by the data. Br J Nutr. 2018 10; 120(8):958-959.
    View in: PubMed
    Score: 0.526
  4. Common scientific and statistical errors in obesity research. Obesity (Silver Spring). 2016 Apr; 24(4):781-90.
    View in: PubMed
    Score: 0.445
  5. White hat bias: examples of its presence in obesity research and a call for renewed commitment to faithfulness in research reporting. Int J Obes (Lond). 2010 Jan; 34(1):84-8; discussion 83.
    View in: PubMed
    Score: 0.287
  6. Drugs associated with more suicidal ideations are also associated with more suicide attempts. PLoS One. 2009 Oct 02; 4(10):e7312.
    View in: PubMed
    Score: 0.284
  7. Testing for differences in distribution tails to test for differences in 'maximum' lifespan. BMC Med Res Methodol. 2008 Jul 25; 8:49.
    View in: PubMed
    Score: 0.261
  8. Detection of gene x gene interactions in genome-wide association studies of human population data. Hum Hered. 2007; 63(2):67-84.
    View in: PubMed
    Score: 0.236
  9. Improving statistical rigor in animal aging research by addressing clustering and nesting effects: Illustration with the National Institute on Aging's Intervention Testing Program data. J Gerontol A Biol Sci Med Sci. 2026 Apr 07; 81(5).
    View in: PubMed
    Score: 0.223
  10. The PowerAtlas: a power and sample size atlas for microarray experimental design and research. BMC Bioinformatics. 2006 Feb 22; 7:84.
    View in: PubMed
    Score: 0.221
  11. Misstatements, misperceptions, and mistakes in controlling for covariates in observational research. Elife. 2024 May 16; 13.
    View in: PubMed
    Score: 0.195
  12. From Model Organisms to Humans, the Opportunity for More Rigor in Methodologic and Statistical Analysis, Design, and Interpretation of Aging and Senescence Research. J Gerontol A Biol Sci Med Sci. 2022 11 21; 77(11):2155-2164.
    View in: PubMed
    Score: 0.176
  13. Best (but oft-forgotten) practices: identifying and accounting for regression to the mean in nutrition and obesity research. Am J Clin Nutr. 2020 02 01; 111(2):256-265.
    View in: PubMed
    Score: 0.145
  14. Regression to the Mean: A Commonly Overlooked and Misunderstood Factor Leading to Unjustified Conclusions in Pediatric Obesity Research. Child Obes. 2016 Apr; 12(2):155-8.
    View in: PubMed
    Score: 0.111
  15. Statistical considerations regarding the use of ratios to adjust data. Int J Obes Relat Metab Disord. 1995 Sep; 19(9):644-52.
    View in: PubMed
    Score: 0.107
  16. A novel generalized normal distribution for human longevity and other negatively skewed data. PLoS One. 2012; 7(5):e37025.
    View in: PubMed
    Score: 0.085
  17. Misuse of odds ratios in obesity literature: an empirical analysis of published studies. Obesity (Silver Spring). 2012 Aug; 20(8):1726-31.
    View in: PubMed
    Score: 0.084
  18. Getting carried away: a note showing baseline observation carried forward (BOCF) results can be calculated from published complete-cases results. Int J Obes (Lond). 2012 Jun; 36(6):886-9.
    View in: PubMed
    Score: 0.078
  19. Use of causal language in observational studies of obesity and nutrition. Obes Facts. 2010 Dec; 3(6):353-6.
    View in: PubMed
    Score: 0.077
  20. Missing data in randomized clinical trials for weight loss: scope of the problem, state of the field, and performance of statistical methods. PLoS One. 2009 Aug 13; 4(8):e6624.
    View in: PubMed
    Score: 0.070
  21. Obesity--still highly heritable after all these years. Am J Clin Nutr. 2008 Feb; 87(2):275-6.
    View in: PubMed
    Score: 0.063
  22. Microarray data analysis: from disarray to consolidation and consensus. Nat Rev Genet. 2006 Jan; 7(1):55-65.
    View in: PubMed
    Score: 0.055
  23. HDBStat!: a platform-independent software suite for statistical analysis of high dimensional biology data. BMC Bioinformatics. 2005 Apr 06; 6:86.
    View in: PubMed
    Score: 0.052
  24. Comparison of linear weighting schemes for perfect match and mismatch gene expression levels from microarray data. Am J Pharmacogenomics. 2005; 5(3):197-205.
    View in: PubMed
    Score: 0.051
  25. Effect of Box-Cox transformation on power of Haseman-Elston and maximum-likelihood variance components tests to detect quantitative trait Loci. Hum Hered. 2003; 55(2-3):108-16.
    View in: PubMed
    Score: 0.044
  26. Repeatability of published microarray gene expression analyses. Nat Genet. 2009 Feb; 41(2):149-55.
    View in: PubMed
    Score: 0.016
  27. PYY3-36 as an anti-obesity drug target. Obes Rev. 2005 Nov; 6(4):307-22.
    View in: PubMed
    Score: 0.014
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Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.