scholarly journals Sample Size Calculations for Main Effects and Interactions in Case–control Studies using Stata's nchi2 and npnchi2 Functions

2003 ◽  
Vol 3 (1) ◽  
pp. 47-56 ◽  
Author(s):  
Catherine L. Saunders ◽  
D. Timothy Bishop ◽  
Jennifer H. Barrett
Keyword(s):  
Sample Size ◽  
Null Hypothesis ◽  
Case Control ◽  
Case Control Studies ◽  
Gene Environment ◽  
Sample Size Calculations ◽  
Main Effects

The non-central X2 distribution can be used to calculate power for tests detecting departure from a null hypothesis. Required sample size can also be calculated because it is proportional to the non-centrality parameter for the distribution. We demonstrate how these calculations can be carried out in Stata using the example of calculating power and sample size for case–control studies of gene–gene and gene–environment interactions. Do-files are available for these calculations.

scholarly journals Power and sample size for multivariate logistic modeling of unmatched case-control studies

2017 ◽  
Vol 28 (3) ◽  
pp. 822-834
Author(s):  
Mitchell H Gail ◽  
Sebastien Haneuse
Keyword(s):  
Logistic Regression ◽  
Sample Size ◽  
Case Control ◽  
Simulation Methods ◽  
Case Control Studies ◽  
Control Data ◽  
Logistic Analysis ◽  
Sample Size Calculations ◽  
Control Designs ◽  
Univariate Analyses

Sample size calculations are needed to design and assess the feasibility of case-control studies. Although such calculations are readily available for simple case-control designs and univariate analyses, there is limited theory and software for multivariate unconditional logistic analysis of case-control data. Here we outline the theory needed to detect scalar exposure effects or scalar interactions while controlling for other covariates in logistic regression. Both analytical and simulation methods are presented, together with links to the corresponding software.

Power and Sample Size Calculations for Genetic Case/Control Studies Using Gene-Centric SNP Maps: Application to Human Chromosomes 6, 21, and 22 in Three Populations

2005 ◽  
Vol 60 (1) ◽  
pp. 43-60 ◽  
Author(s):  
Francisco M. de La Vega ◽  
Derek Gordon ◽  
Xiaoping Su ◽  
Charles Scafe ◽  
Hadar Isaac ◽  
...  
Keyword(s):  
Sample Size ◽  
Case Control ◽  
Human Chromosomes ◽  
Case Control Studies ◽  
Sample Size Calculations

scholarly journals Genotype Pattern Mining for Pairs of Interacting Variants Underlying Digenic Traits

Genes ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1160
Author(s):  
Atsuko Okazaki ◽  
Sukanya Horpaopan ◽  
Qingrun Zhang ◽  
Matthew Randesi ◽  
Jurg Ott
Keyword(s):  
Null Hypothesis ◽  
Pattern Mining ◽  
Genetic Diseases ◽  
Frequent Pattern Mining ◽  
Case Control ◽  
Frequent Pattern ◽  
Permutation Testing ◽  
Case Control Studies ◽  
P Values ◽  
Dna Variants

Some genetic diseases (“digenic traits”) are due to the interaction between two DNA variants, which presumably reflects biochemical interactions. For example, certain forms of Retinitis Pigmentosa, a type of blindness, occur in the presence of two mutant variants, one each in the ROM1 and RDS genes, while the occurrence of only one such variant results in a normal phenotype. Detecting variant pairs underlying digenic traits by standard genetic methods is difficult and is downright impossible when individual variants alone have minimal effects. Frequent pattern mining (FPM) methods are known to detect patterns of items. We make use of FPM approaches to find pairs of genotypes (from different variants) that can discriminate between cases and controls. Our method is based on genotype patterns of length two, and permutation testing allows assigning p-values to genotype patterns, where the null hypothesis refers to equal pattern frequencies in cases and controls. We compare different interaction search approaches and their properties on the basis of published datasets. Our implementation of FPM to case-control studies is freely available.

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