Computational protocol: The PiGeOn project: protocol for a longitudinal study examining psychosocial, behavioural and ethical issues and outcomes in cancer tumour genomic profiling

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Protocol publication

[…] Mean differences in outcomes will be compared using t-test (continuous) or chi-squared tests (dichotomous). Non-parametric tests will also be used where appropriate. Temporal changes in scales will be investigated by calculating the difference between time-points. Multiple (continuous outcomes) or logistic (dichotomous outcomes) regression will be used to adjust for the effect of confounders and identify predictors of outcome. Linear mixed models and logistic mixed models will be carried out in R: A language and environment for statistical computing using package nlme for the linear mixed model and lme4 for the logistic mixed model. Assumptions of normality of residuals and homogeneity of variance will be checked visually though diagnostic residual plots. Multivariable models will be constructed with the inclusion of all potential confounders, and those predictors that show weak evidence for an association with the outcome in univariate analysis. Backwards elimination followed by forwards addition will be used to select predicting variables in the final model. Known and identified confounders will be included regardless of their statistical significance. Collinear independent variables will be identified and removed. […]

Pipeline specifications

Software tools nlme, lme4
Application Mathematical modeling
Organisms Homo sapiens
Diseases Neoplasms