Why we should use simpler models if the data allow this: relevance for ANOVA designs in experimental biology.
BACKGROUND
Analysis of variance (ANOVA) is a common statistical technique in physiological research, and often one or more of the independent/predictor variables such as dose, time, or age, can be treated as a continuous, rather than a categorical variable during analysis - even if subjects were randomly assigned to treatment groups. While this is not common, there are a number of advantages of such an approach, including greater statistical power due to increased precision, a simpler and more informative interpretation of the results, greater parsimony, and transformation of the predictor variable is possible.


RESULTS
An example is given from an experiment where rats were randomly assigned to receive either 0, 60, 180, or 240 mg/L of fluoxetine in their drinking water, with performance on the forced swim test as the outcome measure. Dose was treated as either a categorical or continuous variable during analysis, with the latter analysis leading to a more powerful test (p = 0.021 vs. p = 0.159). This will be true in general, and the reasons for this are discussed.


CONCLUSIONS
There are many advantages to treating variables as continuous numeric variables if the data allow this, and this should be employed more often in experimental biology. Failure to use the optimal analysis runs the risk of missing significant effects or relationships.
Lazic
Stanley E
SE
Department of Physiology, Development and Neuroscience, University of Cambridge, CB2 3DY, UK. stan.lazic@cantab.net
D000704:Analysis of Variance; D000818:Animals; D018687:Antidepressive Agents, Second-Generation / Q000008:administration & dosage / Q000494:pharmacology; D001695:Biology / Q000379:methods*; D003068:Coercion; D003627:Data Interpretation, Statistical; D004305:Dose-Response Relationship, Drug; D005473:Fluoxetine / Q000008:administration & dosage / Q000494:pharmacology; D046353:Immobility Response, Tonic / Q000187:drug effects; D008297:Male; D051381:Rats; D017207:Rats, Sprague-Dawley; D012044:Regression Analysis; D012106:Research / Q000706:statistics & numerical data*; D013550:Swimming; D013997:Time Factors
D018687:Antidepressive Agents, Second-Generation; D005473:Fluoxetine