  N-of-1 trials allow inference between two treatments given to a single
individual. Most often, clinical investigators analyze an individual's N-of-1
trial data with usual t-tests or simple nonparametric methods. These simple
methods do not account for serial correlation in repeated observations coming
from the individual. Existing methods accounting for serial correlation require
simulation, multiple N-of-1 trials, or both. Here, we develop t-tests that
account for serial correlation in a single individual. The development includes
effect size and precision calculations, both of which are useful for study
planning. We then evaluate and compare their Type I and II errors and interval
estimators to those of usual t-tests analogues via Monte Carlo simulation. The
serial t-tests clearly outperform the usual t-tests commonly used in reporting
N-of-1 results. Examples from N-of-1 clinical trials in fibromyalgia patients
and from a behavioral health setting exhibit how accounting for serial
correlation can change inferences. These t-tests are easily implemented and
more appropriate than simple methods commonly used; however, caution is needed
when analyzing only a few observations. Keywords: Autocorrelation; Cross-over
studies; Repeated measures analysis; Single-case experimental design;
Time-series
