TY - JOUR

T1 - The analysis of continuous data from n-of-1 trials using paired cycles

T2 - a simple tutorial

AU - Senn, Stephen

N1 - Publisher Copyright:
© The Author(s) 2024.

PY - 2024/2/16

Y1 - 2024/2/16

N2 - N-of-1 trials are defined and the popular paired cycle design is introduced, together with an explanation as to how suitable sequences may be constructed. Various approaches to analysing such trials are explained and illustrated using a simulated data set. It is explained how choosing an appropriate analysis depends on the question one wishes to answer. It is also shown that for a given question, various equivalent approaches to analysis can be found, a fact which may be exploited to expand the possible software routines that may be used. Sets of N-of-1 trials are analogous to sets of parallel group trials. This means that software for carrying out meta-analysis can be used to combine results from N-of-1 trials. In doing so, it is necessary to make one important change, however. Because degrees of freedom for estimating variances for individual subjects will be scarce, it is advisable to estimate local standard errors using pooled variances. How this may be done is explained and fixed and random effect approaches to combining results are illustrated.

AB - N-of-1 trials are defined and the popular paired cycle design is introduced, together with an explanation as to how suitable sequences may be constructed. Various approaches to analysing such trials are explained and illustrated using a simulated data set. It is explained how choosing an appropriate analysis depends on the question one wishes to answer. It is also shown that for a given question, various equivalent approaches to analysis can be found, a fact which may be exploited to expand the possible software routines that may be used. Sets of N-of-1 trials are analogous to sets of parallel group trials. This means that software for carrying out meta-analysis can be used to combine results from N-of-1 trials. In doing so, it is necessary to make one important change, however. Because degrees of freedom for estimating variances for individual subjects will be scarce, it is advisable to estimate local standard errors using pooled variances. How this may be done is explained and fixed and random effect approaches to combining results are illustrated.

UR - http://www.scopus.com/inward/record.url?scp=85185411214&partnerID=8YFLogxK

UR - https://pubmed.ncbi.nlm.nih.gov/38365817

U2 - 10.1186/s13063-024-07964-7

DO - 10.1186/s13063-024-07964-7

M3 - Article

C2 - 38365817

AN - SCOPUS:85185411214

SN - 1745-6215

VL - 25

JO - Trials

JF - Trials

IS - 1

M1 - 128

ER -