Statistical issues on the analysis of change in follow-up studies in dental research.
OBJECTIVE
To provide an overview to the problems in study design and associated analyses of follow-up studies in dental research, particularly addressing three issues: treatment-baselineinteractions; statistical power; and nonrandomization.


BACKGROUND
Our previous work has shown that many studies purport an interacion between change (from baseline) and baseline values, which is often based on inappropriate statistical analyses. A priori power calculations are essential for randomized controlled trials (RCTs), but in the pre-test/post-test RCT design it is not well known to dental researchers that the choice of statistical method affects power, and that power is affected by treatment-baseline interactions. A common (good) practice in the analysis of RCT data is to adjust for baseline outcome values using ancova, thereby increasing statistical power. However, an important requirement for ancova is there to be no interaction between the groups and baseline outcome (i.e. effective randomization); the patient-selection process should not cause differences in mean baseline values across groups. This assumption is often violated for nonrandomized (observational) studies and the use of ancova is thus problematic, potentially giving biased estimates, invoking Lord's paradox and leading to difficulties in the interpretation of results.


METHODS
Baseline interaction issues can be overcome by use of statistical methods; not widely practiced in dental research: Oldham's method and multilevel modelling; the latter is preferred for its greater flexibility to deal with more than one follow-up occasion as well as additional covariates To illustrate these three key issues, hypothetical examples are considered from the fields of periodontology, orthodontics, and oral implantology.


CONCLUSIONS
Caution needs to be exercised when considering the design and analysis of follow-up studies. ancova is generally inappropriate for nonrandomized studies and causal inferences from observational data should be avoided.
Blance
Andrew
A
Leeds Dental Institute, University of Leeds, Clarendon Way, Leeds, UK.
Tu
Yu-Kang
YK
Baelum
Vibeke
V
Gilthorpe
Mark S
MS
D000704:Analysis of Variance; D018865:Dental Research / Q000379:methods*; D015340:Epidemiologic Research Design; D005163:Factor Analysis, Statistical; D005500:Follow-Up Studies; D006801:Humans; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D017063:Outcome Assessment, Health Care / Q000379:methods; D036301:Qualitative Research; D016032:Randomized Controlled Trials as Topic