[Publications of dissertations on unconventional medical therapy and diagnosis procedures--a contribution to "publication bias"].
BACKGROUND
The problem of the so-called publication bias has received much attention during the last few years. It may be a major threat to the results of meta-analyses and systematic reviews. Its implications are not completely clear, and successful strategies to deal with this problem are far from being established. Some studies have begun to identify characteristic items which de- or increase the likelihood of publication of research results.


OBJECTIVE
It was to be investigated, whether or not a publication bias can be proved for results of medical theses on all kinds of complementary medical subjects and which modifying factors can be identified.


METHODS
All 140 medical theses on aspects of complementary medicine of the years 1982-1992 were included. Data were extracted, and publications were searched for in MEDLINE and by personal communication with the authors and supervisors. Factors that may influence the likelihood of publication were identified by means of bivariate analysis and logistic regression analysis.


RESULTS
Publications were found for 53 (37,9%) of medical theses included. The most dominant factors which were found to influence the likelihood of publication were 'positive result' (OR 2.337; 95% CI 0.733; 7.450), 'high-level statistical analysis' (1.483; 95% CI 0.710; 3.097), 'supervisers with a high publication output' (1.477; 95% CI 0.164; 13.267), and 'candidates of younger age' (0.691; 95% CI 0.388; 1.230).


CONCLUSIONS
As it was found for other medical subjects, an apparent publication bias was shown in complementary medicine, with positive results having a greater chance of publication than negative ones.
Zimpel
T
T
Abteilung Medizinische Biometrie, Universitätsklinikum Heidelberg, Deutschland.
Windeler
J
J
D000529:Complementary Therapies / Q000193:education*; D004500:Education, Graduate; D016239:MEDLINE; D011643:Publishing / Q000706:statistics & numerical data*