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Background: Lymphoceles (LC) represent a well-described rare complication post-radical prostatectomy (RP). Our aim was to determine risk factors and to develop possible prevention strategies for LC in a community-based study. Methods: Data from 1163 RP-patients from 67 clinics between January 2002 and December 2004 were retrospectively evaluated. Patients underwent pelvic imaging procedures/LC-management during 3 weeks of rehabilitation post-RP. Results: LC were identified in 304 patients (26%). Lymphadenectomy was carried out in 92% of patients (1001/1086 patients), from which 28% had LC (n = 277) versus 14% without lymphadenectomy (12/85, P = 0.007). Complications (lower limb edema, pain, thrombosis, infection and bladder compression) were observed in 9% of patients (28/304; 2.4% of total patients); necessitating therapy. LC therapy was carried out in 59 patients (5.9%) with pelvic lymph node dissection (PLND) and in no patients (0%) without PLND (P = 0.021). Risk factors included were patients' age, body mass index, prostate volume, TNM-classification, number of removed lymph nodes, previous surgery/therapy, heparin prophylaxis, surgical instruments and pelvic lymphadenectomy. Univariate analysis showed lymphadenectomy as the only significant risk factor for the development of LC post-RP (P = 0.007). When applying multivariate analyses using stepwise logistic regression, only lymphadenectomy was associated with a significant risk for lymphoceles (odds ratio = 2.6, 95% CI = 1.3-4.9, P = 0.004). Adjusting for other factors, no other factor came close to being significant (P < 0.05). All symptomatic LC were successfully treated without further sequelae. Conclusions: Subclinical LC post-RP are more common than thought, and rarely necessitate intervention. Pelvic lymphadenectomy represents the only significant factor contributing to LC-development. Because of this, prevention remains difficult.

For a given research question, there are usually a large variety of possible analysis strategies acceptable according to the scientific standards of the field, and there are concerns that this multiplicity of analysis strategies plays an important role in the non-replicability of research findings. Here, we define a general framework on common sources of uncertainty arising in computational analyses that lead to this multiplicity, and apply this framework within an overview of approaches proposed across disciplines to address the issue. Armed with this framework, and a set of recommendations derived therefrom, researchers will be able to recognize strategies applicable to their field and use them to generate findings more likely to be replicated in future studies, ultimately improving the credibility of the scientific process.

Statisticians have been keen to critique statistical aspects of the “replication crisis” in other scientific disciplines. But new statistical tools are often published and promoted without any thought to replicability. This needs to change, argue Anne-Laure Boulesteix, Sabine Hoffmann, Alethea Charlton and Heidi Seibold