Correction of sampling bias in a cross-sectional study of post-surgical complications

Ronen Fluss*, Micha Mandel, Laurence S. Freedman, Inbal Salz Weiss, Anat Ekka Zohar, Ziona Haklai, Ethel Sherry Gordon, Elisheva Simchen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


Cross-sectional designs are often used to monitor the proportion of infections and other post-surgical complications acquired in hospitals. However, conventional methods for estimating incidence proportions when applied to cross-sectional data may provide estimators that are highly biased, as cross-sectional designs tend to include a high proportion of patients with prolonged hospitalization. One common solution is to use sampling weights in the analysis, which adjust for the sampling bias inherent in a cross-sectional design. The current paper describes in detail a method to build weights for a national survey of post-surgical complications conducted in Israel. We use the weights to estimate the probability of surgical site infections following colon resection, and validate the results of the weighted analysis by comparing them with those obtained from a parallel study with a historically prospective design.

Original languageAmerican English
Pages (from-to)2467-2478
Number of pages12
JournalStatistics in Medicine
Issue number14
StatePublished - 30 Jun 2013


  • Length bias
  • Post-surgical infections
  • Prevalence
  • Weighted analysis


Dive into the research topics of 'Correction of sampling bias in a cross-sectional study of post-surgical complications'. Together they form a unique fingerprint.

Cite this