Effect of different sampling techniques on odds ratio estimates using hospital-based cases and controls

Potential biases introduced by the use of hospital admission records have rarely been discussed in the veterinary literature. Veterinary Medical Teaching Hospital (VMTH) patient records kept at the University of California, Davis (UCD) School of Veterinary Medicine provide a unique opportunity to perform in-depth analyses on the effect of different control selection (sampling) techniques on odds ratio (OR) estimates for disease risk factors in a retrospective case-control study. Horses with Corynebacterium pseudotuberculosis abscesses (134) and the (secondary) study base population (source for controls) were identified, and a 'gold standard' OR for each category of the factors admission type, age, breed and sex was derived. Example data were used to calculate sampling ratios (SRs), defined as the ratio between any sample proportion (of an arbitrary risk factor) and the study base proportion for this risk factor. Sampling ratios different from 1.0 introduced biases into the observed OR estimates, when compared with the 'gold standard' OR. Three randomized samples (simple random, stratified random, systematic sampling), one matched (on date of admission) and three different diagnosis samples ('colic', 'cuts and lacerations', 'fractures') were selected from the study base, and the SRs for all categories of the four factors were derived. The matched and two different disease samples ('colic' and 'fractures') had especially wide ranges of observed SRs (and large errors in the OR estimates), whereas simple random and systematic sampling had comparably narrow ranges (less biased OR estimates). For the three randomized sampling techniques under study, repeated sampling was used to derive SR distributions. The SRs were approximately normally distributed. Analysis of variance and covariance showed that simple random and systematic sampling provided SR distributions with means closest to 1.0 (expected value) and small standard deviations. The OR estimates obtained from records selected by these two sampling techniques therefore were least biased. The findings demonstrate the importance of selecting appropriate sampling techniques in addition to properly defining the study (base) population. Sampling design introduces uncertainty into the OR estimates. The direction of the bias, however, depends on the OR between factor and disease in the source population (the 'gold standard'), and on the direction and magnitude of the SR. When combining the results from single and repeated sampling we conclude that sampling design is most influential on the range of the observed SRs (single samples), on the absolute deviation of the SR from 1.0 (expressed as SR delta Mean) and on the SR standard deviation (SD) (repeated sampling).