Corroborating evidence by exploring sources of bias in observational spinal cord injury studies

Observational studies investigating large real-life datasets are a valuable resource in clinical research. Understanding the imperfect nature of clinical data, statistical approaches factoring in known confounders are instrumental for rigorously addressing bias.1 Our recent work identifying pneumonia and postoperative wound infections (Pn/Wi) as risk markers for impaired long-term functional recovery and survival after spinal cord injury (SCI)2 was considered as a strong statistical analysis.3 However, some unexplored putative confounders in terms of nonrandom loss to follow-up, temporal changes in clinical practice, and exclusion criteria were discussed.3 In order to evaluate and objectivize for the probability of attrition and temporal and selection bias, we apply and discuss an array of analytical tools extending beyond the format of the original publication.2