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Purpose: Use of real‐world data (RWD) for external controls added to single‐arm trials (SAT) is increasingly prevalent in regulatory submissions. Due to inherent differences in the data‐generating mechanisms, biases can arise. This paper aims to illustrate how to use quantitative bias analysis (QBA). Methods: Advanced non‐small cell lung cancer (NSCLC) serves as an example, where many small subsets of patients with molecular tumor subtypes exist. First, some sources of bias that may occur in oncology when comparing RWD to SAT are described. Second, using a hypothetical immunotherapy agent, a dataset is simulated based on expert input for survival analysis of advanced NSCLC. Finally, we illustrate the impact of three biases: missing confounder, misclassification of exposure, and outcome evaluation. Results: For each simulated scenario, bias was induced by removing or adding data; hazard ratios (HRs) were estimated applying conventional analyses. Estimating the bias‐adjusted treatment effect and uncertainty required carefully selecting the bias model and bias factors. Although the magnitude of each biased and bias‐adjusted HR appeared moderate in all three hypothetical scenarios, the direction of bias was variable. Conclusion: These findings suggest that QBA can provide an intuitive framework for bias analysis, providing a key means of challenging assumptions about the evidence. However, the accuracy of bias analysis is itself dependent on correct specification of the bias model and bias factors. Ultimately, study design should reduce bias, but QBA allows us to evaluate the impact of unavoidable bias to assess the quality of the evidence. [ABSTRACT FROM AUTHOR]

Serious developmental disabilities (DD) are quite common and affect approximately 2% of all school-aged children. The impact of DDs with respect to the need for special education services, medical care and the demand on family members can be enormous. While this impact can be magnified for families with more than one child with a DD, little is known regarding the epidemiology of recurrence of DDs. When the cause of a DD is unknown, genetic counsellors rely on recurrence risk estimates which for DDs are over 10 years old. The objectives of our study were to: (1) assess the contribution of recurrent cases to the prevalence of DDs; (2) provide current, population-based recurrence risk estimates; and (3) examine characteristics of the first affected child as predictors of recurrence. Two population-based data sources were used to identify all children born to the same mother during the period 1981–91 in the five-county metropolitan Atlanta area with at least one of four DDs: mental retardation (MR), cerebral palsy, hearing loss, or vision impairment. Recurrence risk estimates for these DDs ranged from 3% to 7% and were many times higher than the background prevalences. The risk of recurrence of DDs was greatest for MR– approximately eight times greater than the baseline MR prevalence. Isolated mild MR (IQ 50–70) was highly concordant between siblings with MR. Sex, race, and birthweight of the index child, maternal education, and maternal age were not significantly associated with recurrence risk. Further research is needed to investigate the roles of genetic and environmental factors on the recurrence of DDs, particularly isolated mild MR. [ABSTRACT FROM AUTHOR]