Natural history studies provide important information about disease progression for rare conditions such as ALS. Enrolling participants at time of diagnosis (incident cases) permits prospective data collection throughout the course of their disease. Enrolling participants well after diagnosis with retrospective data collection (prevalent cases) adds value to a dataset but can bias the dataset towards a longer-surviving cohort. Statistical methodologies are available to minimize this bias.

Data was collected from 1,852 subjects from nine multidisciplinary ALS centers. Participants’ enrollment date relative to symptom onset had a median of 0.4 months (IQR 0.0-5.6). 40% of participants were incident cases. 893 participants died during the period. Remaining participants were censored at the date of last visit or date lost to follow up. Survival was estimated with and without left truncation. Left truncation is a statistical method used to address the delay between onset and study enrollment. If both methods return similar results, left truncation might be unnecessary.

The median survival estimates with and without left truncation adjustment were 29 and 32 months, respectively. in this cohort, an unadjusted survival estimate (without left truncation) would overestimate survival by three months. Though a small difference, such a variance may skew secondary analyses of prognoses of population.

Left-truncation minimizes bias when estimating survival in datasets combining incident and prevalent cases at enrollment. This adjustment allows researchers to make use of prevalent cases who are willing to share data while keeping the integrity of survival analysis.