A flexible parametric approach for estimating continuous-time inverse probability of treatment and censoring weights

Comprehensive sequential genetic analysis delineating frequency, patterns, and prognostic impact of genomic dynamics in a real-world cohort of patients with lower-risk MDS

The acquisition of subsequent genetic lesions (clonal evolution, CE) and/or the expansion of existing clones (CEXP) contributes to clonal dynamics (CD) in myelodysplastic syndromes (MDS). Although CD plays an important role in high-risk patients in disease progression and transformation into acute myeloid leukemia (AML), knowledge about CD in lower-risk MDS (LR-MDS) patients is limited due to lack of robust longitudinal data considering the long clinically stable courses of the disease. In this retrospective analysis, we delineate the frequency and the prognostic impact of CD in an unselected real-world cohort of LR-MDS patients. We screened 68 patients with a median follow-up of 40.5 months and a median of 7.5 (range: 2-22) timepoints for CE and CEXP detected by chromosomal banding analysis, fluorescence in situ hybridization, sequencing, and molecular karyotyping. In 30/68 patients, 47 CE events and a CD rate of 1 event per 4 years were documented. Of note, patients with at least 1 CE event had an increased probability for subsequent treatment. Unexpectedly, CE did not correlate with inferior outcomes, which could be reasonably explained by CD detection triggering the subsequent start of a disease-modifying therapy.

Impact of discretization of the timeline for longitudinal causal inference methods

In longitudinal settings, causal inference methods usually rely on a discretization of the patient timeline that may not reflect the underlying data generation process. This article investigates the estimation of causal parameters under discretized data. It presents the implicit assumptions practitioners make but do not acknowledge when discretizing data to assess longitudinal causal parameters. We illustrate that differences in point estimates under different discretizations are due to the data coarsening resulting in both a modified definition of the parameter of interest and loss of information about time-dependent confounders. We further investigate several tools to advise analysts in selecting a timeline discretization for use with pooled longitudinal targeted maximum likelihood estimation for the estimation of the parameters of a marginal structural model. We use a simulation study to empirically evaluate bias at different discretizations and assess the use of the cross-validated variance as a measure of data support to select a discretization under a chosen data coarsening mechanism. We then apply our approach to a study on the relative effect of alternative asthma treatments during pregnancy on pregnancy duration. The results of the simulation study illustrate how coarsening changes the target parameter of interest as well as how it may create bias due to a lack of appropriate control for time-dependent confounders. We also observe evidence that the cross-validated variance acts well as a measure of support in the data, by being minimized at finer discretizations as the sample size increases.