Illustrating an Adaptive Prespecification Framework for Observational Research: Target Trial Emulations Comparing...

Rigid prespecification can be impractical for noninterventional studies using secondary datasets, where data-driven flexibility is often required. Using target trial emulations comparing immunomodulator treatments for COVID-19, we piloted an adaptive strategy that accommodates warranted mid-course refinements within a prespecified framework. Our preregistered protocol outlined an initial study plan along with predetermined diagnostic thresholds and contingencies. Implementation proceeded through sequential phases, allowing researcher decisions to be guided by prespecified criteria under varying degrees of blinding to results. The adaptive approach led to alterations in the underlying target trial and to the analysis plan used for emulation, strengthening the plausibility of causal assumptions and improving the relevance of findings. During the initial baseline phase, indicated contingencies included sample restrictions, redefining treatments from class-level to product-specific comparisons, a revised propensity score model, and weight truncation. In the subsequent postbaseline phase, diagnostic checks triggered a modified causal contrast, inverse probability of censor weighting to address noncompliance, cause-specific hazard estimation to contextualize competing events, and additional reporting of hazard ratios for progressively truncated follow-up periods. For a secondary study objective, the adaptive framework allowed for some iterative attempts to improve validity while providing a clear stopping point. Similar approaches could lend transparent structure to the process of learning what causal questions the data are equipped to support. Beyond guarding against researcher bias, prespecification of adaptive protocols may promote more robust designs by encouraging investigators to be explicit about their assumptions, strategies for interrogating those assumptions, and specific criteria for determining when and how deviations may be required.