To reduce the running time of static analysis tools upon program changes, incremental static analyses reuse and update pre-existing results. Such analyses must efficiently detect and remove outdated results. We introduce three novel, complementary result invalidation strategies for incremental modular analyses. The core idea of our work is to alternate invalidation with computation. We apply our strategies to a recent, state-of-the-art incremental modular analysis that suffers from imprecision, and evaluate them on soundness, precision, and performance. Our strategies lead to precision improvements compared to an incremental analysis without invalidation, though the precision of a full reanalysis is not yet matched. On most benchmarks, our incremental analysis performs well. However, on some benchmarks our analysis performs poorly as the changes drastically change program behaviour, for which the changes are difficult for an incremental analysis to handle.