Nonidentifiability in the presence of factorization for truncated data
截断数据中存在分解条件时的不可识别性
Summary A time to event, $X$, is left-truncated by $T$ if $X$ can be observed only if $T<X$. This often results in oversampling of large values of $X$, and necessitates adjustment of estimation procedures to avoid bias. Simple risk-set adjustments can be made to standard risk-set-based estimators to accommodate left truncation when $T$ and $X$ are quasi-independent. We derive a weaker factorization condition for the conditional distribution of $T$ given $X$ in the observable…