LT and LC biases in drug dependence epidemiology

Perhaps more than is the case for any other neuropsychiatric or behavioral condition, except perhaps suicide ideation, attempts, and planning, we should have special concern about left-truncation and left-censoring biases in "clinical investigations" of quitting drug use or being in remission or recovery after having become drug dependent.

Leaving aside these biases, think about denominator and numerator issues.

When we estimate newly incident use or dependence, we generally have large denominators to work with. The S.E. help us deal with uncertainty such as the relatively small numbers of users who will die within the first 1-2 years after first use, or soon after dependence onset.

In contrast, the estimates for quitting among cases are constrained by the relatively small number of observed cases in each survey. Wide S.E. as a result, even if we ignore the LT and LC biases.

Make sure you can describe LT versus LC in the survey context.

A paper that I think over-values our epidemiological survey evidence on quitting: 

URL for Heyman, 2013

Perhaps "clinical investigation" samples are better than epidemiological samples in this domain of inquiry, if they can document zlT and LC bias constraints better than we can?