Point in time (or Landmark) analyses are the standard in most Chronic pain indications due to the agency requirement that benefit for chronic pain conditions be shown to last for some period of time. While they may provide immediate relief, sometimes full pain relief is only achieved after some initial lapse (usually a few days) and the drugs continue to provide pain relief only as long as the drug is taken regularly. These types of studies are typically analyzed using MMRM (or ANCOVA) at a particular time point, usually 12 weeks, to show that at the end of 12 weeks the subjects have experienced pain relief. In contrast, in Acute pain studies the pain is caused by some temporary condition and is expected to resolve naturally over time (as the condition causing the pain resolves), and therefore landmark (point in time) studies are not common. Instead, medication is given on a schedule that is meant to provide analgesia over a fixed period of time. For instances of breakthrough pain or lack of pain relief, rescue medication is given between scheduled doses. The effectiveness of this type of drug is measured on the basis of the amount of cumulative relief that is provided over the fixed period of time the study medication is administered (typically TOTPAR, SPID or AUC analyses). However, the usual landmark analyses may not be appropriate for analgesic treatments meant to treat long term chronic pain, but that are meant to be administered only once and to last for a longer period of time. These types of drugs are not taken on a regular basis, but are possibly re-dosed PRN as pain returns. This type of drug is, in effect, similar to drugs that provide relief for acute pain, and therefore, an analysis strategy similar to that for acute pain may be more appropriate. This poster explores the operating characteristics of a landmark MMRM analysis vs a cumulative relief AUC (or SPID) analysis under various conditions that might simulate potential drug effects over time of this type of treatment, including the impact of missing data, overall effect size, and timing of maximal treatment effect.