Several studies were excluded because Imatinib purchase their definitions of PA or PP were missing or unclear. Many included studies were unclear whether biochemical validation applied to PP only or both PA and PP. These studies were excluded. For each study/comparison, we recorded nine study characteristics: (a) publishing year, (b) sample size, (c) whether the follow-up occurred at 6 or 12+ months, (d) definitions of relapse (any smoking vs. 7 consecutive days), (e) number of weeks in the grace period before PA period began, (f) number of biochemical verifications on which PA was based, (g) type of control group (placebo or no drug), (h) length of treatment, and (i) varenicline versus other treatments. We used all participants randomized in the denominators whenever possible; however, this was often unclear.

We recorded number abstinent (i.e., numerator) when possible based on actual numbers in tables or the text but often these had to be calculated from recorded percent abstinent. On rare occasions, these were obtained from relapse/survival curves or bar graphs using a Digimatic software that estimates numerical data from such graphs. We did not measure study quality with a formal scale because there is no consensus on whether this influences meta-analytic outcomes (Balk et al., 2002). Data synthesis The study had four aims. First, we examined the relationship between PA and PP abstinence rates by determining mean values for each, the correlation between the two, the difference between the two, and the ratio of the two. Second, we examined the ability to estimate one from the other using a metaregression analysis.

Third, we determined ��effect sizes�� when PA versus PP outcomes were used. Effect sizes are different measures of the therapeutic effect of a medication, that is, the difference in outcomes between active and control conditions. For this aim, we used three effect sizes. The first two are the odds ratio (OR) and the relative risk (RR). The third is what we term ��the difference in percent abstinent between the active and control conditions�� (DIFF); for example, if the active quit rate was 30% and the control quit rate was 20%, the DIFF would be 10%; this has also been termed risk difference or absolute risk reduction (Fleiss, 1994; Shadish & Haddock, 1994). The pros and cons of using these measures have previously been reviewed (Hughes & Callas, 2007).

The fourth aim was to determine whether study characteristics listed above moderated the relationship of PP to PA. To examine the ability to estimate PA from PP and vice versa, we used Hierarchal Linear Modeling (HLM) software to conduct weighted, two level metaregression with study (n = 28) as Level 2 factor (Raudenbush, Bryk, Cheong, Congdon, & du Toit, 2004; Thompson & Higgins, 2002). HLM takes into account that some of the studies provided more than one comparison. To normalize outcomes, percent Anacetrapib abstinences were transformed into logits (p/1 ? p).