Addictive Behaviors 33 (2008) 1123–1130

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Addictive Behaviors

Using the Timeline Followback to determine time windows representative ofannual alcohol consumption with problem drinkers ☆

Shervin Vakili a,⁎, Linda Carter Sobell b, Mark B. Sobell b, Edward R. Simco b, Sangeeta Agrawal c

a Addiction Centre, Calgary Health Region, Canadab Center for Psychological Studies, Nova Southeastern University, United Statesc University of Nebraska Medical Center, United States

a r t i c l e i n f o

☆ Research for this article was supported in part by tin this paper was conducted as part of the senior auth⁎ Corresponding author. Addiction Centre, 1403 29t

E-mail address: shervin.vakili@calgaryhealthregion

0306-4603/$ – see front matter © 2008 Elsevier Ltd.doi:10.1016/j.addbeh.2008.03.009

a b s t r a c t

Keywords:Alcohol

When assessing individuals with alcohol use disorders, measurement of drinking can be aresource intensive activity, particularly because many research studies report data for intervalsranging from 6 to 12 months prior to the interview. This study examined whether data fromshorter assessment intervals is sufficiently representative of longer intervals to warrant the useof shorter intervals for clinical and research purposes. Participants were 825 problem drinkers(33.1% female) who were recruited through media advertisements to participate in acommunity-based mail intervention in Toronto, Canada. Participants' Timeline Followback(TLFB) reports of drinking were used to investigate the representativeness of different timewindows for estimating annual drinking behavior. The findings suggest that for aggregatedreports of drinking and with large sample (e.g., surveys), a 1-month window can be used toestimate annual consumption. For individual cases (e.g., clinical use) and smaller samples, a 3-month window is recommended. These results suggest that shorter time windows, which aremore time and resource efficient, can be used with little to no loss in the accuracy of the data.

© 2008 Elsevier Ltd. All rights reserved.

DrinkingTimeline FollowbackDrinking time-intervalsProblem drinkers

1. Introduction

The primary approach to gathering drinking data for epidemiological, clinical, and research purposes has been throughretrospective self-reports (Sobell & Sobell, 2003). Over the past 30 years, many studies, summarized in several literature reviews(Babor, Steinberg, Anton, & Del Boca, 2000; Connors & Maisto, 2003; Sobell & Sobell, 2003), have found that when data aregathered under appropriate conditions (i.e., assurance of confidentiality, interviewed in a research or clinical setting, alcohol free)self-reports are more complete and reliable than data gathered from other sources (e.g., biochemical tests, collateral reports, andofficial records).

While there are several different drinking assessment methods (e.g., Dawson, 2000; Rehm et al., 1999; Room 1990; Sobell &Sobell, 2003), two methods have stood out as providing the most precise data: Concurrent Recall (CR), and Daily DrinkingEstimation (DDE). Concurrent Recall methods (e.g., self-monitoring; IVR) collect data when the drinking occurs or shortlythereafter (e.g., same day, multiple times a day), whereas DDE methods collect drinking data retrospectively. Because drinking isrecorded when it occurs, CR measures would be expected to be somewhat more accurate as they are subject to fewer memoryproblems than retrospective methods. Several studies have compared the Timeline Followback (TLFB), the most frequently usedDDEmethod, with CR drinking methods and although some have found that CRmethods resulted in a slightly greater frequency ofdrinking reported than the TLFB (Carney, Tennen, Affleck, Del Boca, & Kranzler, 1998; Samo, Tucker, & Vuchinich, 1989; Searles,

he National Institute on Alcohol Abuse and Alcoholism grant AA08593 to L.C. Sobell. The research describedor's doctoral dissertation.h Street NW, Calgary, AB, Canada T2N-2T9. Tel.: +1 403 944 2002 (work); fax: +1 403 944 2056..ca (S. Vakili).

All rights reserved.

1124 S. Vakili et al. / Addictive Behaviors 33 (2008) 1123–1130

Helzer, & Walter, 2000; Sobell, Bogardis, Schuller, Leo, & Sobell, 1989), two recent studies have found that the TLFB compared veryfavorably with two different CR methods (IVR: Mundt, Moore, & Bean, 2006; hand held computers: Bernhardt et al., 2007). Even ifCR methods have a slight advantage in accuracy, in most cases they cannot be used to gather pretreatment assessment databecause the behavior is recorded immediately or shortly after it occurs (reviewed in Sobell & Sobell, 2003). The only way to gatherpretreatment data prospectively would be to have individuals self-monitor before they begin treatment. However, such aprocedure has two serious drawbacks. First, it necessitates delaying treatment for the sole purpose of gathering pretreatment dataprospectively (e.g., 3 to 12 months), and such a procedure would be ethically objectionable. Second, self-monitoring might bereactive, raising questions about whether the assessment data are representative of pretreatment drinking. Consequently, DDEmethods, and particularly the TLFB, have been the procedure of choice when gathering retrospective reports. The TLFB, whichobtains retrospective estimates of daily alcohol consumption in standard drinks using a calendar format (Sobell, Maisto, Sobell, &Cooper, 1979; Sobell & Sobell, 1992), is the most psychometrically supported retrospective self-report method and was used in thepresent study. Its psychometric characteristics have been extensively evaluated in over three dozen studies (Agrawal, Sobell, &Sobell, 2008; Dum, Voluse, Buerman, Sobell, & Sobell, 2008) and it has been endorsed by the National Institute on Alcohol Abuseand Alcoholism (Sobell & Sobell, 2003) and the American Psychiatric Association (Sobell & Sobell, 2003, 2008). It has also beenfound to have excellent internal consistency when used for intervals up to a year in length (Sobell & Sobell, 2003).

Several studies have shown that some participants refuse to complete lengthy drinking questionnaires (Cunningham, Ansara,Wild, Toneatto, & Koski-Jännes, 1999; Miller & Del Boca, 1994; Sobell et al., 2002). While many participants in these studies agreedto complete a less time consuming drinking measure (e.g., QDS; Sobell et al., 1999), such measures can only provide limited data(i.e., quantity, frequency). Because the assessment of drinking takes time places a burden on study participants it can lead to non-compliance or attrition. Therefore assessments should be as short as possible while still collecting satisfactory data. In this regard,the length of the temporal window for which drinking data are collected is important.

While short time windows (e.g., 7 to 30 days) typically require less time and are often used to assess drinking in clinical andresearch settings (Sobell & Sobell, 2003), several studies have found that individual drinking patterns have considerable variabilityover shorter intervals (Alanko & Poikolainen, 1992). Further, although at the level of group data random sampling and assignmentwill to some extent control for non-systematic individual variability, they cannot control for systematic temporal changes. Forexample, the time of entry into treatment is not necessarily random, but may follow a change in usual drinking patterns. Similartemporal issues affect nonclinical samples (e.g., significant seasonal changes in drinking have been found in several studies; Alanko& Poikolainen, 1992; Cho, Johnson, & Fendrich, 2001; Fitzgerald & Mulford, 1987). Because of the above variations, it has beenargued that using short time frames to estimate annual drinking runs the risk of yielding biased data (Lemmens & Knibbe, 1993).For example, Alanko and Poikolainen (1992) used a 1-week window to estimate annual drinking and found deviations around thetrue annual measure ranging from 50–185%, depending on the sampled week. It has also been argued that longer time windowscould result in memory errors (Lemmens, Knibbe, & Tan, 1988).

Although the length of the time window over which drinking is measured is an important factor for research and clinicalstudies, it has received little empirical study. The present study, a secondary data analysis, used assessment data from a previousrandomized clinical intervention to evaluate the representativeness of different time window lengths for describing aggregatedmeasures of problem drinkers' reports of annual drinking. In addition, variables that could affect the representativeness of sampledtime windows were examined. These variables included: (a) Gender and Age: (i) Men report higher levels of drinking than women(Centers for Disease Control and Prevention, 2000), and (ii) older males (N32 years of age) have more regular drinking patternsthan younger males (Cahalan, Cisin, & Crossley, 1969; Johnson, Armor, Polich, & Stambul, 1977) (b) Stability of Drinking Patterns:Because variability in drinking patterns over time (e.g., seasonal changes, memory, idiosyncratic reasons for entering treatment)maymake a short window unrepresentative of annual drinking, the effect of drinking variability on representativeness of sampledtime windows was evaluated; (c) Alcohol Problem Severity: Several studies suggest that alcohol problems can impact drinking datacollection in several ways (e.g., “heavy” drinkers appear to underreport their drinking; Cahalan, 1987; Lemmens et al., 1988). Threemeasures of drinking severity [rating alcohol problems asmajor; number of past quit attempts; scores on the Alcohol Use DisordersIdentification Test (AUDIT; Reinert & Allen, 2007)] were evaluated for their relationship to the representativeness of drinkingwindows; (d)Motivation to Change Drinking: Individuals who report higher motivation to change drinkingmay pay more attentionto the magnitude of their drinking; and (e) Because certain Demographic Variables (i.e., employment, marital status, education,ethnicity) might impact the stability of drinking over time, their effect on the match between reported vs. estimated annualdrinking rates was examined (no directional hypotheses were posited).

2. Methods and materials

2.1. Participants

This study used data from a community-based mail intervention (Sobell et al., 2002) with problem drinkers. The study,conducted in Toronto, Canada, was approved by a joint Addiction Research Foundation/University of Toronto Institutional ReviewBoard. Potential participants calling in response to advertisements for people concerned about their drinking (Sobell et al., 2002)were screened by telephone. Participant inclusion criteria included: (a) being of legal drinking age (19 in Ontario, Canada); (b) noreports of past formal treatment or self-help for alcohol problems; and (c) reported drinking, on average, more than 12 drinks (1drink=13.6 g of absolute alcohol) per week or consuming 5 or more drinks on 5 or more days in the past year. As noted in theoriginal report of this study (Sobell et al., 2002), the reason specific drinking levels were used as study inclusion criteria was that

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epidemiological studies suggest an increased risk of social and health problems associated with weekly consumption of more than12 drinks. Other brief interventions have also used similar drinking entry criteria. The majority of the initially ineligibleparticipants, 90.4% (613 of 678), reported having received prior treatment or having participated in a self-help group, 6.9% (n=47)reported drinking below the inclusion criteria, 2.2% (n=15) were excluded for other reasons, and .4% (n=3) were underage.

Of the 2434 callers, 1756 individuals were initially eligible and mailed an informed consent and assessment materials, and 825completed and returned the materials. The current study involves a secondary data analysis from this study and only usesassessment data obtained from participants before they were randomly assigned in the study. In this regard, only study proceduresrelevant to the secondary data analysis will be described, as other details about the study procedures and design have beenreported in detail elsewhere (Sobell, Brown, Leo, & Sobell, 1996a; Sobell et al. 1996b, 2002).

The mean (SD) age of the 825 study participants was 47.5 (11.8) years and 66.9% were male. Sixty one percent of participantsweremarried, 30.7% had completed university, 60.4%were employed full-time, and 62.3%worked inwhite-collar jobs. Participantsreported having had a drinking problem for a mean (SD) of 11.4 (9.2) years, with a mean (SD) of .5 (1.5) of alcohol-related arrestsand .1 (1.3) hospitalizations. Participants' mean (SD) score on the Alcohol Use Disorders Identification Test (AUDIT) was 20.2 (6.2).Participants reported drinking on a mean (SD) of 5.4 (1.7) days per week and consuming a mean (SD) of 5.9 (2.8) drinks on dayswhen they drank in the year preceding the intervention (Sobell et al., 2002). Thus, although the current participants had neverbeen in treatment, they reported significant alcohol problems similar to participants in studies of brief interventions (Bien, Miller,& Tonigan, 1993; Heather, 1994; Sobell, Sobell, & Gavin, 1995; Sobell, Sobell, & Leo, 2000).

2.2. Procedures

2.2.1. Timeline Followback (TLFB)The TLFB (Sobell & Sobell, 1992, 2003) was used to evaluate the representativeness of different time windows for describing

annual drinking data. Participants completed and mailed in TLFB reports of their drinking covering a 1-year period prior to theintervention. From participants' TLFB reports, total number of drinks consumed, mean number of drinks per drinking day, andpercent days drinking were calculated for the entire year and for different timewindows (e.g., 1 week, 2 week, 1 month, 2 months,up to 11 months). Time windows started from the last recorded pre-treatment TLFB date (i.e., closest date to the interview date)and went backwards. Estimated annual drinking rates were calculated from data captured from shorter time windows. Forexample, to estimate total number of annual drinks from total drinks reported in a 1-week period, the 1-week valuewasmultipliedby 52.

To assess whether drinking variability affected the time window that is representative of annual drinking, the number ofreported drinks per week for each of the 51 available weeks was calculated for each participant (because a year was defined as360 days only 51 completeweeks could be calculated), and the standard deviation of these values was used as a drinking variabilityindex.

2.2.2. Alcohol Use Disorders Identification Test (AUDIT)The AUDIT, a 10-item scale developed by the World Health Organization (Saunders, Aasland, Babor, De La Fuente, & Grant,

1993), identifies a person's subjective assessment of the severity of their drinking (Connors & Volk, 2003). As demonstrated inseveral major reviews, the AUDIT has very good psychometric properties (e.g., test–retest reliability, internal consistency,sensitivity, specificity: Connors & Volk, 2003; Reinert & Allen, 2007). AUDIT scores range from 0–40 and a score of ≥8 is suggestiveof an alcohol problem.

2.2.3. Motivation to change drinkingMotivation to change drinking was measured by two questions used in previous studies (e.g., Sobell & Sobell, 1993, 2005): (1)

“At this moment, how important is it that you change your current drinking (rated from 0 to 100, where 0=not important at all, and100=the most important thing in my life I would like to achieve now)?” (2) “At this moment, how confident are you that you willchange your current drinking (rated from 0% to 100%, where 0%=I do not think I will change, and 100%=I think I will definitelychange)”?

2.2.4. Subjective evaluation of drinking problem severityAs in past studies (e.g., Sobell & Sobell, 1993), participants rated the severity of their alcohol problem on a 5-point scale (1=not a

problem at all, to 5=very major problem).

2.2.5. Analytic strategiesTotal number of drinks consumed in the year (derived from the full TLFB) was compared to estimated annual values calculated

from each shorter time windows using Pearson correlation coefficients and corresponding significance tests. Next, a series ofbivariate regressions was performed to evaluate the degree of error associated with estimating total number of annual drinksbased on data from shorter time windows.

To assess if any of the hypothesized variables described in the Introduction systematically affected the degree to whichestimated annual drinking rates differed from reported annual values, the variables were entered as independent variables in ahierarchical multiple linear regressionmodel with the difference between reported and estimated total number of annual drinks asdependent variables. The variability index was entered as the first step in the regression because we hypothesized the other

Table 1Pearson product-moment correlations between reported total number of drinks per selected time window, and total number of drinks estimated from shortersampling time windows for the same period

Time windows a Reported average number of drinks

1 week (31.2) 2 weeks (63.2) 1 month (136.1) 2 months (275.4) 3 months (414.4) 6 months (825.3) 12 months (1638.01 week – .963 ⁎ .917 ⁎ .870 ⁎ .850 ⁎ .807 ⁎ .780 ⁎

2 weeks – .964 ⁎ .923 ⁎ .902 ⁎ .860 ⁎ .831 ⁎

1 month – .974 ⁎ .954 ⁎ .914 ⁎ .888 ⁎

2 months – .988 ⁎ .958 ⁎ .933 ⁎

3 months – .982 ⁎ .962 ⁎

4 months .993 ⁎ .975 ⁎

5 months .998 ⁎ .982 ⁎

6 months – .987 ⁎

7 months .992 ⁎

8 months .995 ⁎

9 months .997 ⁎

10 months .998 ⁎

11 months .999 ⁎

Note. N=825.⁎ pb .001, two-tailed.a Timewindows start from the last recorded pre-treatment TLFB date (closest date to the interview date) and go backwards. Therefore, the 1-weekwindow is the

week closest to the interview date.

Table 2Summary of bivariate regression analyses for predicting total number of annual drinks from smaller sampled time windows

Time windowa B Std. error of the estimate

1 week .675 ⁎ 594.92 weeks .755 ⁎ 529.01 month .837 ⁎ 437.22 months .908 ⁎ 342.03 months .953 ⁎ 258.14 months .966 ⁎ 212.65 months .977 ⁎ 178.86 months .984 ⁎ 151.57 months .989 ⁎ 121.88 months .991 ⁎ 99.19 months .993 ⁎ 77.510 months .996 ⁎ 54.011 months .998 ⁎ 32.1

Note. Each line represents a separate regression analysis; N=825.⁎ pb .001.a Time windows start from the last recorded pre-treatment TLFB date (closest date to the interview date) and go backwards.

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)

variables would most probably impact the representativeness of the estimated to the reported annual rate through their impact onvariability of drinking patterns. A separate regression model was utilized for each annual estimate (for a total of 13 multipleregressions).

3. Results

To control for inflation of the type I error rate, a Bonferroni adjustment was made for the significance tests and the adjustedalpha level was set to .001 (.05/43). Table 1 shows the Pearson product-moment correlations between the reported number ofdrinks per timewindow (as reported on the one-year TLFB) and values for the same timewindows estimated by extrapolating fromshorter timewindows. As timewindows get shorter, the correlations with reported annual values generally decrease. However, allcorrelations in Table 1 were significant (pb .001), and there was a minimum of 60.8% shared variance between the estimated andreported total number of drinks. Correlations between mean drinks per drinking day from smaller time windows and from theannual TLFB were high (minimum .84 when comparing 1 week time window to entire TLFB) and significant at pb .001.

A series of bivariate regression analyses was performed to assess the degree of error that could be expected when estimatingtotal number of annual drinks from a shorter time window. All cases with studentized deleted residuals≥3.3 were identified asoutliers (Tabachnick & Fidell, 1996) and the regressions re-runwithout these cases. Generally, removal of the outliers improved thedegree of shared variance between the dependent and independent variables and reduced the standard error of the estimate. Forexample, r2 for estimated total number of annual drinks from a 1-week time window increased from .608 (N=825) to .694(N=815), and the standard error of the estimate was reduced from 594.9 to 493.1 when outliers were deleted.

Table 2 summarizes the regression findings and shows that using smaller timewindows to estimate annual drinking can lead tolarge errors and that this error decreases as the time window gets larger. The standard error of the estimate decreased from 594.5

Fig.1. Reported annual vs. estimated total number of drinks from shorter timewindow (timewindows) scatterplots with fitted regression lines and 95% confidenceinterval bands and prediction interval bands.

Table 3Reported mean drinks per drinking day per sampled time window (STW) and Pearson product moment correlations with annual mean drinks per drinking day

STWa N Mean SD Correlation with annua

1 week 785 6.18 3.27 .844 ⁎

2 weeks 809 6.12 3.07 .887 ⁎

1 month 820 6.05 2.99 .934 ⁎

2 months 823 6.06 2.94 .967 ⁎

3 months 825 6.04 2.90 .977 ⁎

4 months 825 6.01 2.88 .985 ⁎

5 months 825 6.00 2.86 .989 ⁎

6 months 825 5.98 2.85 .992 ⁎

7 months 825 5.97 2.85 .994 ⁎

8 months 825 5.96 2.85 .997 ⁎

9 months 825 5.96 2.85 .998 ⁎

10 months 825 5.95 2.85 .999 ⁎

11 months 825 5.94 2.84 1.000 ⁎

12 months 825 5.94 2.83 –

Note. Some Ns are smaller than 825 because not all participants had a drinking day in the sampled period.⁎ pb .001.a STWs start from the last recorded pre-treatment TLFB date (closest date to the interview date) and go backwards. Therefore, the 1 week window is the closes

STW from the interview date.

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when using a 1-week time window to 32.1 when using an 11-month time window, corresponding to a 95% decrease in error.Together, these findings suggest that for the present sample, short time windows such as 1 month can provide a reasonableestimate of annual drinking when using aggregated data (e.g., to obtain survey data from a large sample). However, using shortertime windows can result in large errors in annual drinking figures for individual data (e.g., when gathering assessment data fromindividual cases).

Fig. 1 presents scatterplots with fitted regression lines and 95% confidence interval bands and prediction interval bands ofreported total number of drinks in the past 12 months vs. estimated total number of drinks for the past 12 months calculated byextrapolating from selected time windows. These figures illustrate that the reduction of scatter of individual cases from theregression line as the time window increases can be rather large (over 1000 drinks/year). Taken together with the correlationspresented in Table 1, it is evident that the correlations become relatively stable and high (N .96) at intervals of three months andlonger, a desirable feature for use in clinical situations.

l

t

Table 4Summary of bivariate regression analyses with mean drinks per drinking day calculated from selected sampling time windows (STW) predicting the 12-monthvalue

STWa B Std. error of the estimate

1 week b .722 ⁎ 1.5022 weeks c .812 ⁎ 1.2991 month d .877 ⁎ 1.0032 months e .932 ⁎ .7273 months f .954 ⁎ .6044 months f .969 ⁎ .4885 months f .981 ⁎ .4116 months f .988 ⁎ .3587 months f .988 ⁎ .3008 months f .991 ⁎ .2359 months f .993 ⁎ .18410 months f .995 ⁎ .12311 months f .998 ⁎ .069

Note. Each line represents a separate regression analysis. Some Ns are smaller than 825 because not all participants had a drinking day in the sampled period.⁎ pb .001.a STWs start from the last recorded pre-treatment TLFB date (closest date to the interview date) and go backwards. Therefore, the 1-week window is the closes

STW from the interview date.b N=785.c N=809.d N=820.e N=823.f N=825.

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t

Statistics for mean drinks per drinking day with Pearson product-moment correlations between the reported mean drinks perdrinking day andmean drinks per drinking day estimated from shorter timewindows are presented in Table 3. As shown, themeandrinks per drinking day are very similar regardless of the time windows sampled. Further, the correlations improve with largersampled windows. Scatterplots utilizing mean drinks per drinking day were very similar to those for total number of drinks.

A series of bivariate regressions was performed with mean number of drinks per drinking day in the past 12 months as thedependent variable and mean number of drinks per drinking day extrapolated from shorter time windows as independentvariables. The findings of these regressions are summarized in Table 4. The standard errors of the estimate are small (≤1.5) anddecrease as the shorter time window increases in length, indicating that one can expect the mean number of drinks per drinkingday in the past week to be a reasonable indication of the mean number of drinks per drinking day during the past year. Removingoutliers (n=4) identified via the studentized deleted residual≥3.3 method resulted in a slight improvement in shared variancebetween the estimated and provided values (r2 [785]= .71; r2 [781]= .72) and a reduction in the standard error of the estimate of .10for drinks per drinking day from a 1 week time window predicting the reported drinks per drinking day in the past 12 months.

Analyses performed for percent days drinking yielded very similar results to those reported for total number of annual drinksand mean drinks per drinking day. To conserve space these results will not be reported here, but are available from the seniorauthor. Likewise, scatterplots for the variable of percent days drinking were very similar to those for total number of drinks.

The hierarchical multiple regression analyses described under analytic strategies were performed using the difference betweenreported annual drinking (total number of drinks) and annual drinking estimated from smaller time windows as the dependentvariables (13 total regressions). The two motivation variables assessed participants' importance of changing (M=70.7, SD=19.05)and their confidence in their ability to change (M=66.3, SD=24.88), each rated on a 100-point scale. The motivation variables weremoderately correlated (r= .29, pb .01). Nine participants who had deleted residuals of ≥3.3 were identified as outliners andanalyses were re-run without these participants. No significant differences in models were observed when these nine cases wereexcluded. Only the first two overall models (for the 1 week and 2 week time windows) were significant. The calculated variabilityindex accounted for less than 1% of the variance in the dependent variable. All the demographic, drinking history, and motivationvariables were entered in step 2. Generally, they increased the degree of variance accounted for by about 2% in the 1 week and2 week regressions. Thus, the amount of variance explained by these models was so modest as to be considered negligible.

4. Discussion

In this study, problem drinkers' self-reported annual drinking data derived from the TLFB were used as the standard againstwhich shorter drinking time windows were compared. For aggregated data, and with large samples, time windows as short as onemonth provided reasonable estimates of annual drinking rates. However, time windows of three months or longer arerecommended for use with individual cases (e.g., clinical uses) or small sample sizes because at the individual case level large overor under estimates of annual drinking can result when short time windows are used. These findings are consistent with Lemmensand Knibbe (1993) who also found temporal changes in drinking to be larger at the individual as compared to aggregated level.

The findings for the variables mean number of drinks per drinking day and percent days drinking were similar to those for totalnumber of drinks. Although short time windows provided reasonable annual estimated drinking rates for these variables for

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aggregated data as indicated by high correlations, individual data from short time windows produced deviations from the annualrates. Errors of up to 1.5 drinks per drinking day and 16.9% of days drinking were foundwhen these variables were calculated usinga 1 week window compared to an annual time window. Such deviations could be clinically important.

The results from our secondary data analyses of one-year pretreatment data derived from the TLFB suggest that for large samplesizes data should be collected for a minimum of 1 month for aggregated reports. In practical terms, the 1-month correlation of .89accounts for nearly 80% of the variance that, while not ideal, has to be balanced against the increased time and resource burdenassociatedwith increasing the length of the timewindow. For individual cases, small samples, or wheremore precision is essential,however, it is recommended that drinking data be collected for a minimum of 3 months, which would account for at least 92.5% ofthe variance and lead tomore acceptable individual variations. The scatterplots for drinking variables showed a dramatic reductionin scatter from the regression line up to 3 months with much smaller improvements when time windows were increased beyond3 months. The added value in variance explained in going beyond 3 months in data collection is small and usually would not besufficient to justify the use of longer time windows. In order to account for observed seasonal variations in drinking and asrecommended by Sobell, Sobell, Klajner, Pavan, and Basian (1986), if a window of one month or less is used to collect drinkingbehavior, the study should inquire about whether the reported rates are representative of participants' usual drinking and if not,longer windows should be sampled.

Regression analyses to predict conditions that would allow for the use of shorter time windows for some people were notsuccessful. Weekly drinking variability and several demographic variables including gender failed to predict similarity betweenestimated and reported annual drinking rates. One reason for this may be that in the current population, shorter time windowsyielded a good estimate of annual drinking rates.

Although the individuals in the present study had never been in treatment, their mean AUDIT score was 20.2, which while high,was similar to participants in other brief interventions of problem drinkers. Nevertheless, additional research will be needed todeterminewhether these findings can be generalized to participants withmore serious alcohol problems. In addition, whether thecurrent findings, based on assessment data, will generalize to follow-up outcome data awaits further test.

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