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The basic concept of timestamp concurrency control algorithms in centralized systems can easily be extended to a distributed database. Since both the basic and conservative TO algorithms (discussed earlier in this chapter) rely on timestamps for resolving con icts, it is important that timestamps be a good representative of the relative age of each transaction. In a centralized system, the physical clock reading is used as the timestamp. The physical clock obviously produces a true indication of each transaction s age the smaller the timestamp the older the transaction is. As mentioned earlier, we do not actually have to use the physical clock reading of the local system as the timestamp. We can use a logical clock (LC) that is incremented for each event in the system as the transaction timestamp, since it provides for the same relative ordering for a transaction s age. In a distributed system, on the other hand, we cannot use any local physical clock readings or any site s logical clock readings as our global timestamps, since they are not globally unique. An indication of the site ID (which is globally unique) needs to be included in the timestamp of a transaction. The issue with using a site s physical clocks is called drifting when two or more clocks show numbers that are different from each other. To see the impact of clock drifting on the TO algorithms, consider two sites, S1 and S2, that generate the same number of transactions during a period of time. If the clocks at these two sites are relatively close, then the ages of transactions at these sites are also relatively close. But, if the clock at S1 is much faster than the

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is the longer, the better But beware longer studies are more expensive, have more missing data, and may lead to outdated results When deciding on the length of follow-up, remember that before you can determine when the event is likely to occur, it must actually occur for enough people under study If the target event never occurs during data collection, all observations are censored The researcher has little information, knowing only that it generally takes longer than this period for the event to occur There is no universally appropriate length of follow-up The answer depends on many factors To decide on a reasonable follow-up period, you must consider the shape of the anticipated hazard function, the probable median lifetime, the sample size, and your proposed statistical analyses.

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As we show later in the section on determining sample size, a good rule of thumb is to follow participants long enough for at least half of them to experience the target event during data collection This ensures suf cient information for estimating a median lifetime and provides reasonable statistical power What have researchers done in practice Noting that exsmokers often start smoking again soon after quitting, McFall (1978) suggested that smoking relapse studies use a 6- to 12-month follow-up And in our review of studies on smoking relapse published during the 1980s (Singer & Willett, 1991), we found that this guideline is widely accepted; the modal follow-up period was 1 year, and this period yielded an average censoring rate below 50% However, Nathan and Skinstad (1987) noted that 3- or 6-month post-treatment follow-ups are likely to be insuf cient .

Figure 16.3. Cross-section of cooked ham: (A) high-quality cooked ham in which the muscular integrity has been respected and (B) low-quality where muscle integrity has been partly lost.

; 2 years or more are probably necessary to determine the long-term effects of a treatment program (p 333) When studying infrequent events, even 5 years of data collection may be insuf cient In their review of the link between alcoholism and suicide, for example, G E Murphy and Wetzel (1990) lamented the fact that many of the available studies are relatively short: less than 10 years (p 387) Before deciding on the length of data collection, be sure to consider the substantive rami cations of your choice It is clear that variation across studies in the length of follow-up explains some of the seemingly discrepant conclusions about treatment ef cacy that arise in the literature Length of follow-up has been identi ed as a major explanatory factor in several literature reviews, including G E Murphy and Wetzel s (1990) review of suicidality among alcoholics.

And even when it has not been identi ed as a key explanatory factor, its impact seems certain In their review of 26 longitudinal studies of teenage alcohol and other drug use, for example, Flay and Petraitis (1991) found that the length.

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