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4: Skinning Gmail . . . . . . . . . . . . . . . . . . . . . . . . . . 29
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a continuous function of time (as re ected, e.g., in the parenthetical inclusion of the symbol t in the expression for the baseline hazard function, 0 (t)). When data are collected in discrete time, however, either because the events only occur, or are only measured, at speci c times perhaps every week, month, academic semester, or year researchers should consider a different class of survival methods known as discrete-time survival analysis. The method is easy to apply, facilitates the estimation of the baseline hazard function, encourages the testing of the proportionality assumption, and enables researchers to t hazard models using procedures available in most statistical computer packages. For all these reasons, we encourage its wider application to studying questions about time. We describe the discrete-time survival analysis approach in detail in two papers (Singer & Willett, 1993; Willett & Singer, 1993) and in a forthcoming book (Singer & Willett, in press); here, we simply give an overview. A researcher conducts a discrete-time survival analysis by altering the data structure, transforming the standard one-person, one-record data set (the person data set) into a one-person, multipleperiod data set (the person-period data set). In the new person-period data set, a dichotomous variable is created to summarize the pattern of event occurrence in each discrete time period for every person in the sample. If relapse into cocaine use were being studied, for instance, in each discrete time interval this variable (RELAPSE) would be coded 0 if no relapse occurred and 1 if it did occur. So, for instance, an ex-addict who relapsed in the sixth month after treatment would have six lines of data in the new person-period data set, and in each line RELAPSE would take on a value speci c to that interval the rst ve being 0, the last being 1. The researcher also creates a set of time indicators that index and distinguish the discrete time intervals themselves. Under the discrete-time approach, the relationship between the dichotomous event summary (RELAPSE) and predictors (including the time indicators) can be t using a modi cation of standard logistic regression programs. Interactions among predictors, and between predictors and the time indicators, are easily included by forming cross products in the person-period data set and using them as predictors. Adding these interactions to main-effects models facilitates easy testing of the proportional-hazards assumption, and if the assumption is violated, retention of the interactions in the tted model ensures the appropriate estimation of the effects. The ability to use standard logistic regression software to t discrete-time hazard models brings this methodology within easy grasp of all empirical researchers. The logistic regression parameter estimates, standard errors, and goodness-of- t statistics are exactly those required for
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It should be rather obvious that all the rows in PROJ1 have corresponding rows in the MPLS_DEPTS fragment, and similarly, all the rows in PROJ2 have
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This second category of request headers allows the client to send additional information about itself, such as the type and version of the software the client is running, and about its user, such as authentication information. The list is shown in Table 3.17. The Authorization request header allows the client to send authorization information to validate the user s request to access a speci c resource as indicated by the request URI. As discussed earlier, there are two types of authentication supported in HTTP/1.1: basic and digest authentication. It is important to note that this header eld does not prevent access to malicious acts but only allows tracking user agents as they access some restricted information. If the server can validate the username and password in the speci ed realm then it will serve the request; otherwise, a 401 unauthorized or a 403 Forbidden response code is returned to the client.
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