The results (outcome) of cardiac surgery can be measured in several ways. The type of variable used as the measure of a particular outcome determines the statistical methods that should be used for its analysis. For example, some administrative outcomes are captured by continuous variables, such as hospital charges (in dollars) or length of stay (in days). Other outcomes are collected as categorical variables, such as discharge destination (eg, acute-care facility, specialized nursing facility, or home). Health-related quality of life is another kind of outcome, which is often measured by the Medical Outcome Study (MOS) 36-Item Short-Form Health Survey (SF-36),1 the Sickness Impact Profile,2 or disease-specific quality-of-life measures, and can be transferred to quality-adjusted life years (QALYs).3 Economic endpoints have been used increasingly, such as cost-effective ratio.4
However, the major outcomes of interest to clinicians are described by variables that indicate the occurrence of (usually adverse) events, such as death, stroke, infection, reoperation, etc. Statistically, we must differentiate between two fundamentally different types of events based on their timing: early (one-time) events and late (time-related) events. Different types of analyses are used for these two types of events. We divide the areas of statistical inquiry into three major categories based on the goals of the analysis: s ummarize, compare, and model. This chapter will describe and illustrate the statistical methods used most often in each situation.
In cardiac surgery, early events are those occurring within 30 days of surgery or before hospital discharge, whichever is later. By the time of the analysis, the early outcome of every patient presumably is known. Thus, every patient has a “yes” or “no” value for the event being studied, and an estimate of the probability of the event can be determined by the ratio of patients with the event to total patients, usually multiplied by 100 and expressed as a percentage.
Late, Time-Related Events
Late events are those that occur after discharge and more than 30 days after surgery. The analysis of these events is complicated by two considerations. First, the time of occurrence must be taken into account because, for example, a death at 6 months will have a different effect on the analysis than a death at 6 years. Second, in the usual ongoing analysis, some patients will have experienced a late event, whereas others will not have experienced an event but are still alive and at risk for the event and may have it in the future. Their event status is termed censored, which means that it is known not to have occurred by the time of the latest follow-up. For example, a patient in the study who had surgery 5 years ago and is still alive has a time of death that is not yet known. But we have partial information about his or her survival ...