Concepts of Time in Clinical Research

Clinicians have an intuitive appreciation for the relevance of time to health and disease. Each day brings snapshots of individual patients: their current health and a profile of the factors that predict risk for disease. As these snapshots accumulate, the clinician is witness to the development of disease, the unfolding of natural history, and the consequences of interventions for prognosis. Patients invariably ask questions relating to time for which clinicians need answers: How long until I am better? How long will I need this therapy? How long will I live? Patients also want to know about their future risks for disease and whether these risks can be decreased. The time frames of reference for many questions may differ for patients and clinicians because patients seek answers to their immediate questions and clinicians maintain a long-term perspective. Time inherently flows through the natural history of disease, from its onset to its end. Clinicians may be less aware of the temporally dynamic processes that originally lead to the development of disease. Many risk factors for disease are fluid in time: age, diet, smoking, work experiences, and the quality of the environment, for example. We now know that many of the devastating chronic diseases that are the leading causes of death in developed countries are the end stages of lengthy processes of change and deterioration. For example, chronic obstructive pulmonary disease usually arises after decades of lung function loss caused by cigarette smoking (1), the most common types of cancer are thought to occur after multiple genetic changes in a cell lead to unrestrained replication (2), and the origins of coronary artery disease can be traced back to childhood (3). Infectious diseases, although often acute in onset, may have lengthy natural histories, and lines between acute and chronic diseases have blurred as we have learned that some chronic diseases may be caused by infection (4). An understanding of these processes is crucial to reducing morbidity and mortality through primary prevention (by minimizing or eliminating risk factors) and secondary prevention (by screening). An appreciation of the picture of health and disease over time is equally fundamental to public health. For centuries, government organizations have recorded births and deaths and have monitored the size of the population. Together, these data constitute the numerators and denominators for fundamental measures of population health: the total mortality rate and the infant mortality rate. We now also track the incidence of some diseasesthat is, the occurrence of new casesand calculate the incidence rate. For example, in the United States, we have maintained a national registry of incident cancer cases (the Surveillance, Epidemiology, and End Results [SEER] program of the National Cancer Institute) (5) since 1973, and soon almost all cases of cancer will be registered nationally. The SEER program alerted us to the increased incidence of non-Hodgkin lymphoma and documented the dramatic improvement in survival rates for men with testicular cancer. We systematically monitor rates of death and disease as part of our national surveillance strategy. Surveillance may show benefits of prevention (for example, decreasing incidence of lung cancer and decreasing mortality rates in U.S. men [5]) or problems that need further evaluation as a basis for intervention (for example, the recent unexplained increase in asthma prevalence [6]). The temporal dimensions of biomedical research should reflect the multiple axes of time that are relevant to a particular disease (Figure 1). For the clinician, the age of the patient is always relevant because it determines the underlying risk for disease and death. Survival time is another key variable: When measured from the time of diagnosis it is the basic standard of prognosis, and when measured from the start of treatment it tracks the consequences of therapy. Calendar time may also be of interest as diagnostic and therapeutic methods become more effective over time. For causes of disease, the time since exposure began, the age at which exposure began, and the duration of exposure may all be relevant. Exposure may occur continuously, stop and start, or occur instantaneously. From the population perspective, changes in rates of disease over time are central to surveillance, and the characteristics of the population itself may change over time as well. Figure 1. Hypothetical etiologic exposure and phases of disease over time and age. This paper provides both a perspective on the central role of time in clinical and population research and an introduction to the statistical methods used to explore and optimally use data that are collected over time. It describes key measures of disease occurrence in time and the observational study designs (the cohort study and its variants) that explicitly incorporate the passage of time into the follow-up of participants. Clinical trialsexperimental studies that include randomization and a comparison groupalso involve follow-up of participants in time. Although the core longitudinal study designs for clinical and epidemiologic research are not new, biostatistical methods that fully allow for the effects of time have been developed and applied only in recent decades. Use of these methods is illustrated by examples of their application to the many clinical and public health questions that have been raised about HIV infection and AIDS. Tracking the Occurrence of Disease The fundamental measures of disease occurrence are indicators of the rate of disease onset at a particular point in time. The incidence rate describes the risk for transition from being free of a particular disease to having it, although a clear transition point may not be identifiable for some chronic diseases. The mortality rate is a similar measure of the risk for death (7). The prevalence rate is the proportion of a population that has a disease at a particular time. When assessed repeatedly, these measures take on the added dimension of calendar time. As disease rates are tracked over time, the population is dynamic in its structure, with younger persons aging and some risk factors, such as smoking, increasing and decreasing over time. When interpreting temporal trends in disease rates, researchers may see variations that reflect age, period, and cohort effects. Wade Hampton Frost, the first professor of epidemiology at the Johns Hopkins School of Hygiene and Public Health (and, for that matter, in the United States), has been credited with developing a cohort analysis of data on vital statistics to separate these effects. This age- and birth-cohort analysis tracked specific patterns of tuberculosis mortality rates in Massachusetts from 1880 to 1930 (Figure 2) (8). His graphical presentation successfully disentangled age and cohort effects. In a previous analysis of cross-sectional mortality rates, Frost had incorrectly assumed that tuberculosis incidence involved a postponement of maximum risk to older ages; in this analysis, however, he found that the mortality rates from tuberculosis in later life reflected those in early life. Nine years earlier, in 1930, Andvord (9) had published a similar but overlooked analysis of tuberculosis mortality rates. Today, we use specific statistical methods to more formally separate the effects of age, period, and cohort (10). Figure 2. Age- and birth-cohort analysis of specific patterns of tuberculosis mortality rates in men in Massachusetts, 1880 to 1930 . American Journal of Epidemiology The same types of data and analyses of rates remain in use today. Recent reports, for example, have addressed infectious disease mortality rates throughout the century (11), trends in liver cancer mortality rates (12), and incidence of cardiogenic shock (13). The recent perplexing and alarming increase in asthma incidence was identified by tracking time trends of mortality and prevalence (6). Data on incidence over time, however, were notably lacking because it is difficult to count incident cases of asthma. Describing the Prognosis of Disease Prognosisthe outcome of disease over timeis determined by following persons with a disease and monitoring recovery or death. The follow-up study design, which is called the cohort study, has its origins in the need for information on length of life and the course of disease. The earliest life tables, developed in the 17th century by Graunt and Halley from cross-sectional mortality counts, were intended to project deaths with aging, inherently acknowledging the passage of time. A life table organizes information on survival of participants on the time axis of interest, which is often age, time since diagnosis, or time since treatment. Farr, a British statistician and public health official, used the life table as an indicator of population health (14). Actuarialists also used life tables to project the risks incurred by insured persons, and a century ago the experience of insurance policyholders was pooled across many companies to provide a view of mortality rates associated with various diseases (15, 16). At about the same time, the cohort design was used in several studies to track mortality rates of persons with tuberculosis who were discharged from sanatoriums after receiving therapy. In one of the earliest of these studies, Brown and Pope (17) traced more than 1000 persons who were discharged from the Adirondack Cottage Sanitarium in Saranac Lake, New York (which would later become the famed Trudeau Sanitorium). They compared patient survival rates in their life table with those in a life table that Farr had prepared using data from the entire British population. Sartwell (18) noted that this study represents the first application of the life-table method in a clinical follow-up study. In 1938, Pearl (19) published a study in Science that described the survival rates of 6813 men in east Baltimore, Mar