How much loss to follow-up is acceptable in long-term randomised trials and prospective studies?

There is increasing evidence that health and development in adult life is influenced or ‘‘programmed’’ by factors, including nutrition, operating during foetal life and infancy. Early nutrition in a variety of animal species, including primates, has been demonstrated to influence later outcomes, including the likelihood of cardiovascular disease, learning and behaviour problems and longevity (see supplementary data). Evidence of programming in humans has, until recently, come largely from historical observational studies that showed associations between small size in early life and adult disease risk (see supplementary data). These cohorts were constructed from available health records. They by necessity relied on indirect measures of maternal and infant nutrition and often lacked detailed data on potential confounding variables. Many cohorts enrolled people who were born in the first half of the 20 century, and it is possible that the nature and size of any associations are different in contemporary populations. Thus, although these studies have generated considerable interest they have been unable to examine direct associations with diet or establish whether associations are causal and cannot, therefore, be used to inform infant feeding recommendations. This has led to a greater emphasis on the need for RCTs to test early nutritional interventions and prospective observational cohorts. RCTs are generally accepted as methodologically the best approach for informing health policy. They can equalise unknown as well as known confounding factors and so can demonstrate causation; they permit estimation of effect size and so can be used to assess likely economic benefits; and they can, if adequately powered, measure expected adverse effects and thus address safety. Nevertheless, in the context of nutritional programming of disease later in life, they have certain limitations. For example, some trials cannot be performed because they would be unethical (eg, breast feeding versus formula feeding). In addition, although certain measures during childhood or adolescence are predictive of final adult outcome (eg, cognitive function), other diseases will not become apparent for decades, necessitating the use of ‘‘proxies’’ of later disease risk that can be measured at younger ages. Contemporary prospective observational studies are also recognised to be important in investigating nutritional programming in humans and are complementary to RCTs. They identify defined (often large) populations, measure them precisely and follow them up longitudinally. If the studies are population based they might also be more generalisable. They have better measures of exposure and confounding factors than historical cohort studies, and, of particular relevance to programming research, they may include detailed early physiological, biological and social data, allowing more complete adjustment for confounding factors. Given the obvious requirement for long-term follow-up in the investigation of nutritional programming of health outcomes, an important issue has become increasingly apparent, affecting both RCTs and prospective observational studies, namely, cohort attrition or loss to follow-up. This is an important issue for the field, with implications for study design and funding. The aim of this paper is to consider the statistical implications of attrition in both RCTs and cohort studies to identify the most effective ways of dealing with attrition when analysing and reporting studies. Factors influencing follow-up rates, measures that can be taken to minimise attrition in future studies and potential alternative approaches to the problem are discussed in the appendices. The paper uses examples from studies examining the effects of nutritional interventions or early-life nutrition on later outcomes, but many of the conclusions are applicable to studies in other fields.