Our investigation of phase III and IV RCTs published in high impact journals reveals several practices that may limit the relevance of study findings to older individuals. First, approximately 20% of clinical trials exclude older adults from participation based on age criteria alone, and nearly half of the remaining trials employ non-age-based eligibility criteria that could disproportionately exclude older adults with complex health status. Second, trials rarely assess highly age-relevant outcomes such as functional status and quality of life. Third, subgroup analyses that examine treatment effects by age do not routinely utilize rigorous methodology. These common practices should be addressed in order to expand and strengthen the evidence guiding policy and treatment decisions for complex, older patients.
Our review of trial age limits provides an update to a previous assessment of RCTs between 1994 and 2006 that found that 39% of trials excluded adults over the age of 65.23
While it is encouraging that trials have reduced their use of explicit upper age limits, eligibility criteria that could disproportionately exclude older individuals, such as functional limitations, cognitive impairment, and clinician discretion, remain pervasive. These exclusion criteria likely contribute to the under-representation of complex, older (and often typical) patients in large clinical trials. Trials that exclude these patients may be less likely to evaluate outcomes and side effect profiles that are clinically meaningful to older adults. In fact, we found that only one in four trials examined at least one outcome that was a measure of health status, physical function, or quality of life. While budget or resource limitations, or aims to establish efficacy, may be cited as a reason to exclude individuals in poor health or those with functional limitations, studies of interventions that are likely to be extended to patients with these characteristics should attempt to include representative individuals. Likewise, if a condition is prevalent in older individuals, or if there is a physiological reason to suspect that an intervention’s effects may differ by age, then it is especially important to recruit older adults.
To our knowledge, this study is the first detailed examination of age-specific subgroup analyses. When performed appropriately, subgroup analyses can provide important information about heterogeneity of treatment effect related to baseline risk or physiology.21
Previous evaluations of subgroup analyses, however, have suggested that they frequently utilize flawed statistical techniques,20,24–30
or make false conclusions due to multiple comparisons or inattention to potential confounders.20,31,32
Our review suggests that many of these missteps continue to occur in age-specific subgroup analyses. For example, fewer than half (n
30, 43.5%) of the reviewed studies reported that subgroups were prespecified. In addition, age was usually analyzed as a dichotomous or categorical variable (n
36, 85.7%), a strategy that is sometimes used to simplify the reporting of results, but one that reduces the power available to detect a treatment effect.31,33
We also found that researchers’ confidence in the reliability of their age-specific subgroup analyses was often overstated, with few mentioning that study findings were exploratory or underpowered. In several instances, non-significant results were used to claim that a treatment effect was consistent across age groups, without acknowledging a lack of power to detect clinically significant differential age effects. At the same time, few of the studies claiming statistically significant heterogeneity by age fully explored potential confounding reasons for heterogeneity (such as more comorbidities or poorer health status, on average, in older subjects). While the implications of these methodological problems will vary based on the circumstances of each trial, such errors can promote misinterpretation of evidence. For example, a clinician may conclude that a treatment is safe in complex older patients, not realizing that the trial was underpowered to detect differential effects by age or that the study did not examine potential confounders. Alternatively, a clinician may think that older adults receive less benefit when the actual mechanism is comorbidity severity or poor health status, resulting in undertreatment of robust elderly patients and over-treatment of sicker younger patients.
Recent changes in FDA and funding agency guidelines reflect a growing awareness of the need for better evidence to guide treatment decisions for older individuals. At the same time, there is great interest in comparative effectiveness research, which aims to identify effective treatment strategies to inform health policy and more personalized medicine. This research is unlikely to improve quality of care in a cost-effective manner, however, if our most complex, expensive patients are not adequately represented. Researchers are now encouraged to report clinical trial data by age and to identify differences in safety or effectiveness associated with age.16,17
These guidelines could be strengthened further by requesting that trials report how well their study sample represents the population of interest, and by encouraging clinical justification for upper age limits and eligibility criteria that might disproportionately affect older individuals. In addition, these guidelines could serve as a reminder that any age-specific subgroup analysis should be adequately powered and specified a priori in order to be considered a reliable finding of the study.
There are also a number of steps that research investigators can take in order to increase the relevance of study results for older patients in the general population (Text Box
). For example, to the degree that it is clinically and economically feasible, studies should include multimorbid individuals of all ages reflective of the general population. Whenever possible, trial participants should also be monitored for outcomes that have greater relevance to older individuals, including quality of life, health status, and physical function, as well as side effects of special concern to these older patients, such as the exacerbation of geriatric conditions like incontinence, dementia, falls, and adverse polypharmacy effects.
In addition, we propose several guidelines for research investigators that we believe will improve the quality of age-specific subgroup analyses (Text Box
). These guidelines expand on previous subgroup analysis recommendations by CONSORT and others,21,22,34,35
while incorporating additional principles that are especially relevant to age-specific analyses. For example, we recommend that investigators who hypothesize that an age-related heterogeneous treatment response may occur attempt to adequately power their study to detect this effect. If the study is underpowered to find this effect, nonsignificant subgroup analysis results should not be interpreted as evidence that treatment effects are consistent across age groups. A useful strategy is to optimize power by analyzing age as a continuous variable33
(unless another functional form is hypothesized), and then to report results for different age categories for the sake of clarity. We also recommend that any differential treatment effect found in older adults be explored to determine whether this finding may be attributed to potential confounders, such as health status or comorbidities. Subgroup analyses that examine heterogeneity by overall risk (as determined by a multivariable risk prediction tool) may be especially powerful at assessing risks and benefits for older individuals with multiple comorbidities and will generally be far more effective than age alone at identifying how treatment effects differ for such individuals.35,36
Findings from this systematic review may be limited by several factors. First, we selected journals for review based on their impact factor, their relevance for a general medicine audience, and their likelihood of publishing trials investigating major morbidity or mortality as an outcome. Circulation was included because it is a high-impact journal that frequently publishes RCTs that inform treatment standards in primary care (i.e., prevention and treatment of coronary artery disease, hypertension, heart failure, and stroke). To ensure that our results were not significantly biased by the inclusion of one subspecialty journal, we repeated our main analyses after excluding studies published in Circulation. Our primary findings did not change substantially for any of our major findings. In general, however, our results should not be generalized to trials published in most subspecialty journals.
There are also potential limitations to our proposed recommendations. Including complex, older individuals in clinical trials is not without challenges. There are likely to be costs associated with expanding trial eligibility, and with monitoring for age-relevant outcomes and side effects. In addition, certain comorbidities, functional or cognitive limitations, and social circumstances may be contraindications to treatment in some instances. Some of these conditions may also represent significant barriers to trial participation and increase drop-out or non-adherence, especially when a trial requires multiple clinic visits or substantial changes to a person’s daily routine. In certain instances, these barriers may be overcome by reducing trial participation burden, but we also recognize that it will not always be practical or feasible to expand trial eligibility or track all relevant outcomes and side effects.
Nevertheless, failing to adapt study design and analysis practices to meet the needs of our increasingly complex aging population will perpetuate the current shortage of information guiding clinical care for these patients. Our findings suggest opportunities for the FDA, funding agencies, and research investigators to improve the current evidence by increasing the representation of these individuals in clinical studies, and by adhering to appropriate analytic techniques when examining age-related heterogeneity. These measures will enhance our understanding of treatment effects in older patients and will improve the quality of evidence that informs guideline development at the policy level and treatment decisions for individual patients.