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Individual Treatment Effects: Implications for Research, Clinical Practice, and Policy
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Individual Treatment Effects: Implications for Research, Clinical Practice, and Policy

Jennifer S. Graff, PharmD; Thaddeus Grasela, PharmD, PhD; David O. Meltzer, MD, PhD; and Robert W. Dubois, MD, PhD
A framework for researchers, providers, payers, or public health bodies identifies when it is most critical to distinguish between "average" population and individual patient response.
Funding for comparative effectiveness research (CER) has focused attention on what treatments work best under what specific clinical circumstances, and for whom. Because not all patients respond in the same way, treatment decisions, clinical guidelines, and coverage policies applied in a “one-size-fits-all” fashion based upon the population “average” response may lead to suboptimal outcomes. Existing frameworks focus on why patients respond differently to treatments. We propose a framework that identifies when these differences are likely to be clinically important. Scenarios are presented in which it may be most critical for clinical decisions and policies to distinguish between the average and the individual patient so that treatment recommendations provide the greatest benefits for the largest number of patients. We provide recommendations for researchers to help identify issues to study, for providers to help assist them in recommending optimal treatment for individual patients, and for payers or public health bodies to help balance societal needs with those of the individual.

Am J Manag Care. 2014;20(7):544-551
Because not all patients respond in the same way, treatment decisions, clinical guidelines, and coverage policies applied in a “one-size-fits-all” fashion based upon the population “average” response may lead to suboptimal outcomes.This article includes:
  • A framework to identify when it may be most critical for clinical decisions and policies to distinguish between the “average” and the individual patient so that treatment recommendations provide the greatest benefits for the largest number of patients.

  • Recommendations to help researchers identify areas to study, help providers make optimal treatment recommendations for individual patients, and help payers or public health bodies to note when it is necessary to balance societal needs with those of the individual.
The hypothetical Ms Jones, aged 55 years, has successfully managed her recurrent depression with sertraline. She plans to join her new state health exchange. Based upon the Essential Health Benefits Final Rule, the health exchange need only cover as many medications per class as are included in that state’s benchmark plan.1 Her exchange chose to offer fluoxetine rather than sertraline. Some researchers have posited that selective serotonin reuptake inhibitors (SSRIs) do not substantially differ in terms of maintaining remission; however, these studies have generally examined “average” efficacy.2

Determining which treatment works best for a population, on average, differs from determining what works best for individual patients.3 Ms Jones asks her provider if she is likely to relapse or see less benefit if she switches to the new preferred drug. To aid in decisions that Ms Jones and other patients have to make, there is increasing interest in and funding of comparative effectiveness research (CER) by the Patient-Centered Outcomes Research Institute (PCORI).4 While many CER studies will compare the benefits and the harms of alternative treatments in large real-world populations, leading to conclusions based on the “average” treatment effect for a certain population, there is increased discussion and funding to attempt to understand which patients respond differently.5-7

Because patients do not all respond in the same way, treatment decisions, clinical guidelines, and coverage policies applied in a “one-size-fits-all” fashion based upon this average response may prove suboptimal. As an example, although percutaneous coronary interventions achieve similar benefits with less morbidity than bypass surgery for many patients, recent studies indicate that bypass surgery leads to better outcomes in patients with complicated diabetes.8

Achieving a proper balance between population-based approaches and individualized decision making is critical. Depending upon how it is applied, CER could yield substantial benefit for some, little benefit for many, and even harm to others. Various authors and prior frameworks focused on why treatment responses differ among individuals; they cited such factors as variability in the underlying clinical condition or patient population, differential risk in therapeutic potential, vulnerability to adverse effects, and patient preferences.9 This variability may be attributed to genetic determinants, sociodemographic factors, disease characteristics, or patient comorbidities.10-14 For further explanation and clinical examples, see the Table.

In contrast to prior frameworks that address the cause of variability, we focus on the implications of heterogeneity by addressing whether those potential differences are likely to be clinically substantial and should therefore influence the therapeutic choice. Our framework can assist providers as they consider whether to apply a particular CER result to an individual patient, to payers whose policies influence what choices are available, and to policy makers who guide the financing, delivery, access, and quality of healthcare. Both frameworks (why treatment response differs and the implications of treatment response differences for patient care) can be used by the research community to identify clinical areas in need of further investigation.

For Ms Jones’ provider, the ability to predict when treatment response will likely differ for specific patients is important to enable individualized treatment. Ideally, evidence would be available comparing the benefits with the harms of treatment options in patients similar to Ms Jones. However, this level of evidence rarely exists. In an environment of inevitable uncertainty, Ms Jones, her providers, payers, and other policy makers will still have decisions to make. We present a framework that identifies when it may be most critical to distinguish between the average patient and the individual.

Framework to Assess the Impact of Individual Treatment Effects

When treatment response is unpredictable, how risky is it to apply population average results to individual patients? The answer depends upon: (1) the clinical consequences of delaying optimal treatment; (2) the underlying diversity in patient attributes; (3) the likelihood of response to similar treatments in similar ways (treatment independence); and (4) patient preferences (Figure). We provide illustrations for each factor below. It should be understood that these factors are not meant to provide a prescriptive or definitive answer, but rather to be considered collectively as a framework for dialogue and presumably improved decision making.

Clinical Consequences of Delaying Optimal Treatment. For certain diseases (eg, hay fever, fibromyalgia), the patient and provider have time to try several treatments without the threat of irreversible consequences. Patients may have uncomfortable symptoms, but their conditions do not irreversibly deteriorate, and trying additional therapies is not likely to jeopardize long-term symptom control. Ms Jones—whose depression has been successfully controlled on sertraline—should feel safe to switch her therapy to fluoxetine, under close observation. Should failure occur, she would have the opportunity to begin the next therapeutic option. In other circumstances (ie, a patient with a more acute depressive presentation, or in whose case the switch in therapy results in discontinuation) the risks may well be far greater, and the consequences much less reversible (eg, suicide attempt). Other scenarios include patients who may have only 1 chance for treatment success prior to disease progression (eg, chemotherapy in oncology) or organ damage (eg, sepsis resulting in kidney failure). In circumstances when the consequences of being “wrong” are low—or when they are high but information exists and is accessible regarding who responds best—policies that narrow treatment choices may be appropriate. In other circumstances, when there is a high degree of treatment diversity, little evidence to determine which patients are likely to respond well, and the consequences of suboptimal treatment are high, policies should permit greater flexibility and access to treatment choices for the patient and provider.

Diversity in Patient Attributes. Some clinical conditions are well characterized by a set of defining attributes (eg, ST–vs non–ST-elevated myocardial infarction), whereas others are far more heterogeneous (eg, systemic lupus erythematosus presenting with dermatologic, arthritic, or vascular complications).10 Applying population results to individual patients is likely to be more appropriate in the former and more worrisome in the latter. Perhaps Ms Jones’ depressive disorder manifests as anxiety and agitation rather than the classic symptoms of sadness, anorexia, or insomnia. For her, the optimal treatment would address the former symptoms, whereas other patients with the latter manifestations may need alternative treatments.

Population average results might blur these important distinctions and using these blended results could lead to suboptimal patient outcomes. Diversity in clinical attributes, outcomes, and response to treatment may also reflect comorbidities. One patient may have potential adverse consequences due to concomitant medications for a secondary disease while another may have a higher side effect risk due to renal dysfunction. A third patient may not suffer from any of the above. Unless a CER study analysis differentiates those patient subtypes or can predict which outcomes are likely, guidelines based upon the CER study might be appropriate for some patients but clinically inappropriate for others.

In general, the patient’s clinical presentation is known to the provider, who can target the treatment regimen accordingly. In some environments, electronic data systems have the breadth and depth of data and the clinical logic applied to it (eg, prior depressive hospitalization within the last month vs no hospitalization; presence vs absence of comorbidities; or potential drug interactions) so that clinical decision support tools and payer policies that ac- cess that electronic information could similarly tailor appropriate access to needed medications. At other times, the clinical diversity may depend upon patient symptomatic complaints, patient adherence, laboratory test results, or imaging findings—elements not “visible” to most pay- ers or automated systems that influence care. In these circumstances, policies should offer patients and providers more flexibility to select the appropriate treatment. When the condition is well defined, or if the implications of diversity are readily accessible, application of population-level treatment protocols or protocols that can account for this diversity may be appropriate.

Likelihood of not responding to similar treatments in similar ways (treatment independence). Based upon the success or failure of prior therapy, variation in response may be predictable. For example, if a patient fails 1 angiotensin-converting-enzyme (ACE) inhibitor for management of hypertension either for reasons of efficacy or safety, she is unlikely to respond well to another ACE inhibitor. Similarly, if amoxicillin is not successful for a urinary tract infection, another penicillin-like agent is less likely to resolve the problem compared with an agent in another therapeutic class (eg, ciprofloxacin, sulfamethoxazole/ trimethoprim). In these scenarios, the responses to available treatments within the same drug class are highly correlated, and treatment options are “dependent” on prior treatment success or failure. In contrast, for patients with depression like Ms Jones who are faced with a change in regimen, success with sertraline may not be matched by similar success with fluoxetine (eg, another SSRI).15 Here, treatment response is more “independent” of prior treatment success or failure.

Independence may vary from illness to illness, across treatment alternatives, or within a class of treatments.16 From a decision-making standpoint, when treatment independence is low (or dependence is high), patients may need to try only 1 therapy in a particular class since related treatments are not likely to be substantially more or less beneficial. In these circumstances, policies that narrow choices within a drug class may be appropriate. However, when treatment independence is high, response to treatment “A” does not eliminate the need for trials of treatments “B” or “C” even when the drugs are within the same therapeutic class. For these patients, access to multiple medications and flexibility to choose among them is needed. For Ms Jones, especially if her disease becomes unstable, the restricted antidepressant formulary of her state exchange that requires her to switch from sertraline to fluoxetine may prove problematic.

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