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The American Journal of Managed Care July 2014
Managed Care Patients' Preferences, Physician Recommendations, and Colon Cancer Screening
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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
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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.
Patient Preferences. For Ms Jones, tolerability regarding such potential side effects as weight gain or sexual dysfunction may influence her treatment adherence and subsequently her ability to make the optimal therapeutic choice. These types of preferences vary from person to person and reflect attitudes associated with preferred outcomes (eg, increased survival vs greater quality of life); tolerability regardingside effects (eg, willingness to accept being drowsy); differences in socio-cultural preferences (eg, low-sodium diets in the American South); route of administration (eg, oral vs injectable medications), or willingness to make behavioral changes (eg, active vs sedentary lifestyle). Preferences also may change over time as symptoms worsen, conditions progress, or patients adapt to their health status.

There is increasing evidence that patient preferences influence health outcomes and costs.17 In treating hepatitis C, preferences for efficacy and treatment side effects are associated with adherence to antiviral therapy, a key component of achieving sustained viral response.18 For several common conditions (eg, cardiac angina, benign uterine fibroids, benign prostatic hyperplasia, hip pain, knee pain, and back pain), overall medical care costs were shown to be reduced when patient preferences were considered in the decision-making process.19 For example, when joint replacement candidates were informed of the treatment benefits and risks, fewer patients chose to undergo costly surgeries.20 Unfortunately, population-level comparative studies rarely assess patient preferences and we don’t know whether individual preferences might lead to therapeutic choices which differ from the published "average" results.

For a provider, determining optimal treatment for an individual patient requires factoring in the available population-level evidence on benefits, risks, and costs to inform an individual patient’s preferences. In some conditions (eg, hospital patients with pneumonia or acute myocardial infarction), patient preferences may not greatly differ and a standardized treatment protocol could be more appropriate. However, in patients with preference-sensitive conditions or conditions in which the potential risks and benefits of treatments differ greatly (eg, prostate cancer, bleeding due to uterine fibroids), it would be important to elicit those preferences and enable patients to choose among the alternative--approaches: to not, in other words, strictly apply population-level findings. In scenarios where patient preference plays a critical role in determining the optimal choice among alternatives with different costs (eg, when adherence or patient expectations are important), a tension will exist between efficient resource allocation and patient choice. In some cases, Higher risk and clinical impact of heterogeneity there may be limited evidence to justify a costly treatment for a terminal condition. In other cases, patient preference may come at a cost to other health plan members. In these cases, health plans and policy makers will need to consider the plan’s purpose and deliberate on how benefit designs or policy statements could account for these tradeoffs to minimize costs and optimize health outcomes.

Implications for Research

Although genomic information will continue to offer the potential to predict individual response, it will not resolve all sources of variability. Research directly comparing treatment alternatives in the same patient is needed. However, direct assessment of treatment alternatives in the form of crossover studies or n-of-1 designs infrequently occurs. It is beyond the scope of this paper to address, but others researchers have discussed the benefits and limitations of using alternative research methods and analyses (eg, risk prediction models, latent growth mixture models, classification and regression analyses, and non-parametric models) to elucidate who will likely respond or will not to particular treatment approaches.21

For experimental and non-experimental studies, pre-specification of subgroups and collection of multiple baseline patient characteristics (eg, clinical, demographic, patient preferences) can help identify which patients differ in their treatment response and point to potential causal effects. Methods research on how to best elicit patient preferences in clinical practice, and how to utilize information generated by patient-powered information sources and networks, could improve our ability to identify patient preferences; demonstration projects could incorporate these approaches into shared decision making. For payers and policy makers, studies on the clinical and economic impact of preferences on patients’ ability to manage their health are needed.22 The information on the clincial and economic impact of patient preferences when treating depression would likely assist Ms Jones’ healthcare provider in answering her question about the advisability of switching to the health exchange’s preferred medication, and it would enable her payer to support rational decision making and balance the tension between costs and preferences.

PCORI’s focus on patient variation and patient sub- groups will improve our knowledge of heterogeneity.7 However, simply funding research on the topic will not be sufficient. Among top-tier journals, only 1 in 3 trials reported heterogeneity in treatment response.23 To expand the availability of this evidence, journal editors could adopt usage of a CONSORT-like statement, in which authors would indicate the likelihood of heterogeneity (eg, high, medium, or low); report whether measures of heterogeneity were assessed; and list the factors that explain any heterogeneity.24-27 Transparent pre-specification of subgroups (eg, via time-stamped analysis plans on clinicaltrials.gov) and standardized subgroup reporting (eg, via online appendices, in databases managed by clinical condition consortia, or as specified by data standards groups) would enable meta-analysts to use this information and better explore patient differences. In turn, these meta-analyses would be able to identify areas in which individual treatment response varies or to suggest future research priorities.

Implications for Providers

As detailed CER results become available, providers will need to focus beyond the study-level conclusions and understand the patient associated with treatment response characteristics. With the large number of CER studies anticipated,28 it is likely that providers will increasingly depend upon professional societies and clinical practice guidelines to assist them in sorting through this voluminous and oftenconflicting information. Providers would be greatly assisted if guideline developers would synthesize the information on treatment variability, treatment independence, and patient preferences, and then formulate practice recommendations with this level of detail. These same concepts should be considered by those responsible for developing quality metrics, so that their clinical and policy decisions will not be affectedinappropriate conclusions based on misleading population-level data.29-31

In the future as mobile health (mHealth) communications between patients and providers become more widely adopted, providers will be able to quickly view a patient’s symptoms, preferences, and clinical markers and determine if subsequent trials of alternative treatments are needed. As bedside information technology becomes more prevalent and sophisticated, providers will gain greater ability to offer evidence-based and individualized treatment recommendations. By mining large electronic databases, Ms Jones’ provider could identify patients “similar” to her and determine how they responded to a switch from sertraline to fluoxetine.32,33

Evidence regarding treatment independence could also guide providers on the likely “success rate” with second-line treatment. When treatment independence is high, rather than immediately switching to a medication in a different class, it may be appropriate to switch to other agents in the same class. In cases where treatment dependence is high (eg, treatment response is correlated with prior treatment success or failure due to similar pharmacologic or pharmacodynamic characteristics), altering the treatment strategy by using a drug in a different therapeutic class may be the preferred course. Finally, tools and training are needed to facilitate the provider-patient discussion of treatment options that incorporate individual preferences for possible risks and benefits.

Implications for Payers

Payers typically make population-based rather than highly individualized coverage and reimbursement decisions. This choice reflects limitations in the available information as well as the need to avoid creating an overly complex benefit design. When treatment independence is low, there is either minimal variation in treatment response or more variability that is predictable based on information accessible to the payer. In such scenarios, there is little risk of clinical deterioration; therefore, insurance designs that direct therapy toward preferred options through utilization management (eg, narrow or closed formularies, therapeutic substitution, step-therapy, or prior authorization) or by instituting financial incentives (eg, lower copayments for preferred tiers) may be acceptable.

Conversely, logistical burdens and financial incentives to access what the payer has deemed optimal treatment may be less acceptable when 1 or more of the following is present: 1) marked variation in treatment effects; 2) minimal ability to predict individual response by the payer or clinical system; 3) treatment response with 1 treatment is not correlated to response with alternative treatments; 4) the consequences of suboptimal initial therapy are high. In these cases, more flexible utilization management designs or provider-directed decision making (within a range of evidence-based therapies) may be preferable.

Treatment heterogeneity also raises ethical dilemmas in reimbursement. Patients with a given biologic predisposition may find that treatments that are optimal for them are not the preferred agents based upon population “average” results. Currently, these patients would be required to pay higher amounts to access the non-preferred medications. To account for ethical standards of fairness,34 patient co-payments for these patients should be similar to those of the preferred treatment options for the population “average.” With the increased sophistication of benefit design and electronic records, it may be feasible to account for biology or genetics and tailor co-payments accordingly.35

CONCLUSIONS

 
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