Payers' Views on the Heterogeneity of Treatment Effect in Oncology

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The American Journal of Pharmacy Benefits, May/June 2017, Volume 9, Issue 3

When making coverage decisions in oncology, payers find difficulties in translating evidence on treatment effect heterogeneity into coverage policies.

Objectives: The heterogeneity of treatment effect (HTE) refers to the nonrandom variability in response to treatment. This study describes how US payers use HTE evidence when formulating coverage policies for oncology drugs.

Study Design: We employed a qualitative approach using semistructured, in-depth interviews with 15 payers.

Methods: An interview guide was developed based on theory and pilot interviews. Themes that emerged from this content analysis were summarized along with verbatim quotes from payers.

Results: All payers agreed that HTE is important to incorporate into coverage policies of oncology treatments. The FDA label is the most overwhelming determinant of whether HTE evidence gets incorporated into a coverage policy. If not in the FDA label, payers find it difficult to use HTE evidence due to the inability to precisely differentiate responders from nonresponders and the logistical difficulty to operationalize HTE. All payers reported that randomized controlled trials are the most trusted source to establish HTE evidence. In addition to the FDA label, payers also consider treatment guidelines, the quality/magnitude of HTE evidence, the availability of effective alternative substitutes, treatment line, cancer aggressiveness, and politics. When a biomarker and a companion diagnostic are involved, the degree to which HTE evidence is incorporated into coverage policies will also depend on the clinical and analytic validity of the test and the ability to accurately and pragmatically distinguish responders from nonresponders.

Conclusions: Payers’ oncology coverage decisions are affected by a myriad of factors. Payers require more definitive HTE evidence to make more efficient coverage decisions.

Am J Pharm Benefits. 2017;9(3):-0

The FDA approval of costly anticancer medications that yield a modest incremental survival benefit has become more prevalent in the past few years, with the price range reaching $10,000 per month in 2013 compared with $4500 per month in 2003.1 Between October 2012 and October 2013, the FDA approved 9 anticancer drugs.

Although this significant number of new drug approvals in a 12-month period indicates increased drug development activity and improvements in regulatory processes (eg, accelerated approvals), most approved drugs were associated with high price tags and a modest survival benefit.2

Healthcare payers are facing great challenges when making coverage decisions, with uncertainty surrounding the value of oncology treatments specific to their plan members.3 Therefore, it is important that payers appropriately assess patient heterogeneity in response to treatment and use that information to inform coverage policies for oncology drugs.

Heterogeneity of treatment effect (HTE) refers to the nonrandom variability in the response to a treatment among different individuals given their characteristics. Kravitz et al define HTE as the “magnitude of the variation of individual treatment effects in the population”4; they further explain that, when HTE is present, the modest average effects realized in clinical trials often reflect a mixture of patients, where a group experiences the average effect, others experience less than or more than the average effect, and some may be harmed by treatment.4

Failing to account for HTE could mean that healthcare payers may initially be paying for treatments that are ineffective, and for additional downstream costs attributed to initially ineffective or harmful therapies.

If HTE evidence is used efficiently, payers can spare patients unnecessary adverse effects if they are not likely to clinically benefit and encourage the use of appropriate therapies in patients with markers suggestive of good response, such as patients with acute lymphoblastic leukemia (ALL) who have a positive Philadelphia chromosome and would respond better to imatinib.

Strategies have been incorporated by healthcare payers to restrict access to anticancer medications depending on an individual’s response.5,6 For example, if a biomarker predicts whether a drug is expected to work, payers may decide to pay for that test to determine whether the drug should be given.

Subsequently, payers would use the test result to determine whether to reimburse for the drug where a negative finding typically would not be reimbursed. Similarly, if a specific biomarker predicts duration of therapy, payers would test that biomarker and reimburse for the duration of therapy as indicated by the test result. Step-therapy programs have been used if a marker predicts which drugs should be used first from several alternatives.

Although restriction strategies have been commonly used in coverage policies, the process by which HTE evidence informs the implementation of these strategies in coverage policies is understudied. The purpose of this study was to further understand the payers’ decision-making process, particularly to elicit their views on how they use HTE evidence when formulating coverage policies for oncology drugs.

METHODS

A qualitative approach using semi-structured in-depth interviews was employed to answer our research question.

Sampling Approach

Payers were selected using a 2-stage sampling approach. In the first stage, we used an expert sampling method (a specific case of purposive sampling) and contacted 20 payers through our professional network.7 We approached payers based on the following criteria: 1) they had a pharmacy or medical background, 2) they had been involved in pharmacy and therapeutics (P&T) committee meetings in their organization, and 3) they had reviewed HTE evidence in their organization to any extent. We received responses from 18 payers (90% response rate), of whom 16 satisfied the criteria and were interested in participating; of these 16 payers, 3 initially agreed but then dropped out.

The second stage—snowball sampling—was used with payers recruited from the first stage to further recruit other payers to increase our sample size.8 Fifteen payers participated in this study.

Our sample represented payers with pharmacy and medical backgrounds and a variety of payer groups. Although it is true that many P&T committee members are not oncologists and do not have detailed knowledge of oncology, the health plans make sure that there are oncologists on their committees as either standing or ad hoc members to assist the committee in their exploration of new oncology agents.

Interview Guide and Features

We developed an interview guide containing 3 sections: orientation, interview questions, and closing remarks (Table 1). The orientation included asking payers whether they agreed to be voice recorded, provided a rationale and context of why this study was important, and provided an orientation to technical terms. Payers were also asked if they had any questions before the interview proceeded. The interview guide had 4 types of questions: background, high-level, detailed, and closing. The third section involved closing remarks to thank interviewees for their time.

The interview questions were developed to understand the factors that affect the value judgments involved when making a coverage decision based on the analysis of a decision framework developed by Eddy.9 Pilot interviews were conducted to narrow the focus of the questions and to ensure their relevance to oncology. Although the interview guide was semi-structured, it was not uncommon that interviewees would switch the order of questions according to what they felt was most important to address during the interview.10

Interview questions were sent to the interviewees beforehand to ensure that they understood the questions and were prepared during the interview.

Each interview was scheduled to last approximately 30 to 60 minutes. During the interview, payers were provided with a decision-making framework and asked in question 5 (Table 1) to modify it to reflect real-world decision making. After the first interview, and in cases where major modifications took place, subsequent interviewees reviewed the updated conceptual framework as informed by the preceding interview.

Content Analysis

The content analysis involved reviewing the written notes from the interviews and complementing them with the audio recordings. Major thematic areas that emerged from the answers were coded and categorized under 1 of the 4 domains: 1) the importance of incorporating HTE evidence in coverage policies of anticancer drugs, 2) the factors that would impact the decision to restrict access to anticancer drugs, 3) sources of HTE evidence, and 4) the future of incorporating HTE evidence into coverage policies. Themes that emerged from the analysis were summarized along with verbatim quotes from payers to support each theme.

RESULTS

Study Sample

Our target was to recruit 12 to 15 individuals representing the various payer groups. Of the 20 individuals who were contacted, 15 were interviewed. The final sample included 15 payers and related organizations representing 11 payer groups located in the United States providing health insurance to 172.5 million individuals (Table 2). The following section describes the themes that emerged during the interviews and are supplemented with verbatim quotes from payers (Table 3).

Payers’ Views on the Role of Incorporating HTEEvidence in Coverage Policies

Theme 1: HTE is important to incorporate into coverage policies, yet most payers do not delve into HTE details unless it is specifically indicated in the FDA label.

All payers were familiar with the concept of HTE (although not the term) and its application in coverage policies. They unanimously agreed that HTE has a critical role in defining coverage policies in oncology; however, there is variation in the extent to which they incorporate HTE evidence in coverage policies.

Ten payers indicated that they do not delve into the details of HTE unless it is in the FDA label. Two of the 10 backed this approach explicitly, responding, “If HTE was not indicated in the FDA label, it was hard to justify a coverage policy that incorporates HTE.”

Small-sized payers indicated that they frequently rely on their pharmacy benefit managers’ recommendations. One payer indicated that they are heavily regulated and legally required to cover anticancer drugs. Only 1 payer stated that they are very sophisticated in their approach and have in-house technology assessment groups that thoroughly investigate HTE.

Theme 2: The inability to precisely differentiate responders from nonresponders and the logistical difficulty to operationalize HTE are reasons why most payers do not focus on HTE. The payers interviewed for our study did not believe that science has advanced far enough that it can be used to clearly differentiate responders from nonresponders. For example, 1 payer gave the example that a negative finding from testing a biomarker does not exclusively mean lack of benefit; instead, it indicates that a patient will not benefit as much compared with a patient with a positive result.

When asked about clinical and sociodemographic factors, all payers agreed that although it does not make sense biologically to differentiate responders from nonresponders, they are used in prior authorizations. For example, Eastern Cooperative Oncology Group (ECOG) performance status is used by many payers in coverage policies for patients taking sipuleucel-T for metastatic prostate cancer.

Patients must be in the ECOG 0 or 1 group to qualify for coverage. Additionally, for large payers, it is logistically difficult to come up with different workloads and clinical pathways for all patients. Therefore, P&T committees trust that physicians will steer patients to their most effective treatment option without searching for HTE evidence and incorporating it into coverage policy.

Payers’ Views on Sources of HTE Evidence

Theme 3: Randomized controlled trials (RCTs) are the most trusted source to establish evidence on treatment effectiveness, and this extends to evaluating HTE evidence. All payers agree that well-conducted RCTs in peer-reviewed journals remain the most reliable source for establishing HTE evidence. However, 5 payers cautioned that RCTs cannot answer all types of HTE questions. For example, due to the narrowly defined population included in RCTs, some HTE questions cannot be answered, such as, “How does the treatment effectiveness vary by comorbidity status?”

Interestingly, none of the payers were able to weigh in on the statistical methods as proof of HTE within RCTs (eg, interaction terms, growth mixture models); however, post hoc subgroup analyses seemed to be the most familiar method. Questions that require longer follow-up would be expensive to answer using RCTs, in which case observational studies may be of importance.

In addition to RCTs, all 15 payers used some combination of Medicare-approved compendia, observational studies, systematic reviews, meta-analyses, and pragmatic clinical trials. Three payers indicated that observational studies are not enough to support coverage decisions, but are more compelling as evidence to support appeals.

Theme 4: Although most payers agreed that biomarkers are most useful in predicting response to treatment, all payers stressed that the quality and magnitude of evidence determine the usability of any predictor.

There is a continuum of the predictive value of the predictor categories (ie, biomarkers are generally more useful than clinical factors) that are more useful than sociodemographic factors in predicting response to treatment. However, the quality and magnitude of evidence is always the key to the usability of these factors. Payers unanimously indicated that they prefer the “cut and dry” predictors and tend to avoid the controversial gray area.

One reason why payers think biomarkers are more appealing is that they are more related to the pathophysiology of the disease and the mechanism of action of the drug compared with other factors. Five payers added that there is a lot of HTE evidence that is “no good” and does not give a clear-cut answer.

The consensus was that if there is not good enough HTE evidence, payers would be less likely to use the evidence until more definitive HTE evidence arises. Others indicated that in oncology, payers must use the best available evidence. For example, prior to the breast cancer Oncotype DX panel, 2 payers indicated that they used the patient’s age and tumor size at point of surgery for prior authorization criteria.

Payers’ Views on Factors That Could Affect Coverage Restrictions of Oncology Drugs

Theme 5: Factors that influence the general health technology assessment process also would come into play when assessing HTE evidence. The FDA label was the most overwhelming determinant for restricting coverage. All payers stated that it is almost impossible for an oncology drug to be covered for certain subgroups unless that specific indication was on the FDA labeling.

In the situation where the label has a companion diagnostic test built into it, payers found it easy to formulate a coverage policy based on the companion diagnostic. However, when the HTE was in relation to a sociodemographic feature, it was somewhat harder to translate that HTE information into policy. The example that was given by 2 payers was belimumab, a drug used to treat systemic lupus erythematosus.

Two RCTs demonstrated the effectiveness and safety of belimumab in patients with lupus, excluding previously treated patients and those with the more severe lupus.11,12 African American patients participating in the 2 RCTs did not appear to respond to belimumab, leading the FDA to caution against its use in this patient population in its labeling. Both payers indicated that they would still cover it for African Americans but would use a “watch and wait” approach.

Other highly weighted factors that affect the incorporation of HTE evidence into coverage policy are the oncology treatment guidelines (mostly National Comprehensive Cancer Network [NCCN]), the quality and magnitude of evidence (RCTs weighted more than other sources), the availability of effective alternative therapies, line of treatment, and the aggressiveness of cancer. Thirteen of the respondents were involved in the clinical evaluation team and not in the economic evaluation; therefore, most were reluctant to speak about the costs of drugs or companion diagnostic tests.

Theme 6: The degree to which HTE evidence is incorporated into coverage policies depends on the predictive value of the characteristic in question and the ability to accurately and pragmatically distinguish responders from nonresponders.

One of the most cited factors that would determine the degree to which HTE evidence can be incorporated into coverage policy is the predictive value of the characteristic in question. Three payers stated that testing for Philadelphia chromosome positivity in patients with ALL when taking imatinib is a good example of a characteristic that is highly predictive of response to treatment; it is also considered one of the “easier” decisions for payers to make.

The clinical and analytic validity of the test used to detect the presence of a specific genotype is one of the major factors that payers talked about. If a test can, to a reasonable extent of sensitivity and specificity, identify the specific genetic arrangement that is associated with improved or worsened response to treatment, then this test would be used to guide therapy in clinical pathways and would be covered.

One payer also stated that operationalization of the test at the point of prescribing can impact whether the test gets covered. The payer identified 3 questions to determine whether the test is easy or difficult to operationalize: is the test FDA approved?

How complicated is it to perform the test? How long to get the results? Another payer identified the background prevalence of the mutation that predicts response to treatment: when the background prevalence of the desired mutation is low, the payer indicated that it makes more sense to use the companion diagnostic test to identify those small numbers of patients (eg, crizotinib for non—small cell lung cancer).

Another payer pointed out the concept of the physician practice gap. A physician practice gap is a clinically important difference between the distribution of treatment practices occurring in a given provider population compared with the pattern that would be expected in that setting if providers followed evidence-based best practice guidance. If physicians follow guidelines (eg, NCCN), then there is no need for a policy to restrict access; however, if there is a significant practice gap, a coverage policy becomes necessary.

When asked to choose which of the 2 scenarios in question 4 (Table 1) is more likely to result in a policy change—scenario 1: a subgroup is worse off in terms of response to treatment relative to the general population; and scenario 2: a subgroup is better off in terms of response to treatment relative to the general population—8 payers said that scenario 2 would more likely trigger a policy change compared with scenario 1.

The justifications varied from payers caring more about poor responders to making sure they are steered to better drugs because scenario 1 has an overall general population benefit. However, 2 of the 8 payers stressed that the magnitude of difference would impact whether a policy should change. Four payers were indifferent and indicated both scenarios would trigger a policy change if the evidence was there, and 3 payers did not know how to answer.

DISCUSSION

Payers Need Better HTE Evidence to ImproveCoverage Policies

Most payers stated that much of the HTE evidence in the literature is not useful. They expressed the need for high-quality, definitive HTE evidence that can be used to inform coverage policies. Most believe that until personalized medicine—particularly, genomic medicine—evolves to produce more definitive HTE evidence, the ability for payers to use the available HTE evidence is limited.

Transparency and Standardization

From the pilot interviews, we proposed a coverage decision-making framework that used HTE evidence to payers and asked them to review and update the framework. Although all payers agreed that the framework (Figure) largely represented what happens in the real world, they called for a more transparent and standardized approach to evaluating and integrating HTE evidence into coverage policies.

Only a couple of payers have formulary meetings made open to the public. Additionally, only 1 payer indicated that they have a standardized approach to evaluating HTE. The majority dealt with HTE issues as they arose or depending on the significance of the topic.

Bridging the Gap Between Payers and Researchers

Based on the information gathered from the interviews, it seems that researchers are producing HTE evidence that is not directly useful to healthcare payers. Researchers should ideally engage payers before, during, and after conducting research to generate HTE evidence. Additionally, payers should have standards that define the level of HTE evidence acceptable to inform coverage policies.

The Present and Future of Genomic Testing

A payer stated that there are 2 systems that mirror the current and potential future of genomic testing. The first is the just-in-time (JIT) system, in which a patient population eligible for receiving the drug undergoes genomic testing when they start the drug.

The payer explained that although JIT is efficient in the current environment, it is not going to be as efficient in the future. A just-in-case system is expected to be more efficient, and it involves preemptive genotyping of an individual’s entire genetic makeup, so if that patient receives the drug of interest, his or her genetic information will be readily available.

Thus, because the technology and cost of testing is outpacing the rate to implement clinically, it will soon be cost-effective to preemptively test a therapy even if the patient does not take the drug because of economies of scale.

CONCLUSIONS

Healthcare payers appreciate the important role that HTE plays in making more efficient coverage policies, but cautioned that the inability to precisely differentiate responders from nonresponders and the logistical difficulty to operationalize HTE can reduce the value of such evidence. In the decision-making process, payers emphasized several factors that affect the incorporation of HTE evidence into oncology coverage policies, but emphasized that the FDA label is the biggest influencer. When effectively incorporated into coverage policies, HTE can help refocus the healthcare delivery system on the evidence-based medicine and value of care received.

Acknowledgments

The authors would like to thank Anna Hung, James Grana, Robert Navarro, and Joseph DiPiro for helping with payer recruitment.

Author Affiliations: Real World Evidence/Data Analytics, Pharmerit International, (AA), Bethesda, MD; Federal Employee Program Integrated Care Management, Blue Cross Blue Shield Association (DY), Washington, DC; Pharmaceutical Health Services Research Department, School of Pharmacy, University of Maryland (FP, CDM), Baltimore, MD

Source of Funding: None.

Author Disclosures: Dr Aly is an employee of Pharmerit. Dr Mullins has been a consultant for Bayer, Insmed, Janssen/Johnson & Johnson, Mundipharma, Novo Nordisk, Pfizer, Regeneron, and Sanofi; has received grants from Bayer, Merck, Novartis, and Pfizer; and has received honoraria from Bayer. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (AA, CDM, FP, DY); analysis and interpretation of data (AA, FP, DY); drafting of the manuscript (AA, DY); critical revision of the manuscript for important intellectual content (CDM, FP, DY); administrative, technical, or logistic support (AA); and supervision (FP, CDM).

Address Correspondence to: Abdalla Aly, PhD, Pharmerit International, 4350 East West Hwy, Ste 430, Bethesda, MD 20814. E-mail: aaly@pharmerit.com.

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