Healthcare policy implications of heterogeneity of treatment effects

The emerging relevance of heterogeneity of treatment effect in clinical care: a study using stage IV prostate cancer as a model

AIM

Heterogeneity of treatment effect (HTE) occurs when patient factors modify a treatment's effect on health outcomes due to interactions between these factors and the treatment. This article reviews evidence regarding HTE in stage IV prostate cancer (S4PC).

METHOD

A systematic literature review was conducted in the MEDLINE and PubMed databases. Inclusion criteria required that articles examine the treatment-related impact of HTE factors on survival, adverse events or health-related quality of life in S4PC patients. The quality of evidence was graded good, fair or poor based on Agency for Healthcare Research and Quality guidelines.

RESULTS

The search identified 2659 articles, of which 92 met the inclusion/exclusion criteria. HTE in S4PC was studied for biologic factors including age, race, clinical signs/symptoms, measures of S4PC disease severity, genetic factors, laboratory data, prior treatment, concurrent medications and comorbidities. Nonbiologic factors that were studied included social, geographic and dietary factors. Age and race seldom showed any correlation with S4PC outcomes.

CONCLUSION

Diverse biologic and nonbiologic factors contribute to HTE in S4PC. This review in S4PC also provides an approach for examining HTE for other medical conditions. Ultimately, such knowledge can help oncologists prescribe more personalized medicine, help patients make more informed treatment choices, and inform policy-making and treatment coverage decisions.

Individualized cost-effectiveness analysis

John Ioannidis and Alan Garber discuss how to use incremental cost-effectiveness ratios (ICER) and related metrics so they can be useful for decision-making at the individual level, whether used by clinicians or individual patients.

Do we know how to set decision thresholds for diabetes?

The diagnosis of diabetes, based on measured fasting plasma glucose level, depends on choosing a threshold level for which the probability of failing to detect disease (missed diagnosis), as well as the probability of falsely diagnosing disease (false alarm), are both small. The Bayesian risk provides a tool for aggregating and evaluating the risks of missed diagnosis and false alarm. However, the underlying probability distributions are uncertain, which makes the choice of the decision threshold difficult. We discuss an hypothesis for choosing the threshold that can robustly achieve acceptable risk. Our analysis is based on info-gap decision theory, which is a non-probabilistic methodology for modelling and managing uncertainty. Our hypothesis is that the non-probabilistic method of info-gap robust decision making is able to select decision thresholds according to their probability of success. This hypothesis is motivated by the relationship between info-gap robustness and the probability of success, which has been observed in other disciplines (biology and economics). If true, it provides a valuable clinical tool, enabling the clinician to make reliable diagnostic decisions in the absence of extensive probabilistic information. Specifically, the hypothesis asserts that the physician is able to choose a diagnostic threshold that maximizes the probability of acceptably small Bayesian risk, without requiring accurate knowledge of the underlying probability distributions. The actual value of the Bayesian risk remains uncertain.

Improving pharmacovigilance in Europe: TPMT genotyping and phenotyping in the UK and Spain

Thiopurine S-methyltransferase (TPMT) is the rate-limiting step in the conversion of thiopurine drugs including azathioprine (AZA) to inactive metabolites. Heritable deficiency of TPMT activity increases risk for adverse events, most notably, myelosuppression leading to leukopenia and neutropenic sepsis. The reported European Commission study was undertaken to identify current evidence for the clinical utility of testing for TPMT status and extent of uptake, by either genotyping or phenotyping, in the clinical setting. Data presented here for the UK and Spain indicate that there has been a considerable increase in the uptake of TPMT testing in recent years. There are some data that support routine TPMT testing before AZA prescribing for reducing AZA-related adverse events. Key data include evidence in favor of TPMT testing in addition to the current practice of routine monitoring for reducing the number of AZA-related episodes of myelosuppression, averting deaths from neutropenic sepsis and improving health-related quality of life. Further data are needed for determining the cost-effectiveness of routine TPMT testing.