Betting on Death: Moral Dilemma

I recently re-read the book “What Money Can't Buy: The Moral Limits of Markets” by Michael J. Sandel. The example used in the book about the viatical industry and the moral dilemma associated with this make me think about the similar dilemma we are facing in the event-driven clinical trials where the event is unfortunate outcome (for example, morbidity  and mortality).

A viatical settlement (from the Latin "viaticum") is the sale of a policy owner's existing life insurance policy to a third party for more than its cash surrender value, but less than its net death benefit. Such a sale provides the policy owner with a lump sum. The third party becomes the new owner of the policy, pays the monthly premiums, and receives the full benefit of the policy when the insured dies.

"Viatical settlement" typically is the term used for a settlement involving an insured who is terminally or chronically ill.

The viatical industry started in the 1980s and 1990s, prompted by the AIDS epidemic. It consisted of a market in the life insurance policies of people with AIDS and others who had been diagnosed with a terminal illness. Here is how it worked: Suppose someone with a $100,000 life insurance policy is told by his doctor that he has only a year to live. And suppose he needs money now for medical care, or perhaps simply to live well in the short time he has remaining. An investor offers to buy the policy from the ailing person at a discount, say, $50,000, and takes over payment of the annual premiums. When the original policyholder dies, the investor collects the $100,000.

It seems like a good deal all around. The dying policyholder gains access to the cash he needs, and the investor turns a handsome profit – provided the person dies on schedule.

With viaticals, the financial risk creates a moral complication not present in most other investments: the investor must hope that the person whose life insurance he buys dies sooner rather than later. The longer the person hangs on, the lower the rate of return.

The anti-HIV drugs that extended the lives of tens of thousands of people with AIDS scrambled the calculations of the viatical industry.

The viatical industry can extend to people with other terminal diseases such as cancer. However the concept is the same and the moral issues are the same: betting the people to die sooner than later.

In clinical trials with event-driven design where the event is bad such as death, cancer recurrence, pulmonary exacerbation, transplantation rejection,…), we may face the same dilemma. While the intention of new treatment is to prevent the bad event from happening, as the trial sponsor, we also hope that these bad events can occur more often so that we can finish the study early and have the study results available earlier.

Suppose there is a cancer clinical trial where the primary efficacy endpoint is time to death and suppose we design a randomized, double-blind study to compare two treatment groups: an experimental treatment group and a control group, we will calculate the sample size to see how many death events are needed to have at least 80% statistical power to show the treatment difference. Then based on the accrual rate and dropout rate, we can further calculate the number of subjects needed to have the desired number of death events. During the study, we can check the aggregate death rate to see if the actual results are in line with the assumptions. If the death rate is below our assumptions, we should be happy since the lower death rate could indicate the experimental treatment works, however, as the trial sponsor, we would not be happy since the lower death rate will indicate the longer trial to accrue the requirement number of death events.

As the trial sponsor, we may want to employ the enrichment strategies to select the population who may be likely to die therefore the death events can be accrued quickly. As mentioned in FDA’s guidance “Enrichment Strategies for Clinical Trials to Support Approval of Human Drugs and Biological Products”, this type of enrichment strategy is called prognostic enrichment. Here is what is said in FDA’s guidance:

IV. PROGNOSTIC ENRICHMENT STRATEGIES—IDENTIFYING HIGH-RISK 169 PATIENTS

A wide variety of prognostic indicators have been used to identify patients with a greater likelihood of having the event (or a large change in a continuous measure) of interest in a trial. These indications include clinical and laboratory measures, medical history, and genomic or proteomic measures. Selecting such patients allows a treatment effect to be more readily discerned. For example, trials of prevention strategies (reducing the rate of death or other serious event) in cardiovascular (CV) disease are generally more successful if the patients enrolled have a high event rate, which will increase the power of a study to detect any given level of risk reduction. Similarly, identification of patients at high risk of a particular tumor, or at high risk of recurrence or metastatic disease can increase the power of a study to detect an effect of a cancer treatment. Prognostic enrichment strategies are also applicable, or potentially applicable, to the study of drugs intended to delay progression of a variety of diseases, such as Alzheimer’s disease, Parkinson’s disease, rheumatoid arthritis, multiple sclerosis, and other conditions, where patients with more rapid progression could be selected; it is possible, of course, that such patients might be less responsive to treatment (i.e., that rapid progression would be a negative predictor of response), and that would have to be considered.  

For any given desired power in an event-based study, the appropriate sample size will depend on effect size and the event rate in the placebo group. Prognostic enrichment does not increase the relative risk reduction (e.g., percent of responders or percent improvement in a symptom), but will increase the absolute effect size, generally allowing for a smaller sample size. For example, reduction of mortality from 10% to 5% in a high-risk population is the same relative effect as a reduction from 1% to 0.5% in a lower risk population, but a smaller sample size would be needed to show a 5% vs. 0.5% change in absolute risk. It is common to choose patients at high risk for events for the initial outcome study of a drug and, if successful, move on to larger studies in lower risk patients.

While this enrichment strategy is good for the trial sponsor and makes the clinical trial smaller, it also gives a bad taste because we are betting that the selected study population will have high death rate and that the patient die sooner.