The days of “Doctor, do whatever you think is the best for me” are slowly disappearing in oncology. Treatment decisions are becoming more varied and complex, and patients are being asked to take on a more active role in decision making around their own health outcomes.
As a medical oncologist, every day, I see patients struggling with the myriad choices – ranging from treatment plans, side effects, quality of life, and prognosis – they are asked to consider as they face their cancer journey.
Below, I describe ten health literacy principles that I hope will empower patients and advocates to be informed and active participants in these important life decisions.
- Does the endpoint matter to you?
Many treatment recommendations are made based on results from clinical trials. However, clinical trials may be measuring things that may not matter to you. For example, for a patient with advanced cancer, whether the intervention improves longevity or quality of life may be most important. However, the trials may be measuring only whether the drug shrinks the tumor. A shrinking tumor may not always lead to a longer or better life. It is important to clearly understand what benefits and risks of the treatment are proven and what are only presumed.
- Dying with a cancer is not the same as dying from the cancer
Being diagnosed with a cancer doesn’t necessarily mean cancer will be the cause of death. Some cancers progress so slowly that some other causes – say, stroke – may lead to death before cancer becomes fatal. This is also the reason why many cancer screening tests may not be helpful. For example, screening for thyroid cancer is not recommended because although some thyroid mass may be detected in screening, they may not be aggressive enough to cause trouble during one’s life. For patients who are already diagnosed with a metastatic cancer, undergoing screening tests for other cancers is also futile because any newly diagnosed cancer is less likely to be as aggressive as the already existing metastatic cancer.
- Different people have different values
Even when survival is the endpoint of trials, different people will value it differently. How important improving survival by three months is vs the expense of toxicities will differ from patient to patient. Some patients may prefer compromise in survival if they are allowed to spend their end of life with family members and friends. The hidden costs of treatment (commute to cancer centres, time, financial costs, etc.) should all be factored into such decision making.
- There are always uncertainties in medicine
In medicine, it is impossible to guarantee individual outcomes. How long are you going to live? Is this treatment going to benefit you? There will never be definite answers to such questions. However, you can ask for a range of probable outcomes such as the best case and the worst-case scenarios and the most common scenario. This will help you in making your own decision about different choices.
How long are you going to live? Is this treatment going to benefit you? There will never be definite answers to such questions.
- The plural of anecdotes is not data
Sensational news headlines such as “a new miracle drug cures cancer” are usually based on a case study of a handful of patients. Sometimes, celebrities promote certain tests or treatments because they believe those interventions saved their lives. However, the problem with such stories is that there is selection bias – several hundreds and thousands of other people who undergo a similar test or treatment will not benefit and may in fact suffer harms. However, these people do not have the motivation to appear in the media to expound the futility or the harms of these interventions.
- What is the alternative?
It’s important to ask what would happen in the absence of intervention. What would happen if you did not do the screening test or did not undergo the surgery or take the medication? These “what-if” questions are important for making decisions and the answer to such “what-if” questions come through randomized trials.
- It’s difficult to establish causality without a randomized trial
The alternative case scenario would be best known only through randomized trials. Randomized trials randomly allocate half of patients to the intervention and the other half to the standard care (the control group) and compare outcomes. This randomization ensures that patients in the intervention arm are not systematically different from patients in the control arm, so that the difference in outcomes can be assumed to be due to the intervention which can be statistically tested. Without such randomized trials, it is difficult to say whether an observed finding is due to the intervention or chance alone. So, when an intervention is recommended, you should ask if it has been tested in a randomized trial.
- Statistically significant is not the same as clinically meaningful
Often the headlines read “Drug X significantly improved survival over standard treatment.” Here the term “significantly” usually means statistically significant. This simply means that the difference in survival is probably due to the drug rather than chance. However, the improvement in survival could only be a few days. It may be statistically significant and real, but it may not be meaningful at all. We have had cancer drugs that are approved because of statistically significant results that are clinically meaningless, such as delaying progression by only three days!
- Watch both relative and absolute risks
A new cancer treatment may claim to improve survival by 50 per cent. This improvement may seem like a huge benefit at first glance but that could simply be prolonging survival from two months to three months. Similarly, a statement may claim “only two patients in the trial suffered serious side effects.” However, it could be two of 100 patients, which is a two per cent risk, which can be substantial when the therapy is offered to several thousand patients.
- Individual-level and population-level decisions may not align
Policy decisions need to be made on a population-level based on data. This differs from individual level decisions, which may be based on values. An individual may think it’s worth undergoing one year of a toxic therapy to reduce the risk of cancer relapse by five per cent, but for others it may seem too much risk for too little benefit. A country may decide paying 100,000 dollars for a month of extra life is not worthwhile while an individual may think there is no price cap to human life. It is important to separate data from value judgments, especially for population level decisions.
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