M. G. Myriam Hunink

Decision Making in Health and Medicine: Choosing the best treatment

Firstly, do no (net) harm. (adapted from) Hippocrates Introduction Some treatment decisions are straightforward. For example, what should be done for an elderly patient with a fractured hip? Inserting a metal pin has dramatically altered the management: instead of lying in bed for weeks or months waiting for the fracture to heal while blood clots and pneumonia threatened, the patient is now ambulatory within days. The risks of morbidity and mortality are both greatly reduced. However, many treatment decisions are complex. They involve uncertainties and trade-offs that need to be carefully weighed before choosing. Tragic outcomes may occur no matter which choice is made, and the best that can be done is to minimize the overall risks. Such decisions can be difficult and uncomfortable to make. For example, consider the following historical dilemma. Benjamin Franklin and smallpox Benjamin Franklin argued implicitly in favor of the application to individual patients of probabilities based on previous experience with similar groups of patients. Before Edward Jenner’s discovery in 1796 of cowpox vaccination for smallpox, it was known that immunity from smallpox could be achieved by a live smallpox inoculation, but the procedure entailed a risk of death. When a smallpox epidemic broke out in Boston in 1721, the physician Zabdiel Boylston consented, at the urging of the clergyman Cotton Mather, to inoculate several hundred citizens. Mather and Boylston reported their results (1): Out of about ten thousand Bostonians, five thousand seven hundred fifty-nine took smallpox the natural way. Of these, eight hundred eighty-five died, or one in seven. Two hundred eighty-six took smallpox by inoculation. Of these, six died, or one in forty-seven.

Decision Making in Health and Medicine: Elements of decision making in health care

And take the case of a man who is ill. I call two physicians: they differ in opinion. I am not to lie down and die between them: I must do something. Samuel Johnson Introduction How are decisions made in practice, and can we improve the process? Decisions in health care can be particularly awkward, involving a complex web of diagnostic and therapeutic uncertainties, patient preferences and values, and costs. It is not surprising that there is often considerable disagreement about the best course of action. One of the authors of this book tells the following story (1): Being a cardiovascular radiologist, I regularly attend the vascular rounds at the University Hospital. It’s an interesting conference: the Professor of Vascular Surgery really loves academic discussions and each case gets a lot of attention. The conference goes on for hours. The clinical fellows complain, of course, and it sure keeps me from my regular work. But it’s one of the few conferences that I attend where there is a real discussion of the risks, benefits, and costs of the management options. Even patient preferences are sometimes (albeit rarely) considered. And yet, I find there is something disturbing about the conference. The discussions always seem to go along the same lines. Doctor R. advocates treatment X because he recently read a paper that reported wonderful results; Doctor S. counters that treatment X has a substantial risk associated with it, as was shown in another paper published last year in the world’s highest-ranking journal in the field; and Doctor T. says that given the current limited health-care budget maybe we should consider a less expensive alternative or no treatment at all. They talk around in circles for ten to 15 minutes, each doctor reiterating his or her opinion. The professor, realizing that his fellows are getting irritated, finally stops the discussion. Practical chores are waiting; there are patients to be cared for. And so the professor concludes: ‘All right. We will offer the patient treatment X .’ About 30% of those involved in the decision-making process nod their heads in agreement; another 30% start bringing up objections which get stifled quickly by the fellows who really do not want an encore, and the remaining 40% are either too tired or too flabbergasted to respond, or are more concerned about another objective, namely their job security.

Decision Making in Health and Medicine: Psychology of judgment and choice

We are pawns in a game whose forces we largely fail to understand. Dan Ariely Introduction This book has discussed a host of methods and approaches to analyzing decisions, to predicting alternate and optimal outcomes, and to applying quantitative and analytic approaches to understand and explain choices. Most of the chapters up until now have emphasized technical methods and are likely to have challenged and expanded your analytic skills. For this final chapter we will change gears to discuss the side of the brain that is commonly thought of as less analytical and more creative, the ‘right brain’ contribution to decision making. In this concluding chapter we will discuss the psychology underlying judgment and choice. Think back for a moment to the example in Chapter 4 about genetic susceptibility for breast cancer. Here was the situation we were considering: Genetic susceptibility for breast cancer A 25-year-old woman has a strong family history of breast cancer, including a sister who developed the disease at age 35. Her sister has undergone genetic testing for cancer predisposition and has been found to carry a mutation in the BRCA1 breast cancer gene. The woman is concerned about her own risk of breast cancer and chooses to be tested. She is found to have the same mutation, and is told that her lifetime risk of developing breast cancer is approximately 65%. If she does nothing at all with this information, her chance of surviving to age 70 is 53% (compared with all women’s survival probability of 84%). She has a number of options open to her: (1) careful surveillance, including regular mammography and magnetic resonance imaging (MRI), which would increase her chance of surviving to age 70 to 59%; (2) prophylactic mastectomy – surgical removal of both breasts – which would increase her chance of surviving to age 70 to 66%; (3) prophylactic mastectomy now plus prophylactic oophorectomy – surgical removal of the ovaries – when she turns 40, which would increase her chance of surviving to age 70 to 79%. Both surgical options increase her chance of survival beyond that of surveillance, but carry some personal costs – mastectomy can affect sexual function and body image, oophorectomy causes early-onset menopause and prevents child bearing. Does the benefit of risk reduction with surgery outweigh the personal costs of these interventions?

Decision Making in Health and Medicine: Interpreting diagnostic information

The interpretation of new information depends on what was already known about the patient. Harold Sox Diagnostic information and probability revision Physicians have at their disposal an enormous variety of diagnostic information to guide them in decision making. Diagnostic information comes from talking to the patient (symptoms, such as pain, nausea, and breathlessness), examining the patient (signs, such as abdominal tenderness, fever, and blood pressure), and from diagnostic tests (such as blood tests, X-rays, and electrocardiograms (ECGs)) and screening tests (such as Papanicolaou smears for cervical cancer or cholesterol measurements). Physicians are not the only ones that have to interpret diagnostic information. Public policy makers in health care are equally concerned with understanding the performance of diagnostic tests. If, for example, a policy maker is considering a screening program for lung cancer, he/she will need to understand the performance of the diagnostic tests that can detect lung cancer in an early phase of the disease. In public policy making, other types of ‘diagnostic tests’ may also be relevant. For example, a survey with a questionnaire in a population sample can be considered analogous to a diagnostic test. And performing a trial to determine the efficacy of a treatment is in fact a ‘test’ with the goal of getting more information about that treatment.

Decision Making in Health and Medicine: Estimation, calibration, and validation

Essentially, all models are wrong, but some are useful. George E. P. Box Introduction As discussed in Chapter 8, ‘good decision analyses depend on both the veracity of the decision model and the validity of the individual data elements.’ The validity of each individual data element relies on the comprehensiveness of the literature search for the best and most appropriate study or studies, criteria for selecting the source studies, the design of the study or studies, and methods for synthesizing the data from multiple sources. Nonetheless, Sir Michael David Rawlins avers that ‘Decision makers have to incorporate judgements, as part of their appraisal of the evidence, in reaching their conclusions. Such judgements relate to the extent to which each of the components of the evidence base is “fit for purpose.” Is it reliable?’(1) Because the integration of a multitude of these ‘best available’ data elements forms the basis for model results, some individuals refer to decision analyses as black boxes, so this last question applies particularly to the overall model predictions. Consequently, assessing model validity becomes paramount. However, prior to assessing model validity, model construction requires attention to parameter estimation and model calibration. This chapter focuses on parameter estimation, calibration, and validation in the context of Markov and, more generally, state-transition models (Chapter 10) in which recurrent events may occur over an extended period of time. The process of parameter estimation, calibration, and validation is iterative: it involves both adjustment of the data to fit the model and adjustment of the model to fit the data. Parameter estimation Survival analysis involves determining the probability that an event such as death or disease progression will occur over time. The events modeled in survival analysis are called ‘failure’ events, because once they occur, they cannot occur again. ‘Survival’ is the absence of the failure event. The failure event may be death, or it may be death combined with a non-fatal outcome such as developing cancer or having a heart attack, in which case the absence of the event is referred to as event-free survival. Commonly used methods for survival analysis include life-table analysis, Kaplan–Meier product limit estimates, and Cox proportional hazards models. A survival curve plots the probability of being alive over time (Figure 11.1).