Skills

Use of decision analysis in clinical practice and policy

© Pearson Professional Lid 1997

treatment with antibiotics (hltervention) reduce the chance of being in pain (outcome) compared with no treatment (comparison group)?

2. In children with acute otitis media, how much does antibiotic treatment increase the chance of diarrhea, vomiting or rash compared with no treatment?

All courses of action involve harms as well as benefits. Decision analysis is one way of making explicit which alternatives are being considered and what the trade-off is in terms of harms and benefits.

In this paper, we draw on an example in child health to illustrate two ways in which decision analysis can be used by policy- makers, clinicians and patients: (1) to structure a decision, so that all important interventions and outcomes are considered; and (2) to guide decisions by quantitatively combining the probability that something will happen, with how different outcomes are valued.

A decision that general practitio0ers address on most working days is whether or not to prescribe antibiotics for children with acute ear infection or otitis media. Implicit in their decision is the weighing up of the benefits and harms of antibiotic treatment with the benefits and harms of not treating. As for many clinical decisions, there is no single 'correct decision' that can be recommended. Instead, clinicians need to assess the absolute risk of different outcomes based on the best available evidence (or have summaries of the evidence provided for them in the form of decision aids or guidelines), and then explicitly weigh these risks with how individual patients value different outcomes. Fortunately, there is a systematic review by Glasziou et al on the Cochrane Database of Systematic Reviews, which compares the effects of antibiotic treatment and placebo in children with acute otitis media, t

In order to structure this decision, we need to define three things: the population, the possible interventions (and comparison groups) and the important outcomes we, and the parents/patients want to consider. 2 Imagine you are confronted by a 2-year-old child with otitis media, who has had a temperature and has been awake most of the night in pain. The population we are interested in is young children with acute otitis media. To keep things simple, we will consider only two possible h~tervention options: treatment with antibiotics or no

treatment. Although there are many possible outcomes (e.g. perforation, mastoiditis), we will concentrate on two of the most common and important in a Western, industrialized, general practice setting: anyone who has young children will "know that pain is the most distressing and immediate concern, while vomiting, diarrhea, and rash are the most common adverse effects of antibiotic treatment.

Drawing a simple decision tree makes more explicit which questions need to be addressed to decide whether to treat or not. The first branch of the tree is shown as a square node and represents the point of decision. The branches further to the fight are shown as circles, and each represents the probability of an event occurring. What we have to do is to fill in each of these probabilities and then to attach a 'utility value' to each possible outcome (the end of each branch on the extreme fight). To make this decision, we need to answer two principal questions:

1. In children with acute otitis media (patient population), how much does

Question 1

The systematic review by Glasziou et al gives us information about the chance of pain at 24 hours and at 2-7 days. Treatment had no effect on pain at 24 hours (odds ratio = 0.99 [95% confidence interval 0.72-1.36]), but there was a reduction in the risk of pain at 2-7 days in the treated group (odds ratio 0.59 [95% CI 0.44-0.79]). Prolonged pain is likely to be important for children and parents, so it seems justified to consider pain at 2-7 days as an important benefit of treatment.

The information needed is the probability of pain at 2-7 days in treated and untreated children. However, this information cannot be derived directly from the meta-analysis, as these trials included patients who received placebo, not patients who received no treatment at all. As patients given placebo do better than the general population, this 'baseline' risk should be derived from cohort studies. Glasziou et al estimate that 20% 3 of untreated children would still be in pain after 2 days, and using this figure and the odds ratio we can calculate that the absolute risk of pain at 2-7 days in the treated group is 13% (95% CI 10-16%) (see formula in box, Fig. 1). The absolute reduction in pain in

Treatment /

Acute otitis media m / I

~ x N , N No treatment

Pain ( ~ 0.13 (]

No pain 0.87 ~

Pain 0.2

( ~ N o pain 0.8 <1

The probability of pain in the untreated group has been estimated to be 20% from cohort studies. The probability of pain in the treatment group is calculated from the patient's expected event rate if untreated (PEER), i.e. 20% or 0.2, and the odds ratio for the treatment effect, derived front the systematic review (odds ratio = 0.59).

odds ratio x PEER The formula is: absolute risk of pain in treated children =

1 - PEER x (1 - odds ratio) 0.59 x 0.2 0.118

- - = 0.128 (i.e. 0.13 or 13% of treated children would still have pain) 1 - 0 . 2 ( 1 - 0 . 5 9 ) 0.918

Note: this formula (derived from Sackett et al. ref 2 would not be needed if the treatment effect had been given as a relative risk rather than an odds ratio.

Fig. 1 Pain at 2-7 days according to treatment for otitis media.

JUNE 1997 EVIDENCE-BASED HEALTH POLICY AND MANAGEMENT 25

Evidence-based Health Policy and Management

thoge treated compared with not treated is simply the difference between the absolute risk of pain in untreated and treated children (i.e. 7%) (Fig. 1).

Question 2

Diarrhea and vomiting can be caused by antibiotic treatment or by otitis media, as shown by the baseline risk of diarrhea, vomiting or rash ranging from < 1% to 30%, in children in the placebo group in the three studies which recorded this outcome (overall estimate 11%). The odds ratio for vomiting, diarrhea or rash was 1.97 (1.19-3.25), and if we use 11% as the patient expected event rate (see formula, Fig. 1) we can calculate the absolute risk of diarrhea, vomiting or rash in the treatment group to be 20% (95% confidence interval 13-29%): an increase of 9%.

At the most simple level, in deciding whether to treat or not treat I00 children with acute otitis media, we have to trade-off 9 extra children developing diarrhea, vomiting or rash on treatment against failing to prevent pain in 7 children if we do not treat. This decision depends on how bad we (parents, patient and clinician) believe each of these adverse outcomes is, If we value diarrhea, vomiting or rash the same as pain then we would tend to favor no treatment. However, i f we thought that pain was > 9/7 i.e. 1.3 or more times worse than diarrhea, vomiting or rash, then we would choose antibiotics.

This numerical approach provides a way in which clinicians can quantify the trade-off between principal harms and benefits in their day-to-day clinical decisions. However, policy-makers may want to consider other

Treatment

Acute otitis media S

DVR $ 0.2 q 0.25

0.8 <1 0.5

DVR No pain ~ 0.20 <] 0.75

0.87 " J ~ N i l ~ 0.8 q 1.0

~ DVR Pain 0.11 (] 0.25

No treatment 0.89 <] 0.5

DVR 0.75

0.8 " - ' ~ <l I.O 0.89

$ the probability of pain and diarrhea, vomiting or rash (DVR) in the treatment group is derived by multiplying the probabilities along the line (0.13 x 0.20) and the utility value for both pain and D & V of 0.25 (0.13 x 0.20 x 0.25 = 0.026). These values are then summed for each sub-branch of the treatment option (total = 0.885), and then compared with the sum of the sub-branches for the no treatment option (total = 0.876). The preferred option is the one with the highest value, i.e. treatment.

Fig. 2 Probabilities and utilities of outcomes with or without treatment.

outcomes (Fig. 2); in particular, the fact that treatment itself is not pleasant, and may be costly. Expansion of the tree allows us to allocate values directly to the different outcomes, to multiply these by the probabilities of pain and diarrhea, vomiting or rash (assuming that these are independent), and to sum the resulting utilities for each option: treatment or no treatment.

As in the example above, treatment is the preferred option (utility value 0.885 > 0.873) if we value pain as being 1.5 times worse than diarrhea, vomiting or rash (utility value for pain = 0.5, and for diarrhea, vomiting or rash = 0.75). However, the preferred option changes if we consider that treatment itself has even a small disutility: given a utility of 0.98 for treatment, the preferred option becomes not to treat (0.98 x 0.885 = 0.817).

In summary, decision analysis is a tool that can be used to varying degrees of complexity to help clinicians and policy- makers be more systematic, explicit and rational in their decision-making by separating the probability of an outcome from how that outcome is valued.

Ruth Gilbert and Stuar t Logan Senior Lecturers in Clinical Epidemiology

Centre for Evidence-Based Child Health Department of Epidemiology and

Biostatistics Institute of Child Health

London, UK

References

1. Glasziou P, llayem M, Del Mar C B. Treatments for acute otitis media in children: antibiotic versus placebo. In: Douglas R, Berman S, Black R E et al, eds. Acute respiratory infections module of the Cochrane Database of Systematic Reviews (update 02 December 1996). Available in The Cochrane Library (database on disk and CD-ROM). The Cochrane Collaboration, Issue 1. Oxford: Update Software, 1997. Update quarterly

2. Saekett D L, Richardson W S, Rosenberg 'iV, ltaynes R B. Evidence-based medicine. London: Churchill Livingstone, 1997

3. Rosenfield R, Bertrees J, Gieblnk G, Canafax D. Clinical efficacy of antimicrobial drugs for acute otitis media. J Pcdiatr 1994; 124:335

26 EVIDENCE-BASED HEALTH POLICY AND MANAGEMENT JUNE 1997