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BMJ 2004;328:371 (14 February), doi:10.1136/bmj.328.7436.371
Cosetta Minelli, research associate in evidence synthesis1, Keith R Abrams, professor of medical statistics1, Alex J Sutton, lecturer in medical statistics1, Nicola J Cooper, research fellow in health services research1
1 Centre for Biostatistics and Genetic Epidemiology, Department of Health Sciences, University of Leicester, Leicester LE1 6TP
Correspondence to: K R Abrams keith.abrams{at}le.ac.uk
Design Probabilistic clinical decision analysis.
Setting Hypothetical population of white UK women aged 50 years with different baseline risks for breast cancer.
Main outcome measure Gain or loss in quality adjusted life years (QALYs).
Results Women free of menopausal symptoms showed a net harm from HRT use, which increased for increasing baseline risk of breast cancer. Those with a baseline risk of 1.2% would expect a loss in QALYs of 0.4 months (-0.03 QALYs, 95% credibility interval -0.05 to -0.01). The main analysis showed HRT to be on average beneficial in women with symptoms, with benefit decreasing with increasing baseline risk of breast cancer. The results were sensitive to the assumed value of quality of life with menopausal symptoms, therefore a contour plot was developed to show the probability of net harm for a range of different values and baseline risks.
Conclusions HRT for primary prevention of chronic diseases in women without menopausal symptoms is unjustified. Perceived quality of life in women with symptoms should be taken into account when deciding on HRT. Thus, a decision analysis tailored to an individual woman is more appropriate in clinical practice than a population based approach.
The prevention of chronic diseases, particularly osteoporosis, has been a strong consideration in prescribing HRT, but potential risks need to be reviewed.13 14 16 20 Several decision analyses have assessed the risks and benefits of HRT, yet the only one that took into account recent evidence from randomised controlled trials, particularly the women's health initiative trial, was qualitative.13 21-28 The women's health initiative trial could not recommend HRT for primary prevention of chronic diseases; in fact the trial was halted on the basis of an interim analysis. In balancing harms and benefits, the researchers did not consider menopausal symptoms, and the results are not sufficient evidence on which to develop a strategy for HRT use.
We performed a decision analysis on the benefits and harms of HRT, based on the best currently available evidence. We considered combined HRT in women free of menopausal symptoms (when HRT might be given for primary prevention of chronic diseases) and in women with symptoms.
Quality adjusted life years (QALYs) were used to study the impact of each outcome. These are standard measures, which evaluate deterioration in length of life along with changes in quality of years of life left when comparing different health states.29 We evaluated the "net gain" in QALYs with HRT, with a positive value for overall benefit and a negative value for overall harm. Our target population was white women in the United Kingdom aged 50, with or without menopausal symptoms, who had used combined HRT for five years.
Decision model
We used the net benefit model.30 The basic equation on which the model is based is net benefit = (risk levelxrisk reduction) - harm. We extended the model to include multiple outcomes of benefit and harm by assuming additive effects for both and by considering the associated uncertainty. This approach consisted of subtracting the harm of an intervention from its benefit, with both expressed as a common measure of effect. The measure we chose was quality of life (QoL), as this allowed us to evaluate the impact on overall health of relief of menopausal symptoms from HRT.31 We estimated the net benefit of HRT in patients with different baseline risks for breast cancer, the most relevant adverse outcome given the magnitude of the relative risk associated with HRT and the background incidence and mortality in the study population. The model identifies a baseline risk threshold above which potential harms outweigh benefits.
Model structure
Figure 1 shows the model. The average loss in QALYs from harms was calculated by multiplying the estimated number of cases caused (five year cumulative incidence and the relative risk increase of the disease) by the loss in QoL associated with the disease over five years. Since QoL was evaluated only for women who were still alive with the disease, we also considered the impact of HRT on mortality by multiplying the number of cases caused by the five year cause specific mortality. The effect of HRT on disease occurrence was limited to the treatment period, but the effect on QoL and mortality was over five years from occurrence. The projected impact on overall QALYs due to excess deaths was based on the average life expectancy of a UK woman of 80 years and an annual discount rate of 3%.
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We followed the same structure for benefits, but we considered the relative risk reduction of the outcome associated with HRT with prevented rather than excess cases of the disease and associated deaths.
For breast cancer, we considered baseline risks ranging from 0.1% to 50% rather than an average risk. We assessed high risk levels to determine the suitability of HRT in women with BRCA1 or BRCA2 gene mutations, in whom the risk of breast cancer can approach 50%.32 33 For menopausal symptoms we considered only the relative risk reduction in symptoms and the impact on QoL because symptoms were experienced by the whole target population and did not lead to any cause specific mortality. See bmj.com for full details of model.
Model assumptions and data for model
Our model is based on three assumptions: the additive nature of the QoL lost or gained for risks or benefits of HRT; the constant increase in relative risk of breast cancer associated with HRT for all levels of baseline risk, with an absolute risk increase increasing linearly with baseline risk; and the constant absolute risk increase and absolute risk reduction associated with HRT for all other outcomes at all levels of baseline risk of breast cancer. The assumption of additive effects implies that there is no interaction in the effect of HRT on different outcomes at any level (baseline risk, relative risk increase or relative risk reduction, QoL weight).
See bmj.com for data used in the model. We selected incidence, mortality, and QoL data for the target population when available; otherwise we used data on more general populations.
Relative risk reduction and relative risk increase
We calculated the relative risk reduction for benefits and relative risk increase for harms from the relative risk of developing each outcome in HRT users compared with non-usersthat is, relative risk reduction = 1 - relative risk, and relative risk increase = relative risk - 1. The data were based on three randomised controlled trials, reviewed in a recent meta-analysis, although for the heart and estrogen/progestin replacement study II trial we used updated results.3
14
16 We estimated the pooled relative risk, using a fixed effect meta-analysis. As none of the three trials considered relief of menopausal symptoms, we based the relative risk on a recent meta-analysis of trials carried out by the Cochrane Collaboration.34
Quality of life
We chose QoL data on the basis of the methods used, in particular the time trade-off method. For this method participants are asked to make trade-offs between a shorter life span in "perfect" health compared with a longer life span with the condition under study. Respondents were individuals in the community, who were not affected by the disease. Exceptions were endometrial cancer and menopausal symptoms, for which data based on the time trade-off and unaffected women in the community, respectively, were not found.
For QoL associated with menopausal symptoms, we used the QoL weight per year of 0.75 (0.66 to 0.83).31 This was assessed using hypothetical scenarios depicting mild and severe symptoms in 63 postmenopausal women. The result that women might give up a quarter of a year of life to live the rest of the year without menopausal symptoms is surprising, particularly when considering the much higher QoL weights reported for all other outcomes0.89 for breast cancer, for example.
We therefore carried out our analysis across a range of QoL values (0.4 to 1.0). This not only allowed us to assess the sensitivity of the results to the QoL weight assumed for menopausal symptoms, but also enabled us to obtain results tailored to individual women according to their perceived QoL with symptoms and their baseline risk of breast cancer.
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Our main analysis showed HRT to be on average beneficial in women with menopausal symptoms, with the magnitude of benefit decreasing with increasing baseline risks of breast cancer. The results are, however, sensitive to the value assumed for the QoL associated with symptoms: figure 2 shows the results for a QoL value of 0.75 (95% confidence interval 0.66 to 0.83). For a baseline risk greater than 25% the probability of net harm was greater than 2.5%. In particular, the net benefit for women at low, average, high, and very high baseline risk was, respectively, 10.7 months in perfect health (QALYs 0.89, 95% credibility interval 0.56 to 1.26), 10.6 months (0.88; 0.55 to 1.25), 8.5 months (0.71, 0.33 to 1.12), and 1.2 months (0.10, -0.78 to 0.89). Results were robust when using sensitivity analyses to assess the impact of different QoL values for all outcomes, except menopausal symptoms.
An alternative approach is to consider the implication for an individual woman according to her baseline risk of breast cancer and the utility she ascribes to her own menopausal symptoms (fig 3). For example, a 50 year old woman with a baseline risk of breast cancer over five years of 5.4% (calculated using the Gail predictive model35) and who attributes a utility of 0.90 to her menopausal symptoms (she would rather live four and a half years without symptoms than five years with symptoms), would have a probability of overall harm between 0 and 1%. The data on QoL for menopausal symptoms in this plot were modelled without uncertainty, since the utility is assumed to be obtained directly from the woman rather than being an estimate for an average woman. The probabilities of net harm shown in the plot represent the most plausible point estimates derived from our model.
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In women with menopausal symptoms the magnitude of net benefit associated with HRT depended on the QoL value assumed for the symptoms. This value varies widely owing to the substantial variability in severity of symptoms and perception of their impact on everyday life reported by women.31 36 Given these limitations a tailored approach to an individual woman, based on her own utility and baseline risk, would be more appropriate than a population based approach for decision making.
Our contour plot could be used as a decision aid to help clinicians and women as it predicts the probability of net harm in a woman with a specific baseline risk of breast cancer who can express the utility of her own menopausal symptoms. In this way women can make a decision based on what they consider an acceptable probability.
Our results are discordant with a subgroup analysis of women with menopausal symptoms at baseline in the women's health initiative trial, which showed that although HRT was effective at improving vasomotor symptoms, it had no major impact on health related QoL except a small benefit on sleep disturbance.37 This might reflect different approaches used for measuring health statesfor example, health state measures and preference based methods such as time trade-off. Preference based measures, although less sensitive than psychometric based measures to changes in health state over time, are considered more suitable for making decisions about the suitability of interventions. This is because they assess quality of life as a whole, thus including aspects that may not be properly addressed by health state measures, but which the individual may consider important.38
Limitations of study
We considered only the average risk for outcomes, apart from breast cancer. This limitation could be overcome by using a multidimensional model that allowed baseline risks of additional outcomes to vary, thus producing a net benefit response surface.39
Our model assumes that the harms and benefits from HRT do not continue once treatment stops at five years. However, there is evidence that the effect of HRT declines after stopping treatment, and this could easily be incorporated using a Markov decision modelling approach.40 41
A limitation of any decision model is its dependence on the data used. The results of our model showed sensitivity to QoL for menopausal symptoms whereas they were robust to the assumptions on all other QoL values. Moreover, our results applied to white UK women aged 50 years, although our model could be easily customised to other populations.
We could not assess properly the assumptions on which our model is based. In particular, that the additive risks and benefits of HRT are not likely to be satisfied for some outcomes, since interactions might be present. A further step would be to extend the model to allow for interactions, although with a large number of outcomes a Markov model would be better.
Conclusions
Women with menopausal symptoms on average benefit from HRT, results that concur with the recommendations of the UK Medicines and Healthcare Products Regulatory Agency.42 The results, however, depend on the QoL attributed to symptoms, which in turn greatly varies with severity of symptoms and women's perceptions. Thus, a decision analysis tailored to an individual woman would be more appropriate in clinical practice than a population based approach.
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The full version of this article is on bmj.com
Competing interests: KRA has received research funding from Schering Health Care, a manufacturer of combined HRT, to evaluate a levonorgestrel emitting intrauterine device.
Ethical approval: Not required.
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