When collecting patient-level resource use data for statistical analysis, for some patients and in some categories of resource use, the required count will not be observed. Although this problem must arise in most reported economic evaluations containing patient-level data, it is rare for authors to detail how the problem was overcome. Statistical packages may default to handling missing data through a so-called 'complete case analysis', while some recent cost-analyses have appeared to favour an 'available case' approach. Both of these methods are problematic: complete case analysis is inefficient and is likely to be biased; available case analysis, by employing different numbers of observations for each resource use item, generates severe problems for standard statistical inference. Instead we explore imputation methods for generating 'replacement' values for missing data that will permit complete case analysis using the whole data set and we illustrate these methods using two data sets that had incomplete resource use information. Copyright © 2002 John Wiley & Sons, Ltd.

ObjectivesSurveys in various countries suggest 17% to 80% of doctors prescribe ‘placebos’ in routine practice, but prevalence of placebo use in UK primary care is unknown.MethodsWe administered a web-based questionnaire to a representative sample of UK general practitioners. Following surveys conducted in other countries we divided placebos into ‘pure’ and ‘impure’. ‘Impure’ placebos are interventions with clear efficacy for certain conditions but are prescribed for ailments where their efficacy is unknown, such as antibiotics for suspected viral infections. ‘Pure’ placebos are interventions such as sugar pills or saline injections without direct pharmacologically active ingredients for the condition being treated. We initiated the survey in April 2012. Two reminders were sent and electronic data collection closed after 4 weeks.ResultsWe surveyed 1715 general practitioners and 783 (46%) completed our questionnaire. Our respondents were similar to those of all registered UK doctors suggesting our results are generalizable. 12% (95% CI 10 to 15) of respondents used pure placebos while 97% (95% CI 96 to 98) used impure placebos at least once in their career. 1% of respondents used pure placebos, and 77% (95% CI 74 to 79) used impure placebos at least once per week. Most (66% for pure, 84% for impure) respondents stated placebos were ethical in some circumstances.Conclusion and implicationsPlacebo use is common in primary care but questions remain about their benefits, harms, costs, and whether they can be delivered ethically. Further research is required to investigate ethically acceptable and cost-effective placebo interventions.

ObjectiveTo explore ongoing symptoms, unmet needs, psychological wellbeing, self-efficacy and overall health status in survivors of prostate cancer. Patients and MethodsAn invitation to participate in a postal questionnaire survey was sent to 546 men, diagnosed with prostate cancer 9-24 months previously at two UK cancer centres. The study group comprised men who had been subject to a range of treatments: surgery, radiotherapy, hormone therapy and active surveillance. The questionnaire included measures of prostate-related quality of life (Expanded Prostate cancer Index Composite 26-item version, EPIC-26); unmet needs (Supportive Care Needs Survey 34-item version, SCNS-SF34); anxiety and depression (Hospital Anxiety and Depression Scale, HADS), self-efficacy (modified Self-efficacy Scale), health status (EuroQol 5D, EQ-5D) and satisfaction with care (questions developed for this study). A single reminder was sent to non-responders after 3 weeks. Data were analysed by age, co-morbidities, and treatment group. ResultsIn all, 316 men completed questionnaires (64.1% response rate). Overall satisfaction with follow-up care was high, but was lower for psychosocial than physical aspects of care. Urinary, bowel, and sexual functioning was reported as a moderate/big problem in the last month for 15.2% (n = 48), 5.1% (n = 16), and 36.5% (n = 105) men, respectively. The most commonly reported moderate/high unmet needs related to changes in sexual feelings/ relationships, managing fear of recurrence/uncertainty, and concerns about the worries of significant others. It was found that 17% of men (51/307) reported potentially moderate-to-severe levels of anxiety and 10.2% (32/308) reported moderate-to-severe levels of depression. The presence of problematic side-effects was associated with higher psychological morbidity, poorer selfefficacy, greater unmet needs, and poorer overall health status. ConclusionWhile some men report relatively few problems after prostate cancer treatment, this study highlights important physical and psycho-social issues for a significant minority of survivors of prostate cancer. Strategies for identifying those men with ongoing problems, alongside new interventions and models of care, tailored to individual needs, are needed to improve quality of life.

The choice of algorithm will be dependent on the study aim. Individuals outside the United Kingdom may find it more useful to use the multinomial results, which can be used with different country-specific tariff valuations. However, these algorithms should not replace prospective collection of utility data.

Non-communicable diseases are the leading global causes of mortality and morbidity. Growing pressures on health services and on social care have led to increasing calls for a greater emphasis to be placed on prevention. In order for decisionmakers to make informed judgements about how to best spend finite public health resources, they must be able to quantify the anticipated costs, benefits, and opportunity costs of each prevention option available. This review presents a taxonomy of epidemiological model structures and applies it to the economic evaluation of public health interventions for non-communicable diseases. Through a novel discussion of the pros and cons of model structures and examples of their application to public health interventions, it suggests that individual-level models may be better than population-level models for estimating the effects of population heterogeneity. Furthermore, model structures allowing for interactions between populations, their environment, and time are often better suited to complex multifaceted interventions. Other influences on the choice of model structure include time and available resources, and the availability and relevance of previously developed models. This review will help guide modelers in the emerging field of public health economic modeling of non-communicable diseases.