about the strengths <strong>and</strong> limitations in respective approaches see the review report bySteen Carlsson et al. [54]. In our study the analysis was performed using aggregate data.The most serious criticism of the aggregate approach is the issue of ecological fallacy.This fallacy means that individual members of a group are assumed to have the averagecharacteristics of the group at large. However, statistics that accurately describe groupcharacteristics do not necessarily apply to individuals within that group. Therefore, itmay be argued that patient level data might produce more precise estimates of anintervention in contrast to aggregate data. However, Grunfeld <strong>and</strong> Griliches (1960, p.1)[55] showed that “aggregation of economic variables can, <strong>and</strong> in fact frequently does,reduce…specification errors. Hence, aggregation does not only produce an aggregationerror, but may also produce an aggregation gain.” In particular, patient-level data aresurely more subject to selection effects (the sickest patients might get the newest – oroldest – treatments) than aggregate data.However, it is still important to acknowledge that the aggregate approach does notproduce estimates of the effects of specific treatments. From the present study we areonly able to draw conclusions about the impact of pharmaceutical innovation onlongevity on an aggregate level, <strong>and</strong> not the impact of specific classes or substances. Itis possible that some treatments contributed significantly to the results, while othergroups have not contributed at all, or even had a negative impact. Nevertheless, thisstudy <strong>and</strong> other similar studies are useful as complements to studies on disaggregateddata.To conclude: although studies on an aggregate level have their limitations, they canprovide useful evidence about the overall value of innovation. This information couldbe useful for evaluating <strong>and</strong> designing pharmaceutical policies on a system level, sincethese policies are important determinants of the use <strong>and</strong> uptake of new drugs. Studies ofthe impact of medical innovation on longevity <strong>and</strong> other health outcomes can beconducted using experimental, or quasi-experimental or observational design. The mainlimitation to the interpretation of observational studies, such as ours, is often thepossible presence of unobserved confounders. Selection bias is one of the majorproblems of causal inference based on observational data. We used difference-indifferences(DID) models, which is a quasi-experimental technique used in econometricsthat measures the effect of a treatment at a given period in time, while avoidingconfounding factors, even if DID models do not overcome all bias problems [56]. In ourequations we include variables to represent disease fixed effects <strong>and</strong> year fixed effects,respectively, <strong>and</strong> inclusion of these effects is therefore a difference-in-differencesmodel. Since our models include year <strong>and</strong> disease fixed effects, they will control for theoverall increase in Swedish longevity <strong>and</strong> for stable between-disease differences inmortality.Pharmaceutical innovation is not the only type of medical innovation that is likely tocontribute to longevity growth. Other medical innovation, such as innovation indiagnostic imaging, surgical procedures, <strong>and</strong> medical devices, is also likely to affectlongevity growth. Therefore, measures of these other types of medical innovationshould be included in the longevity model. Unfortunately, longitudinal disease-levelmeasures of non-pharmaceutical medical innovation are not available for Sweden <strong>and</strong>the omission of these variables could result in an overestimation of the effect of theintroduction of pharmaceuticals. However, analysis of longitudinal disease-levelmeasures of non-pharmaceutical <strong>and</strong> pharmaceutical medical innovation available forthe U.S. during the period 1997-2007showed that the rate of pharmaceutical innovation22
is not positively correlated with the rate of medical procedure innovation <strong>and</strong> may benegatively correlated with the rate of diagnostic imaging innovation. This suggests thatfailure to control for other medical innovation is very unlikely to result inoverestimation of the effect of pharmaceutical innovation on longevity growth, <strong>and</strong> mayeven result in underestimation of this effect.A correlation between two variables doesnot necessarily imply that one variable causes the other. The model must be wellspecified such that there is a theoretical reason to believe that any such spuriouscorrelation is avoided. We believe the theoretical reasons as well as empirical findingsfrom experimental clinical research (on the impact from different treatments <strong>and</strong> effectson morbidity <strong>and</strong> mortality, <strong>and</strong> resource consumption) can be used as complementaryevidence of the policy relevance of studies such as ours.23
- Page 5 and 6: undersöks är relaterat till över
- Page 7 and 8: Executive SummaryLife expectancy ar
- Page 9 and 10: 1. IntroductionLongevity has consta
- Page 11 and 12: 2. Background2.1. Determinants infl
- Page 13 and 14: 2.3. Access, uptake and use of (new
- Page 15 and 16: 3. Value of innovationIncreased lon
- Page 17 and 18: Figure 1. Mean age at death: Actual
- Page 19 and 20: Table 1. Estimation of incremental
- Page 21: This could be explained by the fact
- Page 25 and 26: incremental innovation and on dynam
- Page 27 and 28: 18. Godman, B., Sakshaug, S., Berg,
- Page 29 and 30: 56. Bertrand, M., Duflo, E., Mullai
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