Data interpretation and analysis

I adapted elements of Ragin’s constructivist approach to social science research (see Figure 3.2) in my approach to data analysis, which calls for an iterative approach to building a case study (Ragin 1994). I alternated between analyzing data across my three sources to build provisional representations of the innovation process (via research memos and papers) and drawing on sociological and political economy literatures to conceptualize the analytical frame through which I was interpreting these provisional representations. By analytical frame, I refer to the literatures and concepts described in chapter 1 which provided tentative lenses through which to initiate my exploration but which shifted and evolved through the research process.49 I brought these analytic frames into juxtaposition with the representations of the innovation process yielded by the data and evidence I had collected in order to identify gaps, continuities, and tension

points. These juxtapositions either encouraged me to either gather more data (i.e. when an analytical concept revealed gaps in my current data collection) or review the sociological and political economy literatures (i.e. when a piece of evidence could not be explained by the existing analytic frame I had been using at the time). As represented in Figure 2.2, this iterative approach between my data and my analytical resources allowed for a progressive approach to building a valid representation of the case study.

49 _{For more on these analytical frames, see Chapter 1 and the sections devoted to the entrepreneurial state,}

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Figure 2.3 Data analysis approach to case study development

Source: Adapted from Ragin

(1994)

This representation amounted to a sociological ‘account’ of the innovation process behind sofosbuvir-based treatments. Much like a clinician combining a patient history with quantitative data from diagnostic tests to make a clinical diagnosis, I take ‘account’ as a double-entendre a la Stark (2000): both a set of numbers (such as costs of drug development, revenues, patients treated), as well as a narrative of the innovation process.50 Each gave the other context – whereas numbers enabled a quantitative picture for risks and rewards, narrative detailed the nature and significance of social processes that could help make sense of those quantitative metrics. Through the combination of numbers and narrative in a clinical account of the innovation process, I could make a sociological and political economy diagnosis of the mechanisms that produced Gilead’s pricing and yielded a particular set of innovation and public health outcomes.

The data analysis itself fell largely into two modes of work. First, I built a detailed historical record of the innovation process. This record was a ‘live’ document which I updated throughout my data collection process and drew from all three sources of data. Second, I tracked key financial figures across the innovation process from documents as well as the S&P Capital and NIH databases, in keeping with my aim of not only capturing the narrative but also the numbers

50

Stark (2000:5) captures the intertwined nature of narrative and numbers: “Etymologically rich, the term ‘account’ simultaneously connotes bookkeeping and narration. Both dimensions entail evaluative

judgments, and each implies the other: Accountants prepare story lines according to established formulae, and in the accountings of a good storyteller we know what counts. In everyday life, we are all bookkeepers and storytellers. We keep accounts and we give accounts.”

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that composed the account. These financial figures included the costs of research and development for sofosbuvir, the funding and financing behind the innovation process from multiple public and private actors, the revenues accrued by Gilead for sofosbuvir, as well as the distribution of these financial flows to shareholders and senior leadership. I used a third strategy of analysis – qualitative coding - to a lesser degree. I only used coding on earnings call transcripts to identify the strategic interests communicated by investment analysts and Gilead’s senior leadership. However, I did not code my individual semi-structured interviews as I was not interpreting their ‘talk’ for discursive patterns but rather for key events, relationships with other actors, and elaborations of technological and political-economic processes. I tagged these data points as I reviewed my interview notes and integrated them into the emergent and provisional narrative.

To ensure greater validity, I subjected this narrative to two further methods: within-case triangulation and counter-factuals. Both approaches helped test provisional representations of the innovation process and refine the empirical chapters and argument laid forth. Within the case, I triangulated across accounts provided by different actors and documentary sources to reconcile discrepancies or gaps in the narrative (Stake 2005). I also compared key events and processes in the innovation process with other compounds and firms in the hepatitis C arena beyond Gilead and sofosbuvir: a key example was comparing acquisitions of hepatitis C assets by a number of large companies. Such comparisons helped test alternative mechanisms and understand the extent to which I was capturing the right political-economic dynamics rather than isolated events or viewpoints.

Additionally, I used counter-factual reasoning at specific points in the provisional narrative to challenge and sharpen my representation of the innovation process. Counterfactual reasoning involved posing alternative causal processes that could have occurred and run counter to the ones established via empirical research (Collier 2011; Fearon 2011; Levy 2009). As Levy (2009) puts it, “a theory that specifies the consequences of both X and not X tell us more about the empirical world than a theory that specific only the consequences of X.” For example, my analysis of Gilead’s acquisition of Pharmasset is based on asking what might have happened to Pharmasset if it had attempted to remain a stand-alone company and if Gilead had not existed as an acquisition specialist capable of leveraging its significant capital. Posing the counterfactual strengthened my analysis of the mechanisms by which speculative financial markets and

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shareholders of incumbent pharmaceutical companies like Gilead influence the trajectory of drug pricing.

I also used my provisional representations of the innovation process to garner feedback during my investigation. In one instance, the publication of a peer-reviewed article in the British Medical Journal in July, 2016 generated further feedback from two peer reviewers, BMJ’s editors as well as written responses from Gilead Sciences and other scholars of pharmaceutical innovation (Roy and King 2016). Throughout the research, my supervisor, Dr. Lawrence King, as well as discussions with several peers researching pharmaceuticals also guided and improved my methodology and analysis. Additionally, I presented my research at several major conferences, including at the European Association for the Study of Science and Technology (EASST) in Barcelona (in September of 2016) and at the Harvard Medical School’s MD/PhD conference in the social sciences and humanities, at which I was able to gain useful feedback.

By (1) alternating between my data and the analytic frame, (2) interpreting my data with provisional narrative-building and retrieval of key numbers, (3) performing within-case

comparisons and counter-factuals, and (4) engaging in ongoing feedback and consultation with multiple colleagues and advisors, I produced an analytically valid representation of the hepatitis C innovation process from the data I collected.