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Defining the Intervention Logic of Access-To-Data Measures: A Problem Analysis

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Part of the Munich Studies on Innovation and Competition book series (MSIC,volume 16)


Given the overall objective of proposing the rules on access to non-summary clinical trial data, this chapter examines why the status quo of access to trial data is problematic and how a policy analysis could be conducted. It starts with a brief overview of the basic principles of designing a regulatory intervention and methodology for the problem analysis developed and applied by the European Commission. After taking a closer look at the available evidence on the industry’s data-sharing practice, concerns regarding the ability of trial sponsors to exercise de facto exclusive control over non-summary data are discussed in detail. Finally, given these concerns, the problem drivers, policy objectives and the overall intervention logic of access measures are outlined.


  • Access to clinical trial data
  • Data-sharing policy
  • Industry bias
  • Policy intervention
  • Problem analysis
  • Problem drivers
  • Regulatory intervention
  • Reproducibility
  • Research biases
  • Underutilised potential

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Fig. 6.1
Fig. 6.2


  1. 1.

    Breyer (1979), p. 552.

  2. 2.


  3. 3.

    Such freedom is subject to limitations that can be introduced in line with the principle of proportionality and ‘only if they are necessary and genuinely meet objectives of general interest recognised by the Union or the need to protect the rights and freedoms of others’. CFR, art 52(1).

  4. 4.

    Below (nn 18–19).

  5. 5.

    European Commission (19 May 2015) Better Regulation ‘Toolbox’ supplementing SWD(2015) 111 [hereinafter Better Regulation ‘Toolbox’], p. 67 (further defining efficiency as ‘a situation where no one can be made better off without someone else being made worse off’).

  6. 6.

    Ibid (emphasis added).

  7. 7.


  8. 8.


  9. 9.

    Bator (1958), p. 351.

  10. 10.

    Ibid (emphasis added).

  11. 11.

    Better Regulation ‘Toolbox’ (n 5), p. 68.

  12. 12.

    Todorova T (2014) Archive the transaction-cost roots of market failure. MPRA Paper No 66757, p. 7 (arguing that ‘all forms of market failure could be explained by transaction cost’).

  13. 13.

    See e.g. Better Regulation ‘Toolbox’ (n 5), p. 140.

  14. 14.

    See e.g. Bator (1957), pp. 57–58; Breyer (1982), pp. 15–34; Ogus (1994), pp. 29–46.

  15. 15.

    See e.g. Scott (2012), pp. 36–37; Baldock (2007), p. 21.

  16. 16.

    Feldman (2008), p. 721.

  17. 17.

    Reiter (2018), p. 765 (noting that efficiency has been at the centre of economic theory ‘since ancient times, and is an essential element of modern microeconomic theory’).

  18. 18.

    Better Regulation ‘Toolbox’ (n 5), p. 338.

  19. 19.

    OECD (2009), p. 97.

  20. 20.

    Just et al. (2004), p. 41.

  21. 21.

    As discussed in Chap. 8.

  22. 22.

    Mandelkern Group on Better Regulation (13 Nor 2001) Final report, p. i [hereinafter Mandelkern report].

  23. 23.

    Bergkamp (2003), pp. 677 ff.

  24. 24.

    Better Regulation ‘Toolbox’ (n 5), p. 22. Article 5 of the EC Treaty stipulates that ‘[a]ny action by the Community shall not go beyond what is necessary to achieve the objectives of th[e] Treaty’.

  25. 25.

    European Commission (19 May 2015) Better Regulation Guidelines. SWD(2015) 111 final [hereinafter Better Regulation Guidelines].

  26. 26.

    Ibid pp. 16–17. See also Better Regulation ‘Toolbox’ (n 5), p. 59; Mandelkern report (n 22), p. ii (defining regulatory impact assessment as ‘an effective tool for modern, evidence-based policy making, providing a structured framework for handling policy problems [that] should be an integral part of the policy making process at EU and national levels [as] it allows that decision to be taken with clear knowledge of the evidence’).

  27. 27.

    Better Regulation Guidelines (n 25), pp. 90–91.

  28. 28.

    Better Regulation ‘Toolbox’ (n 5), p. 67.

  29. 29.

    Ibid p. 66.

  30. 30.

    Ibid p. 97.

  31. 31.


  32. 32.

    Ibid p. 269.

  33. 33.

    Ibid pp. 269–270.

  34. 34.

    Better Regulation ‘Toolbox’ (n 5), pp. 65 ff.

  35. 35.

    Mandelkern report (n 22), p. 82.

  36. 36.

    Ibid p. 97 (emphasising the importance of the benefit-cost ratio, as it allows to rank the alternatives based on efficiency). See also Better Regulation Guidelines (n 25), p. 29; Patton, Sawiski and Clark (2016), p. 263 (noting that where ratios of discounted benefits to discounted costs of alternative instruments are compared, the ‘alternative with the highest benefit-cost ratio does not necessarily have the highest net present value’).

  37. 37.

    Better Regulation Guidelines (n 25), p. 23.

  38. 38.

    Ibid pp. 65–67.

  39. 39.

    The adoption of the EU Clinical Trials Regulation was preceded by the impact assessment. While the Regulation introduced the mandatory publication of summaries of CSRs and the optional sharing of IPD among the novelties, the Impact Assessment Report of the European Commission does not contain specific problem analysis underlying these requirements. European Commission (17 Jul 2012) Impact assessment report on the revision of the ‘Clinical Trials Directive’ 2001/20/EC accompanying the document Proposal for a Regulation of the European Parliament and of the Council on clinical trials on medicinal products for human use, and repealing Directive 2001/20/EC, SWD(2012) 200 final, vol. I and II.

  40. 40.

    Better Regulation ‘Toolbox’ (n 5), p. 65.

  41. 41.

    Reg 536/2014/EU, art 56(1).

  42. 42.

    Institute of Medicine of the National Academies (2015), p. 64.

  43. 43.

    Krumholz et al. (2014), p. 499 (concluding, based on the review of clinical data sharing practices of 12 leading research-based pharmaceutical companies, that ‘[i]t is clear that a sea change in concept and action has occurred, at least in industry’).

  44. 44.

    Doshi (2014).

  45. 45.


  46. 46.

    On the importance of such data, see Chap. 8, Sect. Notably, the statistics on access to data through the portal evidence a considerable interest in non-listed data, which the trial sponsors did not declare as accessible. CSDR. Metrics. Accessed 26 Mar 2021.

  47. 47.

    Doshi (2014).

  48. 48.

    Ibid. But see Sydes et al. (2015) (pointing out the risk of identification, self-identification, data distortion and data dredging that might justify the ‘controlled access approach’ to clinical trial data sharing).

  49. 49.

    Goldacre et al. (2017).

  50. 50.

    The median start date of the policy commitments was 2011; the median start date of adopting data sharing policies among the surveyed companies was 2012.

  51. 51.


  52. 52.

    Miller et al. (2019).

  53. 53.


  54. 54.

    Nevitt et al. (2017). The sample included 760 IPD meta-analyses published between 1987 and 2015.

  55. 55.


  56. 56.

    Murugiah et al. (2016). The sample comprised 60 trials sponsored by 20 leading pharmaceutical companies and involved over 5000 participants.

  57. 57.

    Mayo-Wilson et al. (2015).

  58. 58.

    Greifman et al. (2015).

  59. 59.

    Nisen and Rockhold (2013), p. 477. One of the investigators, whose research project was approved but discontinued, characterised the experience of using the portal as ‘not user-friendly’ and ‘highly inconvenient’ due to the password system, further mentioning that ‘every click is tracked’ and that it is ‘difficult to export data’. E-mail of 27 Aug 2017 (on file with the author).

  60. 60.

    Nisen and Rockhold (2013), p. 477.

  61. 61.

    See generally Gøtzsche et al. (2006).

  62. 62.

    Greifman et al. (2015).

  63. 63. Accessed 26 Mar 2021.

  64. 64. Accessed 26 Mar 2021.

  65. 65.

    Institute of Medicine of the National Academies (2015), p. 1; Doshi et al. (2013).

  66. 66.

    Better Regulation ‘Toolbox’ (n 5), p. 66.

  67. 67.

    Ibid p. 67. See also Better Regulation Guidelines (n 25), p. 20.

  68. 68.

    Institute of Medicine of the National Academies (2015), p. 18 (with further references).

  69. 69.

    Mullane et al. (2018), p. 2.

  70. 70.

    Bollen et al. (2015), p. 4.

  71. 71.

    As discussed in Chap. 3, Sect.

  72. 72.

    Chow and Liu (2004), p. 82.

  73. 73.

    Due to the biological variability of the study subjects, both reproducibility and replicability are generally difficult to achieve in biomedical research. See Begley and Ioannidis (2015), p. 117. Perfect replicability in the field of biomedical research is practically unfeasible. See Mullane et al. (2018), p. 4; Porter (2016), p. 447 (observing that ‘few results can be expected to replicate with high precision’). In this regard, it is worth emphasising that research replicability can be problematic not only in the case of industry-sponsored trials but academic research as well. See Institute of Medicine of the National Academies (2012), p. 69 (with further references).

  74. 74.

    While two pivotal well-controlled trials are usually required to fulfil the regulatory requirement for the substantial evidence on efficacy, it cannot be guaranteed that the results can be reproduced even if the same trial protocol is followed. Chow and Liu (2004), p. 82.

  75. 75.

    Goodman et al. (2016).

  76. 76.

    See generally Nüesch et al. (2009); Tierney and Stewart (2005).

  77. 77.

    Atkins et al. (2004). In the context of this study, two types of reproducibility—reproducibility of the trial results and conclusions of the primary analysis—are used as defined in this section.

  78. 78.

    Higgins et al. (2017), p. 8:3.

  79. 79.


  80. 80.

    Ibid p. 8:1. See also the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) (1998) ICH harmonised tripartite guideline. Statistical principles for clinical trials. E9, p. 32 (defining operational and statistical biases as ‘[t]he systematic tendency of any factors associated with the design, conduct, analysis and evaluation of the results of a clinical trial to make the estimate of a treatment effect deviate from its true value’).

  81. 81.

    Higgins et al. (2017), p. 8:4.

  82. 82.

    Schulz et al. (1995).

  83. 83.

    For definitions, see Higgins et al. (2017), pp. 8:7–8:9.

  84. 84.

    Lo and Field (2009). See generally Montaner et al. (2001).

  85. 85.

    Lexchin (2012). See also Lundh et al. (2017) (observing that commercial sponsors can influence the study results in several potential ways including ‘the framing of the question, the design of the study, the conduct of the study, how data are analyzed, selective reporting of favorable results, and spin in reporting conclusions’ (with further references)).

  86. 86.

    Lundh et al. (2017) (emphasis added).

  87. 87.

    Ibid (arguing that ‘[t]here are many subtle mechanisms through which sponsorship may influence outcomes, and an assessment of sponsorship should therefore be used as a proxy for these mechanisms’).

  88. 88.

    See e.g. The pharmaceutical persuaders – the industry, the doctor, and the clinical trial (1961). See also Shapiro (1978), pp. 166 ff.

  89. 89.

    See e.g. Gøtzsche (2013); Goldacre (2014); Dukes et al. (2015); Fisher (2009).

  90. 90.

    Williams et al. (2018), p. 180 (further noting that ‘[t]here is also a tendency by the community at large to assume that researchers in industry are automatically guilty of a conflict of interest until proven otherwise’ (with further references)).

  91. 91.

    Williams et al. (2018), p. 179 (with further references).

  92. 92.

    See e.g. Sismondo (2008); Bekelman (2003); Lexchin et al. (2003); Schott et al. (2010); Golder and Loke (2008).

  93. 93.

    Bekelman et al. (2003); Lexchin et al. (2003); Friedberg et al. (1999); Cho and Bero (1996); Als-Nielsen et al. (2003); Kjaergard and Als-Nielsen (2002).

  94. 94.

    Lexchin et al. (2003).

  95. 95.

    The sample included pharmacoeconomic reports, meta-analyses and systematic reviews identified through searching the Medline database records from January 1966 to December 2002 and the Embase database records from January 1980 to December 2002.

  96. 96.

    Lundh et al. (2017). The publication is an update of the earlier systematic review published by the authors that examined the association between the industry sponsorship with the publication of the study outcomes favourable to the sponsor. See Lundh et al. (2017).

  97. 97.


  98. 98.


  99. 99.

    Lexchin (2012), p. 254. See also Stern and Lemmens (2011) (proposing to impose fraud liability to prevent the practice of ghost-writing in the medical literature).

  100. 100.

    Trial ‘seeding’ refers to the practice of conducting post-marketing studies for ‘the sole purpose of getting doctors to start to use a product with the aim of establishing the drug as a regular part of the doctor’s prescribing’. Lexchin (2012), p. 255.

  101. 101.

    Clifford et al. (2002) (pointing out that ‘the absence of significant associations between funding source, trial outcome and reporting quality reflects a true absence of an association or [such absence] is an artefact of inadequate statistical power, reliance on voluntary disclosure of funding information, a focus on trials recently published in the top medical journals, or some combination thereof’). See also Lundh et al. (2012) (concluding that ‘sponsors are usually involved in the analysis and reporting of results in industry-sponsored trials, but their exact role is not always clear from the published papers’ and proposing that ‘[j]ournals should require more transparent reporting of the sponsors’ role in crucial elements such as data processing, statistical analysis and writing of the manuscript and should consider requiring access to trial protocols, independent data analysis and submission of the raw data’).

  102. 102.

    See e.g. Jackson (2019), p. 499; Kaur and Choy (2014), p. 25; Naci et al. (2015); Institute of Medicine of the National Academies (2015), p. 141; AllTrials. What does all trials registered and reported mean? Accessed 26 Mar 2021.

  103. 103.

    Hoffmann et al. (2017).

  104. 104.

    Ibid. Such materials can include consent forms, statistical analysis plans, blank case report forms and descriptions of measurements and interventions.

  105. 105.

    See above (nn 75–76) and the accompanying text.

  106. 106.

    Hoffmann et al. (2017).

  107. 107.

    Lundh et al. (2017).

  108. 108.

    E-mail from Joel Lexchin of 28 Aug 2017 (reproduced with the permission of Professor Lexchin; on file with author). On difficulties in accessing primary research data, see e.g. Doshi et al. (2013).

  109. 109.

    See e.g. Skovlund (2009), p. 260 (pointing out that ‘unless the design and statistical analysis of a clinical trial are appropriate, results cannot be considered reliable and no confidence can be placed in the subsequent clinical interpretation’).

  110. 110.

    CIOMS (2016), p. 1.

  111. 111.

    Guyatt et al. (2008), p. 925 (noting that quality of evidence is ‘a continuum; any discrete categorisation involves some degree of arbitrariness’).

  112. 112.

    Atkins et al. (2004) (explaining that the strength of a recommendation refers to ‘the extent to which we can be confident that adherence to the recommendation will do more good than harm’).

  113. 113.

    Robinson and Goodman (2011); McGauran et al. (2010).

  114. 114.

    Chan et al. (2004).

  115. 115.

    The sample comprised 101 randomised trials approved in 1994–1995 and corresponding to 122 published journal articles.

  116. 116.

    Chan et al. (2004), p. 2457.

  117. 117.

    Ebrahim et al. (2014). The number of the examined studies indicates that publications based on IPD re-analysis are rather rare.

  118. 118.

    Ibid p. 1030.

  119. 119.

    Ibid p. 1027.

  120. 120.


  121. 121.

    An adverse event is defined as ‘any untoward medical occurrence in a subject to whom a medicinal product is administered and which does not necessarily have a causal relationship with this treatment’. Reg 536/2014/EU, art 2(1)(32).

  122. 122.

    A serious adverse event is defined as ‘any untoward medical occurrence that at any dose requires inpatient hospitalisation or prolongation of existing hospitalisation, results in persistent or significant disability or incapacity, results in a congenital anomaly or birth defect, is life-threatening, or results in death’. Reg 536/2014/EU, art 2(1)(33).

  123. 123.

    Off-label use refers to the situations where an approved drug is used for treating a different condition or a different age group. Off-label uses can also involve clinically significant variations such as a different dosage regime or route of administration.

  124. 124.

    On the discovery of off-label uses, see e.g. DeMonaco et al. (2006). The use of the authorised medicinal products in clinical practice, including off-label use, is not regulated at EU level. See Weda et al. (2017), p. 13.

  125. 125.

    European Ombudsman (8 Jun 2016) Decision on own initiative inquiry OI/3/2014/FOR concerning the partial refusal of the European Medicines Agency to give public access to studies related to the approval of a medicinal product, para 44 (emphasis added).

  126. 126.

    Institute of Medicine of the National Academies (2015), p. 32 (with further references).

  127. 127.

    Ibid p. 74.

  128. 128.


  129. 129.

    Hoffmann et al. (2017) (pointing out that, where study results are unusable and non-replicable by others, ‘the entire trial investment becom[es] a sunk-cost’).

  130. 130.

    As discussed in Chap. 9, Sect.

  131. 131.

    Better Regulation ‘Toolbox’ (n 5), p. 80.

  132. 132.


  133. 133.

    Reg 536/2014/EU, rec 17, art 6(1)(b)(i). The EU Clinical Trials Regulation does not harmonise the division of the tasks and responsibilities between the national competent authorities and ethics committees in respect to the scientific and ethical parts of the assessment. Reg 536/2014/EU, art 4.

  134. 134.

    Reg 536/2014/EU, art 47.

  135. 135.

    Reg 536/2014/EU, art 2(2)(30).

  136. 136.

    Reg 536/2014/EU, art 48, rec 44.

  137. 137.

    Reg 536/2014/EU, art 48.

  138. 138.

    Reg 536/2014/EU, art 56(1). Besides, Recital 51 of the Regulation states that ‘information generated in a clinical trial should be recorded, handled and stored adequately for the purpose of ensuring subject rights and safety, the robustness and reliability of the data generated in the clinical trial, accurate reporting and interpretation, effective monitoring by the sponsor and effective inspection by Member States’ (emphasis added).

  139. 139.

    Chapter 4, Sect.

  140. 140.

    Reg 536/2014/EU, annex IV(D).

  141. 141.

    Reg 536/2014/EU, art 42. A SUSAR refers to ‘a serious adverse reaction, the nature, severity or outcome of which is not consistent with the reference safety information’; a ‘serious adverse event’ is defined as ‘any untoward medical occurrence that at any dose requires inpatient hospitalisation or prolongation of existing hospitalisation, results in persistent or significant disability or incapacity, results in a congenital anomaly or birth defect, is life-threatening, or results in death’. Reg 536/2014/EU, art 2(2)(34) and (33).

  142. 142.

    Reg 536/2014/EU, annex III, para 2.1(3).

  143. 143.

    Dir 2001/83/EC, arts 8(3), 10, 10a, 10b and 11 and annex I. See also Reg 726/2004/EC, art 6(1).

  144. 144.

    Dir 2001/83/EC, annex I, Introduction and general principles, para 2.

  145. 145.

    Reg 726/2004/EC, rec 23.

  146. 146.

    Reg 726/2004/EC, art 5(2).

  147. 147.

    Reg 726/2004/EC, art 61(6).

  148. 148.

    Dir 2001/83/EC, art 21(4). The results of pharmaceutical and pre-clinical tests and clinical trials ‘must enable a sufficiently well-founded and scientifically valid opinion to be formed as to whether the medicinal product satisfies the criteria governing the granting of a marketing authorisation. Consequently, an essential requirement is that the results of all clinical trials should be communicated, both favourable and unfavourable’. Dir 2001/83/EC, annex I, para 5.2(a).

  149. 149.

    Reg 726/2004/EC, rec 25 (emphasis added).

  150. 150.

    European Ombudsman (19 May 2010) Draft recommendation of the European Ombudsman in his inquiry into complaint 2560/2007/BEH against the European Medicines Agency.

  151. 151.

    Advice to the European Medicines Agency from the Clinical Trial Advisory Group on Clinical Trial Data Formats (CTAG2)—Final advice to EMA (30 Apr 2013), p. 4. Accessed 26 Mar 2021. See Koenig et al. (2015), p. 16 (explaining that ‘[a]t the level of EMA/CHMP, decision making in relation to licensure of new drugs (positive/negative opinion) is currently based on rapporteurs’ assessment work without access to clinical trials data on patient level in electronic format’). Such assessment is ‘based on thorough review of protocols, analysis plans and clinical trial reports, and usually does not involve processing of patient raw data to replicate analyses carried out by the Sponsor/Applicant’ Ibid. See EFSPI (25 Apr 2013) European Federation of Statisticians in the Pharmaceutical Industry (EFSPI) Position on European Medicines Agency (EMA) access to clinical trial data initiative, p. 6 (stating that if ‘the new EMA policy will allow re-analysis of patient level data, EFSPI would be interested to know whether it would be possible for EMA to increase their capabilities including expertise and resources to be able to re-analyse patient level data they receive in a regulatory submission, similar to how some other regulatory authorities review regulatory dossiers’). Accessed 26 Mar 2021. See also Senn (2007), p. 463 (referring to the EMA as ‘a statistician-free zone’).

  152. 152.

    Skovlund (2009).

  153. 153.

    European Commission (17 Jul 2012) Impact assessment report on the revision of the ‘Clinical Trials Directive’ 2001/20/EC accompanying the document Proposal for a Regulation of the European Parliament and of the Council on clinical trials on medicinal products for human use, and repealing Directive 2001/20/EC, SWD(2012) 200 final, vol. II, pp. 22–23 (indicating that the number of clinical and validation assessors in the EU NCAs involved in the assessment of clinical trials ranges between zero and two in the majority of the EU Member States).

  154. 154.

    Koenig et al. (2015), pp. 10–11.

  155. 155.

    GDPR, art 9(2)(a).

  156. 156.

    GDPR, art 89(2). Derogations can be applied to the right of access, the right to rectification, the right to restriction of processing and the right to object to processing of personal data.

  157. 157.


  158. 158.

    GDPR, art 89. Such safeguards can be implemented through the organisational and technical measures in line with the principle of data minimisation. Ibid.

  159. 159.

    In Germany, for instance, the derogations were implemented under Bundesdatenschutzgesetz that, apart from stipulating the appropriate safeguards (‘angemessene Maßnahmen’) for protecting the interests of data subjects, introduced the criterion ‘erheblich überwiegen’ (considerably outweigh). This additional requirement means that the data controller’s interests in data processing should considerably outweigh the interests of the data subject in restricting data processing. Bundesdatenschutzgesetz vom 30. Juni 2017 (BGB1. I S. 2097) § 27(1).

  160. 160.

    GDPR, rec 159 (emphasis added).

  161. 161.

    GDPR, rec 26. Besides, Recital 4 of the GDPR reinforces the idea of balancing personal data protection against other fundamental rights, freedoms and principles recognised under the Charter of Fundamental Rights, including the freedom of the arts and sciences, in accordance with the principle of proportionality.

  162. 162.

    See Chap. 4, Table 4.1.

  163. 163.

    Hypothetically, IPD accessed under the EU Transparency Regulation could be used for exploratory research purposes. However, such use would exceed the scope of the right of access to documents, as argued in Chap. 4, Sect.

  164. 164.

    Exploratory IPD analysis, arguably, falls outside the scope of the substantive and procedural matters covered by the EU Clinical Trials Regulation and the EU Drug Authorisation Regulation.

  165. 165.

    Institute of Medicine of the National Academies (2015), p. 141 (with further references).

  166. 166.

    Ibid p. 32.


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Kim, D. (2021). Defining the Intervention Logic of Access-To-Data Measures: A Problem Analysis. In: Access to Non-Summary Clinical Trial Data for Research Purposes Under EU Law. Munich Studies on Innovation and Competition, vol 16. Springer, Cham.

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