With the passing of the Affordable Care Act and the recent ratification provided by the US Supreme Court in King Vs Burwell for federal tax subsidies, there is now a strengthening movement toward providing more transparency and accountability in the health care industry in America. Pharma and drug companies, an important part of the health value chain, suffer from a number of transparency issues. While the ACA made some progress in enabling this transparency by mandating that pharma companies make public drug related payments made to doctors, much remains to be done on other fronts such as publication of clinical trials data and full disclosure of drug side effects. A few, such as Dr. Ben Goldacre, the founder of AllTrials, have launched public movements to campaign for more data transparency in the pharma and drug industry. More data transparency, however, can be a double-edged sword. There are benefits, however, there are practical considerations as well.
Providing transparency around clinical data can be valuable. When clinical data on a class of antidepressants called selective serotonin-reuptake inhibitors (SSRIs) was analyzed, an increased risk of suicide among adolescents from the use of SSRIs was discovered. Similarly, when the raw clinical data of Tamiflu was analyzed, Tamiflu’s efficacy in fighting viral infections and reducing hospital admission rates was brought into question. Like any large scale statistical analyses of data, clinical data analysis upon which drug companies, regulators and government agencies depend for risk evaluation and approvals, can have anything ranging from egregious mistakes to subtle biases. These can stem from a number of factors, including selection bias in the controlled trials, or mistakes in interpreting statistical significance. The latter, in which the statistical model either lacks statistical power (thus increasing the likelihood of false negatives) and/or the threshold for detecting significance is not enough number of standard deviations (thus increasing the likelihood of false positives), are fairly common in the scientific research community. Couple this with other exacerbating factors, such as research scientists lacking appropriate skills in advanced statistical analysis, a prevalent tendency toward publishing positive hypotheses as opposed to negative ones, and ineffective peer reviews of clinical research findings, and one has a perfect storm in which such mistakes can be fairly common. Greater transparency of clinical data allows any external third party to review and validate the research findings and thus bring to light any potential issues and insights that may have escaped the research team’s due diligence or the government agency’s regulatory scrutiny. Thousands of clinical trials have never been registered with oversight agencies and results from around half of all clinical trials remain unpublished. Making that data available to statisticians would almost certainly lead to new discoveries and clinically useful findings (quoted directly from an article in The Economist).
The noble intention behind the push for greater transparency however may not translate into desirable effects and worse may have unintended consequences. One of the biggest fears is inappropriate analyses and interpretations of the clinical datasets. In a litigious environment, with pharma and drug companies already battling an image of being inhumane greedy corporates least concerned with the ability of the people to afford exorbitant drug prices, this can spell disaster. And it may serve as a strong innovation disincentive for the pharma industry in the long term, with the opportunity cost of not experimenting with novel treatment techniques ultimately being borne by consumers in the form of shortened life spans and/or degraded quality of life. Even when there is less room for misinterpretation, practical challenges with replicating the results of experiments may prevent one from exactly reproducing the results of clinical trials. It is a well-established fact that experimenters employ tacit knowledge and improvisations that are not always captured in experimental setup and process steps. Furthermore, many research teams may use proprietary models to analyze and interpret raw clinical data to arrive at their conclusions – models that they may be averse to sharing with the public, but which nonetheless are critical to arriving at the proper conclusions. The cost of full data disclosure for drug companies is not even discussed many times, but there is a non-trivial cost component to retaining and disclosing such data to the public at large.
So, mandating full disclosure of raw clinical data is just one of the items in an entire menu that needs to be put in place if indeed the objective is to improve the safety, efficacy and efficiency of the pharma and drug industry. The field of biostatistics can go long ways in educating researchers on correctly employing and interpreting clinical datasets. Independent data monitoring committees to oversee the construction and execution of clinical trials to ensure appropriate application of analytic techniques could be in place to provide guidance as the experiments are being conducted. Big data and modern statistical analytic techniques could be developed further to provide researchers with means to more effectively analyze data. In the process of doing all this, if we can help prevent even minor mistakes or incorrect interpretations of drug data, we will have made medicine that much safer for mankind.