Improvement in clinical trial disclosures and analysts’ forecast accuracy: evidence from the pharmaceutical industry

The list of participating biomedical journals is available at www.​icmje.​org/​journals.​html.

The ClinicalTrials.gov is an online clinical trial registry developed by the National Library of Medicine for the National Institute of Health. It was first available to the public on February 29, 2000.

The first three phases of clinical trials generally take six to seven years, and the final review and approval by the Food and Drug Administration (FDA) takes another one to two years.

In 2005, the State of Maine passed a public law, Maine Sec 1.22 MRSA C.605, which requires pharmaceutical companies to publicly disclose clinical trial information for products that are or have been FDA approved for marketing and are or have been dispensed, administered, delivered or promoted in Maine. A drug may not be advertised in the State of Maine unless the manufacturer has disclosed key information about the trial, including potential or actual adverse effects of the drug. According to this law, clinical trial means a clinical investigation as defined by the FDA that involves any trial to test the safety or efficacy of a drug or biological product with one or more human subjects and that is intended to be submitted to, or held for inspection by, the FDA as part of an application for a research or marketing permit (Maine Public Law 2005).

Expert network is a group of professionals with specialized expertise in the client’s area of interest. For example, investors in the pharmaceutical field may gain valuable insights from certain doctors. These research outfits pool together vast networks of experts who can provide unique perspectives and insights to various industries (Davidowitz 2015).

The authors would like to thank an anonymous referee for this comment.

The number of trails registered is zero if no record is identified on ClinicalTrails.gov.

See, for example, Kadapakkam and Zhang (2014) and Guo, Lien, and Dai (2016).

ACCRUAL _i,t  = (ΔCA _i,t – ΔCL _i,t – ΔCASH _i,t  + ΔSTD _i,t – DEP _i,t  + ΔTP _i,t )/TA _i,t . Where ACCRUAL _i,t is the scaled accruals of firm i in year t; ΔCA _i,t is the change in current assets of firm i from year t–1 to year t; ΔCL _i,t is the change in current liabilities of firm i from year t–1 to year t; ΔCASH _i,t is the change in cash of firm i from year t–1 to year t; ΔSTD _i,t is the change in current portion of long-term debt of firm i from year t–1 to year t; DEP is the depreciation and amortization expense of firm i in year t; ΔTP _i,t is the change in income tax payable of firm i from year t–1 to year t; and TA _i,t is the total assets of firm i in year t. To reduce measurement error, we follow Dhaliwal et al. (2012) to convert the absolute value of ACCRUAL into an indicator variable that takes a value of 1 if a firm’s average absolute accruals during the testing period is greater than the median of the sample, and 0 otherwise. The result is consistent with that of using the absolute value of ACCRUAL.

Following Dhaliwal et al. (2012), we use a rolling window of 10 years and require a minimum of three years of EPS to calculate the standard deviation.

The six-year testing period covers the two-year post-regulation period (2005 and 2006), and four-year pre-regulation period.

According to the description provided by the U.S. Department of Commerce, firms with an SIC code of 2834 are pharmaceutical preparation firms engaged in manufacturing, fabricating, or processing drugs in pharmaceutical preparations for human or veterinary use.

The authors wish to thank an anonymous referee for this comment.

See, for example, Pana et al. (2015); Guo (2016); Wu, Shen, and Chen (2016).

Appendix C presents details about our implementation of the propensity score matching analysis and balancing tests.

That is, the average treatment effect on the treated (ATT). See further explanation of ATT in Appendix C. Several matching algorithms have been proposed in the literature. However, there is no clear rule to determine which one is more appropriate (Heinrich et al. 2010). To make sure that our findings are not driven by the selection of a particular strategy, we use three of the most commonly employed matching algorithms (i.e., nearest-neighbor matching, radius matching, and kernel matching) to estimate an average treatment effect on the treated (ATT) in this study.

If the promising compound is determined to be reasonably safe based on animal testing during the pre-clinical trials, an IND is filed to request approval for clinical trials to test the compound on humans. If the compound can successfully pass the first three phases of clinical trials on human beings, an NDA is filed to request approval for marketing and large-scale manufacturing, which is then followed by the Phase IV clinical trials.

To ensure balance of the propensity score across treatment and comparison groups, we obtain an estimate of the propensity score’s distribution by splitting the sample by quintiles (blocks) of the propensity score. A starting test of balance is to ensure that the mean propensity score is equivalent in the treatment and comparison groups within each of the five quintiles (See, e.g., Imbens 2004).