Projection Methodolgies in the Health Care Space

The pharmaceutical and medical device industry is an interesting case study in the development of alternative marketing paradigms compared to the consumer space. We will mainly use examples from the pharmaceutical industry in what follows, however similar paradigms exist for the medical device space. Much of these differences among pharmaceuticals and consumer products are well described in Mickey C. Smith’s excellent book “Pharmaceutical Marketing: Strategy and Cases”, October 24, 1991. There is an extensive discussion by several additional authors. The standard 4 P’s as developed by McCarthy, Perreault and Cannon in “Basic Marketing. A Managerial Approach”, 1960, are also discussed. We will not go into detail in our current discussion, except to note that targeting promotional effort is one of the four legs associated with the 4 P’s. The pharmaceutical industry has been a data rich industry historically. Getting a product to market is an? huge expense, currently estimated at nearly $1Billion (need a reference). However, once a product has been approved by regulatory agencies, and has received patent protection, gross margins are typically over 90%. Even the company with the highest market capitalization, Apple, does not enjoy these gross margins. Consequently, if a pharmaceutical manufacturer is to be successful in realizing a net present value above their research and development costs, they must be able to successfully target the population that will “use” their product. There are several data providers to the pharmaceutical industry that serve as arm’s length partners in this endeavor. The largest data provider is IMS. In 2010 IMS was taken private at an estimated price of just under $6 Billion. Another larger data provider is W-K, a division of XXX. I NEED HELP HERE. WHAT DID W-K MORPH INTO? Several niche data providers exist such as Aileron, Pearl Diver Technologies, Health Market Science, Health Products Research and others. The current author sold Data Analysis Systems, Inc. (DAS) to Quintiles in 1998. Quintiles merged DAS into its Verispan group shortly thereafter. Verispan was subsequently acquired by Surveillance Data, Inc. (SDI). IMS acquired SDI in 2011. Such is the nature of this constantly changing landscape. A venerable offering by IMS is their DDD (Drug Distribution Data) offering to the pharmaceutical industry. Originally major pharmaceutical companies historically sold directly to pharmacies. In the 1980’s (CHECK THIS DATE) these major pharmaceutical companies found it preferable to ship their product through major wholesalers such as Bergen Brunswick, Cardinal Health and others. This is ably discussed in the Smith reference mentioned above. When this shift occurred, the pharmaceutical industry lost a geographically specific sales data source. IMS, through DDD, captured a significant enough amount of data shipment from the wholesalers to the retail pharmacies to plug this data hole. In the early 1980’s (CHECK THIS DATE), Pharmaceutical Data Services (PDS), then a division of Pharmaceutical Card Services (PCS), responded to an industry desire to have data available at the individual prescriber level. This was a major task, since at that time there were approximately 60,000 (CHECK THIS NUMBER) pharmacies and more than 600,000 prescribers in the United States. PDS started with a small panel of less than 5,000 pharmacies. This number proved too few to perform an accurate projection to the prescriber population in the US. This panel soon grew to over 10,000 pharmacies, and eventually developed a panel of approximately 40,000 (CHECK THIS NUMBER) pharmacies in the US, enough to statistically project activities to most pharmacies in the US and most prescribers in the US. Thus was born the first micro marketing data base for the US pharmaceutical industry. Promotional effort aimed at prescribers could be adequately tracked by these data. IMS, a latecomer to this space, entered with their offering in the 1990’s. Not to be outdone, IMS received patent number 5,420,786 on May 30, 1995 to enable them to protect their methodology in this space. This is interesting because by patenting their approach, IMS held it out to the public. This could have allowed the public to make improvements and add-ons to the IMS approach. To the best of our knowledge, IMS has never entered in into a lawsuit for patent infringement over their patent discussed above. Obtaining a patent was also interesting for IMS since the patent seems to be based on standard statistical “ratio estimation” as outlined in one of the patent references, “Sampling Techniques”, W. G. Cochran, John Wiley, New York 1977. IMS does make an adjustment for distances of unsampled pharmacies versus sampled pharmacies, but such adjustments are well known. Kolesar and Blum in “Square Root Laws for Fire Engine Travel Distances”, Management Science, 1973, established that travel time distances are non-linear between urban fire companies, and by extension other geographically dispersed, but otherwise similar, entities. Professor Kolesar won the Lanchester prize in 1975 and the 1976 NATO Systems Science prize for this work. This antecedent work on non-linear travel distances is not found in the IMS patent. Statistically, the problem with the IMS patent is simply that it is not based on appropriate Bayesian estimators to improve estimates in large dimensional situations. As stated above, the IMS patent attempts to adequately estimate product usage in over 30,000 zip codes, and for more than 600,000 prescribers. In 1961, James and Stein showed how in three or more dimensions, the sample mean of a sample from a multivariate normal distribution was inadmissible (Estimation with Quadratic Loss, Proceedings of the Fourth Berkeley Symposium, pp.361-379). Statistically, “inadmissible” means that there are better estimators that can uniformly improve upon the IMS estimate. In all practical cases these are Bayesian estimators. Another readable discussion of Bayesian estimators is found at the following link (http://en.wikipedia.org/wiki/Bayes_estimator). The recent monograph, “Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction” by Bradley Efron, IMS Monographs, 2010 also discusses the power of Bayesian estimation procedures in large dimensional parameter estimation situations. Possibly the one paper that discusses geospatial estimation from a Bayesian point of view would be “Hierarchical spatio-temporal mapping of disease rates”, by Waller, Carlin, Xia and Gelfand, Journal of the American Statistical Association 92: 607-617. As mentioned in the title, this paper also adds the temporal component to the geospatial component. Since many of the pharmaceutical industry data sources project their data monthly, the temporal component is also of interest. A final marketing reference is presented applying Bayesian estimation to the marketing application of conjoint analysis methodologies. This article appears in the Journal of Marketing Research, authored by Min Ding, vol. XLIV, May, 2007, 214-223, “An Incentive-Aligned Mechanism for Conjoint Analysis. The article abstract states “This article specifies, analyzes, and validates a rigorous and practical truth-telling mechanism (game) for conjoint applications. The mechanism requires only one real product variation and has truth telling in conjoint as its Bayesian Nash equilibrium, thus making it possible to incentive align participants in most conjoint applications. Using the iPod package as the context, the author shows empirically that the mechanism substantially improves purchase prediction compared with a standard conjoint procedure”. With the above discussion, it may seem difficult to understand the lack of widespread use of Bayesian methodologies in projection approaches in the pharmaceutical marketing space. It may be because practitioners have difficulty with the concept of a prior probability. But as stated above, the industry does not have a dearth of data, many ancillary data sources can be utilized to form an informative prior distribution. Consider the following application. The following is made up from fictitious data. Remicade® is an important product for the Janssen Biotech division of Johnson and Johnson. Is has been approved for eight medical indications by the US Food & Drug Administration. They are rheumatoid arthritis, Crohn’s disease, pediatric Crohn’s disease, psoriatic arthritis, ulcerative colitis, ankylosing spondylitis, pediatric ulcerative colitis and plaque psoriasis. Let us assume that Janssen marketing and sales management wants to establish their own and their competitor’s market share at local levels of geography. The Data Insight Group (DIG) (WE NEED A WEBSITE!!!), working with Pearl Diver Technologies (www.pearldiverinc.com) has obtained federal government reimbursement data. Let us concentrate on the state of New Jersey, and posit that Janssen has ten sales territories in that state, comprising one sales district. This is an assumption based on absolutely no knowledge, simply a fabrication for discussion purposes. Let us also assume that we have been able to establish a Box-Jenkins times series analysis of the relationship between Pearl Diver data and IMS DDD data. Thus, the IMS DDD data can serve as an appropriate prior distribution for the Pearl Diver data. Note that the IMS data does not include the indication information, but the Pearl Diver data does.