Medical Inspection – Health Blog

We divided new drug introductions into two steps: (1) first-in-class drugs to reach the market and (2) later follow-on drugs in established therapeutic categories (i.e., with previously existing USC codes). In this research, our analysis used two logistic regression models: one for first-in-class drugs and a second for follow-on drugs. First-in-class drugs represent a potentially greater innovation than follow-on drugs, which enter the market at a later date in an existing drug class.

First-in-Class Drugs

The first-in-class logistic model has a significant model chi square of .0000 and correctly predicts a robust 80% of the cases (Table 4). In the final logistic regression model, seven variables were statistically significant. In order of relative importance, as measured by the log odds, these variables were: (1) Pre-product Launch Company Prescribing Loyalty, (2) Total Pre-product Launch Prescribing Volume, (3) Age, (4) Pharmaceutical Marketing Support, (5) Practice Type, (6) Clinical Investigator Experience, and (7) Specialty.
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Early adopters of first-in-class drugs tended to write a greater percentage of their prescriptions, three months before product launch, for drugs from the pharmaceutical company marketing the new drug than were physicians who were not early adopters. Pre-product Launch Company Prescribing Loyaltywas the most significant variable in the model explaining adoption of first-in-class drugs.

Second in importance was the absolute number of prescriptions that the physician wrote three months before a ever, the variable was no longer significant as part of a multivariate model.

Table 4 “Model 1″: Early Adoption of First-in-Class Drugs

Follow-on Drugs

The follow-on drug logistic model also had a highly significant model chi square of .0000 and correctly predicted a strong 75% of the cases (see Table 5). Some variables in the second model were similar to those in the first-in-class model, and several had distinctly different explanatory roles.
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The most important variable in the second model, Total Pre-product Launch Drug Class Prescribing, was able to be present only in a model that examined drugs for which an established therapeutic category existed at the time of the new drug launch. The number of prescriptions written in the new drug’s therapeutic class was a key explanatory variable for understanding the adoption of another drug in that therapeutic class. The more prescriptions written in a drug class by a physician, the greater the likelihood that the physician would adopt a new drug in that therapeutic class.

Physicians who did not write any prescriptions at all in the drug class were unlikely to prescribe a new drug in that class. New adopters in this product launch (i.e., Total Pre-product Launch Prescribing Volume). The greater the number of total prescriptions written for all types of drugs, the greater the chances of writing prescriptions for the new drug. High prescribing volume may indicate a high patient flow. Physicians with a high patient flow may be particularly alert to new drugs to address the unfulfilled medical needs of some of their patients.

Age was inversely related to the likelihood of being a new drug adopter, although the relationship was not completely linear. The youngest doctors, those under 36 years of age, and the oldest doctors, those over 65, were the least likely to be new adopters. However, these age groups represented reasonably small groups in this study: 3% and 12%, respectively.

The relative level of Pharmaceutical Marketing Support put behind the new drug was also a key variable. The more money spent in support of the new product launch, the greater the chance of that drug’s early adoption by the physicians in this study. This marketing support may include such activities as increased product detailing and free samples provided by company sales representatives, larger DTC advertising, and more advertising in professional publications. The relative role of each could not be assigned in this study.
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The nature of the practice (Practice Type) was critical as well. Office-based physicians were more likely to adopt a first-in-class drug than were hospital-based doctors, who might work with more restrictive formularies. For example, hospital-based physicians may see a higher percentage of patients who are taking a product on some type of public formulary. Even if these physicians wished to prescribe the new drug, they might be unable to do so early in the new drug-adoption process.

Table 5 “Model 2″: Early Adoption

Physicians who participated in at least one of the phase 3 clinical trials of the new drug had a greater chance of being early adopters (Clinical Investigator Experience). They were familiar with the drug for some time, and they had the most extensive experience using the drug in clinical settings.

Consistent with findings in other literature, specialists in the drug’s therapeutic area tended to be early adopters more often than generalists or other specialists (Specialty in Tables 3, 4, and 5).
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Total Pre-product Launch Prescribing Volume constituted the second most important variable in predicting whether a doctor would be an early adopter of a new drug, as was the case with first-in-class drugs. The more prescriptions a physician has written, the greater the chance that he or she will become a new drug adopter for follow-on drugs as well.

The relative amount of money spent in support of the new drug by the pharmaceutical company, the Pharmaceutical Marketing Support, was the third most significant explanatory variable.

Board Certification was a statistically significant variable in this model, although it was not in the first model.

Office-based physicians, specialists, and those who participated in a drug’s phase 3 clinical trials tended to be early adopters of follow-on drugs in this study (Practice Type, Specialty, and Clinical Investigator Experience in Table 3).

In stark contrast to the first model, Pre-product Launch Company Prescribing Loyalty was not a significant variable. Age was not an important explanatory variable in the follow-on drug model.

The Sex of a physician was not a significant factor in either model. Female physicians were less likely to be new drug adopters, but the difference was not statistically significant. kamagra uk

Several variables were common to the adoption models for both types of drugs, including (1) Total Pre-product Launch Prescribing Volume, (2) Clinical Investigator Experience, and (3) Specialty and Office-Based Practices. The most striking difference, though, between the two models was the role played by Pre-product Launch Company Prescribing Loyalty. It was not present in the follow-on drug model, yet it was the most important variable in the first-in-class model. In this case, the higher the percentage of drugs represented by the launch company in a physician’s total prescribing, the more likely that physician was to be an early adopter of a drug from that company. It may well be that the variable reflected increased detailing by a pharmaceutical company to that physician. However, the variable might also indicate a degree of confidence and trust in that company, or in that company’s sales representatives, by the physician prescribing a therapeutically novel new drug from that company.

Because a novel drug represents a new class of drug, company trust may be a factor in a physician’s decision to be an early adopter of a novel drug from that company. If increased detailing were the only factor at work in explaining the importance of this variable, we would expect to see the variable in both models; however, it was present only in the first-in-class model.

The explanatory importance of trust was further supported by an examination of the subset of physicians who tried the first-in-class drug within the first six months of a product’s launch but who later stopped prescribing that drug. These physicians were statistically similar in virtually every respect to the early adopters (physicians who continued to prescribe the new drug after its adoption within the first six months). This similarity included demographic variables such as age and sex as well as the practice and prescribing variables.

The one important exception was Pre-product Launch Company Prescribing Loyalty. Early adopters demonstrated a statistically significantly higher percentage (.0001) of their drugs from the pharmaceutical company marketing the new drug than did physicians who first prescribed the drug but who eventually stopped prescribing it. The willingness of some physicians to be early adopters of a first-in-class drug was highly related to their total level of prescribing drugs from the company bringing the new novel drug to market, and it probably reflected a degree of trust by the physicians in that company and their representatives to provide a safe and efficacious novel drug.

After reviewing the results by indication as part of our overall analysis, we found no significant, systematic differences in the overall pattern of results between the chronic and acute (short-term) indications.
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LIMITATIONS

The study design had several limitations:

1. The data were restricted to drugs for selected outpatient indications. The dynamics might differ for in-patient or other outpatient indications.

2. Although the study population covered a broad range of physicians, it was not a statistically projectable sample to the entire U.S. physician population.

3. The study covered U.S. data only. The U.S. is the only major pharmaceutical market that currently allows data on individual physician prescribing patterns to be tracked and sold without explicit physician approval. Most countries prohibit the selling of these data under any circumstances, and a few countries allow the data to be sold with the express agreement of the physician. It is probably impossible at present to replicate this study outside the U.S. because of the absence of widespread individual physician-prescribing data.

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