The Impact of Health Insurance Mandates on Drug Innovation: Evidence from the United States

Transcription

1 The Impact of Health Insurance Mandates on Drug Innovation: Evidence from the United States Natalie Chun Asian Development Bank 6 ADB Avenue Mandaluyong City 1550 Philippines Minjung Park Haas School of Business, UC Berkeley 2220 Piedmont Avenue Berkeley, CA USA November 21, 2011 Abstract An important health policy issue is the low rate of patient enrollment into clinical trials which may slow down the process of clinical trials and discourage their supply, leading to delays in innovative lifesaving drug treatments reaching the general population. In the US, patients cost of participating in a clinical trial is considered to be a major barrier to patient enrollment. In order to reduce the barrier, some states in the US have implemented policies requiring health insurers to cover routine care costs for patients enrolled in clinical trials. This paper empirically evaluates how e ective the policies were in increasing the supply of clinical trials and speeding up their completion, using data on cancer clinical trials initiated in the US between 2001 and Our analysis indicates that the policies did not lead to an increased supply in the number of clinical trials conducted in mandate states compared to nonmandate states. However, we nd some evidence that once clinical trials are initiated, they are more likely to nish their patient recruitment in a timely manner in mandate states than in non-mandate states. As a result, the overall length to completion was signi cantly shorter in mandate states than in non-mandate states for cancer clinical trials in certain phases. The ndings hint at the possibility that the policies might encourage drug innovation in the long run. KEYWORDS: clinical trials; drug innovation; health insurance coverage laws JEL CLASSIFICATION CODE: I18, O38 Correspondence: mpark@haas.berkeley.edu, Phone: , Fax: We thank two anonymous referees and the editor for their insightful comments. 1

2 1 Introduction The innovation of new drugs and therapies against serious and life threatening diseases is heavily reliant on clinical trials, which are studies that assess the potential safety and e cacy of new drugs and therapies under development using human volunteers. However, clinical trials are an extremely long and costly process. In 2003, the time to drug approval from the start of human clinical trials was approximately 8 years and the associated cost was estimated at $802 million [8]. A potentially signi cant contributor that has led to an increase in the length and cost of clinical trials is the low rate of patient enrollment into clinical trials. The National Institute of Health (NIH), a primary US federal agency for medical research, states in its letter to the General Accounting O ce [10] Frequent anecdotal reports of signi cant slowing in subject accrual in NIH-supported clinical trials have been a growing concern for NIH. For the case of cancer, only about 3-5% of the 10.1 million adults with cancer in the US participate in a clinical trial. 1 Low patient enrollment can result in a signi cant delay of clinical trials. Moreover, clinical trial sponsors may decide to conduct fewer clinical trials than socially desirable if they anticipate that they will not be able to recruit enough patients to run the trials in a timely and cost-e ective manner. This can lead to reduced incentives to develop new drugs in the rst place. Thus, addressing the issue of low patient enrollment into clinical trials is potentially crucial to spurring drug innovation. The monetary costs of participation in clinical trials are considered a major factor in low patient enrollment. Prior to the late 1990s, few health insurance providers covered routine care costs for patients enrolled in clinical trials even though these costs were typically covered for patients who received standard, non-experimental methods of care. 2 To address this lack of insurance coverage, a number of states in the US have implemented mandates requiring health insurers to cover routine care costs for patients enrolled in clinical trials. If these policies have the intended e ects, they would increase patient participation in clinical trials, thereby increasing the chance of nishing patient recruitment in a reasonable time frame and reducing the time required to complete the clinical trials. Furthermore, the policies might encourage clinical trial sponsors to conduct more trials by making it worthwhile to initiate clinical trials in locations that were not previously considered cost-e ective due to the expected di culty in patient recruitment. Given the policies implications for drug innovation, it is of signi cant importance to evaluate the e ectiveness of these mandates. 1 Murphy et al. [22] cites around 3% of adults diagnosed with cancer between years of age enroll in cancer clinical trials, while the National Cancer Institute [23] cites that less than 5% of adult patients enroll in cancer clinical trials. 2 The National Cancer Institute [24] states Lack of third-party reimbursement for clinical trials may be one of the most critical barriers to patient participation. The American Cancer Society [1] reported that 60% of patients do not take part in clinical trials due to fears of having their insurance denied. Also, Gross et al. [13] states In addition to these important patient and physician factors, payer reimbursement policies are a frequently cited barrier to recruiting patients into clinical studies. 2

3 This paper examines both supply and trial completion times with the goal of answering the following questions: Did the state-mandated reimbursement policies lead to an increased supply of clinical trials? What were the impacts of the policies on the probability of successful patient recruitment and trial lengths? If recruiting clinical trial participants was a binding constraint for clinical trial sponsors and the policies were e ective in removing the constraint, we would expect that the implementation of such policies would lead to a greater incidence of timely completion of patient recruitment, a shorter trial duration, and an increased provision of clinical trials in mandate states compared to non-mandate states. 3 Our empirical analysis focuses on cancer clinical trials initiated in the US between 2001 and The data was collected from ClinicalTrials.gov, an NIH-operated online registry of clinical trials. As cancer advocates were the main force behind the legislation on insurance coverage for clinical trial participants, laws on state mandates for cancer clinical trials are the clearest and most homogenous across states. State variations on when clinical trial health insurance reimbursement became mandatory are used to identify the e ects. Our analysis indicates that the mandates did not lead to an increased supply in the number of clinical trials in mandate states compared to non-mandate states. Since provision of clinical trials depends on the development of new drugs or therapies to be tested, it is a long-run decision, and the span of seven years in our data may be too short to reveal any meaningful changes to that long-run decision. Examining other shorter-run measures, we nd some evidence that the mandates were e ective. The mandates are found to have increased the chance of successfully nishing patient recruitment for trials in certain phases, and trial lengths tend to be shorter for trials run in mandate states compared to those run in non-mandate states. These results suggest that the mandates helped reduce the costs of conducting clinical trials, which could potentially lead to more innovative activities for new drugs and therapies. Our paper focuses on the state policies impact on aspects of the supply of clinical trials, such as provision and duration of clinical trials, rather than the impact on patient enrollment. However, these two sides are closely connected. If the mandates lead to an increased participation in clinical trials, sponsors may be willing to conduct more trials or may be able to complete trials more quickly in states with mandates. If the mandates do not increase patient enrollment, then there is no reason to expect any change in sponsors provision or duration of clinical trials. 4 In other words, patients response to the mandates is a requisite step in observing changes in the supply aspects of clinical trials. However, patients response to the mandates is 3 Under the policies, routine care costs are paid for by health insurance providers, not clinical trial sponsors. Hence, from the perspective of clinical trial sponsors, states that mandate reimbursement of routine care costs should be more attractive than non-mandate states all else equal, as long as the mandates indeed increase clinical trial participation. 4 Since the mandates require that the nancial burden of routine care costs be borne by health insurance providers, not by sponsors, the mandates have no direct impact on the sponsors costs of conducting trials. 3

4 not a su cient condition for changes in sponsors provision of clinical trials or duration of trials, since the low patient enrollment might not have been a binding constraint in conducting trials or completing them from sponsors perspectives. In sum, how patient enrollment responds to the mandates versus how the provision and length of clinical trials respond to the mandates are closely linked, but the former only tells us whether the lack of insurance coverage was a binding constraint for patients, while the latter contains additional information on whether the level of patient recruitment was a binding constraint for sponsors. We focus on the latter due to lack of data on patient enrollment and given that the latter provides an insight on whether the mandates have any implications for innovation of drugs and therapies. 5 To our knowledge, our paper is the rst to provide a comprehensive investigation of the e ects of the US reimbursement policies. Despite immense interest by policy makers, health professionals and academics in the e ectiveness of these laws, there is little empirical research on the subject. We are aware of only two papers that examined this issue approaching the analysis from the side of patient enrollment [13, 14]. They found only marginal or no e ects on patient enrollment from these policies. Since the papers examined only a select set of government clinical trials, it is not clear a priori whether their nding is representative of impacts for all clinical trials. Our sample, in contrast, covers a comprehensive set of clinical trials by all sponsor types. We further add a di erent dimension to the previous papers by examining the policies impact on supply aspects of clinical trials, such as provision and duration of clinical trials, instead of their impact on patient enrollment. Hence, our paper makes a valuable contribution to the health policy literature by examining the e ects of important, but under-studied, policies. The outline for the rest of this paper is as follows. Section 2 provides background on clinical trials and state insurance policies for cancer clinical trials in the US. We also review the prior literature in Section 2. Section 3 describes the data. Section 4 discusses our econometric framework and presents the results. Section 5 provides a general discussion of the results and limitations and concludes the paper. 2 Clinical Trials and Health Insurance Laws Clinical trials are research studies designed to assess the e cacy and safety of new drugs or therapies under development using human volunteers. They are a crucial step in the drug innovation process and for developing new therapies against serious diseases. The Coalition of Cancer Cooperative Groups, a nonpro t organization, states on its website Clinical trials contribute to the overall knowledge and progress against cancer. Many of today s standard treatments for cancer began in clinical trials. In fact, clinical 5 Suppose that the mandates result in an increase in the number of clinical trial participants but do not a ect the provision or length of clinical trials. Then, while the mandates would have implications for those patients whose nancial burden has been eased, the mandates would not have any implications for innovation of drugs and therapies. 4

5 trials run by cooperative groups have helped establish the e ectiveness of lumpectomy for breast cancer, new chemotherapy drugs for colon cancer, and the combination of chemotherapy and radiation for advanced cervical cancer. Many people treated for cancer are now living longer thanks to the knowledge gained through clinical trials. 6 Results from the evaluation of data generated from the trials are considered by the Food and Drug Administration in its approval decision. According to Conner and Vachani [7], two types of costs are associated with clinical trials, and they substantially di er according to who bears the burden. First are routine care costs, which are costs of treating a person s cancer that would occur whether the person is in a trial or not. Routine care costs include physician visits, hospital stays and laboratory tests, and are paid for by either the patient or health insurer depending on insurance coverage. Second are research costs that are required for conducting the trial. These include costs of data collection, extra doctor visits, extra tests and procedures associated with the trial, and they are typically covered by the sponsoring organization. 7 Prior to the late 1990s, few health insurance providers covered routine care costs for patients enrolled in clinical trials, because treatments provided by clinical trials were considered as experimental and nonstandard. Therefore, patients participating in clinical trials paid for routine care costs even though the same kinds of costs were covered by health insurance providers if they did not participate in clinical trials and instead received standard methods of care. Insurers claimed that clinical trial enrollment signi cantly drove up routine care costs by requiring testing and procedures that typically were not used or administered in the absence of clinical trial participation. However, research has found at most small di erences in routine care costs between clinical trial enrollees and non-enrollees. For example, Goldman et al. [11] estimated that clinical trial enrollees cost 6.5% more than non-enrollees, while Chirikos et al. [5] found no signi cant cost di erence between the two types of patients. The absence of coverage for routine care costs was perceived by the scienti c community and policy makers as a signi cant barrier to enrollment in clinical trials. 8 The perception that lack of insurance coverage deterred patients from participating in clinical trials was a stimulus in the passage of laws where health insurers would cover routine care costs for patients with cancer or life-threatening diseases who are enrolled To our knowledge, nancial compensation to patients for trial participation is limited in its scope as there are ethical concerns that substantial compensation could unduly in uence participation and thus obscure risks, impair judgment or encourage misrepresentation. [12]. According to Grady [12], most clinical research studies o er considerably less than $500 for participation, mainly for parking, meals, inconvenience and travel. Thus, it seems that such nancial incentives are a relatively minor component in patients decision on whether to participate in a clinical trial. 8 Routine care costs are signi cant. According to Wagner et al. [28], cancer patients average one-year routine care costs are $24,660. 5

6 in certain phases of clinical trials. 9 ;10 States started to adopt laws in the mid 1990s that required coverage of routine care costs for clinical trials related to cancer and life-threatening diseases. Table 1 shows which states adopted clinical trial reimbursement laws when. Rhode Island was the rst to initiate such a law in 1994 and 24 more states implemented these laws by In the year 2000, Medicare, which provides health insurance for elderly patients, implemented a nationwide policy of covering routine care costs for all drug clinical trials related to cancer. Medicare coverage was subsequently extended to include reimbursement for enrollees into clinical trials for medical devices used in cancer treatment in Few studies have evaluated the e ects of these policies with the exception of Gross et al. [13, 14]. These studies which exist in the medical literature are primarily concerned with assessing whether these policies increased enrollment numbers. Gross et al. [13] focused on a select group of NCI phase 2 and phase 3 trials to compare changes in enrollment rates between 1996 and 2001 using four states that enacted coverage policies in 1999 against a control group of states that did not implement any coverage policies. Their ndings were mixed. Compared to non-mandate states, mandate states experienced a greater increase in phase 2 trial enrollment, but a smaller increase in phase 3 trial enrollment. 12 Gross et al. [14] aimed to evaluate whether the proportion of older patients into cancer clinical trials increased due to the Medicare policy of covering routine care costs for all elderly patients who enrolled in cancer clinical trials. Using a set of 23 NCI sponsored trials from January 1996 through June 2003 they evaluated whether the percent of elderly participants increased due to the Medicare policy. As no signi cant increase was observed, 13 the authors concluded that the Medicare clinical trial reimbursement policy did not have any e ect on the enrollment of older patients. 9 The term life-threatening disease includes AIDS related conditions and a number of rare diseases such as Parkinson s disease. 10 According to ClinicalTrials.gov, clinical trials typically proceed in 4 phases. Phase 1 is the rst stage of testing in a small group of human subjects and its goal is to evaluate the initial safety of a new drug or procedure. Once initial safety is con rmed in phase 1 trials, phase 2 trials are conducted on a larger group of people to generate more information about its safety and bene ts. Phase 3 trials are randomized experiments during which the performance of the new drug or procedure will be compared to the performance of a current standard treatment. After these phases, the developer of the drug or procedure applies for FDA approval. Once approved, phase 4 trials are conducted to continue evaluation. Due to the di erent nature of phase 4 trials, we focus on trials in phases 1 3 in this paper. 11 Medicare almost purely applies to elderly patients (Medicare also covers some people who cannot work due to disabilities such as Lou Gehrig s disease), and we do not use in our analysis trials that exclusively focus on elderly patients in order to minimize the impact of the Medicare policy on our results. Overall, roughly 25%-30% of participants in cancer clinical trials are people of 65 or older [16]. Since we exclude trials exclusively focusing on elderly patients, the fraction should be even lower in our sample. Our analysis can be viewed as examining whether the state mandates impact on non-elderly patients is signi cant enough to a ect provision and completion time of clinical trials. 12 They used a Poisson model (the same methodology we use in one of our analyses) where the dependent variable is the number of clinical trial participants. 13 They used a logistic regression in which the dependent variable was a dummy for being older than 65 years. 6

7 The prior research suggests that the insurance coverage policies did not have the intended impact of increasing clinical trial participation. It is possible that mandates may not have any e ect on patient enrollment as the lack of insurance coverage is not the only barrier to participation in clinical trials, and probably is not even the most signi cant barrier. Other common reasons for patients unwillingness to participate include distance to trial sites, patient misconception or concerns over drug quality, interference with work and lack of time, among others [17, 26, 27]. Furthermore, many patients are not even aware of the fact that clinical trials might be an option [6]. If these other barriers are insurmountable for many patients, the required coverage of routine care costs might not have any signi cant impact on patient enrollment, and thus no impact on provision or duration of clinical trials. Still, one is left to wonder whether the results from the prior studies are de nitive answers on the e ectiveness of these policies. One potential reason why these studies might not be representative of the overall e ects is that they examined only a select set of government clinical trials while a high proportion of cancer clinical trials are sponsored by private non-pro t organizations as well as pharmaceutical companies and universities. In this paper, we consider a more complete set of clinical trials and examine whether the insurance coverage mandates have an impact on the margin despite the presence of other barriers to patient enrollment. Since there are no state-by-state statistics on enrollments, we examine the number of trials conducted by clinical trial sponsors instead of total enrollments. We also examine whether the probability of successful patient recruitment and lengths of clinical trials are in uenced by the reimbursement policies. Investigating these e ects allows us to test whether the insurance coverage laws have an impact on supply-side aspects of clinical trials, which would have direct implications for drug innovation. 3 Data Our primary source of data was collected from ClinicalTrials.gov with the last date of extraction occurring in April of Clinical trials selected for inclusion in our sample were obtained by performing searches for clinical trials associated with 16 di erent cancer diseases listed in Table 2. From each of the clinical trial reports we gathered information on phase of trial, trial locations, start date and end date of trial, age restrictions, sponsors, date the trial was rst received by ClinicalTrials.gov, and status of trial (recruiting, ongoing, suspended, terminated, successfully completed, or withdrawn). Trials that contained no start date were deleted from the sample and they account for less than 2.5% of the potential sample. As there might be a lag in adding location information we allow a one-year time bu er to adequately 14 Zarin et al. [29] provide a nice discussion of data on ClinicalTrials.gov. 7

8 capture the location decisions of clinical trials that occurred for the last year in the sample. Due to concerns over accuracy of data for earlier trials, we focus on trials whose start dates are post with start dates ranging between 2001 and 2007 in the nal sample. Clinical trials restricted to pediatric care patients or patients over 65 were eliminated from the sample as insurance coverage policies are typically di erent for these age groups such as Medicare for elderly patients. Finally, since this study is interested in the e ects of state mandates within the United States, trials that did not have at least one location listed in the US were deleted from the sample. There are limitations to the data set. The main one is that the set of observed trials is likely smaller than the full set of trials since registration with ClinicalTrials.gov is voluntary. In 2005, the International Committee of Journal Medical Editors (ICJME) started mandating registration on ClinicalTrials.gov for any trial hoping to publish in a key medical journal. As a result, there was a signi cant increase in clinical trial registration in 2005 compared to prior years. Since the ICJME registration policy provided clinical trial sponsors with a very strong incentive to register their trials the registration policy required that trials attempting to publish after September of 2004 should retrospectively register and that trials with start dates after July of 2005 should register before the start of the trial in order to subsequently publish the results 16 we believe our data captures most of trials after 2005, but for earlier trials missing reports are common. Figure 1 shows that the number of trial starts tting our criteria appears to increase over time for each phase, primarily for trials conducted by industry sponsors. This upward trend is likely due to the implementation of the ICJME registration policy rather than a true increase in the number of trials starting in a given year. US government sponsored trials do not show any clear trend probably because US government sponsored trials had a fairly high registration rate even prior to the registration policy. The gure also indicates that across phases there is a di erence in registration incentives as the total number of phase 1 trials is less than the total number of phase 2 trials despite the fact that only a subset of phase 1 trials proceed to phase 2. Although missing reports are not ideal, as long as the probability to register does not di erentially change over time between mandate states and non-mandate states, missing reports would not bias our results. If the change in the registration practice a ects all states equally, across di erent mandate regimes, that change would be entirely absorbed by time dummies we include in the regression, leaving the estimated impact of the mandates unbiased ClinicalTrials.gov started its operation in November 1999, so is in the early phase of operation for ClinicalTrials.gov. As a result, we were concerned about accuracy of the data during that initial phase and decided to focus on post-2001 data. 16 Publication is a very good marketing tool to get doctors to adopt new therapies if the results are good. Publication is also valuable for increasing the pro le of the physician or scientist who is conducting the trial. Hill et al. [15] reviewed internal documents by Merck and found that the marketing department was actively designing trials for their marketing value, while Loscalzo [18] reports the importance of publication for academic institutes. 17 One might worry that the new registration policy could have di erent impacts on mandate states versus non-mandate 8

9 Table 3 provides the list and de nitions of variables used in our analysis. A summary of trial counts broken down by key variables is in Table 4. The number of trials starting in 2007 is larger than the number of trials starting in 2001, with phase 2 accounting for the highest number of registrations. Con rming the graphical results in Figure 1, industry accounts for a much larger percentage of registered trials in 2007 compared to 2001 for all phases, indicating that there were di erential incentives for industry sponsors to register their trials compared to government, university and other non-pro t organizations prior to the ICJME policy. Table 4 shows that a typical trial is conducted in multiple sites and often spread over multiple states. This is especially true for phase 3 trials which require a large number of enrollees for randomization. Table 5 reports the average number of trials that start in a given state and year for states with the mandates and states without the mandates. The underlying composition of mandate states and non-mandate states changes across years as more states start to implement the policy over time. From the table, there is no apparent systematic di erence in the number of clinical trials between mandate states and non-mandate states. Table 6 shows the status of trials in our sample as of April 2009 as well as the average duration of trials conditional on completion mode, measured in months. Since some trials in our data report start dates but not end dates (even when they report their completion modes), the number of observations for each year and phase in Table 6 is smaller than that in Table 4. Since our data is right-censored, the average duration obviously gets shorter for more recently initiated trials (as we move to the right in the table). The clinical trial data was supplemented with data from other sources to provide controls for factors that may a ect clinical trials locations as well as their probability of successful patient recruitment and duration. Measures of new cancer cases and cancer mortalities for each cancer category by state were obtained from American Cancer Society s Cancer Facts & Figures (CFF) yearly reports Information on the elderly population and population with private health insurance by state was gathered from Current Population Surveys. 18 states. Suppose that the number of clinical trial starts does not change over time in both mandate states and non-mandate states, but after the registration policy trials run in mandate states are simply more likely to register due to their greater willingness to publish. In that case, we would observe an increase in the number of registered trials in mandate states, but not in non-mandate states, although the mandates in fact have no impact whatsoever (We thank an anonymous referee for raising this possibility). We empirically attempt to check this possibility by including an interaction between the mandate variable and post-2005 dummy in our Poisson regression (in Section 4.1) where the dependent variable is the number of initiated clinical trials. If it is indeed the case that the registration policy (which was implemented in 2005) a ected mandate states and nonmandate states di erentially in the direction discussed above, we would expect a positive and signi cant coe cient on the interaction between the post-2005 dummy and mandate variable. However, we nd that the coe cient on the interaction term is sometimes positive and sometimes negative and always insigni cant. Thus we do not nd any empirical evidence that the registration policy a ected mandate states and non-mandate states di erentially. 18 We were able to obtain data for , and we assume that the elderly population in a given state was the same for 9

10 As a state s infrastructure is potentially an important determinant of where trials are located and their duration, we gathered data from various sources in order to proxy for the ability of a certain state to support cancer clinical trials. The NCI designates certain medical centers (mostly academic institutions) as NCI cancer centers. These NCI-designated cancer centers have specialists, access to technology, and infrastructure that other locations may not have the ability and funds to provide. Thus, we gathered information on the number of NCI cancer centers for each state in each year. We also obtained state level measures of hospitals from Area Resource Files from the US Health Resources and Services Administration. 4 Methods and Results Our objective is to investigate whether clinical trial reimbursement mandates had a bene cial e ect on the supply-side decisions of clinical trial sponsors and the clinical trial development process by allowing more successful patient recruitment in clinical trials. This has important policy implications as it indicates whether the mandates are an e ective policy lever to use in the face of increasing research and development costs and longer time lengths required to bring important and innovative drugs to market. Several approaches are used to examine whether the adoption of clinical trial mandates led to di erences in key outcomes. Speci cally, we look at whether the policies increased the supply of trials locating within mandate states and whether they decreased the length of trials or led to an increased chance of nishing patient recruiting in a reasonable time frame. 4.1 Impact of Mandates on Provision of Clinical Trials We use a di erence-in-di erences analysis that exploits variation in policy implementation across states to examine whether the number of clinical trial starts increased more in mandate states than in non-mandate states. In this approach, inference of the impact is made by comparing two groups, one treatment and one control [2, 20]. The treatment group consists of states that have adopted the policy and the control group is comprised of states that have not. The key identifying assumption is that the two groups would have experienced the same time trend in the dependent variable in the absence of treatment. Thus, any di erence in changes in the dependent variable between the two groups is attributed to the policy. Our identi cation strategy would be invalid, for instance, if states that expected to have a larger increase in the supply of clinical trials were more likely to adopt the policy than states that expected a smaller increase in clinical trial supply. However, Baquet et al. [3] noted that clinical trial laws were primarily implemented years 2001, 2002 and

11 by states to eliminate nancial factors as a variable in participation in clinical trials. 19 Thus, the policy adoption decisions at the state level are unlikely related to the state s forecast of trends in clinical trial supply, so there is no reason to expect that our identi cation strategy will fail. Furthermore, the adoption of the mandates has not coincided with changes in other policies for clinical trials. This state-level analysis studies the number of clinical trial starts (not the number of enrolled patients we don t have data on this) as an outcome measure, since it is a good measure of how e ective the mandates are in removing constraints faced by clinical trial sponsors in their decision to conduct clinical trials. The number of trials is also a good measure of the degree of potential innovations of new drugs and therapies. Since the dependent variable is a count variable, we use a count data model based on a Poisson distribution for estimation. 20 Let s denote state and t time. Y st is the number of trials that started in state s at time t. 21 M st is de ned to be equal to 1 if the reimbursement policy is in e ect in state s at time t and 0 otherwise. 22 X st represents a series of controls in state s at time t. It includes the state level counts of cancer incidence, NCI-designated cancer centers, individuals covered by private health insurance, the elderly population, and hospitals, as well as the ratio of mortality to new cases of cancer incidence in state s at time t. 23 The Poisson regression model we use is parameterized as follows: E(Y st ) = st = exp( s + t + M M st + x X st ) (1) Pr(Y st = y) = exp( st) st y y! 19 This can be seen by the fact that those who are the most vocal advocates of the state mandates and have exerted signi cant lobbying e orts to implement the legislation were patient advocates such as the American Cancer Society. For instance, an article by the American Society of Clinical Oncology mentions As a volunteer for the American Cancer Society, Mangialardi was instrumental in getting legislation passed in her home state of Illinois that prohibits health insurance companies from dropping a covered individual because he or she enrolled in a clinical trial. ( Since legislations were passed as a response to requests from patient advocates, they are unlikely to be correlated with, say, the expected trend of clinical trial supply. 20 We also tried negative binomial distribution, which is more exible. The results are almost identical to those from Poisson distribution, because the dependent variable is not over-dispersed. Hence, we report results from Poisson regression only. 21 In many cases, a given clinical trial is conducted in multiple states. As long as a trial is conducted in state s, we include that trial in de ning Y st, regardless of whether state s is the sole location of the trial or not. Hence, a trial that is conducted in state a and state b will be included in both Y at and Y bt. 22 When we aggregate our data to the quarterly level, M st will be accordingly adjusted as follows. If a reimbursement policy is introduced in state s in January 2002, M st for the rst quarter of 2002 will be 1. If the policy is introduced in state s in February 2002, M st for the rst quarter of 2002 will be If the policy is introduced in state s in March 2002, M st for the rst quarter of 2002 will be Interpretation of our results will be not a ected by this adjustment. 23 The incidence and mortality measures are summed over the 16 cancer types for each state and year. The ratio of mortality to new cases is computed using the summed measures. (2) 11

12 The speci cation allows for state xed e ects 24 and time xed e ects. M is the main parameter of interest and captures the impact of mandates on the number of initiated trials. The control variables X st are included to account for the fact that some states might experience a greater increase in clinical trials simply because they experience a greater increase in the number of cancer patients, for instance. The infrastructure variables such as NCI designated cancer centers and the number of hospitals are included to account for the possibility that some states may increasingly host cancer trials simply because they have had a greater increase in the capacity and quality of cancer centers to run clinical trials. We cluster standard errors by states to account for within-state association since the prior literature has found that controlling for such a correlation is important to obtain proper con dence intervals in di erence-in-di erences analysis [4]. We estimate the model separately for each phase p = 1, 2 and 3. Table 7 reports the results. Speci cation 1 is the most parsimonious regression which only includes state and time xed e ects and the mandate variable. Speci cation 2 adds X st. Speci cation 3 is identical to Speci cation 2 except it excludes those states where M st is equal to 1 for all t, since states that had the policy in place throughout the sample period might systematically di er from other states in unobserved ways. Conversely, we exclude those states where M st is equal to 0 for all t in Speci cation 4. Speci cations 3 and 4 provide an indication of whether our results are robust to the de nition of control group in di erence-in-di erences analysis. The models are also estimated separately for each sponsor type and phase since sponsors may di er in how they respond to the mandate policies due to di erences in their objective functions. The sponsor types are industry, government, private non-pro t organization and university. We also try an analysis where we combine all phases because one might worry that doing a separate analysis for each phase may omit substitution or trade-o s among trials in di erent phases. These results are not reported due to limited space, but are brie y discussed below. 25 Table 7 shows no empirical evidence that the implementation of the mandates increased the amount of clinical trial activity. The policy mandates have no signi cant e ect on clinical trial activity in almost all speci cations. Even when the coe cient on mandate is estimated to be signi cant (Speci cation 1 for phase 3), the e ect disappears after controlling for other variables (Speci cation 2 for phase 3). In the analysis performed separately for each sponsor type and phase, the estimated coe cient on mandate is insigni cant in about 95% of cases. Combining the analysis for all phases or including interactions between the mandate variable and other regressors, we still get similar results. Thus, no evidence is found that the mandates led to an increased provision of clinical trials. The estimated coe cients on control variables are intuitive: states with a greater increase in infrastructure and cancer patients experience a greater increase in the amount of 24 State dummies will capture time-invariant di erences across states such as income di erence (since income di erence across states is likely to be stable over time). 25 These and other unreported results are available from the authors upon request. 12

13 clinical trial provision. A potential concern is that patients might travel to a di erent state to participate in clinical trials. Since many states mandates cover routine care costs for state residents even if they participate in clinical trials in another state, the presence of reimbursement policy in state S could lead to an increased provision of clinical trials not only in state S but also in other states which residents of state S might travel to for clinical trial participation. Then, the lack of any signi cant ndings may simply arise due to these spillover e ects. Because we do not have any information on travel patterns of clinical trial participants, it is di cult to address this concern directly. However, there are four states (Arizona, California, Nevada and New Mexico) whose mandate restricts the coverage of routine care costs only to clinical trials within that particular state. For these states the impact of the policies should not spill over to non-mandate states. Hence, if mandates spur supply of clinical trials, we should observe a larger increase in the provision of clinical trials in these states compared to non-mandate states (as long as the spillover e ects from other mandate states are not as strong as the direct e ects). Exploiting this idea, we re-run the Poisson regression, limiting our attention to the four states and all non-mandate states, excluding other mandate states whose policies have no geographic restrictions. The results in Table 8 do not show any evidence that mandates increase provision of clinical trials. Rather, the estimated coe cient on mandate is negative and signi cant in a couple of speci cations. We conclude that the lack of any signi cant increase in clinical trials attributable to the reimbursement policies is not due to cross-state travel patterns of clinical trial participants. The dependent variable in the estimation is the number of registered clinical trials, not the number of enrolled patients. Since an increase in patient enrollment is a necessary but not su cient condition for an increase in the number of initiated clinical trials, our nding that the mandates did not lead to an increase in the number of initiated clinical trials does not necessarily imply that the mandates did not increase the number of enrolled patients. It is possible that the number of enrolled patients increased after the implementation of the mandates but sponsors did not conduct more clinical trials because the low number of enrolled patients was not a binding constraint for sponsors or simply because it takes a long time for sponsors to adjust provision of clinical trials in response to the changed environment. The next subsection will examine another dimension, the length of clinical trial completion, to obtain a more comprehensive understanding of the policies impact. Since many trials have multiple trial sites within a state it is possible that some trials may simply have increased the number of trial sites in response to the mandates without increasing the number of distinct trials. We repeat the same analysis using as a dependent variable the number of trial sites instead of the number of trials. The interpretation of estimation results that use the number of trial sites as a dependent variable could be somewhat ambiguous. On one hand, an increase in the number of trial sites could be 13

14 evidence of increased provision of and easier patient access to experimental therapies. On the other hand, it could be evidence that sponsors face di culties in recruiting clinical trial participants, since sponsors sometimes decide to conduct their trials in more sites when they expect low enrollment from each site. 26 With this caveat in mind, we re-run the same set of regressions as before after replacing Y st in equation (1) with the number of trial sites and report the results in Table 9. Unlike the number of trials, the number of trial sites displays signs of over-dispersion (variance is several times larger than mean), so we t the model using a negative binomial distribution instead of a Poisson distribution. Table 9 shows similar results as Table 7. In all regressions, the coe cient on the mandate variable is not signi cant, suggesting that there is no di erential change between mandate states and non-mandate states in their numbers of trial sites. 4.2 Impact of Mandates on Patient Recruitment and Trial Length We showed that the mandates have no e ects on provision of clinical trials. However, as the provision decision is a long-term one clinical trial sponsors might not react to the policies in the span of 7 years that our analysis covers. Thus, we examine shorter-run outcome measures of the probability of successful patient recruitment and trial length as the policies may have a more immediate impact on these outcomes. If low patient enrollment signi cantly slows down the recruitment process and as a consequence delays the entire clinical trial process, while the reimbursement policies are e ective in increasing patient participation in clinical trials, we would expect to see an increased incidence of successful patient recruitment within a reasonable time frame as well as a shortened duration for clinical trials conducted in mandate states compared to those conducted in non-mandate states. This section investigates whether these predictions are borne out in the data. Ideally we would examine whether the duration it takes to complete patient recruitment is shorter in mandate states than in non-mandate states, all else being equal. Unfortunately, the limitation with the data is that it only reports the date when a trial ends, rather than when the trial nishes its patient recruitment. For instance, if a trial is reported as starting in March 2003 and being successfully completed in May 2007, we know that the trial successfully nished its patient recruitment sometime between March 2003 and May 2007, but we do not know exactly when it nished recruitment. To get around this data issue, we perform two types of analyses: The rst exercise examines how the probability of having nished patient recruitment by April 2009 di ers between clinical trials conducted in mandate states and those in non-mandate states. The second exercise examines the entire duration of trials, instead of the duration of patient recruitment. If the mandates speed up the recruitment process we should observe a shortened duration from the start 26 Note that this ambiguity is not present when we use the number of trials as a dependent variable. No rm will conduct more trials due to low enrollment in each trial, because di erent clinical trials are for di erent experimental therapies. On the contrary, multiple sites for a given trial administer the exactly same therapy. 14

15 to the end of trials. In these exercises, an implicit assumption, as we do not directly observe the number of participants in clinical trials, is that the probability of nishing recruitment by April 2009 and time to completion directly depend on clinical trial sponsors ability to recruit and retain a su ciently high number of participants Successful Patient Recruitment We investigate whether the probability of having nished patient recruitment by April 2009 di ers between clinical trials performed in mandate states versus those in non-mandate states, controlling for the starting time (since the age of trials is a signi cant predictor of whether the trials nish recruitment by April 2009) and other variables that may a ect patient recruitment. We apply a probit model de ning the dependent variable to be equal to 1 if the trial has completed its patient recruitment by April 2009 and 0 otherwise. Six possible statuses occur for clinical trials as of April 2009: recruiting, ongoing, suspended, terminated, withdrawn, or successfully completed. ClinicalTrials.gov de nes them as follows: 27 (1) Recruiting: participants are currently being recruited and enrolled, (2) Ongoing: study is ongoing, i.e., patients are being treated or examined, but enrollment has completed, (3) Suspended: recruiting or enrolling participants has halted prematurely but potentially will resume, (4) Terminated: recruiting or enrolling participants has halted prematurely and will not resume; participants are no longer being examined or treated, (5) Withdrawn: study halted prematurely, prior to enrollment of rst participant, (6) Successfully completed: the study has concluded normally; participants are no longer being examined or treated (i.e., last patient s last visit has occurred). 28 Ongoing and successfully completed trials have clearly already nished patient recruitment, while recruiting ones have not yet nished the recruitment process. Since withdrawn trials were halted even prior to enrollment of the rst participant, they are not relevant and are excluded from this analysis. We also exclude suspended trials from the probit analysis as it is unclear whether these trials might resume and eventually nish patient recruitment. The remaining ambiguous case is terminated trials as these were halted prior to nishing recruitment but after some patients were treated. Termination generally occurs because of ethical concerns or because the trial has reached a positive or negative statistical endpoint earlier than expected [25]. Since a trial typically gets early results only if it has enrolled enough patients early on, it might make sense to consider terminated trials as success from the perspective of patient recruitment. However, due Successfully completed trials do not necessarily mean positive outcomes for drug e cacy. Although it is likely that a trial would not be completed if early results from the trial are strongly negative (e.g., the rst few patients treated with the drug die), for less clear-cut cases sponsors might decide to complete the trial in order to have a large enough sample size to more precisely determine drug e cacy. 15