P.Mean: Lasagna's Law on patient recruitment (created 2011-10-24). News: Sign up for "The Monthly Mean," the newsletter that dares to call itself average, www.pmean.com/news.

I sent out a question to an email discussion group asking about documentation of sample size shortfalls in clinical research. Someone suggested that I google "Lasagna's Law." What a great suggestion. Here's what I found.

The nicest summary was

It was one of the pioneers in clinical pharmacology, the American Louis Lasagna, who described in 1970 a today's still well-known phenomenon in clinical trials: The incidence of patient availability sharply decreases when a clinical trial begins and returns to its original level as soon as the trial is completed. For illustration, in 1979 Lasagna commented on a trial where out of 8,000 patients theoretically available just 100 patients in the end participated. Later this phenomenon was popurlarly called Lasagna's Law. As quoted at http://www.pfc.ch/upload/docs/Detailprogramme/The%20A-Z%20of%20Pharm%20Med1.pdf

Several sources point to this article below as the source for Lasagna's Law, though I could not find it online to verify it.

J.A.L. Gorringe, Initial preparation for clinical trials, E.L. Harris, J.D. Fitzgerald, Editors, The principles and practice of clinical trials, Livingstone, Edinburgh/London (1970)

Another original source might be a book published in 1972 (and republished in 1992)

Spilker B, Cramer JA. Patient Recruitment in Clinical Trials.

Empirical support for Lasagna's Law was found in

Johannes C van der Wouden, Annette H Blankenstein, Marcus J H Huibers, Danielle A W M van der Windt, Wim A B Stalman, Arianne P Verhagen. Survey among 78 studies showed that Lasagna's law holds in Dutch primary care research J Clin Epidemiol. 2007;60(8):819-824. Abstract: "OBJECTIVE: Research in general practice has grown considerably over the past decades, but many projects face problems when recruiting patients. Lasagna's Law states that medical investigators overestimate the number of patients available for a research study. We aimed to assess factors related to success or failure of recruitment in general practice research. STUDY DESIGN AND SETTING: Survey among investigators involved in primary care research in The Netherlands. Face-to-face interviews were held with investigators of 78 projects, assessing study design and fieldwork characteristics as well as success of patient recruitment. RESULTS: Studies that focused on prevalent cases were more successful than studies that required incident cases. Studies in which the general practitioner (GP) had to be alert during consultations were less successful. When the GP or practice assistant was the first to inform the patient about the study, patient recruitment was less successful than when the patient received a letter by mail. There was a strong association among these three factors. CONCLUSION: Lasagna's Law also holds in Dutch primary care research: many studies face recruitment problems. Awareness of study characteristics affecting participation of GPs and patients may help investigators to improve their study design." [Accessed on October 24, 2011]. http://www.ncbi.nlm.nih.gov/pubmed/17606178

Lasagna's Law flies in the face of Interational Conference on Harmonization Guidelines on research (ICH E6/R1).

4.2.1 The investigator should be able to demonstrate (e.g., based on retrospective data) a potential for recruiting the required number of suitable subjects within the agreed recruitment period.

In any trial, the incidence of the disease studied will be reduced to 10% of the original estimate. As quoted at http://www.studies-obsgyn.nl/home/page.asp?page_id=887

In clinical research the prevalence of any disease falls to about 10% of what you thought it was the day you start to look for cases for your study. As quoted at http://www.bmj.com/content/327/7414/565/reply

As soon as a clinical trial begins, the supply of patients becomes one-tenth of what it was thought to be beforehand. As quoted in Clinical Trials in Neurology by Roberto J. Guiloff.

If the annual supply of suitable patients when the trial is designed is n, when the trial commences it will reduce to n/10. As quoted in Interpretation and Uses of Medical Statistics by Leslie E. Daly and Geoffrey Joseph Bourke.

One quote implies a five fold discrepancy.

As soon as a study begins, the number of patients available instantly drops from a theoretical pool of 100 percent down to 20 percent; as soon as a study concludes, the pool jumps back to 100 percent. As quoted in Reinventing Patient Recruitment: Revolutionary Ideas for Clinical Trials Success by Joan F. Bachenheimer and Bonnie A. Brescia.

Another quote implies a two fold discrepancy.

At the commencement of a study the incidence of the condition under investigation will drop by one-half, and will not return to its former rate until the study is close or the investigator retires, whichever comes first. As quoted in Modeling Survival Data: Extending the Cox Model by Terry M. Thernau and Patricia M. Grambsch.

Another offers a ten to three fold discrepancy.

The number of patients who are actually available for a trial is about 1/10 to 1/3 of what was originally estimated. As quoted in Principles of Medical Statistics by Alvan R. Feinstein.

Ohters imply a general decline without any quantification.

Before a study actually starts at investigative site, there always seem to be more than enough potential subjects waiting in the wings. For some reason, however, it frequently happens that as soon as the trial starts, these potential subjects disappear. When the study is over, there seems, again, to be plenty of suitable subjects. As quoted at http://clinicalresearchzone.blogspot.com/2008/11/louis-lasagna-do-we-still-remember-him.html

A well recognised phenomenon which states that as soon as a clinical trial begins all the subjects who were thought to be suitable for the study disappear. As quoted at http://pharmaschool.co/jargon03.asp?ID=155

The incidence of the disease under study will drastically decrease once the study begins. It will not return to its previous level until the completion of study (if completion occurs before the investigators retire. As quoted in Statistics in the Pharmaceutical Industry by Charles Ralph Buncher and Jia-Yeong Tsay.

Before a study begins, a large number of relevatn patients are available to the investigator. As soon as the study begins, the patients disappear, only to reappear after the study ends. As quoted in Patient Recruitment in Clinical Research: A Guide to Europe by Danielle Jacobs.

A variant, called Münch's law (note the umlaut, and be sure to also search for Munch's law or Muench's law) uses the previously cited ten fold discrepancy

In order to be realistic, the number of cases promised in any clinical trial must be divided by a factor of at least ten. as quoted in the Dictionary of Pharmaceutical Medicine by Gerhard Nahler and Annette Mollet.

There's lots of speculation at these sites as to why this overestimation of patient availability occurs, such as very narrow inclusion criteria, high refusal rates among patients, and insufficient resources to approach eligible patients.

A major study of recruitment noted that less than one third of trials held by the Medical Research Council and Health Technology Assessment Programmes finished on time with the required number of participants. 41% (47/114) of the studies had delays to the overall start of recruitment, 63% (77/122) had slower than expected early participant recruitment, and 38% (46/122) had slower than expected late participant recruitment.

Journal article: M K Campbell, C Snowdon, D Francis, D Elbourne, A M McDonald, R Knight, V Entwistle, J Garcia, I Roberts, et al. Recruitment to randomised trials: strategies for trial enrollment and participation study. The STEPS study Health Technol Assess. 2007;11(48):iii, ix-105. Abstract: "OBJECTIVES: To identify factors associated with good and poor recruitment to multicentre trials. DATA SOURCES: Part A: database of trials started in or after 1994 and were due to end before 2003 held by the Medical Research Council and Health Technology Assessment Programmes. Part B: interviews with people playing a wide range of roles within four trials that their funders identified as 'exemplars'. Part C: a large multicentre trial (the CRASH trial) of treatment for head injury. REVIEW METHODS: The study used a number of different perspectives ('multiple lenses'), and three components. Part A: an epidemiological review of a cohort of trials. Part B: case studies of trials that appeared to have particularly interesting lessons for recruitment. Part C: a single, in-depth case study to examine the feasibility of applying a business-orientated analytical framework as a reference model in future trials. RESULTS: In the 114 trials found in Part A, less than one-third recruited their original target within the time originally specified, and around one-third had extensions. Factors observed more often in trials that recruq()ited successfully were: having a dedicated trial manager, being a cancer or drug trial, and having interventions only available inside the trial. The most commonly reported strategies to improve recruitment were newsletters and mailshots, but it was not possible to assess whether they were causally linked to changes in recruitment. The analyses in Part B suggested that successful trials were those addressing clinically important questions at a timely point. The investigators were held in high esteem by the interviewees, and the trials were firmly grounded in existing clinical practices, so that the trial processes were not alien to clinical collaborators, and the results could be easily applicable to future practice. The interviewees considered that the needs of patients were well served by participation in the trials. Clinical collaborators particularly appreciated clear delineation of roles, which released them from much of the workload associated with trial participation. There was a strong feeling from interviewees that they were proud to be part of a successful team. This pride fed into further success. Good groundwork and excellent communications across many levels of complex trial structures were considered to be extremely important, including training components for learning about trial interventions and processes, and team building. All four trials had faced recruitment problems, and extra insights into the working of trials were afforded by strategies invoked to address them. The process of the case study in Part C was able to draw attention to a body of research and practice in a different discipline (academic business studies). It generated a reference model derived from a combination of business theory and work within CRASH. This enabled identification of weaker managerial components within CRASH, and initiatives to strengthen them. Although it is not clear, even within CRASH, whether the initiatives that follow from developing and applying the model will be effective in increasing recruitment or other aspects of the success of the trial, the reference model could provide a template, with potential for those managing other trials to use or adapt it, especially at foundation stages. The model derived from this project could also be used as a diagnostic tool if trials have difficulties and hence as a basis for deciding what type of remedial action to take. It may also be useful for auditing the progress of trials, such as during external review. CONCLUSIONS: While not producing sufficiently definitive results to make strong recommendations, the work here suggests that future trials should consider the different needs at different phases in the life of trials, and place greater emphasis on 'conduct' (the process of actually doing trials). This implies learning lessons from successful trialists and trial managers, with better training for issues relating to trial conduct. The complexity of large trials means that unanticipated difficulties are highly likely at some time in every trial. Part B suggested that successful trials were those flexible and robust enough to adapt to unexpected issues. Arguably, the trialists should also expect agility from funders within a proactive approach to monitoring ongoing trials. Further research into different recruitment patterns (including 'failures') may help to clarify whether the patterns seen in the 'exemplar' trials differ or are similar. The reference model from Part C needs to be further considered in other similar and different trials to assess its robustness. These and other strategies aimed at increasing recruitment and making trials more successful need to be formally evaluated for their effectiveness in a range of trials." [Accessed on October 24, 2011].

A similar group is sponsoring a new study, though it appears that the results of this study have not yet be published.

Journal article: Katrien Oude Rengerink, Brent C Opmeer, Sabine L M Logtenberg, Lotty Hooft, Kitty W M Bloemenkamp, Monique C Haak, Martijn A Oudijk, Marc E Spaanderman, Johannes J Duvekot, et al. IMproving PArticipation of patients in Clinical Trials--rationale and design of IMPACT BMC Med Res Methodol. 2010;10:85. Abstract: " BACKGROUND: One of the most commonly reported problems of randomised trials is that recruitment is usually slower than expected. Trials will cost more and take longer, thus delaying the use of the results in clinical practice, and incomplete samples imply decreased statistical power and usefulness of its results. We aim to identify barriers and facilitators for successful patient recruitment at the level of the patient, the doctor and the hospital organization as well as the organization and design of trials over a broad range of studies. METHODS/DESIGN: We will perform two cohort studies and a case-control study in The Netherlands. The first cohort study will report on a series of multicenter trials performed in a nationwide network of clinical trials in obstetrics and gynaecology. A questionnaire will be sent to all clinicians recruiting for these trials to identify determinants--aggregated at centre level--for the recruitment rate. In a case control-study nested in this cohort we will interview patients who refused or consented participation to identify factors associated with patients' consent or refusal. In a second cohort study, we will study trials that were prospectively registered in the Netherlands Trial Register. Using a questionnaire survey we will assess whether issues on hospital organization, trial organization, planning and trial design were associated with successful recruitment, i.e. 80% of the predefined number of patients recruited within the planned time. DISCUSSION: This study will provide insight in barriers and facilitators for successful patient recruitment in trials. The results will be used to provide recommendations and a checklist for individual trialists to identify potential pitfalls for recruitment and judge the feasibility prior to the start of the study. Identified barriers and motivators coupled to evidence-based interventions can improve recruitment of patients in clinical trials." [Accessed on October 24, 2011].

Several other resources tried to quantify the extent to which research projects failed to meet recruitment goals.

Successfully recruiting research participants is a key aspect for the success of a research project. However, this is also a difficult part. Therefore, the Quality Committee of the EMGO Institute started an investigation in 2006 to determine to what extent EMGO research projects were able to successfully recruit participants and what the possible determinants and outcomes of successful and unsuccessful recruitments were. Of the 61 research projects included, one third included less than 90% of the needed participants and 60% of the projects had to extend the inclusion period. As a result, at least 22% of the projects exceeded their research budget. In addition, researchers reported to have withdrawn part of the research protocol, to have skipped/unanswered research questions and to have shortened the follow-up period. As quoted at http://www.emgo.nl/kc/Practical/2%20Recruitment%20of%20research%20participants.html

In 2008, ZonMW published a research performed over the period 2001-2005 among 113 research projects.1 Of these projects, 49% included less than 80% of the needed participants. Quoted from the same source as above.

Almost 90% of trials are delayed, primarily because of patient recruitment problems. Public perception of the clinical trial process and, indeed, the pharmaceutical industry on the whole is largely very negative. As such, patient recruitment can be a lengthy process, and every extra day spent in clinical trials makes a huge dent in profits: pharmaceutical companies can lose out on anything from $600,000 to $8 million. As quoted at http://www.datamonitor.com/store/News/online_recruitment_is_streamlining_clinical_trials?productid=62E17CB0-F4C4-4774-A3A2-598600B85247

The one aspect of conducting clinical trials that sites usually spend the most time working on is Patient Recruitment, and yet, statistics show that despite their efforts, reaching enrollment goals per timeline is frustratingly elusive in many studies. The September 2011 volume of Applied Clinical Trials provides the current statistics: "Today, nearly 80% of clinical trials fail to meet enrollment timelines and up to 50% of research sites enroll one or no patients." (Kremidas, J. (2011). Recruitment roles. Applied Clinical Trials, 20 (9), p. 32.). As quoted at http://blog.gobalto.com/2011/10/04/the-site%E2%80%99s-side-patient-recruitment/

Nearly 90% of clinical trials are completed with significant delays. As quoted at http://appliedclinicaltrialsonline.findpharma.com/appliedclinicaltrials/article/articleDetail.jsp?id=82018

In 2001, over 85 percent of all completed medical research studies experienced recruitment delays, and 34 percent were delayed for more than one month. As quoted at http://biopharmarena.com/home/2007/06/21/clinical-trial-recruitment-role-of-ich-gcp-and-recruitment-strategies-training-of-clinical-sites-staff-in-successful-patient-recruitment-rates/ This article cites the following two references: 1. Gamache V. Minimizing Volunteer Dropout. CenterWatch Monthly. 2002;1:9-12. 2. Lightfoot GD, Getz KA, Hovde M, Sanford SM, Stepp PM, Vogel JR. ACRP's White Paper on Future Trends. Spring 1999.

Thompson Centerwatch pulbishes a regular review of the clinical trials industry and several sources have quoted from the 2007 guide.

Images taken from http://www.cddi.co/index.php/eng/content/download/285/2462/file/Patient%20Recruitment.pdf

The classic reference on this topic is

Journal article: J A Freiman, T C Chalmers, H Smith Jr, R R Kuebler. The importance of beta, the type II error and sample size in the design and interpretation of the randomized control trial. Survey of 71 "negative" trials N. Engl. J. Med. 1978;299(13):690-694. Abstract: "Seventy-one "negative" randomized control trials were re-examined to determine if the investigators had studied large enough samples to give a high probability (greater than 0.90) of detecting a 25 per cent and 50 per cent therapeutic improvement in the response. Sixty-seven of the trials had a greater than 10 per cent risk of missing a true 25 per cent therapeutic improvement, and with the same risk, 50 of the trials could have missed a 50 per cent improvement. Estimates of 90 per cent confidence intervals for the true improvement in each trial showed that in 57 of these "negative" trials, a potential 25 per cent improvement was possible, and 34 of the trials showed a potential 50 per cent improvement. Many of the therapies labeled as "no different from control" in trials using inadequate samples have not received a fair test. Concern for the probability of missing an important therapeutic improvement because of small sample sizes deserves more attention in the planning of clinical trials." [Accessed on October 24, 2011].

and over 96 papers cite this finding. Another classic reference is

Moher D, Dulberg CS, Wells GA. Statistical power, sample size, and their reporting in randomized controlled trials. JAMA1994;272:122-4. [Abstract/FREE Full text]

I'll certainly continue searching for more references along these lines and refine the results for incorporation into the draft grant I am preparing for patient accrual.

This page was written by Steve Simon and is licensed under the Creative Commons Attribution 3.0 United States License. Need more information? I have a page with general help resources. You can also browse for pages similar to this one at Accrual Problems.