Skip to main content Accessibility help
×
Hostname: page-component-8448b6f56d-gtxcr Total loading time: 0 Render date: 2024-04-24T19:21:12.394Z Has data issue: false hasContentIssue false

7 - Research in cancer

Published online by Cambridge University Press:  05 November 2015

Robert Hills
Affiliation:
Cardiff University, Cardiff, UK
Louise Hanna
Affiliation:
Velindre Cancer Centre, Velindre Hospital, Cardiff
Tom Crosby
Affiliation:
Velindre Cancer Centre, Velindre Hospital, Cardiff
Fergus Macbeth
Affiliation:
Velindre Cancer Centre, Velindre Hospital, Cardiff
Get access

Summary

Introduction

It is the responsibility of clinicians to provide the best possible care for their patients. However, this simple statement masks a much more complex issue. How does one know precisely what the best care is for a particular patient? In particular, how does one balance the likely benefits and risks for a particular course of treatment? A new drug may appear promising, but can one really be sure that it represents a real improvement on current practice? Generally speaking, unless the action of a particular treatment is both immediate and breathtaking (such as insulin for diabetic coma), we cannot be absolutely certain which treatment is best for which people. Historical comparisons, or other database-dependent methods, can prove misleading. What is required is a method that will provide reliable, convincing evidence that can be used to inform future practice.

Fortunately, there is such a tool: the randomised controlled trial (RCT). At its heart are two principles. First, through randomisation, any differences between patients receiving one treatment and those receiving another are purely down to chance; therefore, if a sufficiently large difference is detected, then it must be due to the only factor that is systematically different between the two groups, namely the treatment. Second, with large numbers of patients, it becomes easier to detect smaller treatment effects and to conclude that any differences are not the result of chance. This, the statistical aspect of RCTs, is effectively a formalisation of common sense. If one tosses a coin 10 times and gets 6 heads and 4 tails, it is not out of the ordinary; but if one saw 6000 heads and 4000 tails from 10,000 tosses, then one would be concerned that the coin may be biased. The proportion of heads is the same, but larger numbers give stronger evidence of an unfair coin.

This chapter will concentrate on obtaining reliable evidence on the efficacy (whether the treatment works under ideal conditions, usually in a highly selected population) and effectiveness (whether a treatment will be beneficial in a real-life setting) of treatments for cancer. In particular, it will look at the factors that constitute a successful clinical trial, how the ideas can be extended to look at the weight of evidence provided by a number of clinical trials (meta-analysis) and how additional laboratory studies can help assess more modern targeted therapies.

Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2015

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Altman, D. G. (1991). Statistical Methods for Medical Research, 2nd edn. London: Chapman and Hall.Google Scholar
Altman, D. G. (1994). The scandal of poor medical research. Br. Med. J., 308, 283–284.CrossRefGoogle ScholarPubMed
Altman, D. and Bland, J. M. (1995). Absence of evidence is not evidence of absence. Br. Med. J., 311, 485.CrossRefGoogle Scholar
Altman, D. and Bland, J. M. (1999). How to randomise. Br. Med. J., 319, 703–704.CrossRefGoogle ScholarPubMed
Altman, D. and Bland, J. M. (2005). Treatment allocation by minimisation. Br. Med. J., 330, 843.CrossRefGoogle ScholarPubMed
Altman, D. G., Machin, D., Bryant, T. N., et al. (2000). Statistics with Confidence. London: BMJ Books.Google Scholar
Assmann, S. F., Pocock, S., Enos, L. E., et al. (2000). Subgroup analysis and other (mis)uses of baseline data in clinical trials. Lancet, 355, 1064–1069.CrossRefGoogle ScholarPubMed
Burman, W. J., Reves, R. R., Cohn, D. L., et al. (2001). Breaking the camel's back: multicenter clinical trials and local institutional review boards. Ann. Intern. Med., 134, 152–157.CrossRefGoogle ScholarPubMed
Burnett, A. K., Milligan, D. W., Prentice, A. G., et al. (2005). Modification or dose or treatment duration has no impact on outcome of AML in older patients: preliminary results of the UKNCRI AML14 trial. Blood, 106(162A), Abstr. 543.Google Scholar
Burnett, A. K., Russell, N. H., Hills, R. K., et al. (2013). Optimization of chemotherapy for younger patients with acute myeloid leukaemia: results of the medical research council AML 15 trial. J. Clin. Oncol. 31, 3360–3368.CrossRefGoogle Scholar
Buyse, M., George, S. L., Evans, S., et al. (1999). The role of biostatistics in the prevention, detection and treatment of fraud in clinical trials. Statist. Med., 18, 3435–3451.3.0.CO;2-O>CrossRefGoogle ScholarPubMed
Cascinelli, N. (1994). Adjuvant interferon in melanoma – reply. Lancet, 343, 1499 (letter).CrossRefGoogle Scholar
Cascinelli, N., Bufalino, R., Morabito, A., et al. (1994). Results of adjuvant interferon study in WHO melanoma programme. Lancet, 343, 913–914.CrossRefGoogle ScholarPubMed
Collins, R., Peto, R., Gray, R., et al. (1996). Large-scale evidence: trials and overviews. In Oxford Textbook of Medicine, Vol. 1, ed. Weatherall, D., Ledingham, J. G. G. and Warrell, D. A., 3rd edn. Oxford: Oxford University Press.Google Scholar
Creutzig, U., Ritter, J., Zimmermann, M., et al. (2001). Idarubicin improves blast cell clearance during induction therapy in children with AML: results of study AML-BFM 93. Leukemia, 15, 348–354.Google ScholarPubMed
Cullen, M., Steven, N., Billingham, L., et al. (2005). Antibacterial prophylaxis after chemotherapy for solid tumors and lymphomas. N. Engl. J. Med., 353, 988–998.CrossRefGoogle ScholarPubMed
Daniels, J., Gray, R., Hills, R. K, et al. (2009). Laparoscopic uterosacral nerve ablation for alleviating chronic pelvic pain: a randomized controlled trial. J. Am. Med. Ass., 302, 955–961.CrossRefGoogle ScholarPubMed
Dickersin, K., Chan, S., Chalmers, T. C., et al. (1987). Publication bias and clinical trials. Contr Clin Trials, 8, 343–353.CrossRefGoogle ScholarPubMed
Duley, L. and Farrell, B. (2002). Clinical Trials. London: BMJ Books.Google Scholar
Early Breast Cancer Trialists Collaborative Group. (1990). Treatment of Early Breast Cancer: Volume 1, Worldwide Evidence 1985–90. Oxford: Oxford University Press.
Edwards, P., Roberts, I., Clarke, M., et al. (2002). Increasing response rates to postal questionnaires: systematic review. Br. Med. J., 324, 1183.CrossRefGoogle ScholarPubMed
Ellenberg, S., Fleming, T. and DeMets, D. (2002). Data Monitoring Committees in Clinical Trials: A Practical Perspective. Chichester: Wiley.CrossRefGoogle Scholar
Enserink, M. (1996). Clinical trials: fraud and ethics charges hit stroke drug trial. Science, 274, 2004–2005.CrossRefGoogle ScholarPubMed
Green, S. B. and Byar, D. P. (1984). Using observational data from registries to compare treatments – the fallacy of omnimetrics. Stat. Med., 3, 361–370.CrossRefGoogle ScholarPubMed
Guyatt, G. H., Sackett, D. L., Sinclair, J. C., et al. (1995). Users' guides to the medical literature IX: A method for grading healthcare recommendations. J. Am. Med. Ass., 274, 1800–1804.CrossRefGoogle Scholar
Hills, R. K. and Burnett, A K. (2011). Applicability of a “Pick a Winner” trial design to acute myeloid leukemia. Blood, 118(9), 2389–2394.CrossRefGoogle Scholar
Hills, R., Gray, R. and Wheatley, K. (2009). Balancing treatment allocations by clinician or center in randomized trials allows unacceptable levels of treatment prediction. J. Evid. Based Med., 2(3), 196–204.CrossRefGoogle ScholarPubMed
Machin, D., Campbell, M. J., Fayers, P. M., et al. (1997). Sample Size Tables for Clinical Research, 2nd edn. Oxford: Blackwell.Google Scholar
Mehta, C. R. and Pocock, S. J. (2011). Adaptive increase in sample size when interim results are promising: a practical guide with examples. Statist. Med., 30, 3267–3284.Google ScholarPubMed
Miller, F. G. and Kaptchuk, T. J. (2004). Sham procedures and the ethics of clinical trials. J. R. Soc. Med., 97, 576–578.CrossRefGoogle ScholarPubMed
Moher, D., Cook, D. J., Jadad, A. R., et al. (1999). Assessing the quality of reports of randomised trials: implications for the conduct of meta-analyses. Health Technol. Assess., 3 12), i–iv, 1–98.Google ScholarPubMed
Moher, D., Schulz, K. F. and Altman, D. G. for the CONSORT group. (2001). The CONSORT statement: revised recommendations for improving the quality of reports of parallel-group randomised trials. Lancet, 357, 1191–1194.CrossRefGoogle Scholar
Pocock, S. J. (1996). Clinical Trials: A Practical Approach. London: John Wiley and Sons.Google Scholar
QUASAR Collaborative Group. (2000). Comparison of fluorouracil with additional levamisole, higher-dose folinic acid, or both, as adjuvant chemotherapy for colorectal cancer: a randomised trial. Lancet, 355, 1588–1596.
Royston, P., Parmar, M. K. and Qian, W. (2003). Novel designs for multi-arm clinical trials with survival outcomes with an application in ovarian cancer. Stat. Med., 22, 2239–2256.CrossRefGoogle ScholarPubMed
Schulz, K. F. (1995). Subverting randomization in controlled trials. J. Am. Med. Ass., 274, 1456–1458.CrossRefGoogle ScholarPubMed
Specht, L., Gray, R. G., Clarke, M. J., et al. for the International Hodgkins Disease Collaborative Group. (1998). Influence of more extensive radiotherapy and adjuvant chemotherapy on long-term outcome of early-stage Hodgkin's disease: a meta-analysis of 23 randomised trials involving 3,888 patients. J. Clin. Oncol., 16, 830–843.CrossRefGoogle Scholar
Storer, B. E. (1989). Design and analysis of Phase I clinical trials. Biometrics, 45, 925–937.CrossRefGoogle ScholarPubMed
Storer, B. and DeMets, D. (1987). Current Phase I/II designs: are they adequate?J. Clin. Res. Drug Devel., 1, 121–130.Google Scholar
Swinscow, T. D. V. and Campbell, M. J. (2002). Statistics at Square One. London: BMJ Books.Google Scholar
Wheatley, K. (2002.) SAB: a promising new treatment for AML in the elderly?Br. J. Haematol., 118, 432–433.CrossRefGoogle ScholarPubMed
Wheatley, K. and Hills, R. K. (2001). Inappropriate reporting and interpretation of subgroups in the AML-BFM 93 study. Leukemia, 15, 1803–1804.Google ScholarPubMed
Yin, G., Li, Y. and Ji, Y. (2006). Bayesian dose-finding in phase I/II clinical trials using toxicity and efficacy odds ratios. Biometrics, 62(3), 777–784.CrossRefGoogle ScholarPubMed
Young, C. and Horton, R. (2005). Putting clinical trials into context. Lancet, 366, 107–108.Google ScholarPubMed
Yusuf, S., Collins, R. and Peto, R. (1984). Why do we need some large, simple randomized trials?Stat. Med., 3, 409–422.CrossRefGoogle ScholarPubMed
There are many excellent books and articles on different aspects of clinical trials, notably those by Altman (1991), Assmann et al. (2000), Collins et al. (1996), Duley and Farrell (2002) and Yusuf et al. (1984). Additionally, the following references are valuable resources.
Chalmers, I. (1993). The Cochrane Collaboration: preparing, maintaining and disseminating systematic reviews of the effects of health care. Ann. N. Y. Acad. Sci., 703, 156–163.CrossRefGoogle ScholarPubMed
The DAMOCLES Study Group. (2005). A proposed charter for clinical trial data monitoring committees: helping them do their job well. Lancet, 365, 711–722.
Greenhalgh, T. (2006). How to Read a Paper, 3rd edn. Oxford: Blackwell.Google Scholar
Peto, R. (1987). Why do we need systematic overviews of randomized trials?Stat. Med., 6, 233–244.CrossRefGoogle ScholarPubMed
Peto, R., Pike, M. C., Armitage, P., et al. (1976). Design and analysis of randomized clinical trials requiring prolonged observation of each patient. Part I: introduction and design. Br. J. Cancer, 34, 585–612.CrossRefGoogle ScholarPubMed
Peto, R., Pike, M. C., Armitage, P., et al. (1977). Design and analysis of randomized clinical trials requiring prolonged observation of each patient. Part II: analysis and examples. Br. J. Cancer, 35, 1–39.CrossRefGoogle ScholarPubMed

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×