Analysis of clinical trials
 | This articleneeds additional citations forverification.(March 2010) |
Clinical trialsaremedical research studiesconducted on human subjects.[1]The human subjects are assigned to one or more interventions, and the investigators evaluate the effects of those interventions.[1][2]The progress and results of clinical trials are analyzed statistically.[3][4]
Analysis factors
[edit]Intention to treat
[edit]Randomized clinical trialsanalyzed by theintention-to-treat(ITT) approach provide fair comparisons among the treatment groups because it avoids thebiasassociated with the non-random loss of the participants.[5][6]The basic ITT principle is that participants in the trials should be analysed in the groups to which they were randomized, regardless of whether they received oradheredto the allocated intervention.[5]However, medical investigators often have difficulties in accepting ITT analysis because of clinical trial issues like missing data or adherence toprotocol.[6]
Per protocol
[edit]This analysis can be restricted to only the participants who fulfill the protocol in terms of the eligibility, adherence to the intervention, and outcome assessment. This analysis is known as an "on-treatment" or "per protocol" analysis. A per-protocol analysis represents a "best-case scenario" to reveal the effect of the drug being studied. However, by restricting the analysis to a selected patient population, it does not show all effects of the new drug. Further, adherence to treatment may be affected by other factors that influence the outcome. Accordingly, per-protocol effects are at risk of bias, whereas the intent-to-treat estimate is not.[7]
Handling missing data
[edit]Last observation carried forward
[edit]One method of handling missing data is simply toimpute,or fill in, values based on existing data. A standard method to do this is the Last-Observation-Carried-Forward (LOCF) method.
The LOCF method allows for the analysis of the data. However, recent research shows that this method gives a biased estimate of the treatment effect andunderestimatesthe variability of the estimated result.[8][9]As an example, assume that there are 8 weekly assessments after the baseline observation. If a patient drops out of the study after the third week, then this value is "carried forward" and assumed to be his or her score for the 5 missing data points. The assumption is that the patients improve gradually from the start of the study until the end, so that carrying forward an intermediate value is a conservative estimate of how well the person would have done had he or she remained in the study. The advantages to the LOCF approach are that:
- It minimises the number of the subjects who are eliminated from the analysis, and
- It allows the analysis to examine the trends over time, rather than focusing simply on the endpoint.
However, theNational Academy of Sciences,in an advisory report to theFood and Drug Administrationon missing data in clinical trials, recommended against the uncritical use of methods like LOCF, stating that "Single imputation methods like last observation carried forward and baseline observation carried forward should not be used as the primary approach to the treatment of missing data unless the assumptions that underlie them are scientifically justified."[10]
Multiple imputation methods
[edit]The National Academy of Sciences advisory panel instead recommended methods that provide validtype I errorrates under explicitly stated assumptions taking missing data status into account, and the use of multiple imputation methods based on all the data available in the model. It recommended more widespread use ofBootstrapandGeneralized estimating equationmethods whenever the assumptions underlying them, such asMissing at RandomforGEEmethods, can be justified. It advised collecting auxiliary data believed to be associated with dropouts to provide more robust and reliable models, collecting information about reason for drop-out; and, if possible, following up on drop-outs and obtaining efficacy outcome data. Finally, it recommended sensitivity analyses as part of clinical trial reporting to assess thesensitivityof the results to the assumptions about the missing data mechanism.[10]
While the methods recommended by the National Academy of Science report are more recently developed, morerobust,and will work under a wider variety of conditions than single-imputation methods like LOCF, no known method for handling missing data is valid under all conditions. As the 1998International Conference on HarmonizationE9 Guidance on Statisticial Principles for Clinical Trials noted, "Unfortunately, no universally applicable methods of handling missing values can be recommended."[11]Expert statistical and medical judgment must select the method most appropriate to the particularly trial conditions of the available imperfect techniques, depending on the particular trial's goals, endpoints, statistical methods, and context.
References
[edit]- ^ab"Clinical trials".MedlinePlus, US National Library of Medicine. 18 May 2018.Retrieved28 June2022.
- ^"NIH's Definition of a Clinical Trial | grants.nih.gov".grants.nih.gov.Retrieved2022-06-27.
- ^Amrhein, Valentin; Greenland, Sander; McShane, Blakeley B. (1 December 2019)."Statistical significance gives bias a free pass".European Journal of Clinical Investigation.49(12): e13176.doi:10.1111/eci.13176.ISSN0014-2972.PMID31610012.S2CID204702258.
- ^Critical Thinking in Clinical Research: Applied Theory and Practice Using Case Studies.Oxford University Press. 2018.doi:10.1093/med/9780199324491.001.0001/med-9780199324491(inactive 2024-09-13).ISBN978-0-19-027280-7.
{{cite book}}:CS1 maint: DOI inactive as of September 2024 (link) - ^abMontori, Victor M.; Guyatt, Gordon H. (2001-11-11)."Intention-to-treat principle".Canadian Medical Association Journal.165(10): 1339–1341.PMC81628.PMID11760981.
- ^abAlshurafa, Mohamad; Briel, Matthias; Akl, Elie A.; et al. (2012)."Inconsistent Definitions for Intention-To-Treat in Relation to Missing Outcome Data: Systematic Review of the Methods Literature".PLOS ONE.7(11): e49163.Bibcode:2012PLoSO...749163A.doi:10.1371/journal.pone.0049163.PMC3499557.PMID23166608.
- ^Sussman, Jeremy B.; Hayward, Rodney A. (2010-05-04)."An IV for the RCT: using instrumental variables to adjust for treatment contamination in randomised controlled trials".BMJ (Clinical Research Ed.).340:c2073.doi:10.1136/bmj.c2073.ISSN1756-1833.PMC3230230.PMID20442226.
- ^Salim, Agus; MacKinnon, Andrew; Christensen, Helen; Griffiths, Kathleen (2008). "Comparison of data analysis strategies for intent-to-treat analysis in pre-test–post-test designs with substantial dropout rates".Psychiatry Research.160(3): 335–345.doi:10.1016/j.psychres.2007.08.005.PMID18718673.S2CID23997894.
- ^Molnar, F. J.; Hutton, B.; Fergusson, D. (2008)."Does analysis using" last observation carried forward "introduce bias in dementia research?".Canadian Medical Association Journal.179(8): 751–753.doi:10.1503/cmaj.080820.PMC2553855.PMID18838445.
- ^abNational Research Council; Division of Behavioral and Social Sciences and Education; Committee on National Statistics; Panel on Handling Missing Data in Clinical Trials (2010).The Prevention and Treatment of Missing Data in Clinical Trials.pp. 110–112.doi:10.17226/12955.hdl:1942/14310.ISBN978-0-309-15814-5.PMC3771340.PMID24983040.
- ^International Conference on Harmonization, Guidance for Industry E9, Statistical Principles for Clinical Trials,1998
Further reading
[edit]- AR Waladkhani. (2008). Conducting clinical trials. A theoretical and practical guide.ISBN978-3-940934-00-0