Is the Deductive Falsification Approach a Better Basis for Clinical Trial Appraisal?

Author(s): Steffen Mickenautsch*.

Journal Name: Reviews on Recent Clinical Trials

Volume 14 , Issue 3 , 2019

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Abstract:

Background: Inductive reasoning relies on an infinite regress without sufficient factual basis and verification is at any time vulnerable to single contrary observation. Thus, appraisal based on inductive verification, as applied in current clinical trial appraisal scales, checklists or grading systems, cannot prove or justify trial validity.

Discussion: Trial appraisal based on deductive falsification can identify invalid trials and give evidence for the recommendation to exclude these from clinical decision-making. Such appraisal remains agnostic towards corroborated trials that pass all appraisal criteria. The results of corroborated trials cannot be considered more robust than falsified trials since nothing within a particular set of complied trial criteria can give certainty for trial compliance with any other appraisal criterion in future. A corroborated trial may or may not reflect therapeutic truth and may thus be the basis for clinical guidance, pending results of any future trial re-appraisal.

Conclusion: Trial grading following appraisal based on deductive falsification should be binary (0 = Invalid or 1 = Unclear) and single component scores should be multiplied. Appraisal criteria for the judgment of trial characteristics require a clear rationale, quantification of such rationale and empirical evidence concerning the effect of trial characteristics on trial results.

Keywords: Clinical decision-making, deductive reasoning, falsification, inductive reasoning, trial appraisal, single contrary observation.

[1]
Jadad AR, Moore RA, Carroll D, et al. Assessing the quality of reports of randomized clinical trials: Is blinding necessary? Control Clin Trials 1996; 17: 1-12.
[2]
Higgins JP, Altman DG, Gøtzsche PC, et al. The cochrane collaboration’s tool for assessing risk of bias in randomised trials. BMJ 2011; 343: d5928.
[3]
Guyatt G, Oxman AD, Akl EA, et al. GRADE guidelines: Introduction-GRADE evidence profiles and summary of findings tables. J Clin Epidemiol 2011; 64: 383-94.
[4]
Ruse C. Oxford student’s dictionary of current English. 2nd ed. Budapest: Oxford University Press 1990; p. 326.
[5]
Popper K. Conjectures and refutations. London,and New York: Routlege 2002; p. 47.
[6]
Popper K. The logic of scientific discovery. London,and New York: Routlege 2002; pp. 66-, 91, 281.
[7]
Hume D. A treatise on human nature Reprint: Selby-Bigge LA Ed Oxford: Clarendon Press 1896; 86-94.
[8]
Popper K. The two fundamental problems of the theory of knowledge.London and New York: Routlege. 2012; pp. 3-10, 35-86, 147-187, . 188-237.
[9]
Ruse C. Oxford student’s dictionary of current English. 2nd ed. Budapest: Oxford University Press 1990; p. 163.
[10]
Kahneman D, Tversky A. Subjective probability: A judgment of representativeness. In: Kahneman D, Slovic P, Tversky A Eds. Judgment under uncertainty: Heuristics and biases. 1st ed. Cambridge, London, New York, New Rochelle, Melbourne, Sydney: Cambridge University Press 1982; pp. 32-47.
[11]
Berger VW, Alperson SY. A general framework for the evaluation of clinical trial quality. Rev Recent Clin Trials 2009; 4: 79-88.
[12]
Berger VW. Selection bias and covariate imbalances in randomised clinical trials. Wiley 2005; pp. 1-218.
[13]
Mickenautsch S, Fu B, Gudehithlu S, et al. Accuracy of the Berger-Exner test for detecting third-order selection bias in randomised controlled trials: A simulation-based investigation. BMC Med Res Methodol 2014; 14: 114.
[14]
Odgaard-Jensen J, Vist GE, Timmer A, et al. Randomisation to protect against selection bias in healthcare trials. Cochrane Database Syst Rev 2011; 13: MR000012.
[15]
Berkman ND, Santaguida PL, Viswanathan M, et al. The empirical evidence of bias in trials measuring treatment differences. methods research report. (Prepared by the RTI-UNC Evidence-based Practice Center under Contract No. 290-2007-10056-I.) AHRQ Publication No. 14-EHC050-EF. Rockville, MD: Agency for Healthcare Research and Quality; September 2014. Available from: www.effectivehealthcare.ahrq.gov/reports/final.cfm
[16]
Page MJ, Higgins JPT, Clayton G, et al. Empirical Evidence of Study Design Biases in Randomized Trials: Systematic Review of Meta-Epidemiological Studies. PLoS One 2016; 11: e0159267.
[17]
Tierney JF, Stewart LA. Investigating patient exclusion bias in meta-analysis. Int J Epidemiol 2005; 34: 79-87.


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Article Details

VOLUME: 14
ISSUE: 3
Year: 2019
Page: [224 - 228]
Pages: 5
DOI: 10.2174/1574887114666190313170400
Price: $65

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