Why is pharmaceutical supported, sponsored clinical trial research today pure junk & CANNOT be trusted? they use 1) non-important outcomes & not patient-important outcomes 2)secondary outcomes as

by Paul Alexander

primary to declare benefit e.g. remdesivir 3) 'inferior' comparators 4)sub-group estimates that are spurious 5)small sample sizes 6) small event number 7)multiple endpoints 8)no external validity

9)unblinded analysis and they deliberately unblind the subjects essentially ending assessment of efficacy or harms e.g. COVID vaccine trials by Pfizer & Moderna were essentially ended when unblinded and placebo got vaccine

10)they stop ‘early for benefit’ which is red-flag high risk of biased estimates of effect, often over-estimate the treatment effect (a ‘random’ high)

11)often heavily pharma industry sponsored so conflicts of interest that are not declared or managed

12)use unblinded subjects in the primary analysis

13)incorrect analysis of cross-over trials

14)incorrect analysis of cluster randomized trials

15)only publishing of ‘positive’ trials

16) run meta-analyses with elevated publication bias e.g. exclusion of negative studies

17) omission in the reporting (of even collection of surveillance data) of adverse and serious adverse events

18) methodology is flawed and sub-optimal e.g. subverted and biased randomization (breached and failure to consider optimal randomization e.g. simple vs blocking vs stratified vs minimization etc.), breached allocation concealment (improper hiding of the generated sequence and being able to anticipate future assignments to treatment or control trial arms), flawed blinding e.g. no blinding of outcome adjudicators, data analysts etc.

19) selective outcome reporting

20) report relative risk reduction (RRR) and not the needed absolute risk reduction (ARR) or the number needed to treat (NNT)

21) fail to explain and explicitly, the disposition of subjects lots e.g. lost due to attrition, what were the reasons? failing to explain the disposition of missing outcome data is a fatal flaw

22) failure to conduct and report intention to treat analysis (ITT); ‘as treated’ and ‘per protocol’ analyses etc.

23) failure to model ‘best case, worst case’ scenarios

24) failure to properly match e.g. propensity score matching

25) failure to statistically adjust for confounding variables and failure to adjust for the optimal variables

26) failure to document any deviations from baseline

27)failure to properly assess and document baseline imbalances

This list is not exhaustive but I wanted you to keep in mind the issues that plague industry sponsored trials (and clinical research in general) and why you cannot and must not today and a while now, lend any credibility to the study findings. Most research published today is ow low quality, biased, and corrupted by the researchers with flawed very sub-optimal methods, where we cannot have any confidence and certainty in the estimates of effect.