The papers from Koch, Abel, and Urbach do not reject randomization summarily as a means to achieve 1-4, rather they claim it is neither sufficient nor necessary to achieve those criteria. The take-home message is a) An RCT must not necessarily be done to answer every scientific question and b) Any published RCT may not be gold-standard evidence of efficacy.
As an alternative to the RCT, the open label trial (OLTs) is an obvious choice since the presumptive purpose of said trial is to evaluate a novel therapy not readily accessible by the patient population. For the analysis of RCT or OLT, similar principals to analyzing observational studies apply: control of causal factors, block randomization, and so on improve the efficiency and reduce the bias of such studies.
##means to validate certain statistical tests
(are randomized participants "independent" and "identically distributed" per assumptions of t-test, log-rank test, and so on?)
RCT pros: Clusters of correlated participants are likely to be "broken up" in study randomization so that, without contamination, the dependence structure is similar within treatment assignment and methods for independent data estimate the correct standard errors anyway. Similarly, prognostic factors are likely to be balanced between study groups at the time of randomization.
RCT cons: Unless grouped designs are used, no possible way of assigning treatment to correlated participants. Contamination is possible, in fact fairly prevalent and ignored and undermined as a source of dependence among study participants within-and-between arms. Participants will not be identically distributed: Heterogeneity in prognostic factors, even when balanced, lead to attenuating effect estimates over longer study durations, since the higher risk treatment is more likely to "kill off" higher risk participants over a shorter time frame, this can lead to crossing hazards which is inefficient for log-rank tests.
##basis for causal inference,
is the estimated effect the same as a "rewind-time" instance of assigning all treated participants to control, and subtracting those differences
RCT+: assignment of treatment is completely at random, no confounding by indication, blinding (when possible) may reduce risk of differential treatment discontinuation.
RCT-: Differential and non-differential follow-up due to attrition will contribute to imbalanced cluster size. Non-blinded studies introduce risk of differential treatment discontinuation.
##facilitation of masking:
when treatment is randomly assigned, is it possible to administer both treatments in a way that participants do not know what arm they have been randomized to?
RCT+: When an appropriate placebo is available, it can be done. It should be noted that the appropriate use of "placebo" is such that a participant receives standard of care (SOC). For instance, suppose an IND is administered by injection and SOC is a pill. Control participants receive SOC in an (unlabeled) pill form and a saline injection, while active arm participants receive the IND injection and an identical sugar pill.
RCT-: A placebo may not be available. For instance, provenge is a monoclonal antibody therapy for high grade urothelial cancer. Administration of this treatment requires an invasive procedure called leukapheresis. Leukapheresis is too invasive and costly to ethically be performed in the control arm, so provenge-assigned participants will know they are receiving the IND.
##method to balance comparisons groups.
is the expected distribution of "covariates" in the analysis sample equal in distribution between IND-treated and control participants?
RCT+: at time of randomization a 50/50 sample balance of treatment and control groups is noted, as well as an expected probabilistic balance of possible prognostic factors. Re-randomization is possible for batch-entry designs although they are far less prevalent these days.
RCT-: efficient design still requires control of prognostic factors, the optimal design in presence of a treatment effect is not 50/50 balance for most analyses, attrition and unequal cluster size due to loss-to-follow-up commonly means that balanced design is not guaranteed. Randomization does not guarantee balance of prognostic factors.