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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.

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: Randomization does not address contamination: participants as a consequence of their indication and even participation in the study are likely to relate to one another and influence participation and outcomes as a result. Even with blocking, the distribution of prognostic factors is heterogeneous between arms. Those receiving the higher risk treatment and who are at higher risk at baseline are more likely to "die off" sooner, leading to a healthy risk set at future event times (survivor bias). This can lead to crossing hazards which is inefficient for log-rank tests.

As an alternative to the (blinded) 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. Not every question is answered in analyses of randomized sets in RCTs, so 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.

RCT pros: Clusters of correlated participants - so called "contamination" - 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: Randomization does not completely address contamination: participants as a consequence of their indication and even participation in the study are likely to relate to one another and influence participation and outcomes as a result. Even with blocking, the distribution of prognostic factors is heterogeneous between arms. Those receiving the higher risk treatment and who are at higher risk at baseline are more likely to "die off" sooner, leading to a healthy risk set at future event times (survivor bias). This can lead to crossing hazards which is inefficient for log-rank tests.

##means to validate certain statistical tests

##basis for causal inference,

##facilitation of masking:

##method to balance comparisons groups.

means to validate certain statistical tests

basis for causal inference,

facilitation of masking:

method to balance comparisons groups.

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.

RCT-: A placebo may not be available. For instance, provenge is a monoclonal antibody therapy for high grade prostate 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.