![]() ![]() “I came to not believe in the possibility of infinitely many repetitions of identical experiments, as required to be envisioned in the frequentist paradigm.” The following sentence is an excerpt from this piece: Statistical Thinking - My Journey from Frequentist to Bayesian Statistics On a somewhat related note, how exactly is the concept of repetition defined in frequentist statistics? Given that “random sampling” never actually occurs in the design and conduct of an RCT, how should we define/interpret p values in the RCT context?Ģ. Could this misunderstanding be rooted in the fact that 1) p values are used widely in the interpretation of RCT results, and 2) the concept of “random sampling” seems to be built into the definition of a p-value? Some non-clinical people/non-statisticians seem to be under the mistaken impression that random sampling occurs in the design/conduct of RCTs. Many patients with colon cancer live in parts of the world where clinical trials are not conducted. ![]() There is no “master list” of all patients in the country (or world) with colon cancer Įven if there were such a master list, we wouldn’t be able to just pluck patients randomly from the list and force them to enter a clinical trial of a new therapy But this isn’t remotely how clinical research works, for the following reasons: Using the clinical example above, random sampling would require that doctors randomly “pluck” a sample of patients with colon cancer from a master list of ALL patients with colon cancer and then randomly allocate them to one treatment or another. Two uses of a die Recent exchanges on Twitter revealed some confusion as regards the difference between randomisation, which is regularly used in the conduct of clinical trials and random sampling, which is not. Rather, what occurs is “randomization”- a very different process: Random, yes, but sampling or isation? In turn, only those who agree to participate in the trial will be randomized, either to the new therapy (whose intrinsic efficacy is being tested) or to placebo.Īs discussed in this blog, “random sampling” does not occur in the conduct of human experiments. Only if they meet the inclusion criteria for the trial will we next offer them the chance to be randomized. For example, if we want to study the effect of a new chemotherapy drug in patients with colon cancer, we will interview patients with colon cancer as they happen to present for medical care (of their own volition). I’m confused…We are never actually using a “random sample” of patients when we conduct an RCT, yet p values are found throughout trial reports. Otherwise, there are no grounds for using these inferential tools and they become essentially uninterpretable…” that it is or can be treated as a random sample. “When p-values or confidence intervals are displayed, a plausible argument should be given that the studied sample meets the underlying probabilistic assumptions, i.e. The following article discusses inferential problems that arise when random sampling has not occurred: Hirschauer_2020_p_values.pdf The P-value to have the usual meaning assumes that the data model is correct.” It is the probability of observing a statistic as or more extreme than the observed one if H0 is true, i.e., if the population from which the sample was randomly chosen had the characteristics posited in the null hypothesis. “A P-value is something that can be computed without speaking of errors. This is the definition of a p-value provided in section 5.4 of the BBR text (my emphasis in bold): What exactly does a p value mean in the context of an RCT, given that “random sampling” from an underlying population of interest has not occurred? I’ll understand if there’s no way to explain these concepts in layman’s terms.ġ. Unfortunately, I keep getting hung up on them and can’t find clear answers in my reading. As discussed in the Quick Tutorial, this option is especially helpful for doing random assignment by blocks.The questions below are very basic and have probably been asked thousands of times by statistically-naive MDs- apologies in advance. This layout allows you to know that 23 is the third number in the sequence, and 18 is the ninth number over both sets. With Place Markers Across, your results will look something like this: Notice that with this option, the Place Markers begin again at p1 in each set. This layout allows you to know instantly that the number 23 is the third number in Set #1, whereas the number 18 is the fourth number in Set #2. With Place Markers Within, your results will look something like this: This is the default layout Research Randomizer uses. With Place Markers Off, your results will look something like this: Place Markers let you know where in the sequence a particular random number falls (by marking it with a small number immediately to the left).
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