"statistical analysis Statistical analysis should have analysis principles such as ITT or PP. In addition, imputation methods for dropout should be suggested." there is a revision. I watched this video to handle this revision but I could not understand how I can solve it. Could you please help me? what is the expectation of reviewers or editors related to this revision? what should I add to the manuscript? Thank you very much..
I am still confused how if we don't know the outcome of the dropped out subjects? For example, 100 subjects in control group vs 100 subjects in experimental group are followed for survival after 5 years. If there are 10 dropped out subjects in experimental group and (for the sake of simplicity) the rest of them is alive, how should we calculate the survival rate of experimental group? PP analysis: 90/90 = 100% ITT analysis: 90/100? And how about if I study their mortality rate? PP analysis: 0/90 = 0% ITT analysis: 10/100 = 10% (we count the dropped out subjects as "failure") or 0/100 (because we only include the dropped out as they were randomized, without any outcome)? Thank you
But what's the difference between per protocol & completers-only analysis? Sounds like same, as in both cases who adhered to initial randomly allocated group & present till end of the study are been considered.! 4:12imputation methods
Wie finde ich Ergebnisse, die ein Teil oder auch das ganze Wort wie bspw. "leiter" enthalten? Ich möchte sowohl Projektleiter XY, als auch Leiter XY finden. Wenn ich nur leiter oder *leiter eingebe, erhalte ich nur die Ergebnisse, die das Wort Leiter enthalten. Projektleiter wird nicht angezeigt.
As-treated or per-protocol analysis is what we call scientific data, you know, something that actually happened. Imputation means making stuff up. Plausible is not data. ITT determines what it says. You intention. D
“The advantages of randomization…” are presumably for the experimenter who wants to follow his intentions. Unfortunately all kinds of disappointing outcomes have good intentions. But, if the patient died before they took the pill they are not in the experiment. In fact, intention to treat requires that you report that death is an outcome of the trial which may be a test of dental carries prevention. Proponents of using intention to treat will never answer objections like that. They might have to say that that was not the outcome. To determine the outcome, they will examine the teeth of the corpse.
3:15 "leads to an equal distribution of known and unknown confounders ...". EQUAL distribution is hard to achieve, however, randomization is the best action to be taken to get as close as possible to that. I should admit that, at the end of the day, this presentation is very informative and useful. Thanks.
Thank you for the video, can you help me how to prove that is unbiased in this question? Question: Compare the average height of employees in Google with the average height in the United States, do you think it is an unbiased estimate? If not, how to prove it is not matched?
Schön erklärt :) Obwohl ich den Unterschied zwischen Risiko und Chance so rein vom Verständnis (ohne Formel-Erklärung) immernoch nicht verstanden habe =/
Here is a question about group allocation: what if instead of using blocks in order to obtain equal groups, we just repeated simple randomization until we got equal groups for the first time? Would that produce biased results?
