COX REGRESSION and HAZARD RATIOS - easily explained with an example!

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In this video, we will discuss the main concepts behind Cox regression for survival time analysis - easily explained! We will go through hazard ratios, coefficients, p-values and confidence intervals.
I will also give you simple and practical guidelines on how to interpret the results from Cox regression, with an example!
And as always, you can find the full explanation at biostatsquid.com
Hope you like it!
biostatsquid.com/easy-cox-regression-for-survival-analysis/
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Other interesting resources:
Survival analysis review: www.ncbi.nlm.nih.gov/pmc/articles/PMC1065034/
Survival curves and Cox regression: www.ibm.com/docs/en/spss-statistics/saas?topic=statistics-cox-regression

Опубликовано:

26 ноя 2023

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Комментарии : 20

@GAsantewaa 4 месяца назад

Thank you for this simple and short explanation!!!

@user-lj7bo1hd1p 6 месяцев назад

Great explanation! Thanks !

@maxacevedo1725 4 месяца назад

Really helpful, thank you!

@tareknahle9578 Месяц назад

Thank you for this amazing video!

@amandamirandamartins2014 2 месяца назад

this video helped me so much!!!!!

@jlee509 7 месяцев назад

hidden gem of stats

@rianiw2023 5 месяцев назад

thank you for the video! I would like to know what is the best time to collect data for cox regression analysis? in the beginning of treatment, or endpoint (when the event/hazard occurs)?

@juandediostenahorrillo9622 5 месяцев назад

How to deal with a situation where the value of the covariate changes after the treatment?. For example, a person is smoker at the initial period but he quits after some time.

@bodorakotonirina6035 4 месяца назад

thank you for your explanations, in the last example, the confidence interval include the number 1 but the p value is significant, which parameter should we consider to definitely say that the result is significant. Thank you very much

@biostatsquid 4 месяца назад

Hi! Thank you so much for your question, it's a really good one. It's best to follow confidence intervals - they give you a better precision of the estimate (in this case we are estimating the HR). There's a very complete comment with additional links here: www.researchgate.net/post/When_a_confidence_interval_crosses_the_null_hypothesis_1_but_P_value_is_0001_Is_it_significant

@user-nd7ur8jr1m 5 месяцев назад

nice explanation!!! but you might want to balance the volume

@bemtheman1100 3 дня назад

I am a bit confused by the hazard ratio. It seems like its group A is HR times as like to die as group B. So in the smoking example where smoking had a hazard ratio of 7.4. I took non_smokers as 0 being group A and smokers as 1 being group B. Would this mean that non-smokers were 7.4 times as likely to die compared to smokers?

@biostatsquid 2 дня назад

Thanks for your question! The positive HR for smoking means that there is an increase in the hazard for the smoking group compared to the control (non-smoker group) at any given time. Is this what you were asking? As a sidenote: Hazard ratios are a bit different to relative risk - the HR accounts for also the timing of the event (death), whereas the relative risk only checks if it happened or not. An HR = 1 indicates no change in the hazard (probability of death given that you have survived up to a specific time), if HR > 1 it's increased, and if HR < 1 it's decreased. But this does not translate directly to "7.4 times more likely to die", because it's a ratio, not a probability. To get the probability you can use this equation P = HR/(1 + HR). So for example, a hazard ratio of 2 means there's a 67% chance of the smoking group dying first, and a hazard ratio of 3 corresponds to a 75% chance of dying first. A HR of 6.7 means there's an 87% chance a smokers will die before a non-smoker at any given time. Does this make sense? This paper is really useful in case you want to read more about it: www.ncbi.nlm.nih.gov/pmc/articles/PMC478551/

@bemtheman1100 2 дня назад

@@biostatsquid Ahhh I think I was not thinking of things in terms of a group vs control, but was thinking of it in terms of the first group and second group which doesnt make as much sense. Lmao also it being called a ratio should make it obvious to me that it is a ratio and not a probability. I appreciate the clarification, this makes a ton more sense now. Time to finish running this cox-prop model on my GBM survival data. Fingers crossed this paper gets out by Oct T-T

@k4mf0164 4 месяца назад

Hi. Just checking the data in your video, and drug A's HR is e^(-1.8) = 0.1652, not 0.152. I'm guessing a typo with omitted 6? Otherwise, nice explanation, thank you for the videos!

@biostatsquid 4 месяца назад

Hi, thank you for you for your comment! Yes, just a typo, great that you noticed:)

@k4mf0164 4 месяца назад

Also the Age HR is e^(0.2) = 1.221 (not 1.247) and the 95% CI for Age HR on the slide [0.60 - 0.90] doesn't include the given HR, it should be around [1.034; 1.443]?

@biostatsquid 4 месяца назад

Correct! Well spotted:) and definitely - sorry for the confusion! The confidence interval should include the hazard ratio as it is a way of expressing the uncertainty around the point estimate of the hazard ratio. Thanks for your comment, I'm sure more people have the same question:)

@k4mf0164 4 месяца назад

And the CI 95% for Gender HR is [0.349; 0.474] and does not include 1.0. There are just too many errors in the data shown in the video.

@biostatsquid 4 месяца назад

Yep exactly! Thanks! Hope that despite the errors I still made my point across and the idea behind Cox regression was understandable.