In regards to the tooth fillings you would have to install the two types in the same mouth (person) for the study to be signficant. Otherwise mouth chemistry and other "confounders" might skew the results. You also have to have the same dentist do these fillings since there may be difference in the quality of the dentistry form one dentist to another.
Being a graduate in the domain of Public Administration where the curriculum mostly focused on theoretical discussions, I have started an extended academic journey in the field of 'Data'. Your video is so helpful. Helped me to easily understand the basic concept. Definitely, I will follow through. Thanks a lot! Best wishes.
Hi @DATAtab , may i know why survival is focusing on predicting probability rather than a single value ? As per the definition survival outcome is to predict duration ( time to event ). Thanks
if you want to know the mortality at 5 years, and persone A dies at 6 years and person B drops from the study at 3 years (so two years before your measuring point). And you make a table from this data: do you consider both data as censored? and what do you will in in the table? Should you fill in person A: No ( so it did not die at year 5) and for person B not fill anything ( so not Yes and not No)?
Mate, I found your question ambiguous. So, sorry, if this doesn't answer your Q. If question is to know how many will be alive in 5 years, then both the data points you mentioned would be part of analysis, since one passed away in 3 yrs (before the threshold) & the other after the threshold (6yrs). So, neither data point would be censored.
![Kaplan-Meier-Curve [Simply Explained]](http://i.ytimg.com/vi/L_ziqYhksG8/mqdefault.jpg)
![Kaplan-Meier-Curve [Simply Explained]](/img/tr.png)
![Cox Regression [Cox Proportional Hazards Survival Regression]](http://i.ytimg.com/vi/DpZoRqqDgXA/mqdefault.jpg)
If you like, please find our e-Book here: datatab.net/statistics-book 😎
In regards to the tooth fillings you would have to install the two types in the same mouth (person) for the study to be signficant. Otherwise mouth chemistry and other "confounders" might skew the results. You also have to have the same dentist do these fillings since there may be difference in the quality of the dentistry form one dentist to another.
Being a graduate in the domain of Public Administration where the curriculum mostly focused on theoretical discussions, I have started an extended academic journey in the field of 'Data'. Your video is so helpful. Helped me to easily understand the basic concept. Definitely, I will follow through. Thanks a lot! Best wishes.
Love the pace! Great examples and explanation, thank you a lot!
Wow! Perfect video on this topic. I didn't expect such material exist.
Glad you liked it!
I have exam about this tomorrow, you really save my day
Thank you :)
Happy to help!
May God bless you!!. Your videos and explaining are more than amazing. Actually they are beyound description.
Amazing video! Easy to understand. Thanks very much!!!
Glad it was helpful!
this method can be applied to retail to predict when a client will purchase again a determined product?
Very clear!! Thanks 🎉
Just wooow thank you
You're welcome 😊
Great vídeo, i would really apreciate if you make one computating in R.
Tanks
Thanks for the idea! But datatab finances me that I can create free videos : )
Great video!
Awesome explaination, thanks!
Great video 👏
Thanks : )
Really really thank you!
this is really great
Great Video! Thanks
👏👏👏
Glad you liked it!
Strictly speaking one does not "retain" the null hypothesis. One "fails to reject it".
perfect video!!!
great video...thank you
Glad you liked it!
So, a quick question is a survival analysis possible without censored data?
Hi @DATAtab , may i know why survival is focusing on predicting probability rather than a single value ? As per the definition survival outcome is to predict duration ( time to event ). Thanks
Coz every prediction is "likely" to be wrong. So, there is always some uncertainty. N where there is uncertainty, there is probability.
if you want to know the mortality at 5 years, and persone A dies at 6 years and person B drops from the study at 3 years (so two years before your measuring point).
And you make a table from this data: do you consider both data as censored? and what do you will in in the table? Should you fill in person A: No ( so it did not die at year 5) and for person B not fill anything ( so not Yes and not No)?
Mate, I found your question ambiguous. So, sorry, if this doesn't answer your Q.
If question is to know how many will be alive in 5 years, then both the data points you mentioned would be part of analysis, since one passed away in 3 yrs (before the threshold) & the other after the threshold (6yrs). So, neither data point would be censored.
can i get pdf files please
Hi, you can find the content here: datatab.net/tutorial/survival-analysis Regards Hannah
Jabardasti dekhni padti h😢
Really really thank you!