The process of censoring yields data w/ only partial information. The most common example of censoring is *right censoring* in survival analysis, where the time until the event occurred is only known to be longer than some duration because the event had not occurred when the study ended.

learn more… | top users | synonyms

4
votes
0answers
17 views

KDE for censored data

I have a sample of observations where about $30\%$ of the observations are right-censored. I want to fit a kernel density estimator to this sample but I have not found a standard method to do so. Is ...
4
votes
1answer
25 views

How do I compute an average when one of the measurements couldn't be measured?

In my biology class we were doing a lab on respiration rates of peas. We used a respirometer with a small pipet tube (markings up to 1 mL) to measure the volume of gas; by immersing in water, the gas ...
0
votes
0answers
27 views

Need the formula and to understand the number needed to treat

I am reading the article http://www.ncbi.nlm.nih.gov/pubmed/16214598. I am having trouble with the calculation formula (to test it / reproduce it). The relevant information is in page 1282. For those ...
1
vote
0answers
12 views

Treatment of waiting time data with repeated observations for a finite time

A call center is open for 9 hours every day. My data are the times of occurrence of calls, divided in days. Some days are busier than the others and some days no calls at all occur. I have to estimate ...
0
votes
0answers
16 views

Right censored data, abundant in zeros for regression analysis

I am looking at conditioning to stimuli and there in the time taken to perform a certain task. The IV for this data is Conditioning periods ranging from 1-34 periods and the DV is the time taken for ...
0
votes
1answer
17 views

Dealing with Right Censored Data

I'm working with a dataset with n of roughly 300,000 and am trying to build a model to predict the time-to-event from multiple explanatory variables, including past times from the same subjects. ...
1
vote
0answers
45 views

Expected value of a censored poisson

I have the following density (it's a censored model): $$f(y) = \begin{cases} e^{-\lambda}(1+\lambda), & \text{if }y^*=0,1 \\ \frac{\lambda^{y^*}e^{-\lambda}}{y^*!}, & \text{if }y^*=2,3.... ...
0
votes
0answers
6 views

Uneven (right) censoring where the time of truncation is constant

I've collected data comparing the relative success of two different groups of participants on finding the answer to a difficult question. The problem is that not all the participants in the control ...
2
votes
0answers
35 views

Right-censored independent variable in Cox/logistic regression

I have a right-censored continuous independent variable that I want to include in a Cox regression. The variable is a physiologic test which is capped at a certain time, say 120 seconds, due to safety ...
0
votes
0answers
55 views

Censored Data Log Likelihood

Suppose we have a n random samples ($X_1,..., X_n$) from a negative exponential distribution. If lets say we have these n random samples are censored at t, such that ($X_1, ..., X_m$) are observed and ...
0
votes
0answers
15 views

competitive risk in propensity score matched cox regression

I am actually performing a survival analyse using propensity score matched cox regression. My outcome is calculated from biological data contained in a database. I censured each patient at the time ...
0
votes
1answer
26 views

censoring as a result of survey screening question

I have a survey data where respondents were asked if they or an immediate family member had previously used a lawyer and, for those who said yes, they were then asked if they had ever sued. I want to ...
0
votes
0answers
16 views

Censoring for events that don't occur

I have data on when the time between two events A and B. However, I am assuming if the time between them is more than 5 hours then the two events are unrelated and so we assume that on these occasions ...
0
votes
1answer
16 views

Counting total number of cells producing antibody in a 96-well plate using Poisson

I make cells produce an antibody. I then add cells to plates that contain 96 wells (random distribution). I know a cell is in a particular well because the well will be positive for the antibody. ...
1
vote
0answers
19 views

How to find the expected value of a random variable when it is observed only in a range?

I want to estimate an average livespan of a user account which is a time-difference in days between the account open and account closed. I observe the livespan of accounts that were closed before the ...
1
vote
2answers
35 views

How are censorings before the first event dealt with in survival analysis

If you have a dataset, sorted into ascending order by survival time (minimum of censoring and event time), and this dataset contains at least one censoring before the first event (so that the start of ...
2
votes
2answers
45 views

Inference for a distribution with different number of samples due to censoring

I have the following problem in case someone has an idea about how to solve this. Assume three experiments that refer to the same population for a random variable $X$. In the first experiment, I ...
0
votes
1answer
24 views

What is the justification? [duplicate]

I understand 'when' we right censore a survival time. But why do we do so? And how does it affect analysis in survival context. I would appreciate it anyone could recommend some reference.
0
votes
0answers
13 views

Does RMSE accurately represent performance for censored data?

I am working with some remotely sensed data collected at irregular intervals and want to use these observations to evaluate performance of a model. For each site location I have about 4-9 ground truth ...
2
votes
1answer
95 views

Probability density problem

Suppose that the random variable $X$ is uniformly distributed on the interval $[0,1]$ (i.e. $X \sim U(0,1)$) and suppose that $$Z=min(2,2X^2+1)$$ (a) Explain why $Z$ does not have a ...
0
votes
1answer
49 views

Handling survival data where people join at various stages

My question is about survival analysis. According to this censoring is a condition in which the value of a measurement or observation is only partially known. For example, suppose a study is ...
0
votes
0answers
25 views

Data censorship - simple reconstruction of missing data?

I have data from disease symptoms assessment on individuals (over time). Pattern of the disease is always a decreasing sigmoid, the disease starts with a 100% sound tissues, but ends up with a 0% ...
1
vote
1answer
17 views

How to average days until completion when some items haven't completed the cycle?

I am attempting to calculate the average number of days it takes for an order to go through our process - from receipt of order to delivery. When calculating the average days, how should I handle ...
0
votes
0answers
11 views

Handling a censored factor score?

I have an 11 item measure for which I am computing factor scores. It is a censored (floor effect) measure. EFA/CFA analysis indicates I should remove 3 of those items. Whether or not I remove those ...
2
votes
2answers
63 views

Should one use a censored survival model when an event is only observed at death?

A colleague of mine is trying to estimate how neutron radiation exposure changes cancer incidence rates (in mice). He has autopsy data that reports whether a cancer was observed at the time of death, ...
0
votes
0answers
24 views

WinBUGS - sample from the posterior of exponential distributed survival times

as introduction to WinBUGS, I want to simulate the posterior of the Gamma-Exp-Model. Because the posterior is known, I can varify the result. But the true posterior is always shifted to the right. I ...
1
vote
0answers
45 views

Handling Informative Censoring in Survival Analysis

In a survival study with informative censoring (for example, studying the effects of cigarettes on mortality and smokers are more likely to be Lost to Follow Up). This causes the censored data to be ...
2
votes
0answers
31 views

Kernel density estimation with censored data

I have several univariate data sets that I would like to fit with a Kernel density estimator. However, some of the data sets contain left, interval, and right censored observations. How can I ...
1
vote
0answers
91 views

How to estimate mean and variance of censored normal?

Supposing I have data which I know is normally distributed, but because the recording process is right censored, how do I estimate the parameters of the distribution?
2
votes
1answer
39 views

Censoring “Death” in Time-To-Recovery Analysis

I am performing time-to-recovery analysis comparing 2 groups. In both groups, a few subjects died from the disease under consideration (instead of recovering). Is it appropriate to consider the deaths ...
0
votes
0answers
103 views

How to simulate informative censoring in a Cox PH model?

I wish to simulate events from a Cox PH model where the censoring is informative, and to compare parameter estimator quality with estimates obtained from data generated by a Cox PH model with ...
1
vote
0answers
32 views

simulate censored data (right censored) using R statistics [duplicate]

I want to generate right censored data, but I want to be able to pass in a parameter to a function to dictate that a certain percentage of the data will be censored. I have found this R-package: ...
0
votes
2answers
60 views

Survival regression variance estimates

I would like help understanding why a survival regression with no censored data-points does not give the same variance estimates as a linear model (see code below). I think it must be something to do ...
1
vote
0answers
36 views

Heteroscedastic censored regression

I am dealing with a heteroscedastic censored dataset. I tried to use the survival analysis package in R to estimate a linear model for it. So before doing that, I conducted a simulation study, where I ...
0
votes
0answers
14 views

Some details in Cragg's Limited Dependent Variable article

1) Is there anybody who can explain me the precise meaning of the indices and hence, dimensions of the involved matrices of Cragg's equation (18) in his Limited Dependent Variables article from 1971? ...
0
votes
1answer
23 views

Cutoffs to consider for survival tree

In an tree based algorithm a criterion is measured at certain cutoffs for the variable. This cutoffs are the candidate split points for that variable. How does one come up with candidate split points ...
1
vote
1answer
34 views

Left-censoring , $Y_i=\text{max}(T_i,U_i)$

In the book , Statistical Models and Methods for Lifetime Data , in Left-censoring , it is written that Can only observe $Y_i=\text{max}(T_i,U_i)$ . Where , ...
1
vote
0answers
72 views

Censored logistic regression

I have the following problem: We have data with a 0-1 outcome which can occur precisely once. It can occur at any time within a certain time period (say 3 years). For this data set, for some ...
1
vote
0answers
48 views

Question on Tobit Regression

Does Tobit regression assume that the dependent variable is continuous above the lower bound? I am trying to model mortality (ie. "dead" or "living" but not the time) based on a set of independent ...
1
vote
1answer
144 views

Method for fitting censored data in R

I have 320 data points - each has a redshift and a turnover-frequency, and I want to fit a correlation between them (a linear fit). However, 120 of the turnover-frequency values are upper limits. As ...
1
vote
1answer
49 views

How do you compare two “survival times” when there is no censoring per se?

I've gone through the 70+ questions when using "survival no censoring" as my search criteria, but I can't seem to find an answer to this very simple situation. I have patients' length of stay in a ...
2
votes
1answer
149 views

Python Astronomy Censored Data in Lifelines

I am trying to find a correlation between a given data set containing redshifts and turnover frequencies (I have a list of 320 galaxies, and the redshift and turnover frequency (a turnover frequency ...
2
votes
1answer
65 views

Survival estimation when death/censoring is probabilistic

I am trying to estimate survival function, but in case where each event is censoring with probability $p_i$. (That is, I am never sure if the event is right-censoring or death, but I can estimate the ...
1
vote
0answers
41 views

The most general definition of the Likelihood function for continuous data (including truncation and censoring)

How would you rigorously define the likelihood function for censored/truncated observations? Even in most lifetime/reliability literature (where these types of observations are frequently encountered) ...
18
votes
2answers
4k views

What is the difference between censoring and truncation?

In the book Statistical Models and Methods for Lifetime Data , it is written : Censoring: When an observation is incomplete due to some random cause. Truncation: When the incomplete nature of ...
5
votes
3answers
2k views

Right censoring and Left censoring

Wikipedia gives the following definitions: Right censoring: a data point is above a certain value but it is unknown by how much. Left censoring: a data point is below a certain value but it is ...
0
votes
0answers
19 views

Imposing follow-up time cutoffs on censored data for cox regression

When fitting cox regression models, is it valid to impose a cutoff for censored samples to have been followed for at least a given stretch of time for inclusion in the analysis?
1
vote
1answer
118 views

What model fit / predictive accuracy measure can be used to cross validate a Cox PH model with censored data?

How would you go about validating a Cox PH model with censored data? I am trying to run a Cox PH model on a dataset with observations that failed, and observations that are censored. Normally, I use ...
0
votes
0answers
17 views

Correlation of scores, when most scores are the highest

Let's say we have 10,000 observations of two kinds of scores for each item, i.e. each observation has two scores reported. The scores are from 0 to 100. Within each kind of score 9,900 observations ...
5
votes
2answers
537 views

How should I model a continuous dependent variable in the $[0, \infty]$ range?

I have a dependent variable that can range from 0 to infinity, with 0s actually being correct observations. I understand censoring and Tobit models only apply when the actual value of $Y$ is partially ...