# 722 - Cole Lectures

### 24 terms by NadyaMaria

#### Study  only

Flashcards Flashcards

Scatter Scatter

Scatter Scatter

## Create a new folder

### Left truncation

incomplete data due to follow up starting after origin
--> study truncates individuals with events between 0 and W

### Immortal person-time (0-W)

Interval in which:

-participant is not at risk for the event
-participant is not at risk for any censoring event --> because censored observations are assumed to have the event after censoring and are effectively events

### Difference between censoring and truncation

Censoring (RIGHT), you know the people, but you don't know what their values are

Truncation (LEFT),

1.

### Conditions for Selection Bias

1. Need drop-out (under <5% = no worries, over 20% forget about any kind of correction!)
2. associated with exposure
3. associated with outcome

### Confounding

The presence of common cause

### Selection bias

Conditioning on common causes

### Calculating hk Calculating "hazard" [Is there an actual difference between these two things?]

hk = # events / (#at risk * delta-k)

hazard = slope of S(t) / S(t)

Note: hazard is also the negative differential of the log S(t)

### Calculating S(t) at k

1 - (# events/ # at risk)

### Calculating H-km(t) --> Cumulative hazard (=Kaplan-Meier estimate)

H-km(t) = -log(S(t))

Note: Cumulative Hazard is not bounded by 100%

Note: log of the cumulative hazards need to be parallel --> PHA

Note: you also use cumulative hazards to decide about model fit

### Selection bias: Define in-selection

Bias (potentially) creating when adding late entries

### Selection bias: Define out-selection

Bias (potentially) created when you have drop-outs

### Cox Model - Deviance Residuals used for?

To test whether you've gotten the functional form right

-1 per subject
-are like standard residuals (mean = 0, SD = 1, anything outside ±3 is trouble)
--> you can't calculate the deviance, but you can calculate the deviance residual

### Cox Model - Delta-beta residuals

To test outliers, see whether you have any coding problems

-one per regressor, per subject
-see how much each coefficient would change if you deleted that subject

### Differences between Cox and Poisson

Poisson: has explicit saturated model (can calculate deviance)

Cox: no explicit saturated model (can only calculate deviance residuals)

### How to compare nested models?

LRT = -2(Log La - Log Lb)
w/ chi-square distribution with df equal to difference in parameters

AIC

### Poisson and NBR: Difference

Both calculate incidence density (i.e. rate)

-NBR inludes an error term, Poisson has no error term

### Assumptions of Poisson

-PHA
-Mean = Variance
(if variance > mean then overdispersed, if variance < mean, then underdispersed)

### Assumption of Negative Binomial

model error term --> estimate variance

### How do I test for confounding in Incidence Density Ratio (IDR)

ln(CoIDR) --> large change = strong confounder, small change --> not a great confounder

### How to handle time-varying variables?

1. Counting process (in-out)
2.

### Ecologic Fallacy

Inferences about individuals are based on average data for the group to which they belong
--> average effect says nothing about distribution among individuals
-->

### What's the bias called when you make a time-varying variable time-fixed?

misclassification bias!

Example:

## Press Ctrl-0 to reset your zoom

### Please upgrade Flash or install Chrometo use Voice Recording.

For more help, see our troubleshooting page.

Create Set