Course materials | Advanced Epidemiologic Methods

L11: Emulating target trials

Thu, 07 Apr 2022 00:00:00 +0000

Readings

García-Albéniz X, Hernán MA, Logan RW, Price M, Armstrong K, Hsu J. Continuation of annual screening mammography and breast cancer mortality in women older than 70 years. Annals of internal medicine. 2020 Mar 17;172(6):381-9.

Videos

Recordings of lectures are available on the EPID 722 Sakai site.

Exercise

There is no exercise this week. Instead, please work on your final project to design a target trial. Come to the lab session prepared to do a dry run of your final presentation and get feedback from your peers.

Questions

Please use the form below to submit your questions about this week’s reading.

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L10: Per protocol analyses

Thu, 31 Mar 2022 00:00:00 +0000

Readings

Lu H, Cole SR, Westreich D, Hudgens MG, Adimora AA, Althoff KN, Silverberg MJ, Buchacz K, Li J, Edwards JK, Rebeiro PF. Clinical Effectiveness of Integrase Strand Transfer Inhibitor–Based Antiretroviral Regimens Among Adults With Human Immunodeficiency Virus: A Collaboration of Cohort Studies in the United States and Canada. Clinical Infectious Diseases. 2021 Oct 1;73(7):e1408-14.

Notes

We are often interested in estimating the effects of treatments or interventions that are maintained over time, but exposures to these treatments and interventions often vary over time for participants in our datasets. Reasons may include

treatment changes or discontinuation
nonadherence
loss to follow-up
mobility and migration

among others.

This week, we will focus on a simple scenario in which we want to compare risk of death under continuous treatment and under no treatment, but some people in the treatment arm discontinue treatment. In the exercise, we will compare risk under 2 treatments, where people in both arms discontinue treatment.

Videos

Recordings of lectures are available on the EPID 722 Sakai site.

Exercise

Exercise 10 may be found on the course Teams site.

Optional readings

In the reading and during lecture, we discussed using IPW to estimate “per protocol” effects in the presence of a simple time-varying exposure. The reading below illustrates use of g computation to achieve the same goals.

Naimi AI, Perkins NJ, Sjaarda LA, Mumford SL, Platt RW, Silver RM, Schisterman EF. The Effect of Preconception-Initiated Low-Dose Aspirin on Human Chorionic Gonadotropin–Detected Pregnancy, Pregnancy Loss, and Live Birth: Per Protocol Analysis of a Randomized Trial. Annals of Internal Medicine. 2021 Jan 26.

The reading also introduced the idea of emulating a randomized trial using observational data. We will discuss this more next week. In the meantime, you can read more about this idea here:

Hernán MA, Robins JM. Using big data to emulate a target trial when a randomized trial is not available. American journal of epidemiology. 2016 Apr 15;183(8):758-64.

Questions

Please use the form below to submit your questions about this week’s reading.

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L8: Intro to part 2

Tue, 22 Mar 2022 00:00:00 +0000

Readings

No reading for this class session.

Notes

Today we discuss what is coming up in part 2 of the course. The goal of today’s lecture is to explain what we will discuss (and why) in the second part of the course, give you an overview of time varying exposures, and discuss why framing our questions becomes more challenging when factors vary over time.

Videos

Recordings of lectures are available on the EPID 722 Sakai site.

Exam info

Tue, 08 Mar 2022 00:00:00 +0000

The exam will be held in person on March 8th. Additional information will be provided here and in class prior to the exam.

Designing a time-to-event study

Thu, 24 Feb 2022 00:00:00 +0000

Readings

Hernán MA, Sauer BC, Hernández-Díaz S, Platt R, Shrier I. Specifying a target trial prevents immortal time bias and other self-inflicted injuries in observational analyses. Journal of clinical epidemiology. 2016 Nov 1;79:70-5.

Notes and slides

So far, we have discussed the nuts and bolts of analysis of time-to-event data. But in practice, using these tools effectively requires knowledge of study design. This week, we cover some highlights of study design, including overviews of the “target trial” framework and what to do about late entries.

Videos

Recordings of lectures are available on the EPID 722 Sakai site.

Exercise

This week’s exercise is available on the EPID 722 Teams site. Please note that you will need to download the dataset from the data folder. The exercise questions can be found under /Class Materials/Exercises/Exercise 7/Exercise 7.docx. Sample SAS and R code can be found in the same folder.

Optional Reading

This reading presents a nice example of a target trial.

García-Albéniz X, Hsu J, Hernán MA. The value of explicitly emulating a target trial when using real world evidence: an application to colorectal cancer screening. European journal of epidemiology. 2017 Jun;32(6):495-500.

Questions

Please use the form below to submit your questions about this week’s reading.

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Competing events

Thu, 17 Feb 2022 00:00:00 +0000

Readings

Andersen PK, Geskus RB, De Witte T, Putter H. Competing risks in epidemiology: possibilities and pitfalls. International journal of epidemiology. 2012 Jun 1;41(3):861-70.

Slides

The slides for today’s class are available in the class Teams/Sharepoint folder.

Videos

Recordings of lectures are available on the EPID 722 Sakai site.

Exercise

This week’s exercise is available on the EPID 722 Teams site. Please note that you will need to download the dataset from the data folder. The exercise questions can be found under /Class Materials/Exercises/Exercise 6/Exercise 6.docx. Sample SAS and R code can be found in the same folder.

Optional readings

Below are some additional readings about competing events

Cole SR, Lau B, Eron JJ, Brookhart MA, Kitahata MM, Martin JN, Mathews WC, Mugavero MJ. Estimation of the standardized risk difference and ratio in a competing risks framework: application to injection drug use and progression to AIDS after initiation of antiretroviral therapy. American journal of epidemiology. 2015 Feb 15;181(4):238-45.
Edwards JK, Hester LL, Gokhale M, Lesko CR. Methodologic issues when estimating risks in pharmacoepidemiology. Current epidemiology reports. 2016 Dec;3(4):285-96.

Questions

Please use the form below to submit your questions about this week’s reading.

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Generalizability

Thu, 10 Feb 2022 00:00:00 +0000

Readings

Stuart EA, Rhodes A. Generalizing treatment effect estimates from sample to population: A case study in the difficulties of finding sufficient data. Evaluation review. 2017 Aug;41(4):357-88.

Slides

The slides for today’s class are available in the class Teams/Sharepoint folder.

Videos

Recordings of lectures are available on the EPID 722 Sakai site.

Exercise

This week’s exercise is available on the EPID 722 Teams site. Please note that you will need to download the dataset from the data folder. The exercise questions can be found under /Class Materials/Exercises/Exercise 5/Exercise 5.docx. Sample SAS and R code can be found in the same folder.

Questions

Please use the form below to submit your questions about this week’s reading.

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Comparisons of risk

Thu, 03 Feb 2022 00:00:00 +0000

Readings

Sato T, Matsuyama Y. Marginal structural models as a tool for standardization. Epidemiology 2003;14(6):680-6.

Notes

The notes for today’s class are available in the class Teams/Sharepoint folder.

Videos

Recordings of lectures are available on the EPID 722 Sakai site.

Exercise

This week’s exercise is available on the EPID 722 Teams site. Please note that you will need to download the dataset from the data folder. The exercise questions can be found under /Class Materials/Exercises/Exercise 4/Exercise 4.docx. Sample SAS and R code can be found in the same folder.

Questions

Please use the form below to submit your questions about this week’s reading.

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Cox Models

Thu, 27 Jan 2022 00:00:00 +0000

Readings

Bradburn MJ, Clark TG, Love SB, Altman DG. Survival analysis part II: multivariate data analysis–an introduction to concepts and methods. British journal of cancer. 2003 Aug;89(3):431-6

Slides

Download PDF of all slides

Download PDF of Notes

Videos

Recordings of lectures are available on the EPID 722 Sakai site.

Exercise

This week’s exercise is available on the EPID 722 Teams site. Please note that you will need to download the dataset data.dat. The exercise questions can be found under /Class Materials/Exercises/Exercise 3/Exercise 3.docx. Sample SAS and R code can be found in the same folder.

Optional Readings

Hernán MA. The hazards of hazard ratios. Epidemiology (Cambridge, Mass.). 2010;21(1):13–5.

Questions

Please use the form below to submit your questions about this week’s reading.

NOTE: Please submit your question by Wednesday at 5pm.

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Nonparametric estimators of risk

Thu, 20 Jan 2022 00:00:00 +0000

Readings

Clark TG, Bradburn MJ, Love SB, Altman DG. Survival analysis part I: basic concepts and first analyses. British journal of cancer. 2003 Jul;89(2):232-8.

Notes

The notes for today’s class are available via Teams.

Videos

Recordings of lectures are available on the EPID 722 Sakai site.

Notes

As a self check, reflect on how what you learned helps answer the following questions:

What is immortal person time?
What is risk? What is survival?
What is the hazard?
What is a nonparametric estimator and how does it differ from a parametric estimator?
How does the Kaplan-Meier account for late entry and censoring?
What are the pros and cons of allowing late entry?
How can we assess the possible effects of informative drop out?
What is a line diagram and how is it drawn?
Fill in a table like Table 2 in the Cole and Hudgens paper in AIDS given sample data
When is censoring informative?
What do we assume about censoring when using the Kaplan-Meier?
What is the difference between a parameter and an estimator?

Optional Readings:

Cole SR, Hudgens MG. Survival analysis in infectious disease research: describing events in time. AIDS (London, England). 2010 Oct 23;24(16):2423.

Exercise

This week’s exercise is available on the EPID 722 Teams site. The exercise questions can be found under /Class Materials/Exercises/Exercise 2/Exercise 2.docx. Sample SAS and R code can be found in the same folder.

Questions

Please use the form below to submit your questions about this week’s reading.

NOTE: Please submit your question by Wednesday at 5pm.

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L1: Time in epidemiology: Questions and parameters

Thu, 13 Jan 2022 00:00:00 +0000

Readings

Samet JM. Concepts of time in clinical research. Annals of internal medicine. 2000 Jan 4;132(1):37-44.

Notes

Download PDF of Notes

Below is the code to produce line diagrams for each scenario described at the end of the notes. Please check out the course notes and attempt the scenarios outlined there by hand (using pencil and paper) before looking at the answers posted here.

Say have a (small) cohort study of 5 soldiers selected at random from all people enlisting in the armed services between 2004 and 2014 and followed for mortality up to 10 years.

id	age	enlist	last_info	vital_status
1	24	2008-07-01	2022-01-13	alive
2	18	2008-07-01	2011-01-01	dead
3	21	2011-01-01	2016-06-15	dead
4	35	2011-01-01	2022-01-13	alive
5	19	2013-07-01	2022-01-13	alive

Practice drawing 3 line diagrams:

one with age (starting at age 18) on the \(x\) axis
one with calendar time (starting 1 Jan 2008) on the \(x\) axis
one with time since enlistment on the \(x\) axis.

id <- c(1:5)
age <- c(24, 18, 21, 35, 19)
enlist <- c(as.Date("2008-07-01"),
            as.Date("2008-07-01"),
            as.Date("2011-01-01"),
            as.Date("2011-01-01"),
            as.Date("2013-07-01"))
last_info <- c(Sys.Date(),
               as.Date("2011-01-01"),
               as.Date("2016-06-15"),
               Sys.Date(),
               Sys.Date())
vital_status <- c("alive", "dead", "dead", "alive", "alive")
dat <- tibble(id, age, enlist, last_info, vital_status)

#line diagram 1
dat$t1 <- (last_info - enlist)/365.25 + age
dat$w1 <- age
dat <- dat %>%
  mutate(delta1 = ifelse(vital_status == "dead" & t1<=(age+10), 1, 0))
#library(grid)
library(ggthemr)
ggthemr('flat')
line <- ggplot() +
 # geom_segment(aes(x = 0, y = newid, xend = yearw, yend = newid), lty = "dotted") +
  geom_segment(data = dat %>% filter(delta1 == 0),
               aes(x = w1, y = id, xend = t1, yend = id),
               arrow = arrow(length=unit(0.2, "cm"))) +
  geom_segment(data = dat %>% filter(delta1 == 1),
               aes(x = w1, y = id, xend = t1, yend = id)) +
  ylab("ID") +
  scale_x_continuous(name = "Age" ) +
   geom_point(data = dat %>% filter(delta1 == 1),
              aes(x = t1, y = id),
              color = "red", size = 1.5)

line

#line diagram 2
dat$t2 <- (last_info)
dat$w2 <- enlist
dat <- dat %>%
  mutate(delta2 = ifelse(vital_status == "dead" & t2<=(enlist+10*365.25),
                         1, 0))
line <- ggplot() +
  geom_segment(data = dat %>% filter(delta2 == 0),
               aes(x = w2, y = id, xend = t2, yend = id),
               arrow = arrow(length=unit(0.2, "cm"))) +
  geom_segment(data = dat %>% filter(delta2 == 1),
               aes(x = w2, y = id, xend = t2, yend = id)) +
  ylab("ID") +
  scale_x_date(name = "Calendar Date" ) +
   geom_point(data = dat %>% filter(delta2 == 1),
              aes(x = t2, y = id),
              color = "red", size = 1.5)

line

#line diagram 3
dat$t3 <- (last_info - enlist)/365.25
dat$w3 <- 0
dat <- dat %>%
  mutate(delta3 = ifelse(vital_status == "dead" & t3<=(10), 1, 0))

library(ggthemr)
ggthemr('flat')
line <- ggplot() +
  geom_segment(data = dat %>% filter(delta3 == 0),
               aes(x = w3, y = id, xend = t3, yend = id),
               arrow = arrow(length=unit(0.2, "cm"))) +
  geom_segment(data = dat %>% filter(delta3 == 1),
               aes(x = w3, y = id, xend = t3, yend = id)) +
  ylab("ID") +
  scale_x_continuous(name = "Years since enrollment") +
   geom_point(data = dat %>% filter(delta3 == 1),
              aes(x = t3, y = id),
              color = "red", size = 1.5)

line

Videos

Recordings of lectures are available on the EPID 722 Sakai site.

Optional readings

Further reading about time in epidemiologic research:

Cole SR, Hudgens MG, Brookhart MA, Westreich D. Risk Am J Epidemiol. 2015 Feb 15;181(4):246-50

Other resources

Parts of this week’s lecture are based on a talk I gave at SER in 2020.

Exercise

This week’s exercise, along with SAS and R code to get you started, is posted on the Microsoft Teams site for this course.

Questions

Please use the form below to submit your questions about this week’s reading.

Questions are due by Wednesdays at 5pm.

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L0: Introduction

Tue, 11 Jan 2022 00:00:00 +0000

Readings

The syllabus page for this class

Slides

Below are links to the two sets of slides used this week: an introduction to the course and Lecture 1. You can download all slides by clinking the download link below.

Download PDF of all slides

Notes

Notation

Everyone in this course has the preparation required to master the material presented. There is no advanced math in this class, but you may see notation you have not seen before. Get in the habit of translating all notation you come across in this course into English language sentences and ask questions about notation that is unfamiliar.

Some examples:

\(\sum_{i=1}^nX_i\): Sum \(X_i\) over all units (up to unit \(n\)) indexed by \(i\)

\(\prod_{k=1}^m d_k\): multiply \(d\) over all units (up to unit \(m\)) indexed by \(k\)

What you should know after this week

Each week, we will post a list of skills you should have developed (e.g., drawing line diagrams, defining risk, coding a Cox model). Check the Notes section each week to be sure you are keeping up.

Videos

Recordings of lectures are available on the EPID 722 Sakai site.

Optional readings

Further reading about time in epidemiologic research

Cole SR, Hudgens MG, Brookhart MA, Westreich D. Risk Am J Epidemiol. 2015 Feb 15;181(4):246-50

Optional reading (for fun) about learning science and math

Hamming RW. Mathematics on a distant planet. The American Mathematical Monthly. 1998 Aug 1;105(7):640-50.

Exercise

There is no exercise today.

Questions

Each week, you will use a form below to submit your questions about the week’s reading. By tomorrow at 12pm, please navigate to the page for Lecture 1 to find the assigned reading and submit your question.

L9: Informative censoring

Wed, 24 Mar 2021 00:00:00 +0000

Readings

Howe CJ, Cole SR, Lau B, Napravnik S, Eron Jr JJ. Selection bias due to loss to follow up in cohort studies. Epidemiology (Cambridge, Mass.). 2016 Jan;27(1):91.

Slides

Coming soon!

Notes

Coming soon.

Videos

Recordings of lectures are available on the EPID 722 Sakai site.

Exercise

Exercise 9 may be found on the course Teams site.

Questions

Please use the form below to submit your questions about this week’s reading.

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Course feedback

Mon, 01 Jan 0001 00:00:00 +0000

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Final Project

Mon, 01 Jan 0001 00:00:00 +0000

The purpose of the final project is to design a target trial to answer a public health or clinical question.

Components

The final project will consist of 2 parts:

A presentation in class on April 19th or 21st
A 1- to 2-page document summarizing your target trial

Process

Students will work in groups of 3 to form a question and design a target trial to answer that question. Note that the assignment is to design the target trial only; there is no need to discuss how one would emulate the target trial with observational data.

In class presentations will be no more than 7 minutes in length. Groups may divide responsibilities however they see fit. Groups may divide the presentation between all members or elect 1 or 2 group members to speak. After each presentation, groups will field questions from the class and the instructional team.

In addition to the presentations, groups should submit a 1- to 2-page document describing their study question and target trial to the teaching team on the day of their presentation.

Content

A target trial is a hypothetical randomized trial that you would do to answer a question of interest. The target trial need not be feasible nor ethical. Rather, the target trial is a mental device that sharpens questions. Designing a target trial is a useful first step when addressing causal questions. With the target trial in hand, one could either implement the trial, or some version of the trial, or emulate the trial using observational data.

Necessary components of the target trial include:

A. A detailed question, including

Aim
Target population
Actions considered
Origin/timescale
Outcome
Parameter of interest

B. How a target trial would answer the question, including

Aim
Significance
Eligibility criteria
Sampling/recruitment strategy
Actions forming arms of the trial (treatment strategies, exposure plans, what is being manipulated?)
How will actions be assigned?
Follow-up (when does it start and end? when does randomization occur?)
Outcome definitions
Causal contrast (parameter of interest)
Presentation of hypothetical results (table and figure)
Impact (how will results be used?)

As noted above, a third step described in many papers that use target trials to sharpen a question is to emulate the target trial using observational data. For this project, you are not required to outline how you might emulate the target trial using observational data. (But, this is something you will likely need to do in your career, and we discuss how to do so in the lecture on April 7th).

In addition to addressing the components listed above, projects should include a statement on the significance of the question (why this question at this time?) and the potential impact of the work (how will results be used?).

Preparation

On February 24th, we will discuss the design of target trials, including the elements needed for this assignment. On April 7th, we will revisit the design of target trials and discuss how to emulate target trials using observational data. During the lab session on April 12th, groups will have the opportunity to give and receive peer feedback on each other’s target trial designs.

Deadlines

By February 10th: Form 3-person groups and inform teaching team of your group by email
By March 31st: Determine your general study question and send to teaching team
By April 12th: Develop draft of your target trial design to obtain feedback during lab
4: April 19th/21st: Present target trial to class and submit 1-pager

FAQs

What if I don’t have a group?

If you need help finding a group, please reach out to the teaching team and we will place you in a group.

How do I choose a topic?

You may choose the topic of your study question from any area of epidemiology. Note that, for this assignment, you will need to ask a causal question that lends itself to some type of action. To specify a target trial, your exposure/treatment/plan/etc will need to be manipulable. (While descriptive questions are also critically important for epidemiology, a descriptive question will not fill the needs of this assignment). If you would like preliminary feedback on your topic or assistance in finding a topic, please contact the teaching team ahead of the March 31st deadline.

What format should be used for presentations?

Presentations should be no more than 7 minutes in length and should be presented in the style of a short conference presentation or funding proposal. Presentations will be assessed for content and clarity. Slides are allowed but not required. Some visual element should be used.

What is the desired format for the document?

The document must be no more than 1 to 2 pages and can include text and figures. Please strive for brevity and clarity.

Where can I go for help?

Please contact the teaching team with any additional questions.

Line Diagrams

Mon, 01 Jan 0001 00:00:00 +0000

Why would I need code for a line diagram?

Line diagrams are easy to draw by hand, but can get unwieldy for real datasets. But sometimes plotting the line diagram can be useful. This includes settings when you want a quick visual check of

the administrative censoring date
the presence of late entry
the maximum follow-up time
etc

R code to produce line diagrams

Simple simulated data (used in L1)

First, we generate simple data on 20 participants as describe by L1:

“Say you wish to estimate the 5-year risk of death among people entering HIV care. You have a database of people entering HIV care between 2012 and 2020.”

### Generate some data -----
require(dplyr)
set.seed(123)
year0 <- runif(20, min=2012, max = 2020)
t <- runif(20, min=2, max = 15)
dat <- data.frame(year0, t)


dat <- dat %>% mutate(y=ifelse(t+year0>2020 | t>5, 0, 1),
                      t = ifelse(t+year0>2020, 2020-year0, t), 
                      t = ifelse(t>5, 5, t),
                      id = row_number())

dat

##       year0         t y id
## 1  2014.301 5.0000000 0  1
## 2  2018.306 1.6935589 0  2
## 3  2015.272 4.7281846 0  3
## 4  2019.064 0.9358608 0  4
## 5  2019.524 0.4762617 0  5
## 6  2012.364 5.0000000 0  6
## 7  2016.225 3.7751561 0  7
## 8  2019.139 0.8606476 0  8
## 9  2016.411 3.5885199 0  9
## 10 2015.653 3.9124774 1 10
## 11 2019.655 0.3453332 0 11
## 12 2015.627 4.3733268 0 12
## 13 2017.421 2.5794349 0 13
## 14 2016.581 3.4189328 0 14
## 15 2012.823 2.3199779 1 15
## 16 2019.199 0.8014002 0 16
## 17 2013.969 5.0000000 0 17
## 18 2012.336 4.8133032 1 18
## 19 2014.623 5.0000000 0 19
## 20 2019.636 0.3639708 0 20

Produce the line diagram by creating line segments and points in ggplot.

# Calendar time as timescale
library(grid)
library(ggthemr)
ggthemr('solarized')
line <- ggplot() +
  geom_segment(data = dat %>% filter(y==0), aes(x = year0, y = id, xend = year0+t, yend = id),  arrow = arrow(length = unit(0.1, "cm"))) +
  geom_segment(data = dat %>% filter(y==1), aes(x = year0, y = id, xend = year0+t, yend = id)) +
  scale_y_continuous(name = "ID", breaks = c(1, 5, 10, 15, 20), limits = c(0,20))+
  scale_x_continuous(name = "Calendar Time", breaks=c(2012, 2014, 2016, 2018,  2020), limits = c(2012, 2021)) +
  geom_point(data = dat %>% filter(y==1), aes(x = year0+t, y = id), color = "red", size = 0.6) +
  theme(text = element_text(size = 14, family = "Open Sans"))

line

Reorganize plot to show time since entry into HIV care on the x-axis.

# Calendar time as timescale
line2 <- ggplot() +
  geom_segment(data = dat %>% filter(y==0), aes(x = 0, y = id, xend = t, yend = id),  arrow = arrow(length = unit(0.1, "cm"))) +
  geom_segment(data = dat %>% filter(y==1), aes(x = 0, y = id, xend = t, yend = id)) +
  scale_y_continuous(name = "ID", breaks = c(1, 5, 10, 15, 20), limits = c(0,20))+
  scale_x_continuous(name = "Calendar Time", breaks=c(0, 1, 2, 3, 4, 5), limits = c(0, 6)) +
  geom_point(data = dat %>% filter(y==1), aes(x = t, y = id), color = "red", size = 0.6) +
  theme(text = element_text(size = 14, family = "Open Sans"))

line2

Example data (similar to Cole & Hudgens 2010)

Next, we read in some sample data similar to that used in Cole & Hudgens 2010.

##   int     year        w        t cenyear d late newid yearw    yeart
## 1   1 2010.320 4.679838 5.527338    2020 1    1     1  2015 2015.848
## 2   1 2010.321 4.678720 9.557808    2020 1    1     2  2015 2019.879
## 3   1 2010.427 4.573213 9.573213    2020 0    1     3  2015 2020.000
## 4   1 2010.509 4.490943 9.490943    2020 0    1     4  2015 2020.000
## 5   1 2010.645 4.355168 9.355168    2020 0    1     5  2015 2020.000
## 6   1 2010.775 4.224887 9.224887    2020 0    1     6  2015 2020.000

Calendar timescale

### Create second dataset with only events ------
exdat2 <- exdat[exdat$d == 1, ]

### Plot the lines -----
line <- ggplot(data = exdat) +
  geom_segment(aes(x = year, y = newid, xend = yearw, yend = newid), lty = "dotted") +
  geom_segment(aes(x = yearw, y = newid, xend = yeart, yend = newid)) +
  ylab("ID") +
  scale_x_continuous(name = "Calendar Time", breaks=c(2010, 2015,  2020)) +
  geom_point(data = exdat2, aes(x = yeart, y = newid), color = "red", size = 0.5) +
  theme(text = element_text(size = 16, family = "Open Sans"))

line

Time since AIDS diagnosis

line2 <- ggplot(data = exdat) +
  geom_segment(aes(x = 0, y = newid, xend = w, yend = newid), lty = "dotted") +
  geom_segment(aes(x = w, y = newid, xend = t, yend = newid)) +
  ylab("ID") +
  scale_x_continuous(name = "Time since AIDS diagnosis", breaks = c(0,2, 4, 6, 8, 10)) +
  geom_point(data = exdat2, aes(x = t, y = newid), color = "red", size = 0.5) +
  
  theme(text = element_text(size = 14, family = "Open Sans"))

line2

Schedule

Mon, 01 Jan 0001 00:00:00 +0000

This course is divided into 2 main sections:

Basic tools for working with time-to-event data
Tools to handle factors that vary over time

with time at the beginning and end of the course to put these tools into the context of asking and answering important public health questions.

This page provides quick links to the readings , lecture materials , and exercises .

	Tools for Survival Analysis	Content	Reading	Exercise
Jan 13 & 18	Time in epidemiology: Questions and parameters
Jan 20 & 25	Estimators of risk in settings with censoring
Jan 27 & Feb 1	Cox models
Feb 3 & 8	Comparisons of risk
Feb 10 & 15	Generalizability and transportability
Feb 17 & 22	Competing events
Feb 24 & Mar 1	Designing a time-to-event study
Mar 3	Exam Review
Mar 8	Exam
Mar 10	Exam Postmortem
	Incorporating time-varying factors	Content	Reading	Exercise
Mar 22	Taxonomy of time-varying exposures
Mar 24 & 29	Informative censoring
Mar 29 & 31	Per-protocol analyses using IPW
Apr 7 & 12	Target trials
Apr 14	Wellness day
	Wrap-up	Content	Reading	Exercise
Apr 19 & 21	Presentations on target trials
Apr 26	Wrap up: where do we go from here?

Syllabus

Mon, 01 Jan 0001 00:00:00 +0000

Instructor

Dr. Jessie K. Edwards
2102A McGavran-Greenberg
jessedwards@unc.edu

Co-instructor

Dr. Stephen R. Cole
2105E McGavran-Greenberg
cole@unc.edu

Course details

Tuesdays and Thursdays
January – May 2022
3:30 - 4:45 PM
McGG 1304

Teaching Assistants

Katie Mollan (kmollan@email.unc.edu)
Audrey Renson (arenson@email.unc.edu)

Course objectives

Build competence in asking and answering important public health questions
Understand and appreciate time in epidemiologic studies
Address threats to accuracy in epidemiologic studies

Course structure

Each topic will be covered during 2 course periods. The first (typically Thursdays) will be reserved for short, modular lectures, initiated with small group discussions and closed with class-wide discussion. During the second course period on each topic (typically Tuesdays), students will discuss exercises in small groups and we will again close with class-wide discussion of lingering questions.

Readings

One reading will be assigned each week. Students are expected to read thoroughly and submit one question about the week’s reading to the course website by Wednesday at 5pm. Please reference the page, column, and line number that motivated your question. Students will discuss the questions in small groups and the instructors will address a subset of the questions during lecture.

Exercises

Each week, students will complete an exercise outside of class to be discussed during the second course meeting on each topic (typically Tuesday). Students are expected to submit written answers to the exercise questions prior to that course meeting (typically Thursdays). Collaboration on the exercises is encouraged, but all students must submit individual answers to the exercise questions. Exercises will be graded for completion. Exercises require implementation of methods discussed in the context of real data sets. Some exercises will provide SAS or R code to get you started, others will require students to look back at the course notes or previous exercises to find relevant code.

Midterm Exam

The course will contain one in-person mid-term exam during the week before spring break.

Final Project

Students will work in groups of 2-3 students to develop a “target trial” related to a clinical or public health question of interest. Each group will present their target trial in a 5 minute presentation during designated course periods at the end of the semester. In addition to the presentation, each team should submit a 1-page document describing their target trial, due on the date of their presentation. Additional details will be provided before Spring Break.

Grading

The final grade will be based on completion of the weekly exercises (30%), the midterm exam (30%), the final project (30%), and class participation (10%).

Grade	Range
H	[90, 100]
P	[70, 90)
L	[60, 70)
F	[0, 60)

Office Hours

Teaching assistants will hold at least one regularly scheduled help session per week for students to ask questions and seek general assistance with the course, and be available by email. Please check the Teams site for up-to-date information on office hours. Instructors welcome email, and will be available by appointment.

Software

This course is software agnostic. That means that you may use any type of statistical software you like to complete the exercises. Example code will occasionally be provided in SAS and/or R.

Spring 2022 Adjustments

In spring 2022 the University community is experiencing, along with the rest of the world, ongoing COVID pandemic conditions. We hope we can offer an in-person class for the duration of the semester. However, I will be considering the course content and making an assessment about the best way to offer the content for this course given our shared circumstances. For example, in a given week, if I find that many students in the course are sick and are unable to attend, I may choose to offer the course over zoom (synchronously). Please be alert for my weekly announcements (sent to you by email via Sakai).

UPDATE 1/21/22: As a reminder, the School of Public Health has stipulated that all courses are virtual until at least January 31, 2022. We will reassess the situation at that time.

This year

The past 2 years have been tumultuous. The pandemic and political situation have added a great deal of stress to the beginning of 2022. In this course, we strive to create an atmosphere that encourages learning and exploration of ideas without compounding that stress. Please contact the instructors or TAs with any concerns. Above all, remember to be kind to yourself and others as we navigate the coming months.

Feedback

We welcome feedback on the course at any time. Please feel free to email us with your suggestions or leave anonymous feedback via our website here.

Mask Requirement and Community Standards

This semester, while we are in the midst of a global pandemic, all enrolled students are required to wear a mask covering your mouth and nose at all times in our classroom. This requirement is to protect our educational community — your classmates and me – as we learn together. If you choose not to wear a mask, or wear it improperly, I will ask you to leave immediately, and I will submit a report to the Office of Student Conduct. At that point you will be disenrolled from this course for the protection of our educational community. Students who have an authorized accommodation from Accessibility Resources and Service have an exception. For additional information, see the Carolina Together webpage.

Accessibility Resources (ARS)

The University of North Carolina at Chapel Hill facilitates the implementation of reasonable accommodations, including resources and services, for students with disabilities, chronic medical conditions, a temporary disability or pregnancy complications resulting in barriers to fully accessing University courses, programs and activities. Accommodations are determined through the Office of Accessibility Resources and Service (ARS) for individuals with documented qualifying disabilities in accordance with applicable state and federal laws. See the ARS Website for contact information or email ars@unc.edu.

Counseling and Psychological Services (CAPS)

CAPS is strongly committed to addressing the mental health needs of a diverse student body through timely access to consultation and connection to clinically appropriate services, whether for short or long-term needs. Go to their website or visit their facilities on the third floor of the Campus Health Services building for a walk-in evaluation to learn more.

Title IX Resources

Any student who is impacted by discrimination, harassment, interpersonal (relationship) violence, sexual violence, sexual exploitation, or stalking is encouraged to seek resources on campus or in the community. Please contact the Director of Title IX Compliance (Adrienne Allison – Adrienne.allison@unc.edu), Report and Response Coordinators in the Equal Opportunity and Compliance Office (reportandresponse@unc.edu), Counseling and Psychological Services (confidential), or the Gender Violence Services Coordinators (gvsc@unc.edu; confidential) to discuss your specific needs. Additional resources are available at safe.unc.edu.