Schedule

Note

This is the tentative schedule for the Spring 2026 offering of Epi 204. It is generated directly from the course schedule spreadsheet (inst/extdata/epi-204-schedule-2026.xlsx) and will be updated as the quarter progresses. The authoritative, continuously-updated version is on Canvas.

1 Class sessions

library(dplyr)

schedule_path <- here::here("inst/extdata/epi-204-schedule-2026.xlsx")

# Combine the per-textbook reading-reference columns into a single labeled
# string, dropping any that are blank for a given session.
combine_readings <- function(...) {
  values <- c(...)
  labels <- c("Dunn & Smyth", "Dobson", "Vittinghoff", "Notes")
  parts <- mapply(
    function(label, value) {
      if (is.na(value) || trimws(value) == "") {
        return(NA_character_)
      }
      paste0(label, " ", trimws(value))
    },
    labels, values,
    USE.NAMES = FALSE
  )
  parts <- parts[!is.na(parts)]
  if (length(parts) == 0) {
    return(NA_character_)
  }
  paste(parts, collapse = "; ")
}

class_schedule <-
  readxl::read_excel(schedule_path, sheet = "Schedule") |>
  # Drop footnote rows and the tentative "Skipped?" row (no real session date).
  filter(!is.na(Date)) |>
  mutate(Date = format(as.Date(Date), "%b %d")) |>
  rowwise() |>
  mutate(
    Readings = combine_readings(
      `Dunn & Smyth sections`, `Dobson sections`,
      `Vittinghoff sections`, `Lecture notes **`
    )
  ) |>
  ungroup() |>
  transmute(
    Week, Date, Day,
    Topic = Topics,
    Subtopics,
    Deadlines = `Deadlines*`,
    `New work` = `New Assignments`,
    Lecturer,
    Readings
  )

class_schedule |>
  gt::gt() |>
  gt::sub_missing(missing_text = "") |>
  gt::opt_row_striping() |>
  gt::tab_options(table.font.size = gt::pct(80)) |>
  gt::tab_footnote(
    footnote = paste(
      "Homework assignments have an automatic 24-hour grace period",
      "unless otherwise announced."
    ),
    locations = gt::cells_column_labels(columns = Deadlines)
  )

Table 1: Tentative schedule of class sessions (Spring 2026).

Week	Date	Day	Topic	Subtopics	Deadlines1	New work	Lecturer	Readings
1	Mar 31	T	Review: basics of inference	Maximum likelihood estimation			Ezra	Dunn & Smyth 1; Dobson 1.1 - 1.2; Notes Appendix F.1.1 - F.1.8
1	Apr 02	Th	Review: basics of inference	Quantifying uncertainty for MLEs: standard errors, confidence intervals, hypothesis tests, p-values, prediction		HW 1	Ezra	Dunn & Smyth 4; Dobson 1.3, 1.6, 2, 4; Vittinghoff 5.6; Notes F.1.9 - F.4
2	Apr 07	T	Review: linear regression	Interpreting linear regression models			Ezra	Dunn & Smyth 4; Dobson 5; Notes 2.1
2	Apr 09	Th	Review: linear regression	Inference for linear regression models	HW 1	HW 2	Ezra	Dunn & Smyth 2.1 - 2.7; Dobson 6; Vittinghoff 4; Notes 2.2 - 2.3
2.5	Apr 14	T	Review: linear regression	Goodness of Fit for Linear Regression Models			Ezra	Dunn & Smyth 3; Dobson 6; Vittinghoff 4; Notes 2.4 - 2.5
2.9	Apr 16	Th	Review exam (ungraded)		HW 2		Ezra
4	Apr 21	T	Logistic regression	Odds, Odds ratios, risk ratios, risk differences, link functions			Hilary	Dunn & Smyth 5; Dobson 7; Vittinghoff 5; Notes 3.1 - 3.4
3.7	Apr 23	Th	Logistic regression	Interpreting Logistic Regression Coefficients		HW 3	Hilary	Dunn & Smyth 6, 7; Dobson 7; Vittinghoff 5; Notes 3.5 - 3.6
5	Apr 28	T	Logistic regression	Fitting logistic regression models			Hilary	Dunn & Smyth 8; Dobson 7; Vittinghoff 5; Notes 3.4 - 3.10
5	Apr 30	Th	Logistic regression	Diagnostics for logistic regression			Hilary
6	May 05	T	Review session	Inference, Linear Regression, Logistic Regression	HW 3		Both?
6	May 07	Th	Midterm 1	Inference, Linear Regression, Logistic Regression			Hilary
7	May 12	T	Survival analysis	hazard; censoring			Ezra	Dobson 10; Vittinghoff 6; Notes 5
7	May 14	Th	Survival analysis	Kaplan-Meier and Nelson-Aalen curves			Ezra	Dobson 10; Vittinghoff 6; Notes 5
8	May 19	T	Survival analysis	survival regression models		HW 6	Ezra	Dobson 10; Vittinghoff 6; Notes 6
8	May 21	Th	Survival analysis	AFTs?			Hilary?	Dobson 10; Vittinghoff 6; Notes 6
9	May 26	T	Review session	Survival analysis	HW 6	Midterm 2 review (HW 7)	Ezra
9	May 28	Th	Midterm 2	Survival analysis	Midterm 2 review (HW7)		Ezra
10	Jun 02	T	Poisson regression	Poisson regression models		HW 5	Hilary	Dunn & Smyth 10; Dobson 9; Vittinghoff 8; Notes 4
10	Jun 04	Th	Review	Everything above	HW 5		Both	Dunn & Smyth 10; Dobson 9; Vittinghoff 8; Notes 4
Finals	Jun 09	T	Final exam (10:30am-12:30pm)	Everything above, except Poisson	Final (optional)		Both
1 Homework assignments have an automatic 24-hour grace period unless otherwise announced.

2 Assignments and exams

readxl::read_excel(schedule_path, sheet = "Homework and Exams") |>
  filter(!is.na(Assignments)) |>
  mutate(
    Topics = gsub("[\r\n]+", " ", Topics),
    across(
      c(`Date Assigned`, `Date Due*`, `Date Returned (approx.)`),
      ~ format(as.Date(.x), "%b %d")
    )
  ) |>
  rename(
    `Date due` = `Date Due*`,
    `Date returned (approx.)` = `Date Returned (approx.)`
  ) |>
  gt::gt() |>
  gt::sub_missing(missing_text = "") |>
  gt::opt_row_striping() |>
  gt::tab_options(table.font.size = gt::pct(85))

Table 2: Homework assignments and exams (Spring 2026).

Assignments	Topics	Date Assigned	Date due	Date returned (approx.)
Homework 1	Review of probability and inference; data manipulation, visualization, and analysis	Mar 31	Apr 09	Apr 15
Homework 2	Linear regression - theory and application	Apr 09	Apr 16	Apr 22
Homework 3	Logistic regression - theory	Apr 16	Apr 23	Apr 29
Review for Midterm 1	Inference, Linear regression, logistic regression	Apr 27	May 04	May 10
Homework 4	Logistic regression - data analysis	Apr 28	May 05	May 11
Homework 5	Poisson regression - theory and application	May 19	May 26	Jun 01
Homework 6	Survival analysis - theory	May 26	Jun 02	Jun 08
Review for Midterm 2	Poisson regression, survival analysis	Jun 02	Jun 09	Jun 15
Homework 7	Survival analysis - data analysis	Jun 09	Jun 16	Jun 22
Review for Final	Linear regression, GLMs, survival analysis	Jun 01	Jun 12	Jun 18
* all homework assignments are due at 5pm on the due date.