-
Notifications
You must be signed in to change notification settings - Fork 10
/
other-resources.qmd
117 lines (82 loc) · 5.85 KB
/
other-resources.qmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
- @dobson4e is a classic textbook on GLMs.
It was used in UCLA Biostatistics's MS-level GLMs course (Biostat 200C)
when I took it, and it helped me a lot.
It is fairly mathematically rigorous and concise, bordering on terse.
It covers GLMs in detail, and survival analysis briefly, and it also has
helpful chapters on Bayesian methods.
I have adapted examples and explanations from it extensively in these notes.
- @hosmer2013applied is a classic text on logistic regression.
I haven't read it yet.
- @agresti2012categorical is another classic text for GLMs.
I haven't read it yet.
- @agresti2018introcat appears to be a more applied version of @agresti2012categorical.
I haven't read it yet.
There are [extra exercises](https://bcs.wiley.com/he-bcs/Books?action=resource&bcsId=11293&itemId=1119405262&resourceId=44770) and other resources available on the [Student Companion Site](https://bcs.wiley.com/he-bcs/Books?action=index&itemId=1119405262&bcsId=11293)
- @agresti2015foundations has "More than 400 exercises for readers to practice and extend the theory, methods, and data analysis"; might be more theoretical?
- @agresti2010ordinal is specifically about ordinal data.
- @dunn2018generalized is a recent textbook on GLMs.
It doesn't cover time-to-event models,
and it doesn't use the modern [`tidyverse`](https://tidyverse.org/) packages ([`ggplot2`](https://ggplot2.tidyverse.org/),
[`dplyr`](https://dplyr.tidyverse.org/), etc.),
but otherwise it seems great.
- @moore2016applied is a recent textbook on survival analysis. It also doesn't use the `tidyverse`, but otherwise seems great.
- @klein2003survival is a classic text for survival analysis.
I read most of it in grad school, and it was very helpful.
Examples and explanations from it are borrowed extensively in the second half of these notes
(partially filtered through David Rocke's course notes.)
- @kalbfleisch2011statistical is another classic survival analysis text;
I haven't read it yet.
- @kleinbaum2010logistic is a mostly applied-level "self-learning" text for logistic regression;
I read it cover-to-cover before grad school, and found it very helpful.
- @kleinbaum2012survival is the corresponding "self-learning" text for survival analysis;
I read it cover-to-cover before grad school, and found it very helpful.
- @rms2e is another popular textbook. It uses [`ggplot2`](https://ggplot2.tidyverse.org/) but not [`dplyr`](https://dplyr.tidyverse.org/),
and covers logistic regression and survival analysis (no Poisson or NB models?).
An abbreviated but continuously updated version with audio clips is available at <https://hbiostat.org/rmsc/>.
- @fox2015applied is another standard text.
^[I don't have anything to say about this book, because I haven't opened it yet, but I've heard it's great!]
- @mccullaghnelder is a classic, theoretical textbook on GLMs
^[haven't opened it either]
- @dalgaardintroductory covers GLMs and survival analysis at an applied level, using base R
- @vittinghoff2e covers GLMs, survival analysis, and causal inference, using Stata.
The authors are UCSF professors, and it is used for the core Epi PhD courses there.
I read this book nearly cover-to-cover before grad school, and it was hugely helpful for me,
both for statistical modeling and for causal inference (I think it provided my first exposure to DAGs).
- @faraway2016extending has GLMs but not survival analysis
- @selvin2001epidemiologic provides worked-out examples of applications for a wide range of
statistical analysis techniques.
The [Author](https://publichealth.berkeley.edu/people/steve-selvin)
is a retired UC Berkeley Biostatistics professor;
he used it in a graduate-level biostat/epi course.
- @jewell2003statistics is by another UC Berkeley [professor](https://publichealth.berkeley.edu/people/nicholas-jewell);
it mostly covers logistic regression, with one chapter on survival analysis.
- <https://ucla-biostat-200c-2020spring.github.io/schedule/schedule.html> provides course notes for
"Biostat 200C - Methods in Biostatistics C" at UCLA, which is at the Biostatistics MS level.
- <https://online.stat.psu.edu/stat504/book/> provides course notes for
"STAT 504 - Analysis of Discrete Data" at Penn State University.
It includes logistic regression and Poisson regression, as well as 2-way tables and other related topics, and includes SAS code.
- @NahhasIRMPHR is currently in-development
- @clayton2013statistical covers binary regression, count regression, and survival analysis. Haven't started it yet.
- <https://thomaselove.github.io/2020-432-book/index.html> is another set of lecture notes.
- @woodward2013epidemiology covers GLMs and survival; haven't read it yet, but it looks comprehensive.
- @roback2021beyond is recent and uses the `tidyverse`; doesn't appear to cover survival analysis.
- @wood2017generalized is about generalized *additive* models but includes a detailed
summary of GLMs.
- @kutoyants2023introduction appears to be a complete book on Poisson models.
- @hardin2007generalized uses Stata.
- @andrews2012data is a classic "learn-by-example" book with many datasets amenable to GLMs
- @r4epi is another open-source, online textbook like this one;
it is primarily about statistical programming, but it includes
full chapters on
[linear regression](https://www.r4epi.com/linear-regression),
[logistic regression](https://www.r4epi.com/linear-regression-1), and
[Poisson regression](https://www.r4epi.com/poisson-regression).
There is currently (2024/06) a placeholder chapter for [survival analysis](https://www.r4epi.com/cox-proportional-hazards-regression).
- @gelman2006data covers GLMs as well as hierarchical extensions of GLMs.
No survival models?
- @statproofbook is a collection of proofs for results in
probability,
statistics,
and related computational sciences.
- @suarez2017applications covers GLMs but not survival analysis
- <https://drive.google.com/file/d/1VwosGvHtRtKnC7P3ja7RAUawvvudgc9T/view>