Computational Estimation and Prediction in Public Health

Using computational approaches utilizing large datasets to investigate public health

information is an important mechanism for institutions seeking to identify strategies

for improving public health. The art in computational approaches, for example

in health research, is managing the trade-offs between the two perspectives:

first, inference and s econd, p rediction. Many techniques from statistical methods

(SM) and machine learning (ML) may, in principle, be used for both perspectives.

However, SM has a well established focus on inference by building probabilistic

models which allows us to determine a quantitative measure of confidence about

the magnitude of the effect. Simulation-based validation approaches can be used

in conjunction with SM to explicitly verify assumptions and redefine t he specified

model, if n ecessary. On the other hand, ML uses general-purpose algorithms

to find p atterns t hat b est p redict t he o utcome and makes minimal assumptions

about the data-generating process; and may be more effective in a number of situations.

My work employs both SM- and ML- based computational approaches to

investigate particular public health problems. Chapter One provides philosophical

background and compares the application of the two approaches in public health.

Chapter Two describes and implements penalized Cox proportional hazard models

for time-varying covariates time-to-event data. Chapter Three applies traditional

survival models and machine learning algorithms to predict survival times of cancer

patients, while incorporating the information about the time-varying covariates.

Chapter Four discusses and implements various approaches for computing predictions

and effects for generalized linear (mixed) models. Finally, Chapter Five

implements and compares various statistical models for handling univariate and

multivariate binary outcomes for water, sanitation and hygiene (WaSH) data.