title	author	output
Data Exploration and Predictive Modeling: Spontaneous Abortion Prediction	Peace Maddox	html_document df_print paged

Introduction

This notebook is based on this (Esophageal Cancer) project. These techniques are important for contextualizing data and creating predictions based on modeling and visualizations. The data set used for this project is from the (Induced abortion and secondary infertility) study.

Objective

• Exploring the data set (infert) which comes in the "R" data sets package.

• Here is a data usage example below:

require(stats)
model1 <- glm(case ~ spontaneous+induced, data = infert, family = binomial())
summary(model1)
## adjusted for other potential confounders:
summary(model2 <- glm(case ~ age+parity+education+spontaneous+induced,
                     data = infert, family = binomial()))
## Really should be analysed by conditional logistic regression
## which is in the survival package
if(require(survival)){
  model3 <- clogit(case ~ spontaneous+induced+strata(stratum), data = infert)
  print(summary(model3))
  detach()  # survival (conflicts)
}

• Visualizing the relationship between spontaneous abortion case occurrence and age / education / induced abortions.

• Identifying the groups at risk via useful analyzes and graphs.

• Building a well-developed generalized linear model.

• Predicting spontaneous abortion percentages among the groups.

• Testing the robustness of the model via leave-one-out cross validation.

Refer to the pdf document for the full mardown!

Resources

Esophageal Cancer Project

Induced abortion and secondary infertility study

Infertility data

Linear Regression Analysis

Practical advice on variable selection and reporting using Akaike information criterion

Common pitfalls in statistical analysis: Logistic regression

Cross-validation

Cross-validation under separate sampling: strong bias and how to correct it{.uri}

Impact of the Choice of Cross-Validation Techniques on the Results of Machine Learning-Based Diagnostic Applications{.uri}

How to Calculate RMSE in R