CHAPTER 1: Introduction
1.1 Statement of Problem
1.2 Aim and Objectives of the Study
1.3 Justification for the Study
1.4 Significance of the Study
1.5 Definition of Terms

CHAPTER 2:  Literature Review
2.1 Observational Studies
2.2 Case-Control Studies
2.2.1 Bias in Observational Case-Control Studies
2.2.2 Traditional Methods for Bias Reduction
2.3 Propensity Scoring
2.3.1 Propensity Score Definition
2.4 The Use of Propensity Score Technique
2.4.1 Matching with Propensity Scores
2.4.2 Stratification (Subclassification) with Propensity Scores
2.4.3 Regression (Covariance) Adjustments with Propensity Scores
2.5  Logistic Regression
2.5.1 Introduction
2.5.2 The Model
2.5.3 Uses of Logistic Regression
2.6 Preterm Birth
2.6.1 Consequences of Preterm Births

CHAPTER 3: Methodology
3.0 Introduction
3.1 Preliminary Statistical Analysis
3.1.1 Correlation Coefficients
3.2 Propensity Scores
3.2.1 Propensity Score Variable Identification
3.2.2 Variable Selection for Propensity Score Model
3.2.3 Propensity Score Modelling
3.3 Logistic Regression and Propensity Score
3.3.1 Elements of the Propensity Score
3.4 Study Population
3.5 Analysis Selection
3.5.1 Matching
3.5.2 Matching Metric
3.5.3 Matching Variable
3.5.4 Matching Algorithm
3.5.5 Matching Structure
3.5.6 Replacement of Comparison Subjects
3.5.7 Nearest Neighbour Matching on Estimated Propensity Score
3.6 Assessing the Matched Data
3.6.1 The Standardized Bias
3.6.2 Bias Reduction
3.6.3 Test on Means and Proportions
3.7 Analysis
3.7.1 Software

CHAPTER 4: Data Analysis and Interpretation
4.1 Correlations with Baby Status
4.1.1 Correlation of Status with Covariates
4.2 Logistic Regression on Baby‘s Status
4.3 Group Comparisons

CHAPTER 5: Summary, Conclusions and Recommendations
5.1 Summary
5.2 Conclusions
5.3 Recommendations
5.4 Contribution to Knowledge
5.5 Suggested Areas for Further Research

In observational studies, ―case-control groups‖ often exhibit imbalance on covariates. This covariate imbalance is confounded with treatments. It is difficult to attribute differences in responses to the ―treatment‖ because the covariates are also believed to influence the response. Propensity score matching attempts to reduce the confounding effects of covariates, and so allows differences of responses to be attributed to differences of treatments. In addition, the values of the propensity scores can serve as a diagnostic tool to evaluate the comparability of the groups in a quantitative way. When two groups are being compared, the propensity score can be calculated as the predicted probability of group membership from a logistic regression. It represents the ‗tendency‘ for an observation to be in one group or the other. By adjusting for the value of the propensity score in a linear model, one effectively adjusts for any group differences attributed to the variables used to create the propensity score. Here we present an experiment where propensity scores were used to adjust for differences between a case and a control group (treatment group and a non-randomized control group). Propensity scores were created using SPSS Version 16 Binary Logistic Regression Procedure on a Windows Vista platform. A linear model was also estimated using the same. Groups were compared using independent samples t-tests and chi-square tests as appropriate. Standardized differences were calculated and matching was done with Microsoft Excel Version 2007 on a Windows Vista platform. The results showed that the Propensity Score Matching was successful in reducing the bias on the covariates.

In order to make group comparisons, the generally accepted pattern in research consists of the following method:
Formation of treatment and experimental groups, sometimes with a single group serving as its own control.
Mapping treatments to the groups.
Analysing group differences.

 Generalising findings based on groups to tendencies among future individuals. Defining groups is a crucial first step and once they are defined, one would want their composition to be identical. Statistical adjustments, often in the form of blocking variables, variables or covariate analysis could be used to adjust for the pre-treatment group differences.

The random assignment of treatment to groups before comparison is often resorted to because, in theory, this assures that the groups are identical. This, however, is not always practical and does not necessarily result in groups that are equivalent in terms of all the important covariates. It is the expected values of the covariates over numerous replications that are equal.

A substitute to random assignment is a matched-pairs design whereby each member of the first group is matched with a member of the second group on all the factors the researcher considers to be viable and important. According to Rudner & Peyton (2006), in a well-matched pair, it is as if we are using the same individual twice. When matching is adequate, the variables used for matching that might cause confounding.....

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