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Surveillance is the ongoing systematic collection, analysis and interpretation of outcome-specific data for use in the planning, implementation and evaluation of public health practice. Surveillance can have a negative connotation, but we can use it to: Ide...

Family data is correlated that could lead to inflation in test statistics if not accounted for. Many genetic studies contain related individuals. Family-based studies were developed to avoid bias due to population structure; Biological family members are genet...

When multiple markers/SNPs are genotyped in a gene or gene region, the SNPs may be in linkage disequilibrium (LD). Each individual test of association with a marker is correlated with all tests for other markers in LD with that marker. So, instead of testing ...

The goal of supervised learning methods (regression and classification) is to predict outcome/response variable Y using a set of p features (X1, X2... Xp) measured on n observations. We train the machine on 'labeled' data to predict outcomes for unforeseen dat...

In survival data the dependent variable is always survival time (or time until an event with the potential to censor observations). The independent variable can be any type; Continuous, ordinal or categorical. We assume the observations are independently and r...

Review of errors and difference in means/proportions:Type II error is represented by beta and type I error as alphaWhere Z1 - (alpha /2)is the Z-value that creates the target value of alpha in the tail of the distribution. Power decreases as alpha increases. ...

Types of probability samples: Simple random sample - everyone in the population has equal likelihood of being selected The most effective, but often hardest to execute Stratified random sample - we create strata based on some factor and take a rando...

Review: Logistic and Proportional Hazards Regression Model Selection You can look at changes in the deviance (-2 log likelihood change) Deviance - Residual sum of squares with normal data Problem: Deviances alone do not penalize "model complexity" (henc...

Clutsering refers to a very broad set of techniques for finding subgroups, or clusters, in a data set. When we cluster the observations, we partition the profiles into distinct groups so that the profiles are similar within the groups but different from other ...

Classification is often used to describe modeling of a categorical outcome. In binary classification the outcome is two possible values/classes and the goal is the predict the correct class using covariates. Classification rule: A mathematical function to p...

Genotyping arrays can be obtained at pre-selected sites for each sample. Ex. Genotyping sites known to be polymorphic based on prior sequencing. Sequencing is obtaining "every" base in the exome or genome for each sample. Most of the sequence is identical acr...

We use the Poisson Regression to model a risk ratio when we are interested not in whether something occurs but how many times it occurs; Either repeated events or events in a population.Ex. number of hospitalizations, number of infections, etc. This assumes i...

Statistical interaction occurs between two factors if their combined effect is different than what would be expected based on their individual separate effects. In genetics, differences in risk or mean phenotype between genotypes vary according to the exposure...

We could determine if a intervention is meeting the target through a census, sampling plan ,or LQAS. LQAS LQAS is a primary classification tool Lots are classified as performing "acceptable, unacceptable, low or high" The goal is to shift resources...

We've learned about One-Way Analysis of Variance (ANOVA) previously, it is a regression model for one continuous outcome and one categorical variable. It allows us to compare the means of the groups to detect significant differences. A one-way fixed-effects ...

Statistical association analysis is not all about significance. There is much to consider when deciding covariates and choosing a model to represent the relationship. Regression Modeling If we have a small number of variables we can manual assess confoundi...

Missing data is common in epidemiology studies, and always observed in longitudinal studies. Inadequate handling of missing data may cause bias or lead to inefficient analyses.  If an estimate is incomplete, we can remove it without introduce bias. If a variab...

GWAS is performed on millions of SNPs. Because of multiple testing, we use very stringent thresholds for statistical significance. This can greatly reduce power and may not be sufficient to detect associated SNPs. Combining information across studies will impr...

Correlated data occurs in a variety of situations. The four basic types: Repeated measurements data Clustered data designs Spatially correlated data Multivariate data Repeated Measurements Longitudinal data is a response variable collected from the s...

A clinical trial is defined as a prospective study comparing the effect or value of an intervention against a control in subjects. The core components of clinical trials are the population, intervention, control and outcome. The great majority of clinical tria...