BGStats specializes in data analysis and statistical modeling. The science of statistics has developed a wide range of techniques to determine
whether the
fluctuations, changes,
effects, etc. observed
in the data
can be attributed to
some deterministic
component or are dueto chance
variation alone. We recommend and apply the most appropriate analysis methods in order to extract the parameters
of interest from your data.
Our consultants provide the following services:
Designing Statistical Analysis Plans
A data analysis must be properly planned. We elaborate or review statistical analysis plans for your research projects by:
Choosing the appropriate statistical design for the particular data situation, i.e. determining the probabilistic assumptions, specifying the error distributions etc.
Selecting the most powerful analysis techniques for your data.
Exploratory Data Analysis - Data Visualization
We apply modern methods of data presentation, exploratory data analysis, etc. to visualize the interesting components of your data.
We generate professional graphics for your presentations, study reports or publications.
Statistical Modeling
The basic aim of statistical modeling is to derive a mathematical representation of the relationship between one or more response variables and a number of explanatory variables,
including a measure of the inherent uncertainty of any such relationship. Whether your response variables are measured as continuous, categorical or count outcomes, we provide the optimal modeling solutions to extract the parameters
of interest from your data. Our data analysts have years of experience in the following areas:
We provide expertise in the following statistical techniques:
Cluster Analysis
Factor Analysis
Principal Component Analysis
Discriminant Analysis
Classification and Regression Trees (CART)
Correspondence Analysis
Analysis of Data from Designed Experiments
In applied sciences, it is common to design experiments in order to obtain an unbiased estimation of parameters
while controlling for confounding variables. We
specialize in the
following analysis methods for experimental data:
Analysis of Variance (ANOVA)
Analysis of Covariance (ANCOVA)
Repeated Measurements ANOVA
Linear Fixed-Effects Models for Continuous Outcomes
Mixed-effects Models for Replicated, Blocked Designs
Multi-level Models for Split-Plot Experiments
Categorical Data Analysis
Often the outcomes of interest are
frequency counts of
observations occurring
in specified response
categories rather thancontinuous
variables but are
expressed as frequency
counts of observations
occurring in the
response categories. We
apply modern statistical methods to analyze categorical data:
Logit Models - Logistic Regression
Loglinear Models for Contingency Tables
Models for Ordinal Variables
Multinomial Response Models
Exact Tests for Small Samples
Models for Matched Pairs
Analysis of Clustered or Longitudinal data
In applied sciences, one is often confronted with correlated, so-called clustered data, such as data from surveys with complex multi-stage sampling designs, data from cluster randomization trials, multivariate observations, repeated measurements, etc. Longitudinal data in which the outcome variable is repeatedly measured over time are a special kind of clustered data. In the analysis of clustered data, special statistical models are used to adjust for the stochastic dependence structure. We specialize in
Linear Mixed Models for Repeated Continuous Outcomes
Nonparametric Models for Experimental Longitudinal Data
Marginal, GEE and robust longitudinal modelling approaches
Repeated Measurements Analysis of Variance
Statistical Methods for Cluster Randomization Trials
Multilevel Modelling of Complex Survey Data
Models for Overdispersed Count or Categorical Data
Models for Repeated Categorical and Count Outcomes
Survival Analysis
Survival data is a special kind of longitudinal data where the outcome
of interest is the time to a specific event.
One special feature of
survival data, censoring,
occurs when the event
of interest is not
observed for a study
participant during the observational period. Analysis of censored data requires special statistical methodology. We have years of experience in the following analysis techniques:
Standard Survival Analysis Procedures:
Nonparametric estimation of survival and hazard functions:
Life tables, Kaplan Meier curves
Comparing survival distributions from different sample populations
using log-rank and other global test procedures
Cox Proportional Hazards Regression
Parametric Modeling Approaches
Advanced techniques for multivariate survival data:
Multi-state models for
event history data
Competing risk models
Analysis of recurrent or multiple events
Applied stochastic process models,
e.g. Markov models,
etc.
Statistical Methods in Clinical Chemistry and Laboratory Medicine
Those working in clinical laboratories may be familiar with the terms "accuracy", "precision", "linearity", "bias" etc. which occur in clinical method-evaluation studies.
BGStats is able to evaluate the analytical accuracy, imprecision or diagnostic performance of analytical methods or instruments. Whether you are developing a new method, evaluating an existing method, or introducing a new method to your lab, we can perform the evaluation for you,
according to
NCCLS or
IFCC)
standards. Our expertise embraces the following evaluation procedures:
Linearity tests
NCCLS precision profiles
Method comparison & analytical accuracy
Bias Plots
Passing Bablok Regression
Deming Regression
Bayesian algorithms to quantify cross reactions
Sensitivity and specificity in the absence of a gold standard
Receiver Operating Characteristic (ROC) curves
Performance of
diagnostic screening
methods
Methods for Quantitative Synthesis in Medicine
In Evidence-Based
Medicine, modern quantitative methods have become essential tools in the formulation of clinical and public policy.
BGStats provides
know-how in modern quantitative research approaches, e.g.
Meta-Analysis
Decision Analysis
Cost-Effectiveness Analysis
Genetic Analysis
At BGStats we also have experience in the statistical analyses of genetic data. Our services include, among others:
Statistical
analysis of data
from
whole-genome-scans
Hardy-Weinberg Equilibrium Test
Candidate Gene Analysis
Analysis of Single Nucleotide Polymorphism (SNP) - Data
Bayesian Algorithms for Haplotype Reconstruction
Association Analyses: SNPs - Haplotypes - Clinical Outcomes
