Visit our exhibition stand at this important one-day conference run by the Association of Survey Computing.

Venue: Imperial College London

Date: September 30th 2010

Some world’s leading research institutions have used BMDP as their statistical analysis partner for their innovative research projects.

 At Indiana State University, researchers use BMDP Time Series and Repeated Measures progrmas to analyze very large serial thermal data sets to assess and improve the performance of top athletes.

A graduate class on Biomath in UCLA is using BMDP AR to fit differential equation models to their data.

Researchers at University of Pittsburg use BMDP 7M to analyze data generated from over 300 encephalographic sensors. The work will enable rapid and accurate diagnosis of mild head trauma and psychiatric illness.

A leading telecommunications company is using BMDP Survival Analysis Programs 1L & 2L to build sophisticated customer retention models, resulting in the development of customer loyalty programmes and helping to increase customer value over the medium to long term.

Discover much more about BMDP Statistical Software.
Visit our main BMDP Pages
Take a free trial of BMDP to try the software for yourself.
Find pricing information.

• What are the goals of a meta-analysis
• How a meta-analysis differs from a narrative review
• Computing effect sizes and treatment effects
• Computing a combined effect
• Fixed-effect vs. random-effects analyses
• Heterogeneity in effect sizes
• Using analysis of variance and meta-regression
• Criticisms of meta-analysis
• Publication bias

Participants will also learn how to perform a meta-analysis using the software Comprehensive Meta-Analysis, and will have free use of this program for the duration of the course.

Participants will be required to purchase a copy of Introduction to Meta-Analysis (Michael Borenstein, Larry V Hedges,  Julian Higgins, Hannah Rothstein), published by Wiley (2009).

Participants can ask questions and exchange comments with Dr. Borenstein via a private discussion board throughout the period.

The course takes place online at statistics.com in a series of 4 weekly lessons and assignments, and will require 10-15 hours per week. There are no set times when you are required to be online. 

nQuery Advisor easily calculates sample sizes for a wide variety of analyses. This short example shows how to use nQuery Advisor to calculate sample size for a survival analysis study in a cancer research setting.

Sample Size for survival analysis

The following short (4 minute) video shows how nQuery Advisor can be used to calculate sample size for a survival analysis study in an oncology setting.

You can discover much more about nQuery Advisor by visiting our main nQuery Advisor pages. You can even take a free trial of nQuery Advisor to try the software for yourself.

BMDP is recognized by leading statisticians and clinical researchers as one of the best statistical software packages for Survival Analysis. For many years, BMDP has been trusted by leading professionals to provide clear and accurate statistical analysis of research data.

 BMDP’s Survival Analysis programs, 1L &2L, are renowned for their ability to easily handle complex data and to accurately calculate and test survivor functions, Cox proportional hazards model and the log-linear (accelerated failure time) model.

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BMDP Survival Analysis program 1L carries out two major functions:

1. It provides estimates of the survival functions
2. It compares survival functions across different subgroups

Major Features include

• Life Table and Kaplan-Meier estimates of survival distribution
• Survival curves for all or subsets of patients
• Tests for equality of survival curves between groups, including Mantel-Cox, Tarone-Ware, Breslow and Peto-Prentice tests.
• Plots of cumulative survival function, hazard function
• Stratified Analysis
• Brookmeyer-Crowley confidence interval for median survival time
• Special features to handle more unusual analyses types.

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BMDP Survival Analysis Program 2L analyzes survival data for which the time-to-response is influenced by other measured variables. BMDP 2L provides 2 methods of analysis:

1. Cox Proportional Hazards Model
2. Accelerated Failure Time also known as the Log-Linear Model

Major Features Include:

• Cox proportional hazard models with built-in risk functions
• Accelerated failure time models (exponential, Weibull, log-logistic, log normal distributions)
• Optional stepwise variable selections based on two methods
• Competing risk analysis
• User specified time-dependent covariates
• Plots of cumulative survival function, log cumulative hazard function
• Stratified Analysis
• Hypothesis tests for joint significance of subsets of regression co-efficients
• Diagnostic plots (Cox-Snell residuals, standardized residuals Vs. each covariate, log of survival function Vs. time)

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In the comparison sheet that Cytel have used to compare nQuery Advisor and SiZ they have used three headings; Design, Simulation and Analysis.nQuery Advisor does not perform any statistical analysis, it is purely used for calculating power and sample size.
Also, nQuery Advisor uses closed form solutions for calculating sample size as much as possible, thereby eliminating the need for nQuery Advisor to perform simulations. Certain tables require simulation (for example with survival analysis) and where appropriate nQuery Advisor provides this.

In the SiZ document, there are several entries that refer to Non-inferiority/Superiority/Equivalence. In nQuery Advisor there are tables for equivalence for difference of means, ratio of means, difference of proportions. nQuery Advisor labels these tables as Equivalence but by setting the limits appropriately they can all be used for Non-Inferiority and Superiority also. There are examples of this in the nQuery Advisor manual. So it is a little misleading to say nQuery Advisor does not include these tables. The calculations can be done, they are just all under the heading of Equivalence.

nQuery Advisor also contains tables for calculating sample size for Survival Analysis, Agreement and Regression, none of which are present in SiZ. Here is a full list of the tables that nQuery Advisor offers:

http://www.statistical-solutions-software.com/nquery-advisor-sample-size-software/sample-size-tables/

Finally, the SiZ document lists an incorrect price for nQuery Advisor. nQuery Advisor is priced as follows: Academic Licenses US$795 / Euro 650 and Commercial Licenses US$995 / Euro 895. These prices are for perpetual licenses and no annual fees are payable.

Advantages of Meta Analysis

Advantages of meta-analysis (eg. over classical literature reviews, simple overall means of effect sizes etc.) include:

• Derivation and statistical testing of overall factors / effect size parameters in related studies
• Generalization to the population of studies
• Ability to control for between-study variation
• Including moderators to explain variation
• Higher statistical power to detect an effect than in ‘n=1 sized study sample’

Weaknesses of Meta Analysis

Meta-analysis can never follow the rules of hard science, for example being double-blind, controlled, or proposing a way to falsify the theory in question. Weaknesses include:

• Sources of bias are not controlled by the method
• A good meta-analysis of badly designed studies will still result in bad statistics.
• Heavy reliance on published studies, which may create exaggerated outcomes, as it is very hard to publish studies that show no significant results. (File Drawer Problem)
• Simpson’s Paradox (two smaller studies may point in one direction, and the combination study in the opposite direction)
• Dangers of Agenda Driven Bias: From an integrity perspective, researchers with a bias should avoid meta-analysis and use a less abuse-prone (or independent) form of research.

Comprehensive Meta Analysis Software

Comprehensive Meta Analysis is a software application developed by some of the world’s leading meta analysis experts. Comprehensive Meta Analysis is incredibly easy to learn and use, with a clear and intuitive interface. The interactive guide will walk you through all steps in the analysis, allowing new users to be productive within minutes. With Comprehensive Meta Analysis, the logic of meta-analysis comes alive.  Use the program to help explain complex issues, such as the impact of study weights on the combined effect, the implications of heterogeneity, or the distinction between fixed effect and random effects models.

Comprehensive Meta-Analysis will allow you to:

Work with a spreadsheet interface

Compute the treatment effect (or effect size) automatically

Perform the meta-analysis quickly and accurately

Create a high-resolution forest plot with a single click

Perform a cumulative meta-analysis

Perform a sensitivity analysis

Assess the impact of moderator variables

Work with multiple subgroups or outcomes within studies

Assess the potential impact of publication bias

Work with subsets of the data

Sample Size Calculation is a crucial part of study design. The sample size must be chosen correctly to allow a study to arrive a valid conclusions for the given scientific question. A study with a sample size that is too small may produce inconclusive results, while a study with a sample size that is too large will waste scarce resources.

The sample size of a statistical sample is the number of observations that constitute it. It is typically denoted n, a positive integer (natural number). Typically, all else being equal, a larger sample size leads to increased precision in estimates of various properties of the population, though the results will become less accurate if there is a systematic error in the experiment. This can be seen in such statistical rules as the law of large numbers and the central limit theorem. Repeated measurements and replication of independent samples are often required in measurement and experiments to reach a desired precision.

Sample Size Calculation Software

nQuery Advisor is the world’s leading sample size calculation software application. It is used by 1,000’s of researchers, statisticians, clinicians and data analysts at all leading global pharmaceutical and biotechnology companies, at renowned research institutes and universities and importantly, it is also the sample size calcualtion software of choice at regulatory agencies such as FDA.

nQuery Advisor allows you to:

Quickly and easily calculate your sample size and power
Easily generate your sample size calculation justification statement
Compare different sample size calculation options with easily customizable plots
Perform sample size and power calculations for a wide variety of statistical analyses
Work with a sample size calculation package that is used by leading regulatory agencies

Watch a short video on how easy it is to perform sample size calculations using nQuery Advisor

Download a FREE TRIAL of nQuery Advisor

Imputation is the practice of ‘filling in’ missing data with possible plausible values.  Therefore, it can be used when you are trying to analyse data that is incomplete or has “gaps”. This is an excellent way of solving missing-data problem at the start of your analytical process.

The issue of Missing Data is the subject of increasing debate in contemporary statistics. In any given study, missing data can have many causes. For instance, respondents may be unwilling to answer some questions (item non-response) or refuse to participate in a study (unit non-responses). In addition, transcription errors and dropouts in follow up studies and clinical trials can frequently occur.

IN 1987, Rubin and Little suggested that “naive” or unprincipled imputation methods may create more problems than they solve.

Major Advantages of Multiple Imputation:

• Better statistical validity than ad-hoc approaches
• Multiple Imputation is statistically efficient in that it uses the entire observed dataset in the statistical analysis, efficiency being the degree to which all information about the parameter of interest, available in the dataset, is used.
• Multiple Imputation saves money, since for the same statistical power, multiple imputation requires a smaller sample size than listwise deletion
• Once imputations have been generated by a knowledgeable user, researchers can use them for their own statistical analyses.

Multiple Imputation Explained

SOLAS for Missing Data Analysis, developed in conjunction with Prof. Donald B. Rubin, Harvard University, provides researchers with a range of imputation techniques in an easy to use, validated software application. SOLAS offers principled approaches to analyzing data with missing values, featuring 6 different imputation techniques and its own script language so that you can easily record your imputation choices.

Click here to download a free trial of SOLAS for Missing Data Analysis