Statsols 20多年來一直通過統計軟體引領市場。nQuery 是世界上最值得信賴的樣本量和功耗分析軟體之一。
7 New Adaptive Tables
What's New in the nQuery Adapt Module
The Winter 2019 release extends the number of tables on offer for adaptive designs, including for designs used in Phase II clinical trials. 7 new tables have been added.
In this release, the main areas are:
- MCP-Mod (Multiple Comparisons Modelling)
- Simon’s Two-Stage Design
- Group Sequential Tests for Counts/Rates
2 New Bayesian Tables
What's New in the nQuery Bayes Module
The Winter 2019 release extends the tables for sample size calculation using Bayesian credible intervals. This release extends the sample size framework to the single proportion interval case.
A binomial proportion interval is an interval for the most probable values for a given binomial proportion e.g. number of yes/no’s. A credible interval is a Bayesian interval which contains a given proportion of posterior density for the given parameter, here the binomial proportion, within it.
The method in nQuery assumes a highest posterior density (HPD) interval with a beta prior for the proportion with three criteria for sample size provided: average length criterion (ALC), average coverage criterion (ACC), worst outcome criterion (WOC).
Tables are provided for the HPD credible interval, where the final interval of interest will only be the credible interval, and mixed Bayesian/Likelihood interval, where the final interval needs to fulfill both the credible and likelihood (i.e. frequentist confidence interval) error definitions.
The 2 new tables are:
- Binomial Proportion using Credible Intervals Bayes
- Binomial Proportion using MBL Credible Intervals
13 New Core Tables
What's New in the nQuery Core Module
The Winter 2019 release extends the number of tables on offer for classical trial designs. 13 new tables have been added.
In this release, the main areas are:
- Count/Incidence Rate Models
- Post-Marketing Surveillance
- Non-Inferiority Log Rank
Survival analysis is a branch of statistics for analyzing the expected duration of time until one or more events happen.
Calculate the true probability of success and formalize your sample size sensitivity analysis.
Make trials more flexible by using interim results to modify the trial’s course in accordance with pre-specified rules.
Compare two means or compare the mean of your sample data to a known value.
The measure that attempts to determine the strength of the relationship between one dependent variable and a series of other changing variables.
Cluster Randomized Trials
A type of randomised controlled trial in which groups of subjects (as opposed to individual subjects) are randomised.
- CRT Two Means Completely Randomized
- CRT Two Proportions Inequality Completely Randomized
- CRT Two Proportions Equivalence Completely Randomized
- CRT Two Proportions Non-Inferiority Completely Randomized
- CRT Two Proportions Superiority by a Margin Completely Randomized
- CRT Two Means Matched Pairs
- CRT Two Rates Completely Randomized
- CRT Two Rates Matched Pair
- CRT Two Proportions Matched Pair
- CRT Two Survival Curves
- CRT Two Means Non-Inferiority Completely Randomized
- CRT Two Means Completely Randomized with Unequal k's/m's
- CRT Two Means Equivalence Completely Randomized
- CRT Two Means Superiority by a Margin Completely Randomized
Studies that measure the proportion of people who have some characteristic or compare this proportion with another group.
Assessing correlation or relationship between two measurements.
The longitudinal study in which subjects receive a sequence of different treatments.
- Test for Pairwise Mean Differences in a Williams Crossover Design
- Non-Inferiority Test for Pairwise Mean Differences in a Williams Crossover Design
- Equivalence Test for Pairwise Mean Differences in a Williams Crossover Design
- Superiority by a Margin Test for Pairwise Mean Differences in a Williams Crossover Design
- Test for Pairwise Proportion Differences in a Williams Crossover Design
- Non-Inferiority Test for Pairwise Proportion Differences in a Williams Crossover Design
- Equivalence Test for Pairwise Proportion Differences in a Williams Crossover Design
- Superiority by a Margin Test for Pairwise Proportion Differences in a Williams Crossover Design
- Test for Generalised Odds Ratio 2x2 Crossover
- Equivalence Test for Generalised Odds Ratio 2x2 Crossover
- Non-Inferiority Test Generalised Odds Ratio 2x2 Crossover
Other miscellaneous tables for sample size calculation.
- CI for Variance Variances
- CI for Variance Relative Error
- CI for Variance Tolerance Prob
- CI for Two Variances
- CI for Two Variances Relative Error
- CI for Standard Deviation SD
- CI for Standard Deviation Tol
- CI for Standard Deviation Rel Error
- One Sample t-test for Log-Normal data
- Paired t-test for Mean Ratio (logscale)
- Wilcoxon Sign-Rank Test
- Inequality Tests for Two Means in a Cluster-Randomized Design (Unequal k's/m's)
The study and analysis of the distribution and determinants of health and disease conditions in defined populations.
- Vaccine Efficacy Confidence Interval - Cohort Study
- Vaccine Efficacy Confidence Interval - Case-Control Study
- Logistic Regression for Binary Covariate
- Conditional Logistic Regression binary
- Conditional Logistic Regression continuous
- Mendelian Randomized Trial - 2 Means
- Mendelian Randomized Trial - 2 Props
Equivalence / Non Inferiority
Determine if new therapies have equivalent or non inferior efficacies to the ones currently in use.
- Equivalence for One Mean
- Equivalence for Paired Means
- Equivalence for One Mean Ratio
- Equivalence for Paired Means Ratio
- Non-inferiority for One Mean
- Non-inferiority for One Log-Normal Mean
- Non-inferiority for Paired Means Ratio
- Non-inferiority for cross-over design
- Non-inferiority for two-sample ratio on log-scale
- Non-inferiority for cross-over ratio on log-scale
- Non-inferiority for two-sample ratio on original scale
- Non-inferiority for cross-over ratio on original scale
- Equivalence for Two Proportions
- Non-inferiority for two Poisson Rates
- Equivalence for Two Poisson Rates
- Equivalence for Negative Binomial
- Non-inferiority for Negative Binomial
- Equivalence for Negative Binomial Unequal Follow-Up
- Non-inferiority for Negative Binomial Unequal Follow-Up
- Non-Inferiority for CRT Means
- Equivalence for CRT Means
- Superiority for CRT Two Means
- Non-inferiority for single proportions
Features / Utilities
IQ/OQ Validation, Automated Updates, nQuery Knowledge Base and fine tune calculations with the Specify Multiple Factors tool.
Easily conduct a variety of “What-If” scenarios to align your final sample size choice with your scientific and budgetary requirements.
- Power Vs. Sample Size
- Improved Custom Row Plots
- Multiple Boundaries Plot
- Inverse Boundaries Graph
- Boundaries Graph
|Advanced Plus||Advanced Pro||Expert|
Calculate Sample Size
& Power Confidently
for Regulatory Approval
|Integrate Prior Information,
Real World Data and
|Run Sophisticated Adaptive Trials while adhering
to the most up-to-date guidelines
|Custom bespoke solutions
for your sample size
& power requirements
Tables & Procedures
Everything in Advanced,
with the addition of:
Everything in Advanced Plus, with the addition of:
Everything in Advanced Pro, with the addition of:
Sample Size Re-estimation Blinded
Posterior Credible Intervals
Sample Size Re-estimation Unblinded
Continuous Reassessment Method
Group Sequential Trials
On-Demand Access to Sample Size Experts
A Dedicated Account Manager
N of 1 Trials
On-Boarding & Training
MINIMUM (known) system requirements for nQuery Advanced.
- 2 core processor of 1 GHz 'speed'
- 3 GB of available hard disk space
- 2GB RAM
- Windows 10, then it must be higher than version 1511.
- -Please note that Windows 10 S is not supported.
- .NET Framework 4.7.1
SOLAS for Missing Data Analysis，由哈佛大學的Prof. Donald B. Rubin開發，它是一個易於使用且經過驗證的應用軟體，可提供給研究人員一個範圍內的歸集(imputation)技術。SOLAS提供了有原則的方法，利用值丟失（missing value）來分析數據，共有6種不同的估算方法和自己的腳本語言，因此您可以輕鬆地記錄您的歸集選擇。
Multiple Imputation techniques
- Mahalanobis Distance Matching Method
- Predictive Mean Matching Method
- Predictive Model Based Multiple Imputation
- Propensity Score Based Multiple Imputation
- Propensity Score/Predictive Mean/Mahalanobis Distance Combination Method
Single Imputation techniques
- Hot Deck Imputation
- Predicted Mean Imputation (using Regression)
- Last Value Carried Forward (LVCF / LOCF)
- Group Means
- Unique Missing Data Pattern
- New Pre Imputation Marginplots
- Post Imputation Scatterplots
- Customizable plotting facility
- Plots integrated within all analyses
- Wide variety of charts and plots including
- Bar charts
- Mean comparison charts
- Box plots
- Normal probability plots
- Import and export to and from SAS, SPSS, Stata using the Data Transfer feature.
- Saves time, and eradicates file corruption issues
- Easy to use “drag and drop” features
- Save and read files in:
- SAS, SPSS, Stata, Minitab
- R, Excel, JMP, Systat
- S-Plus, CSV & more
- Spreadsheet-like data entry
- Easy specification of variable attributes: Type, role, grouping, cut-points, etc.
- Easy specification of variable transformation
- Script Language Facility
- Post Imputation Analysis
- Descriptive Statistics
- Frequency Tables