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What's New in nQuery?

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Summer 2020 - nQuery Update

From fixed to flexible trial designs, the Summer 2020 nQuery release (v8.6) sees the continued strengthening of nQuery to help biostatisticians and clinical researchers save costs and reduce risk in their clinical trial designs.

26 new sample size tables have been added in total.

nQuery PRO

New Adaptive/Pro Tables

nQuery BASE

New Core Tables

5 New Adaptive Tables

(Included in the Pro Package) 

What's new in nQuery? (Adaptive Module)

The Summer 2020 (v8.6) release sees 5 new sample size tables added to nQuery Advanced PRO tier, covering various new adaptive designs.

In this release, the following areas have been targeted for development:

  • Multi-Arm Multi-Stage Designs (MAMS) GSD for Means
  • Generalized MCP-Mod (Multiple Comparisons Procedure - Modelling)
  • Phase II Group Sequential Tests for Proportions (Fleming’s Design)

Multi-Arm Multi-Stage Designs (MAMS) GSD for Means

Adaptive designs, such as the group sequential design, have become widely used in clinical drug development. However, much of this development has been confined to the two-arm setting. A common situation in clinical trials is that in which an optimal treatment or dose has not been identified prior to the phase III trial. In this situation it may be desirable to begin a Phase II trial (or longer Phase III trial) with several treatment arms and conduct interim analyses that will allow the dropping of less promising arms and potentially seamlessly move those arms into Phase III. This type of design is commonly named a multi-arm multi-stage design.

The Multi Arm Multi Stage (MAMS) group sequential design extends the common two-arm group sequential design to a design in which multiple treatment arms can be compared to a common control arm and allows the trial to be stopped for efficacy or futility based on the best performing arm. In addition, it can be shown that less effective arms may be dropped at interim analyses without affecting the Type I error.

MAMS designs provide the ability to assess more treatments in less time than could be done with a series of two-arm trials and can offer significantly smaller sample size requirements when compared to that required for the equivalent number of two-arm trials.

In this release, a MAMS Group Sequential design has been implemented for a continuous endpoint under normality. This provides a familiar framework with which to conduct a MAMS design and explore the additional flexibility offered.

1 table added has been added:

  • Multiple Arms Multiple Stage (MAMS) Group Sequential Design for Means

Generalized MCP-Mod (Multiple Comparisons Procedure - Modelling

MCP-Mod (Multiple Comparisons Procedure - Modelling) is an increasingly popular statistical methodology for dose-finding Phase IIb trials. Since its development at Novartis, MCP-Mod promises to devise proof-of-concept and dose-ranging trials with greater evidence and data that can prove critical for Phase III clinical trial design.

Combining the robustness of multiple comparisons procedures with the flexibility of modelling, MCP-mod combines these methods to provide superior statistical evidence from Phase II trials with regards to dose selection with the FDA & EMA approving MCP-Mod as fit-for-purpose (FFP).

The MCP-Mod methodology has been well developed for the case of normally distributed, homoscedastic data from a parallel group study design. In practice however many other types of endpoints are encountered and often in more complex settings like longitudinal studies for example. Generalized MCP-Mod extends the original MCP-Mod methodology to the context of general parametric models and for general study designs.

In this release, 3 tables will be added for generalized MCP-Mod. This extends the MCP-Mod methodology for a continuous endpoint (released in nQuery 8.5) to one for binary and count endpoints. Similar to the continuous MCP-Mod table, these new tables focus on the proof-of-concept stage of the study.

3 tables have been added for Generalized MCP-Mod

  • Multiple Comparisons Procedure - Modelling for Poisson Rates (MCP-Mod)
  • Multiple Comparisons Procedure - Modelling for Negative Binomial Rates (MCP-Mod)
  • Multiple Comparisons Procedure - Modelling for Binary Outcome (MCP-Mod)

Phase II Group Sequential Tests for Proportions (Fleming’s Design)

Typically, a phase II trial is designed to have a single stage in which a certain number of subjects are treated and the number of successes/responses are observed. However for ethical and economic reasons it may be of interest to stop the trial early, in particular for futility due to high failure rates at Phase II.

Based on the method described in Fleming (1982), the Phase II group sequential test is a one-sided multiple testing procedure that allows for early termination of the study if interim results are extreme. This is done by testing the accrued data at a number of interim stages and looking at the point estimate for the proportion of successes or responses observed at interim points.

Under this design, at each of the interim analyses, the trial may be stopped for futility if the interim cumulative number of successes is below an acceptance point and the trial may be stopped for efficacy if the interim number of successes is above a rejection point. In addition to the ethical benefit, this type of design allows for greater flexibility and cost savings while retaining operating characteristics similar to a fixed term trial.

In this release, a table will be added for a Phase II Group Sequential Tests for One Proportion. This is commonly referred to as Fleming’s Design.

1 table has been added:

  • Group Sequential Test of One Proportion (Fleming’s Design)

List of New Tables in nQuery's Adaptive Module

Multi-Arm Multi-Stage Designs (MAMS) GSD for Means
  • Multiple Arms Multiple Stage (MAMS) Group Sequential Design for Means
Generalized MCP-Mod (Multiple Comparisons Procedure - Modelling
  • Multiple Comparisons Procedure - Modelling for Poisson Rates (MCP-Mod)
  • Multiple Comparisons Procedure - Modelling for Negative Binomial Rates (MCP-Mod)
  • Multiple Comparisons Procedure - Modelling for Binary Outcome (MCP-Mod)
Phase II Group Sequential Tests for Proportions (Fleming’s Design)
  • Group Sequential Test of One Proportion (Fleming’s Design)

VIEW ALL SAMPLE SIZE PROCEDURES AVAILABLE

How To Update

To access the adaptive module you must have a nQuery Advanced Pro subscription. If you do, then nQuery should automatically prompt you to update.

You can manually update nQuery Advanced by clicking Help>Check for updates.

CLICK HERE FOR FULL DETAILS ABOUT UPDATING

nQuery Advanced Check for Updates

 

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21 New Base Tables

(Included in all packages: Base Plus & Pro Package) 

What's new in nQuery? (Base Module)

21 new sample size tables with additional endpoints and use cases have been added to the base nQuery module. This release summary provides an overview of which areas have been targeted, along with the full list of tables being added.

In this release, the four main areas targeted for development are:

  • Survival/Time-to-Event Trials
  • Cluster Randomized Stepped-Wedge Designs
  • Three Armed Trials Non-inferiority
  • Confidence Intervals for Proportions

Survival/Time-to-Event Designs

Survival or Time-to-Event trials are trials in which the endpoint of interest is the time until a particular event occurs, for example death or tumour regression. Survival analysis is often encountered in areas such as oncology or cardiology. In contrast to studies with non-survival based endpoints, like continuous or binary endpoints for example, the statistical power of a time-to-event study is determined, not by the number of subjects in the study, but rather by the number of events which are observed and this requires additional flexibility when designing and analyzing such trials.

In this release, sample size tables were added in the following main areas:

  • Flexible Log-Rank Test Designs
  • Tests for >2 Survival Curves
  • Piecewise Weighted Log-Rank Test for Delayed Survival Effect
  • One Sample Survival 

Flexible Log-Rank Test Designs

The log-rank test is one of the most common statistical tests used for the analysis of survival data. Its flexibility and interpretability provide useful insights into the comparable hazard rates in survival trials. In this release,

In this release, two tables are added for flexible log-rank test designs. These provide many additional options to give more flexibility over the design and assumptions when designing a survival study. Some of the new areas covered included sample size determination that targets the study length given accrual rates. This moves beyond the fixed study length assumption in previous tables. Secondly, these tables allow piecewise accrual, hazard rates/survival curves and dropout under both the fixed length and accrual rate tables. This flexibility will allow users to test the effect of deviations from uniform accrual, exponential survival curves and other similar assumptions.

2 new tables will be added for this area in the coming release:

  • Log-Rank Test, User-Specified Accrual Rates, Piecewise Survival and Dropout Rates
  • Log-Rank Test, User-Specified Accrual, Piecewise Survival and Dropout Rates

Tests for >2 Survival Curves

Extensions of the common survival methods to the >2 groups context are a useful addition to the methods available for survival analysis. Omnibus and contrast tests are common for other endpoints and should provide the same utility for the survival context.

In this release, two new tables were added that extend standard methods to this context. This release adds a table which uses an ANOVA-like log-rank omnibus test to test for the equality of a number of independent exponentially distributed log hazard rates. This release also adds a contrast test for exponential hazard rates and should be useful for dose-finding studies which have a survival endpoint.

2 new table will be added for this area in the coming release:

  • Omnibus Log-Rank Test of >2 Survival Curves
  • Contrast Test for Equality of >2 Survival Curves

Piecewise Weighted Log-Rank Test for Delayed Effect

There is increasing interest in survival analysis for flexible models due to the increasing occurrence of non-proportional hazards in clinical trials in areas such as immunotherapy. The piecewise weighted log-rank test has been proposed as one potential method that allows a survival analysis to account for a delayed effect. This method splits the trial into two or more time periods which are weighted to create a better estimate of the overall effect by accounting for the different effect sizes expected during different time intervals.

In this release, the Analytic Power Calculation Method based on the Piecewise Weighted Log-rank Test (APPLE) method approach for determining the sample size for a weighted log-rank test for a delayed effect is added. This method is useful in detecting a difference between two hazard functions when there is a delayed onset of the clinical effect. This results in a delayed separation of the survival curves for the experimental and control groups, so the proportional hazard assumption no longer holds. This type of design is often encountered in immunotherapy trials when the immune system takes time to respond to the treatment.

1 new table will be added for this area in the coming release:

  • Piecewise Weighted Log-rank Test for Delayed Effect Survival Model (APPLE Method)

One-Sample Survival

A one-sample trial is simply one in which a single treatment arm is included in the study. As many new treatments in areas where survival analysis is commonly used, like oncology for example, are cost-prohibitive and have slow accrual rates, researchers are often faced with the restriction of conducting single arm trials.

In this release two one-sample tables for survival are included. The first of these builds upon prior one sample log-rank test tables by using an exact parametric approach for the one sample log-rank test which is more appropriate in trials with small to moderate sample sizes where the asymptotic assumptions of other methods may not hold. The second of these tables finds the number of events or confidence interval width for a one-sample confidence interval for a median survival time assuming an exponential survival curve and Type II censoring. The median survival is the time at which 50% of the population have the event.

The 2 new tables added for this area are as follows:

  • One Sample Testing using Exact Parametric Test
  • One Sample Confidence Interval for Median Survival

Cluster Randomized Stepped-Wedge Designs

A parallel cluster-randomized trial (CRT) is one in which entire groups or clusters of subjects are randomized to either the treatment or control group rather than the individual subjects within these clusters. A stepped-wedge CRT is one in which each cluster must receive both the treatment and the control over time, and in which the clusters may cross-over in one-direction only, usually from the control group to the treatment group.

Cluster-randomized designs are often adopted when there is a high risk of contamination if cluster members were randomized individually. Stepped-wedge designs are useful in cases where it is difficult to apply a particular treatment to half of the clusters at the same time.

In this release, three Stepped-Wedge CRT tables are added. The Cluster Randomized Stepped-Wedge design tables contained in this release will cater for both complete and incomplete designs. In a complete stepped-wedge design, all clusters are initially assigned to the control group and a fixed number of clusters switch to the treatment group at each step. In an incomplete design, the number of clusters switching at each step can vary. When a cluster has switched from the control to treatment group, it remains in the treatment group for the remainder of the study. At each point in time, different subjects are measured within each cluster and no subject is measured more than once. The types of incomplete designs available to users will be Balanced, Unbalanced, and Sequential designs. The option is also available for users to enter their own custom design pattern.

The summer 2020 release will see 3 new tables added for this area:

  • CRT Stepped-Wedge design for Inequality of Two Means
  • CRT Stepped-Wedge design for Inequality of Two Proportions
  • CRT Stepped-Wedge design for Inequality of Two Poisson Rates

Non-Inferiority Testing for Three-arm Trials

Non-Inferiority tests are used to test if a proposed treatment is non-inferior to a pre-existing treatment by a specified amount. This is a common objective in areas such as medical devices and generic drug development. Three armed trials compare the effect of an experimental treatment against a reference treatment by comparing both of their effects versus a third placebo group. The main benefit of this type of design is that by including a placebo in the active trial, the effect of the reference treatment versus placebo need not be assumed based on historical trials, but can be calculated concurrently.

In this release, six new tables are added for three armed trials. These will cover this design type for continuous, incidence rate, proportion and survival endpoints using a common framework for the analysis of the trial type.

The summer 2020 release will see 6 new tables added for this area:

  • Non-Inferiority Test for Means in a Three Armed Trial with Common Variance
  • Non-Inferiority Test for Means in a Three Armed Trial with Uncommon Variance
  • Non-Inferiority Test for Poisson Rates in a Three Armed Trial
  • Non-Inferiority Test for Negative Binomial Rates in a Three Arm Trial
  • Non-Inferiority Test for Proportions in a Three Armed Trial
  • Non-Inferiority Test for Exponential Survival in a Three Armed Trial

Confidence Intervals for Proportions

A confidence interval calculates an interval of “probable” values for an unknown parameter. In sample size determination, the target for a study is the desired width (precision) of the calculated interval. Note that intervals are seen as a potential alternative to p-values and some argue more usage of interval based sample size determination.

Proportions are a common type of data where the most common endpoint of interest is a dichotomous variable. Examples in clinical trials include the proportion of patients who experience a tumour regression. There are a wide variety of confidence intervals proposed for binary proportions ranging from exact to maximum likelihood to normal approximations.

In this release, five new tables will be added to the binary proportion confidence intervals for the one sample, two sample and cluster randomized contexts. These tables include sample size determination for a variety of possible confidence intervals such as score intervals like the Farrington & Manning Score, Miettinen & Nurminen Score, Gart & Nam (Skewed) Score, Wilson Score; exact intervals such as the Clopper-Pearson and Cornfield; and asymptotic intervals such as the normal approximation and Log Ratio (Katz).

5 new tables will be added for this area in the coming release:

  • Confidence Intervals for One Proportion
  • Confidence Interval for the Difference between Two Proportions
  • Confidence Interval for the Ratio of Two Proportions
  • Confidence Interval for the Odds Ratio of Two Proportions
  • CRT Confidence Interval for One Proportion

List of New Core Tables

Survival/Time-to-Event Designs
  • Flexible Log-Rank Test Designs
  • Tests for >2 Survival Curves
  • Piecewise Weighted Log-Rank Test for Delayed Survival Effect
  • One Sample Survival
Flexible Log-Rank Test Designs
  • Log-Rank Test, User-Specified Accrual Rates, Piecewise Survival and Dropout Rates
  • Log-Rank Test, User-Specified Accrual, Piecewise Survival and Dropout Rates
Tests for >2 Survival Curves
  • Omnibus Log-Rank Test of >2 Survival Curves
  • Contrast Test for Equality of >2 Survival Curves
Piecewise Weighted Log-Rank Test for Delayed Effect
  • Piecewise Weighted Log-rank Test for Delayed Effect Survival Model (APPLE Method)
One-Sample Survival
  • One Sample Testing using Exact Parametric Test
  • One Sample Confidence Interval for Median Survival
Cluster Randomized Stepped-Wedge designs
  • CRT Stepped-Wedge design for Inequality of Two Means
  • CRT Stepped-Wedge design for Inequality of Two Proportions
  • CRT Stepped-Wedge design for Inequality of Two Poisson Rates
Non-Inferiority Testing for Three-arm Trials
  • Non-Inferiority Test for Means in a Three Armed Trial with Common Variance
  • Non-Inferiority Test for Means in a Three Armed Trial with Uncommon Variance
  • Non-Inferiority Test for Poisson Rates in a Three Armed Trial
  • Non-Inferiority Test for Negative Binomial Rates in a Three Arm Trial
  • Non-Inferiority Test for Proportions in a Three Armed Trial
  • Non-Inferiority Test for Exponential Survival in a Three Armed Trial
Confidence Intervals for Proportions
  • Confidence Intervals for One Proportion
  • Confidence Interval for the Difference between Two Proportions
  • Confidence Interval for the Ratio of Two Proportions
  • Confidence Interval for the Odds Ratio of Two Proportions
  • CRT Confidence Interval for One Proportion

VIEW ALL SAMPLE SIZE PROCEDURES AVAILABLE

How To Update

If you have nQuery Advanced installed, nQuery should automatically prompt you to update.

You can manually update nQuery Advanced by clicking Help>Check for updates.

CLICK HERE FOR FULL DETAILS ABOUT UPDATING

nQuery Advanced Check for Updates

If your nQuery home screen is different, you are using an older version of nQuery.
Please contact your Account Manager.

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See how the new features can be applied to your trial designs

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Flexible Clinical Trial Design
Survival, Stepped-Wedge & MAMS Designs

1

About This Webinar

Using features in nQuery V.8.6, we will explore a select few topics that highlight the additional flexibility available when designing modern clinical trials.

2

What you will learn about

  • Flexible Survival Analysis Designs
  • Stepped-Wedge designs
  • Multi-Arm Multi-Stage (MAMS)
3
Host
Speaker: Ronan Fitzpatrick, Head of Statistics, Statsols
Duration: 60 minutes

Watch Now