The nQuery April 2018 release will add a wide range of sample size tables ranging from extensions of pre-existing tables for a better and clearer user experience to the those based on the latest academic research and user feedback.
In the April 2018 release, we added 52 new sample size tables to the core product and 20 Bayesian tables. This release summary will provide an overview of what areas have been targeted in this release along with the full list of tables being added.
In the April 2018 release, three main overarching areas were targeted for significant improvement. These were:
There is also a number of tables which do not fall into these categories related to areas such as inequality testing for log-normal data, testing of variance and standard deviations and non-parametric tests. These are described at the end of this document.
We will provide background on each of these below and a list of the sample size tables which will be added in the April release. References for each method are provided at the end of this article
1. EpidemiologyEpidemiology is the branch of medicine which primarily studies the incidence, distribution, and possible control of diseases and other factors relating to health. Epidemiological studies are cornerstone of research into areas such as public health, health policy or preventative medicine (e.g. vaccines).
Due to often having to study the effect of medicines and interventions at a more complex society-wide level, processes and methods for epidemiology often adjust for problems that are less prominent in well-controlled clinical trials. These issues include being unable to individually randomise, relying on observational data or attempting to extract causal relationships from highly complex data.
Due to these and other issues, the study designs and statistical methods used by epidemiologists will often have to include adjustments for exogenous effects and have a greater reliance on processes such as pair-matching. While statistical methods for clinical trials provide a useful starting point for getting adequate sample size estimates, there is a growing desire for methods which have found traction in the epidemiological field.
In the April release, 12 new tables will be added with the main areas of focus in the Epidemiology upgrade being the following:
These areas and the tables in each category are explored below.
Case-Control Studies
Case-Control studies are those where the analysis assumes that the effect of a treatment or intervention or prognostic factor can be modelled by comparing the effect on a paired cases and controls. In the epidemiological context, this is most commonly associated with retrospective studies attempting to find a relationship between a risk factor and a disease of interest (e.g. effect of smoking on lung cancer rates) using pre-existing sources such as health databases.
In this context, the nQuery April 2018 release adds an additional four tables which should add additional flexibility when planning a case-control study using nQuery. These are:
These tables complement our pre-existing nQuery tables for chi-squared tests, exact tests, correlated proportions and logistic regression and add options for conditional logistic regression to allow for greater flexibility when exploring sample estimates for case-control studies.
Vaccine Efficacy
Vaccine efficacy studies face significant challenges compared to other clinical trials. These include having a much larger scale due often being nation or region wide campaigns, dealing with rare diseases or conditions and the challenges of doing work in the field rather than in a fully controlled setting. For reasons such as these, vaccine efficacy designs and statistical methods have developed their own approaches and terminology to help the relevant researchers and public or private bodies of interest.
For vaccine efficacy, the nQuery April 2018 release adds two additional tables tailored for finding the sample size for the precision of an estimate of the vaccine efficacy. These are:
In conjunction with our wide range of pre-existing tables for binomial proportions and survival rates, these tables will give researchers in vaccine research more tailored options for their study.
Cluster Randomized Trials
Cluster randomized trials are studies where the unit of randomization is a cluster or group rather than the individual subject. This is a common design when there are natural blocks or clusters such as schools or hospitals. By assigning the same treatment to all subjects within a cluster, the administrative and financial cost of field trials can be reduced significantly. For this reason and others, this design type is very commonly seen in public health policy studies.
For cluster randomized trials, the nQuery April 2018 release includes four additional tables which expand upon our pre-existing options for cluster randomized trials. These are:
These options expand upon our pre-existing tables for cluster randomized trials comparing means, proportions, incidence rates and survival curves and for alternative cluster randomized trials such as the matched-pair design.
Mendelian Randomization
Mendelian Randomization is form of randomization which takes advantage of the growing availability and understanding of genetic information to make causal claims about potential treatments without using the common fully randomized approach. By using well characterised relationships between genes and phenotypes with a known secondary effect on an outcome of interest, mendelian randomization offers the opportunity to use genetic information as a instrumental variable to find the causal relationship between a risk factor of interest and a disease outcome.
For studies which use Mendelian Randomization, the nQuery April 2018 release provides two new tables. These are:
These provide the first tables in nQuery which account for this novel design and innovative approaches such as this will be of active interest in the area
2. Non-inferiority and Equivalence Testing
Non-inferiority and equivalence testing are used to statistically evaluate how similar a proposed treatment is to a pre-existing standard treatment. This is a very common objective in areas such as generics and medical devices. This is particularly important if using a placebo group would be required otherwise.
As non-inferiority and equivalence testing will typically involve evaluation against a well-defined treatment (e.g. RLD), there is a lower incidence of the large parallel studies typically seen in Phase III clinical trials. One-sample, paired samples or cross-over designs are common as these will generally require a lower cost and sample size.
In the April release, we will be adding an additional 20 (22 in the CRT Means reference above are included) tables for non-inferiority and equivalence testing. These are focused on expanding the options available for the non-inferiority and equivalence testing of continuous data, binary data and incidence rates. The focus areas are as follows:
These areas and the tables in each category are explored below.
Continuous Outcome Studies
In the context of non-inferiority and equivalence testing, the comparison of continuous outcomes using means is the most common situation to encounter. A wide range of design types and statistical methods are available for comparing this type of data depending on the assumptions and constraints relevant to proposed study. Common design types in this context would be one-sample, paired, cross-over and parallel studies. The most common statistical methods are based on assuming either that the data is normally distributed and comparing the difference in means (Additive model) or that the data is log-normally distributed and analysing the ratio of (geometric) means (Multiplicative model).
For non-inferiority and equivalence testing of continuous data, the nQuery April 2018 release adds an additional 12 tables. These are as follows:
These tables expand upon the large number of pre-existing tables for non-inferiority and equivalence testing for means to give the largest number of options available to find the sample size.
Binary Outcome Studies
In the context of non-inferiority and equivalence testing, the comparison of binary is less common but has grown in more recently as additional statistical methods have become popularised. Common design types in this context would be one-sample, paired and parallel studies. In the context of binary data, one of the most noticeable aspects is wide variety of options available ranging from relatively simple normal approximation tests to more complex exact methods and sample size methods have followed this trend in regard to binary data analyses generally.
For non-inferiority and equivalence testing of binary data, the nQuery April 2018 release adds an additional 2 tables. These are as follows:
Note that both these tables integrate more exact binomial enumeration methods as an option in addition to the typical normal approximation methods. They also include options for a wide range of proposed statistics with the main categories being chi-squared tests (including option for continuity correction), Z and t-test approximations and several likelihood score statistics (Miettinen and Nurminen, Gart & Nam, Farrington and Manning). These tables expand upon the large number pre-existing tables for the non-inferiority and equivalence testing of binary proportions.
Incidence Rates Studies
In the context of non-inferiority and equivalence testing, the comparison of incidence rates is a relatively uncommon scenario. Incidence rates are where the outcome of interest is the number of events which occur on average in a given time period (i.e. the event rate). The wider availability of software to analyse incidence rates directly rather than relying on normal approximations has seen a growth of interest in methods such as Poisson and Negative Binomial regression. This has naturally extended to the case of non-inferiority and equivalence testing of incidence rate data. The time-dependent nature of incidence rates means that models can integrate greater flexibility for time dependencies and this is reflected in the rapidly growing literature for sample size in the area.
For non-inferiority and equivalence testing of incidence rates data, the nQuery April 2018 release adds 6 tables. These are as follows:
These tables expand upon the pre-existing options for analysing incidence rate data in the context of inequality testing. These methods represent the latest research and in the last two tables can integrate the effects of dispersion and unequal follow-up on the sample size estimate.
3. Correlation and Diagnostic Testing (ROC) Methods
Correlation, agreement and ROC methods are interested in characterising the strength of the relationship between a predictor (e.g. presence of treatment) and outcome (disease progression) of interest. These measures are often used in conjunction with models and statistical testing to characterise the nature of the relationship of interest. These methods are of interest when attempting to communicate the strength of a model or a relationship.
These types of measures are seen throughout statistical practise but are particularly prominent in areas such as diagnostic testing, the social sciences and biomarker studies.
In the nQuery April release, we will be adding an additional 9 additional tables in this area which fall in the following main categories:
These are summarised below.
Correlation and Agreement Measures
Correlation measures are used to characterise the strength of relationship between continuous and/or ordinal outcomes and measures such as Pearson’s correlation are ubiquitous in statistical practise. Agreement measures are used to analyse the strength of the ability of more than one rater (e.g. tester or test) to agree and correctly diagnose the condition of one or more entities (e.g. subject disease status). Both of these are common outcomes of interest in a wide variety of settings.
Due to the ubiquity of these methods, a wide range of measures have been proposed to adjust for scenarios which diverge from the most common correlation and agreement measures (e.g. Pearson correlation and Cohen’s Kappa). Common complications adjusted for are the presence of ordinal instead of continuous variables or divergences from common distributional assumptions (e.g. Normal)
In the nQuery April release, we are adding four additional options in this area. These are as follows:
These add to the options available for other common correlation and agreement measures such as the Pearson Correlation, Lin’s Concordance Coefficient and Cohen’s Kappa Coefficient.
Diagnostic Screening Measures
Diagnostic screening measures are very common in clinical research. These measures are used to assess the performance of a diagnostic test to accurately predict a condition of interest in the population(s) of interest. Areas where this type of analysis have become particularly popular are biomarker studies, machine learning and predictive genetic tests.
Commonly, this strength is characterised by the Area under the Curve (AUC) of the Receiving Operating Curve (ROC) which provides a useful summary measure of screening performance over all potential cut-off points for a screening measure. However, a large number of other statistics may be of interest at specific cut-offs such as sensitivity (a.k.a. recall), specificity and positive predictive value (PPV, a.k.a. precision), among other.
In the nQuery April release, we are adding 5 new tables in this area. These are as follows:
These add to the pre-existing tables already present in nQuery for the testing of AUC values under varying design types and sensitivity and specificity.
In the April release of the nQuery, 11 tables do not fit into the above categorisations. These cover areas such as the testing of log-normal means, the testing of variances and standard deviations and non-parametric tests. These tables are as follows:
These options expand upon nQuery’s pre-existing options in these areas.
Below are the Bayesian tables that have been added nQuery in this release: