Datapharm Australia's highly trained statisticians are experts in producing high quality data analyses and reporting for clinical trials. With 25 years of applied clinical trials experience, no statistical problem is too complex for our team.
As part of a full service contract research organization, our statisticians work seamlessly with Datapharm trial monitors, data managers and medical writers to produce high quality statistical analyses and reporting.
Our statistics services include:
Datapharm statisticians have designed many clinical trials. From small early phase studies to multicentre late phase trials, our statisticians are experts in constructing scientifically defensible study designs for the task at hand. In recent studies, our statisticians have employed the use of parallel, Bayesian adaptive, crossover and factorial designs.
The sample size of a study must be carefully determined to ensure that research time, patient effort and study costs are not wasted. A suitable sample size depends upon a number of factors including experimental design, analysis techniques used, expected drop out rates, measurement variability and the magnitude of the expected treatment effect. Taking these factors into account, we use a combination of proprietary software and in house techniques to arrive at an appropriate sample size for your study.
Allocating patients into active and placebo groups is usually not as simple as flipping a coin. Whilst simple randomisation methods can sometimes be used in larger clinical trials, smaller trials can become unbalanced using such techniques. Our statisticians are experts in randomisation methods that balance treatment groups and control for the influence of covariates a priori.
A well designed clinical database promotes data accuracy, completeness, legibility, timeliness and is amenable to subsequent statistical analyses. As a full service CRO with dedicated data management, statistics and programming teams, we have extensive experience designing cost effective database solutions that support the integrity of your clinical data. Our clinical databases including eCRFs are 21CFR Part 11 FDA compliant.
As CDISC standards become the industry norm, it is advantageous to prepare and program your data according to the standard from the outset. Our statisticians have extensive experience preparing SAS programs to CDISC standards, fast tracking your regulatory submission.
Data conversion and validation are important steps in ensuring that final analysis datasets are an accurate representation of the patients from whom the data were collected. Our statisticians, programmers and data managers work together to check the plausibility of your data and perform data conversion without error.
By specifying inferential analysis and statistical techniques in advance, a well written SAP ensures that your study remains free from bias and compliant with ICH guidelines. Over the past 25 years, Datapharm has produced statistical analysis plans for studies across the full range of therapeutic areas.
Clear interpretation and reporting of statistical analysis results reduce the likelihood of regulatory delays. Our statisticians have a reputation for clear, concise interpretation of complex statistical results.
ICH guidelines specify that clinical trial tables and listings should adhere to a pre-specified format to suit inclusion in the full study report (FSR). Our statisticians and medical writers know what is required to format your tables and listings to global regulatory standards.
To minimise the possibility of error, our statistical analyses results are independently cross checked in house by our statistics and medical teams. On occasion, we also call on external consultants to obtain specialised quality checking.
In the clinical trials setting, a network meta analysis synthesizes both direct and indirect evidence for the effectiveness or safety of an experimental treatment. Unlike traditional meta analysis, which summarises the findings of multiple direct comparisons, a network meta analysis combines the results of two or more studies that have one treatment in common.