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Understanding the Proportional Odds Assumption in Clinical Trials

Statistical Consultancy Team


Ordinal scales are commonly used to assess clinical outcomes; however, the choice of analysis is often sub-optimal.  In 2007, the Optimising Analysis of Stroke Trials (OAST) collaboration showed that ordinal-appropriate analyses of ordinal stroke outcome scales were preferable over binary analysis of a chosen ‘favourable’ outcome[1] but uptake of ordinal methods between 2007 and 2014 has been low [2].

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Topics: Randomization, SAS Programming, Biostatistics Consulting, Statistics, Ordinal Logistic Regression, Proportional Odds Assumption, PROC Logistic, Neurology

The Role of a Statistician In a Pre-Clinical Study

Statistical Consultancy Team

Pre-clinical trials, involving experiments in-vitro (literally ‘in glass’, i.e. in the laboratory, typically involving cells) and in-vivo (‘in animals’) are an essential part of drug development as it is a regulatory requirement  to investigate the safety of new drugs in-vitro and/or in-vivo before they are tested in humans. These trials can also be used to investigate the potential efficacy of new compounds.

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Topics: Pharmacokinetics and Pharmacodynamic, Randomization, Specialist Biometrics CRO, Pre-Clinical Study, Biostatistics Consulting

Conducting Randomization in Clinical Trials [VIDEO]

Statistical Consultancy Team

 

When running a clinical trial the industry standard is a double-blind placebo‑controlled parallel group trial. This is because it is the best way to ensure that the characteristics of subjects in each treatment group are the same, whilst ensuring the investigators cannot anticipate the treatment of a subject.

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Topics: Clinical Trials, Statistical Programming, Randomization, Clinical Documents, Serious Adverse Events (SAEs)

Efficient Data Reviews and Quality in Clinical Trials [Video]

Statistical Consultancy Team

This video is presented by Kelci Miclaus from SAS JMP who was a speaker at Clinical Data Live 2013. Her presentation was is titled: 'Efficient Data Reviews and Quality in Clinical Trials'.

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Topics: Bayesian Statistics, CDISC, FDA, Standardization, Remote Monitoring, Remote Data Capture, Source Data Verification (SDV), Randomization, SAS Programming, On-Site Monitoring, Serious Adverse Events (SAEs), Quality Control, Visualization, Additional Monitoring, Efficient Data Review, Fraud Detection, Patient Safety

Longitudinal Observational Data in a Paediatric Disease Registry

Statistical Consultancy Team

The use of population-based disease registries to support ongoing data collection for long-term safety and clinical outcomes is becoming increasingly common. Data collection methods within registries can vary in terms of completeness and quality. This particular example arose from support to a post-registration commitment for marketing authorisation of a paediatric drug and aims to provide some insight to the techniques and strategies used to monitor paediatric development (growth, sexual maturation) and clinical outcomes of varying severity. The challenges of accounting for irregular follow-up and associated biases are illustrated, and potential statistical solutions described. Recommendations for future reporting are presented as part of the conclusions. 

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Topics: Statistical Programming, Randomization, Paediatric Disease Registry

Complex Randomization in Clinical Trials Need Not Be Complex

Statistical Consultancy Team

 

Whilst delivering statistical consultancy to one of our clients, we designed a clincial trial with five treatment arms: three were various doses of the active compound, one arm was a positive control and the final arm was a negative control.

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Topics: Statistical Programming, Clinical Study Design, Interim Analysis, Randomization

Multiple Forest Plots and the use of SAS Macros

Clinical Programming Team

 

This blog post discusses the SAS/Graph Annotation option and how this can be used in combination with SAS Macros to allow the creation of multiple Forest Plots, giving details on what can and cannot be controlled as part of the macro call. The purpose of this paper is to highlight the methods of using the ANNOTATE Option available with the SAS/GRAPH procedures for producing a Forest Plot using the SAS system and how this can be adapted to allow multiple plots to be created using SAS Macros.

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Topics: Randomization, SAS Programming, SAS Macros, SAS Forest Plots

The Use of Propensity Scoring in Pooling Data across Clinical Trials

Statistical Consultancy Team

A member of Quanticate's statistical consultancy team presented a poster on “Using a Propensity-Pairing Algorithm to Reduce Bias due to Imbalances in Covariates: A Case Study Pooling Data from 5 Kidney Transplant Trials” at a conference for statisticians in the pharmaceutical industry (PSI).  This work uses a range of statistical methods including stepwise logistic regression, conditional logistic regression, principal component scores and mixed modeling.

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Topics: Randomization, Specialist Biometrics CRO, Biostatistics Consulting, Propensity Scoring

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