Delayed-entry models using PROC PHREG in Survival Analysis

Statistical Consultancy Team

Time-to-event data often arise in clinical research, and in many cases represent the primary outcome of interest. These data generally represent the elapsed time between a reference time-point (e.g., treatment randomization) and an event of interest (e.g. death, relapse, etc.).

Whereas right censoring is a feature that is easily accommodated by most existing software, the same doesn’t strictly hold for another feature of survival data, left-truncation. In this post we’ll describe what left-truncation is, when it can arise and provide some SAS code that can be used to derive survival estimates and curves. 

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Topics: Survival Analysis, Statistical Programming, SAS Programming, SAS, PROC PHREG

Assessing Equivalence in Rheumatoid Arthritis in Clinical Trials

Statistical Consultancy Team

In Rheumatoid Arthritis (RA) clinical trials, treatment response is often assessed via the American College of Rhematology (ACR) composite responder score ACR20. This is a binary criterion that incorporates several indices of treatment activity in terms of symptoms reduction and is equal to 1 if at least 20% of improvement between a baseline and post-baseline measurement is observed in tender and swollen joint counts, in at least three out of five other indicators (C-Reactive Protein, patient global assessment of disease activity, physician global assessment of disease activity, patient pain scale, Health Assessment Questionnaire Disability Index), and 0 otherwise.

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Topics: Statistical Programming, Biostatistics Consulting, Rheumatoid Arthritis (RA), PROC NLMIXED

Celebrating Women in Science, Medicine and Mathematics

Marketing Quanticate

Happy International Women's Day! On this day, we wanted to celebrate the success of women within science, mathematics and medicine, and how they have helped shape the pharmaceutical industry in which we work. Without the hard work and success of these individuals, we may not be providing the level of healthcare available today. Today, half of medical graduates are female, as well as doctors and top researchers, it is reported that the number of statisticians equals the number of men. Therefore please read on to learn about some of the most influential women to celebrate international women’s day.

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Topics: Statistical Programming, Medical Writing, Clinical Programming, Drug Development, Nobel Prize, Women in Science

Outcomes Research Programming vs Traditional Clinical Trial Programming

Clinical Programming Team

Outcomes research aka health outcomes research, is the study of the end results of particular health care practices and interventions, in other words it is the study of what happens in the real world to patients when they are given a certain treatment or a certain method of care. Outcomes research studies are used to improve the quality and value of healthcare for patients.

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Topics: Statistical Programming, Clinical Study Design, Clinical Programming, Accessible Data, Big Data, Outcomes Research, Real World Data, Teradata SQL

Creating Custom or Non-Standard CDISC SDTM Domains

Clinical Programming Team

Within the Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model (SDTM), standard domains are split into four main types: special purpose, relationships, trial design and general observation classes. General observation classes cover the majority of observations collected during a study and can be divided among three general classes:

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Topics: Statistical Programming, CDISC Guidelines, CDISC SDTM, SDTM Domains, CDISC, Standardization, Clinical Programming

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)

Utilizing a Bayesian Informative Prior to Reduce Sample Size in Clinical Trials

Statistical Consultancy Team

Bayesian_Informative_Prior_to_Reduce_Sample_Size_in_Clinical_Trials

Bayesian statistics in clinical trials are becoming more widely used in the pharmaceutical industry. By gathering data from historical studies, it is possible to reduce the sample size of the current trial by using an informative prior in the Bayesian analysis. This blog explores five cases in different indications that have historical data on placebo subjects from the literature, and calculates the effective sample size using an informative prior. In some cases, the effective sample size is substantial, but in others there are no sample size savings despite abundant data in the literature.

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Topics: Bayesian Statistics, Bayesian Methods, Statistical Programming, Biostatistics Consulting

Is Multiple Imputation in Clinical Trials Worth the Effort?

Statistical Consultancy Team

In a case study examined to look at Multiple Imputation (MI) in clinical trials, comparing Active to Placebo treatment (at Weeks 2, 4, 6 and 12 of the trial) in adolescents with acne, drop outs were common.  The primary endpoint was the number of lesions at Week 12.  The factors believed to affect the propensity to be missing included age, side effects and lack of efficacy, and thus missing data patterns differ between groups. 

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Topics: Statistical Programming, FDA, SAS Programming, Statisticians in the Pharmaceutical Industry (PSI), Propensity Scoring, Multiple Imputation

Prentice-Wilcoxon Test for Paired Time-to-Event Data

Statistical Consultancy Team
In survival analyses we conventionally compare a time-to-event endpoint between two or more strata; patients are either represented in one strata or the other and the strata are independent of each other.
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Topics: Survival Analysis, Statistical Programming, Statisticians in the Pharmaceutical Industry (PSI), SAS Macros, Prentice Wilcoxon Test

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

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