Tag Archives: research studies

Even Mild COVID-19 Cases Can Deliver Antibody Protection For Life, Study Finds

https://www.dailywire.com/news/even-mild-covid-19-cases-can-deliver-antibody-protection-for-life-study-finds

PROJECT CAMELOT, [May 27, 2021 at 1:06 PM]
Darpa Hydrogel hollow nylon fibers fill the FFP2 class plastic respirators. This Darpa Hydrogel with additives is slowly released from the respirators. When breathing, it enters the human airways.
The swabs used for PCR are also composed of hollow nylon fibers with Darpa Hydrogel.— AFFECTS PINEAL GLAND

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30 studies showing lockdowns don’t work

30 studies showing lockdowns don’t work:

1. https://onlinelibrary.wiley.com/doi/abs/10.1111/eci.13484

2. https://www.medrxiv.org/content/10.1101/2020.07.22.20160341v3

3. https://www.thelancet.com/journals/eclinm/article/PIIS2589-5370(20)30208-X/fulltext

4. https://advance.sagepub.com/articles/preprint/Comment_on_Dehning_et_al_Science_15_May_2020_eabb9789_Inferring_change_points_in_the_spread_of_COVID-19_reveals_the_effectiveness_of_interventions_/12362645

5. https://arxiv.org/pdf/2005.02090.pdf

6. https://www.datascienceassn.org/sites/default/files/Illusory%20Effects%20of%20Non-pharmaceutical%20Interventions%20on%20COVID19%20in%20Europe.pdf

7. https://www.timesofisrael.com/the-end-of-exponential-growth-the-decline-in-the-spread-of-coronavirus/

8. https://www.medrxiv.org/content/10.1101/2020.05.01.20088260v2

9. https://www.medrxiv.org/content/10.1101/2020.04.24.20078717v1

10. https://www.medrxiv.org/content/10.1101/2020.09.26.20202267v1

11. https://www.nicholaslewis.org/did-lockdowns-really-save-3-million-covid-19-deaths-as-flaxman-et-al-claim/

12. https://www.bmj.com/content/371/bmj.m3588

13. https://www.medrxiv.org/content/10.1101/2020.03.30.20047860v3

14. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2652751/

15. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3607803

16. https://imgcdn.larepublica.co/cms/2020/05/21180548/JP-Morgan.pdf

17. https://jamanetwork.com/journals/jama/fullarticle/2768086

18. https://www.medrxiv.org/content/10.1101/2020.10.09.20210146v3

19. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3665588

20. https://www.nber.org/papers/w27719

21. https://www.bmj.com/content/370/bmj.m3543

22. https://www.medrxiv.org/content/10.1101/2020.11.01.20222315v1

23. https://pandata.org/wp-content/uploads/2020/07/Exploring-inter-country-variation.pdf

24. https://www.nejm.org/doi/full/10.1056/NEJMoa2029717

25. https://www.medrxiv.org/content/10.1101/2020.08.04.20168112v1#:~:text=The%20seroprevalence%20of%20COVID%2D19,care%20workers%20in%20Niger%20State

26. https://www.frontiersin.org/articles/10.3389/fpubh.2020.604339/full

27. https://www.tandfonline.com/doi/abs/10.1080/00779954.2020.1844786?journalCode=rnzp20

28. http://www.upmc-biosecurity.org/website/resources/publications/2006/2006-09-15-diseasemitigationcontrolpandemicflu.html

29. https://www.medrxiv.org/content/10.1101/2020.12.25.20248853v1

30. https://www.medrxiv.org/content/10.1101/2020.12.28.20248936v1

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Clinical Trials 101

Clinical trials are research studies that involve people and test new ways to prevent, detect, diagnose, or treat cancer and other diseases. At the conclusion of this webinar, you will be able to demonstrate a basic understanding of the basics of clinical trials, including how they work, protections for participants and factors related to participation.

Source: NCI Events

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anayansi gamboa

Cancer Clinical Trials: What is a Clinical Trial?

Clinical trials are research studies that test how well new medical approaches work in people.

Source: The National Cancer Institute (NCI) supports a vast array of clinical trials designed to test new ways to treat, prevent, detect, or diagnose cancer as well as new methods to improve cancer patients’ quality of life.

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anayansi gamboa

CDISC Clinical Research “A” Terminology

acronym: A word formed from the beginning letters (e.g., ANSI) or
a combination of syllables and letters (e.g., MedDRA) of a name or phrase.
admission criteria:Basis for selecting target population for a clinical trial.
Subjects must be screened to ensure that their characteristics match a list of admission criteria and that none of their characteristics match any single one of the exclusion criteria set up for the study.
algorithm: Step-by-step procedure
for solving a mathematical problem;
also used to describe step-by-step
procedures for making a series of
choices among alternative decisions to
reach a calculated result or decision.
amendment: A written description
of a change(s) to, or formal clarification
of, a protocol.
analysis dataset:An organized collection of data or
information with a common theme arranged in rows and columns and
represented as a single file; comparable to a database table.
analysis variables: Variables used
to test the statistical hypotheses
identified in the protocol and analysis
plan; variables to be analyzed.
approvable letter:An official communication from FDA to an
NDA/BLA sponsor that lists issues to be resolved before an approval can be issued.
[Modified from 21 CFR 314.3;Guidance to Industry and FDA Staff

arm: A planned sequence of elements,
typically equivalent to a treatment
group.

attribute (n): In data modeling,
refers to specific items of data that can
be collected for a class.
audit:A systematic and independent
examination of trial-related activities
and documents to determine whether
the evaluated trial-related activities were
conducted and the data were recorded,
analyzed, and accurately reported
according to the protocol, sponsor’s
standard operating procedures (SOPs),
good clinical practice (GCP), and the
applicable regulatory requirement(s).
[ICH E6 Glossary]
audit report: A written evaluation by
the auditor of the results of the audit.
[Modified from ICH E6 Glossary]
audit trail. A process that captures
details such as additions, deletions,
or alterations of information in an
electronic record without obliterating the original record. An audit trail
facilitates the reconstruction of the
history of such actions relating to the
electronic record.

Source:Applied Clinical Trials

Anayansi Gamboa has an extensive background in clinical data management as well as experience with different EDC systems including Oracle InForm, InForm Architect, Central Designer, CIS, Clintrial, Medidata Rave, Central Coding, OpenClinica Open Source and Oracle Clinical.

Acme Pharma Develops A Drug: Part I

Learn more about how the pharmaceutical industry has traditionally developed and brought drugs to market. Watch part II of this series to learn how Network Fortress can improve the drug development process and save pharma and biotech companies time and money.

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Clinical Trials Terminology for SAS Programmers

Entry Level SAS Programmers

Statistical Programmer:requires him to program using the SAS language to analyze clinical data and produce reports for the FDA

Bioanalyst, Clinical Data Analyst, Statistical Programmer Analyst and SAS Programmer: same as Statistical programmer.

Biotechnology:companies which is a general term used to explain a technique of using living organisms within biological systems to develop micro-organisms for a particular purpose.

protocol:outlined all the procedures and contained detailed plans of the study.

controlled experiment: the clinical trial had patients grouped into different groups such as those in the placebo controlled group which had no active drug. This is how comparisons are made within the controlled clinical trial CFR Part 11:Code of Federal Regulations set by the FDA to regulate food, drug, biologics and device industries. The part 11 specifically deals with the creation and maintenance of electronic records.
Case Report Form or CRF:forms to collect information such as demographic and adverse events. Source Data or the information collected:which include important documents because they contain the core information required to reconstruct the essential capital of the study.
sponsor:company who is responsible for the management, financing and conduct of the entire trial. randomized: subjects that are randomly assigned to groups so that each subject has an equal chance to be assigned to the placebo control
baseline: subjects are assigned to their drug change from baseline:analyses that measure differences between baseline and current visit
placebo or sugar pill:is an inactive substance designed to look like the drug being tested. blinded:they do not know if the drug that they are taking contains the active ingredient.
open-label study:all was out in the open, the drug the subject is assigned to. Pharmacokinetics or PK:analysis of that study showed that with that dosing level, there were high levels of toxicity in the subject.
informed consent: described all the potential benefits and risks involved. TLGs: Tables, Listings and Graphs
trade name:drug name that is collected from the patient and recorded into the source data. For example: Tylenol generic name: refers to its chemical compound. For example: Acetaminophen.
WHO-DRUG: list all the drug names and how they matched to the generic drug names.This dictionary is managed by the World Health Organization MedDRA:This is short for Med (Medical), D (Dictionary), R (Regulatory), and A (Activities).
SAP: Statistical Analysis Plan ANOVA: analysis of variable
confidence interval:gives an estimated range of values being calculated from the sample of patient data that is currently in the study. null hypothesis:lack of difference between the groups in a report
pilot study:perform the same analysis upon an older. DIA: Drug Information Association
CBER: Center for Biologics Evaluation and Research (medical device) CDER: Center for Drug Evaluation and Research (drug)

Source:CDER Acronym List


Anayansi Gamboa has an extensive background in clinical data management as well as experience with different EDC systems including Oracle InForm, InForm Architect, Central Designer, CIS, Clintrial, Medidata Rave, Central Coding, OpenClinica Open Source and Oracle Clinical.

Adverse Event Monitoring for CRAs

During monitoring visits one of the most important and impacting activities that a CRA performs is the source document verification of Adverse Events. The CRA is the eyes for the research sponsor when it comes to proper collection and documentation of subject safety information. Incorrect and inadequate monitoring of adverse events can lead to inaccurate labeling for clinical trials and impact market application inspectional reviews, as well as post marketing labeling. The safety regulatory and ICH definitions will be reviewed and applied to the monitoring process. This includes Causality, Expectedness/Unanticipated, and other important concepts. Case scenarios will be used to apply the information for better learning.

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Introduction to Clinical Trials

Video introducing cancer clinical trials and their use in clinical practice guidelines

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Source: Cancer Guidelines – Canada

Data Management: Queries in Clinical Trials

When an item or variable has an error or a query raised against it, it is said to have a “discrepancy” or “query”.

All EDC systems have a discrepancy management tool or also refer to “edit check” or “validation check” that is programmed using any known programming language (i.e. PL/SQL, C# sharp, SQL, Python, etc).

So what is a ‘query’? A query is an error generated when a validation check detects a problem with the data. Validation checks are run automatically whenever a page is saved “submitted” and can identify problems with a single variable, between two or more variables on the same eCRF page, or between variables on different pages. A variable can have multiple validation checks associated with it.

Errors can be resolved in several ways:

  • by correcting the error – entering a new value for example or when the datapoint is updated
  • by marking the variable as correct – some EDC systems required additional response or you can raise a further query if you are not satisfied with the response

Dealing with queries
Queries can be issued and/or answered by a number of people involved in the trial. Some of the common setups are: CDM, CRA or monitors, Site or coordinators.

Types of Queries

  • Auto-Queries or Systems checks
  • Manual Queries
  • Coding Queries
  • SDV related Queries generated during a Monitor visit
  • External Queries – for external loaded data in SAS format

EDC Systems and Discrepancy Output Examples

InForm

Note: All queries are associated to a single data item relevant to that query.

RAVE

Note: Users are only able to see / perform an action on a query based on their
role and the permissions via Core Config.

Timaeus

Note: Queries are highlighted by a red outline and a Warning icon.

OpenClinica

Note: Extensive interfaces for data query.

Query Metrics – It is important to measure the performance of your clinical trials.
Metrics are the same for all clinical studies but not all EDC systems are the same. Standardized metrics encourage performance improvement, effectiveness, and efficiency. Some common metrics are:

  • Outstanding Query
  • Query Answer Time
  • Average Time to Query Resolution
  • Number of closed discrepancies on all ongoing studies

Data management’s experience with data queries in clinical trials

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Trademarks: InForm is a trademark or registered trademark of Oracle Corporation. Rave is a trademark or registered trademark of Medidata. Timaeus is a trademark or registered trademark of Cmed Clinical Research.


Anayansi Gamboa has an extensive background in clinical data management as well as experience with different EDC systems including Oracle InForm, InForm Architect, Central Designer, CIS, Clintrial, Medidata Rave, Central Coding, OpenClinica Open Source and Oracle Clinical.