EQIPD - User contributions [en]

1.4.3.1 Industry-academia: Research as service

2021-11-16T09:59:07Z

Antonbespalov: /* Resources */

=Background and Expectations=

A significant percentage of drug discovery and development efforts is outsourced to external service providers. Biopharmaceutical industry aims to achieve the same robustness and reliability of research data whether originating from internal laboratories or external collaboration partners.

With this objective in mind, EQIPD has developed experimental record-keeping guidelines for organizations performing non-regulated research under contractual agreement conditions. The practices outlined are intended to safeguard data traceability and data integrity, to facilitate decision making and create reliable scientific evidence to bring the best possible solutions to patients.

Further, EQIPD has developed a tool that allows assessment of research practices at organizations conducting research as a service (e.g., contract research organizations).

'''It is recommended for the sponsors of research to use this tool (see the link below) to qualify service providers prior to concluding the legal contracting paperwork'''.

The tool may also be used by both sponsors and service providers to trigger a more in-depth assessment of research practices by the EQIPD team.

=Resources=

Examples and templates for external collaborators:
* [https://paasp.sharepoint.com/:w:/s/EQIPD/Edznay8k9dZAuNwmwZeuXz8B3D9lgHGyHsKna8nNfLXLeg?e=zc9VBz 1.4.3.1 Expectations for external collaborators.docx]
* [https://paasp.sharepoint.com/:w:/s/EQIPD/EQ5criyu0xtDu4DJOmOTutMBuIoFK-jUxXpVSfixe_OOFw?e=DT4naO 1.4.3.1 Expectations for external collaborators one pager.docx]

Service provider assessment tool:
* [https://paasp.sharepoint.com/:x:/s/EQIPD/EQek_OgEbABCiG9_xQcInYIBdK0FPD7IVZz2SneEP_bMFQ?e=ty6PY4 Excel-based tool] (The tool can be downloaded from the Online-Excel via the tab “File” in the menu, click on “Save as” and click on "Download a copy")

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Next item: [[1.4.3.2 Industry-academia: Research as collaboration]]

1.4.3.5 Expectations from public funders

2021-11-16T09:58:08Z

Antonbespalov: /* Resources */

=Background and Expectations=

EQIPD has developed a tool to support:
* funders willing to communicate their expectations about data management and rigor in study design, conduct, analysis, and reporting
* scientists applying for funding and willing to follow best practices in research rigor

For funders:
* it is expected that the tool is made freely available (e.g. as a link) on the website informing applicants about the scope of the tool
* the tool provides generic expectations formulated by the EQIPD working group and can be further extended by guidance and other information specific to a funding body
* it is expected that different funding bodies will decide as to whether:
** the use of this tool is mandatory or not
** whether a report generated by the tool should be made part of the application for funding
** whether any additional information or evidence should be provided to support answers collected by the tool

For scientists applying for funding:
* the primary use of the tool is to help scientists identify potential gaps in the current practices
* even if the use of the tool is not mandated by the funder, applicants may nevertheless want to add the report generated by the tool to the submission package in order to emphasize the adherence to the research practice expectations formulated by EQIPD

The tool creates a "snapshot" of the environment in which research is conducted and provides scientists/organizations with the opportunity to demonstrate to funders in a structured (comparable) way that they are aware of critical quality measures/requirements and that they have implemented (most of) these in their environment.

It ensures that funders and applicants can align on relevant quality expectations (based on the EQIPD framework) and that all parties involved speak the “same language”.

This tool is NOT supposed to guide scientists how to design and conduct specific experiments.

=Resources=

* [https://paasp.sharepoint.com/:x:/s/EQIPD/ESCLBokAH8ZIuDcre3q5pp0BlN3IzHfYVr8clB9_UbT13w?e=48hlRK Excel-based tool] (The tool can be downloaded from the Online-Excel via the tab “File” in the menu, click on “Save as” and click on "Download a copy")

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Next item: [[1.5.1 Quality policy]]

1.4.3.5 Expectations from public funders

2021-11-16T09:45:59Z

Antonbespalov: /* Background and Expectations */

=Background and Expectations=

EQIPD has developed a tool to support:
* funders willing to communicate their expectations about data management and rigor in study design, conduct, analysis, and reporting
* scientists applying for funding and willing to follow best practices in research rigor

For funders:
* it is expected that the tool is made freely available (e.g. as a link) on the website informing applicants about the scope of the tool
* the tool provides generic expectations formulated by the EQIPD working group and can be further extended by guidance and other information specific to a funding body
* it is expected that different funding bodies will decide as to whether:
** the use of this tool is mandatory or not
** whether a report generated by the tool should be made part of the application for funding
** whether any additional information or evidence should be provided to support answers collected by the tool

For scientists applying for funding:
* the primary use of the tool is to help scientists identify potential gaps in the current practices
* even if the use of the tool is not mandated by the funder, applicants may nevertheless want to add the report generated by the tool to the submission package in order to emphasize the adherence to the research practice expectations formulated by EQIPD

The tool creates a "snapshot" of the environment in which research is conducted and provides scientists/organizations with the opportunity to demonstrate to funders in a structured (comparable) way that they are aware of critical quality measures/requirements and that they have implemented (most of) these in their environment.

It ensures that funders and applicants can align on relevant quality expectations (based on the EQIPD framework) and that all parties involved speak the “same language”.

This tool is NOT supposed to guide scientists how to design and conduct specific experiments.

=Resources=

to be added

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Next item: [[1.5.1 Quality policy]]

1.4.3.5 Expectations from public funders

2021-11-16T09:45:13Z

Antonbespalov: /* Background and Expectations */

=Background and Expectations=

EQIPD has developed a tool to support:
* funders willing to communicate their expectations about data management and rigor in study design, conduct, analysis, and reporting
* scientists applying for funding and willing to follow best practices in research rigor

For funders:
* it is expected that the tool is made freely available (e.g. as a link) on the website informing applicants about the scope of the tool
* the tool provides generic expectations formulated by the EQIPD working group and can be further extended by guidance and other information specific to a funding body
* it is expected that different funding bodies will decide as to whether:
** the use of this tool is mandatory or not
** whether a report generated by the tool should be made part of the application for funding
** whether any additional information or evidence should be provided to support answers collected by the tool

For scientists applying for funding:
* the primary use of the tool is to help scientists identify potential gaps in the current practices
* even if the use of the tool is not mandated by the funder, applicants may nevertheless want to add the report generated by the tool to the submission package in order to emphasize the adherence to the research practice expectations formulated by EQIPD

The tool creates a "snapshot" of the environment in which research is conducted and provides scientists/organizations with the opportunity to demonstrate to funders in a structured (comparable) way that they are aware of critical quality measures/requirements and that they have implemented (most of) these in their environment.

It ensures that funders and applicants can align on relevant quality expectations (based on the EQIPD framework) and that all parties involved speak the “same language”.

This tool is NOT supposed to guide scientists how to design and conduct specific experiments

=Resources=

to be added

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Next item: [[1.5.1 Quality policy]]

1.4.3.5 Expectations from public funders

2021-11-16T09:43:49Z

Antonbespalov: /* Background and Expectations */

1.4.3.5 Expectations from public funders

2021-11-16T09:43:22Z

Antonbespalov: /* Background and Expectations */

1.4.3.5 Expectations from public funders

2021-11-16T09:42:23Z

Antonbespalov: /* Background and Expectations */

1.4.3.4 Academia-academia: Research as collaboration

2021-11-16T09:40:28Z

Antonbespalov: /* Resources */

=Background and Expectations=

to be added

=Resources=

to be added

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Next item: [[1.4.3.5 Expectations from public funders]]

1.4.3.5 Expectations from public funders

2021-11-16T09:39:50Z

Antonbespalov:

=Background and Expectations=

to be added

=Resources=

to be added

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Next item: [[1.5.1 Quality policy]]

1.5.5 Sustainability

2021-10-26T12:26:53Z

Antonbespalov: /* B. Guidance & Expectations */

== A. Background & Definitions ==

This item refers to one of the [[Core Requirements]] (Core Requirement 18 - "Resources for sustaining the EQIPD Quality System must be available") and is, therefore, considered as essential.

This is likely to be the last core requirement to be implemented ([[Implementation Strategy]]).

== B. Guidance & Expectations ==

During the implementation, Process Owner and her/his team get an idea how much resources will be required to maintain the system.

It is up to a Process Owner how this resource estimation and allocation is done and whether this exercise is documented (by default, no extra paperwork such as a written declaration by a process owner is required).

The key criterion is that lack of time or resources is not used as a justification for not following the most appropriate practices.

It is expected that Process Owner will:

* Calculate cost for maintaining Quality System (personnel and resources)
* Assess the availability of resources (including finances) to support maintenance of the Quality System for the next three years

'''PLEASE DO NOT FORGET'''

To consider the positive impact of maintaining higher research rigor standards under the Quality System

== C. Resources ==

Cost-benefit analysis - to be added

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Next item: [[2.1.1 Study protocol]]

1.5.5 Sustainability

2021-10-26T12:26:38Z

Antonbespalov: /* B. Guidance & Expectations */

== A. Background & Definitions ==

This item refers to one of the [[Core Requirements]] (Core Requirement 18 - "Resources for sustaining the EQIPD Quality System must be available") and is, therefore, considered as essential.

This is likely to be the last core requirement to be implemented ([[Implementation Strategy]]).

== B. Guidance & Expectations ==

During the implementation, Process Owner and her/his team get an idea how much resources will be required to maintain the system.

It is up to a Process Owner how this resource estimation and allocation is done and whether this exercise is documented (by default, no extra paperwork such as a written declaration by a process owner is required).

The key criterion is that lack of time or resources is not used as a justification for not following the most appropriate practices.

It is expected that Process Owner will:

** Calculate cost for maintaining Quality System (personnel and resources)
* Assess the availability of resources (including finances) to support maintenance of the Quality System for the next three years

'''PLEASE DO NOT FORGET'''

To consider the positive impact of maintaining higher research rigor standards under the Quality System

== C. Resources ==

Cost-benefit analysis - to be added

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Next item: [[2.1.1 Study protocol]]

1.5.5 Sustainability

2021-10-26T12:25:55Z

Antonbespalov: /* B. Guidance & Expectations */

2.1.11 Preregistration

2021-10-06T05:57:13Z

Antonbespalov: /* C. Resources */

== A. Background & Definitions ==

Preregistration refers to a process of registration of study protocol and data analysis plan before conducting a study.

Registered Report is a maximal form of registration, in which study manuscript that includes the study hypothesis, rationale, methods, experimental protocol and a detailed analysis plan is peer reviewed (Stage 1) before study data is collected. After data collection, the final manuscript that includes results and discussion sections undergoes a conventional peer review (Stage 2) where adherence to the original (Stage 1) procedures is confirmed. Following favorable reviews and, regardless of study results, the manuscript is accepted for publication ([https://www.nature.com/articles/s41562-016-0021 Munafo et al. 2017], [https://openscience.bmj.com/pages/registered-reports-guidelines/ BMJ Open Science, Registered Reports Guidelines]).

Benefits of preregistration:

* Increases transparency
* Saves resources by avoiding unnecessary duplication of efforts
* Serves to reduce:
** The risk of publication bias
** "HARKing" (hypothesizing after the results are known)
** P-hacking (analytical decisions after the results are known)
** In case of a Registered Report, registration helps also against "CARKing" (unjustified critique of the article by reviewers after the results are known; Munafo et al. 2017).

== B. Guidance & Expectations ==
* If study is done to inform a knowledge claim ([[2.1.4 Purpose of research]]), it is strongly recommended to preregister the study protocol before data are collected.
* It is strongly recommended to register systematic review protocols.
* Training on planning and benefits of preregistration is highly recommended.

'''PLEASE DO NOT FORGET'''
* To consider adding this subject to a training program for new employees or refresher training (if appropriate)
* To check for risks of disclosing confidential or otherwise sensitive or proprietary information (e.g. in the context of existing or emerging intellectual property)

== C. Resources ==
Online registry platforms:
* Specialized platforms
** [https://www.preclinicaltrials.eu Preclinicaltrials.eu]
** [https://www.animalstudyregistry.org Animal Study Registry]
** [https://osf.io/rr OSF for Registered Reports]
** [https://www.crd.york.ac.uk/prospero/#guidancenotes_animals PROSPERO, a platform for registering a systematic review of animal studies]
* Broad scope platforms
** [https://aspredicted.org AsPredicted]
** [https://cos.io/prereg Center for Open Science]
* [https://paasp.sharepoint.com/:b:/s/EQIPD/ERA-rz_pc3xDh_uYg0Q-B3cBuPRVFFsNwgQUQZ3Oz5qiRg?e=CXBnL2 Platform comparison]
Publications:
* Nosek BA, Ebersole CR, DeHaven AC et al. (2018) The preregistration revolution. Proc Natl Acad Sci U S A. 115:2600-2606. PubMed [https://pubmed.ncbi.nlm.nih.gov/29531091/]
* De Vries RBM, Hooijmans CR, Langendam MW, et al. (2015) A protocol format for the preparation, registration and publication of systematic reviews of animal intervention studies. Evid Based Preclin Med. 2:1–9. [https://onlinelibrary.wiley.com/doi/full/10.1002/ebm2.7]
* [https://osf.io/8v2n7/ Pre-registration and Registered Reports - A primer from the UK Reproducibility Network]

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Next item: [[2.2.1 Use of SOPs for standard experiments]]

2.1.11 Preregistration

2021-10-06T05:55:40Z

Antonbespalov: /* C. Resources */

== A. Background & Definitions ==

Preregistration refers to a process of registration of study protocol and data analysis plan before conducting a study.

Registered Report is a maximal form of registration, in which study manuscript that includes the study hypothesis, rationale, methods, experimental protocol and a detailed analysis plan is peer reviewed (Stage 1) before study data is collected. After data collection, the final manuscript that includes results and discussion sections undergoes a conventional peer review (Stage 2) where adherence to the original (Stage 1) procedures is confirmed. Following favorable reviews and, regardless of study results, the manuscript is accepted for publication ([https://www.nature.com/articles/s41562-016-0021 Munafo et al. 2017], [https://openscience.bmj.com/pages/registered-reports-guidelines/ BMJ Open Science, Registered Reports Guidelines]).

Benefits of preregistration:

* Increases transparency
* Saves resources by avoiding unnecessary duplication of efforts
* Serves to reduce:
** The risk of publication bias
** "HARKing" (hypothesizing after the results are known)
** P-hacking (analytical decisions after the results are known)
** In case of a Registered Report, registration helps also against "CARKing" (unjustified critique of the article by reviewers after the results are known; Munafo et al. 2017).

== B. Guidance & Expectations ==
* If study is done to inform a knowledge claim ([[2.1.4 Purpose of research]]), it is strongly recommended to preregister the study protocol before data are collected.
* It is strongly recommended to register systematic review protocols.
* Training on planning and benefits of preregistration is highly recommended.

'''PLEASE DO NOT FORGET'''
* To consider adding this subject to a training program for new employees or refresher training (if appropriate)
* To check for risks of disclosing confidential or otherwise sensitive or proprietary information (e.g. in the context of existing or emerging intellectual property)

== C. Resources ==
Online registry platforms:
* Specialized platforms
** [https://www.preclinicaltrials.eu Preclinicaltrials.eu]
** [https://www.animalstudyregistry.org Animal Study Registry]
** [https://osf.io/rr OSF for Registered Reports]
** [https://www.crd.york.ac.uk/prospero/#guidancenotes_animals PROSPERO, a platform for registering a systematic review of animal studies]
* Broad scope platforms
** [https://aspredicted.org AsPredicted]
** [https://cos.io/prereg Center for Open Science]
* [https://paasp.sharepoint.com/:b:/s/EQIPD/ERA-rz_pc3xDh_uYg0Q-B3cBuPRVFFsNwgQUQZ3Oz5qiRg?e=CXBnL2 Platform comparison]
Publications:
* Nosek BA, Ebersole CR, DeHaven AC et al. (2018) The preregistration revolution. Proc Natl Acad Sci U S A. 115:2600-2606. PubMed [https://www.ncbi.nlm.nih.gov/pubmed/?term=the+preregistr%E2%80%8Bation+revolution+and+Nosek]
* De Vries RBM, Hooijmans CR, Langendam MW, et al. (2015) A protocol format for the preparation, registration and publication of systematic reviews of animal intervention studies. Evid Based Preclin Med. 2:1–9. [https://onlinelibrary.wiley.com/doi/full/10.1002/ebm2.7]
* [https://osf.io/8v2n7/ Pre-registration and Registered Reports - A primer from the UK Reproducibility Network]

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Next item: [[2.2.1 Use of SOPs for standard experiments]]

1.3.5 Reward system and incentives

2021-10-06T05:50:36Z

Antonbespalov:

== A. Background & Definitions ==
EQIPD Quality System relies on and should become part of the overall organizational quality culture.

The introduction of a quality system will require efforts from every team member.

Rewards and incentives can help to motivate people to work together for a common goal, such as good research quality.

== B. Guidance & Expectations ==
EQIPD does not expect any formal documents or analyses to be conducted. However, it is highly desirable that the Process Owner reviews the rewards and incentives and, if necessary, aligns them with the quality objectives: Do we encourage / support publication of negative results?

* Are high-quality results recognized / rewarded irrespective of the outcome (positive/negative)?
* Are people encouraged to develop and implement risk-mitigating strategies and procedures? Are these efforts recognized?
* Are people encouraged not to rush experiments but to invest time and resources in developing/using robust methods, optimizing experimental set-ups and to conduct experiments properly according to high-quality standards (even if it takes more time than initially allocated)?
* Are internal processes reviewed to make sure that there is no pressure to generate positive results? If there is such pressure, are there processes to mitigate the risks?
* When evaluating new job applications, is attention paid to rigor of applicants’ research?
* What reward systems/incentives work in your specific environment:
** monetary?
** badges?
** public praise?
** gifts?
* Does the RU have a reward system to incentivize certain behaviors (e.g. team work, innovation)? Monetary or not.
** If yes, is the reward system used to support your quality goals, good research quality, transparency and open science attitude?

'''PLEASE DO NOT FORGET'''

To make sure that the research team members are fully aware of the reward / incentives

== C. Resources ==

* [https://qatc.org/winter-2016-connection/sixty-simple-recognition-and-reward-ideas/ The Quality Assurance & Training Connection (QATC): Sixty Simple Recognition and Reward Ideas]

* [https://sfdora.org/2019/10/20/rewarding-robust-and-reproducible-research-experiences-from-the-pilot-phase-of-implementing-research-assessment-reforms-for-hiring-professors-at-bih-charite-merit-prof/ Experiences from the pilot phase of implementing research assessment reforms for hiring professors at BIH/Charité]

* [https://www.bihealth.org/en/translation/innovation-enabler/quest-center/calls-and-awards BIH QUEST examples Financial incentives to support re-use of open data, preregister and to publish negative (null) results]

* [https://osf.io/kqgez/ Open Research Awards - A Primer from the UK Reproducibility Network]

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Next item: [[1.4.1 Research ethics]]

2021-10-06T05:36:20Z

Antonbespalov: /* C. Resources */

2.1.6 Sample size and power analysis

2021-09-14T07:43:29Z

Antonbespalov: /* C. Resources */

UNDER CONSTRUCTION

== A. Background & Definitions ==

Statistical power is defined as the probability of detecting a statistically significant effect of a pre-specified size. Formally, power is equal to 1 minus the Type II error rate (beta or ß). The Type II error rate is the probability of obtaining a non-significant result when the null hypothesis is false — in other words, failing to find a difference or relationship when one exists.

Balancing sample size, effect size and power is critical to good study design. When the power is low, only large effects can be detected, and negative results cannot be reliably interpreted. The consequences of low power are particularly dire in the search for high-impact results, when the researcher may be willing to pursue low-likelihood hypotheses for a groundbreaking discovery (see Fig. 1 in [https://www.nature.com/articles/nmeth.2738 Krzywinski & Altman 2013]). Ensuring that sample sizes are large enough to detect the effects of interest is an essential part of study design.

Studies with inadequate power are a waste of research resources and arguably unethical when subjects are exposed to potentially harmful or inferior experimental conditions.

Statistical power analysis exploits the relationships among the four variables involved in statistical inference: sample size (N), significance criterion (α), effect size (ES), and statistical power.

== B. Guidance & Expectations ==

General advice - DO:

* Whenever possible, seek professional biostatistician support to estimate sample size.
* Use power prospectively for planning future studies.
* Put science before statistics. It is easy to get caught up in statistical significance and such; but studies should be designed to meet scientific goals, and you need to keep those in sight at all times (in planning and analysis). The appropriate inputs to power/sample-size calculations are effect sizes that are deemed scientifically important, based on careful considerations of the underlying scientific (not statistical) goals of the study. Statistical considerations are used to identify a plan that is effective in meeting scientific goals – not the other way around.
* Do pilot studies. Investigators tend to try to answer all the world’s questions with one study. However, you usually cannot do a definitive study in one step. It is far better to work incrementally. A pilot study helps you establish procedures, understand and protect against things that can go wrong, and obtain variance estimates needed in determining sample size. A pilot study with 20-30 degrees of freedom for error is generally adequate for obtaining reasonably reliable sample-size estimates.
* Generate sample size estimates for a range of power and effect size values to explore the gains and losses in power or detectable effect size due to increasing or decreasing n. This is why the term ‘sample size estimation’ is often preferred over ‘sample size calculation’. Although the arrival at a number for the required sample size is invariably based on (often complex) formulae, the term ‘calculation’ implies an unwarranted degree of precision. The purpose of sample size estimation is not to give an exact number but rather to subject the study design to scrutiny, including an assessment of the validity and reliability of data collection ([https://www.sciencedirect.com/science/article/pii/S1466853X05000714 Batterham & Atkinson 2005]).
* Remember to consider attrition rate (i.e. possibility that some subjects or samples are lost during the conduct of the study or follow-up for technical and other data analysis-unrelated reasons)

General advice - DO NOT:

* Avoid using the definition of “small,” “medium,” or “large” effect size based on Cohen's d of .20, .50, or .80, respectively. Cohen's assessments are based on an extensive survey of statistics reported in the literature in the social sciences and may not apply to other fields of science. Further, this method uses a standardized effect size as the goal. Think about it: for a “medium” effect size, you’ll choose the same n regardless of the accuracy or reliability of your instrument, or the narrowness or diversity of your subjects. Clearly, important considerations are being ignored here. “Medium” is definitely not the message!

* Retrospective power calculations should be avoided, because they add no new information to an analysis (i.e. avoid using observed power to interpret the results of the statistical test). You’ve got the data, did the analysis, and did not achieve “significance.” So you compute power retrospectively to see if the test was powerful enough or not. This is an empty question. Of course it wasn’t powerful enough – that’s why the result isn’t significant. Power calculations are useful for design, not analysis.

Guidance on sample size estimation:

--- to be added / revised (please do not edit - placeholder) ---

.. getting a solid grip on the existing literature in one's topic, drilling down to what effects were identified and obtaining the corresponding ES values either directly from the publication or from appropriate calculations based on the printed documentation.

.. being sure that the estimate you obtain is the one that fits the study design correctly; one cannot necessarily generalize across disparate research designs.

.. and citing the algorithm or software used to generate the estimates. A power calculation result given without this detail can be viewed with suspicion.

--- to be added / revised (please do not edit - placeholder) ---

What to do if you have no choice about sample size:

Limited budget, limited supply of research materials, or a difficult-to-overcome guidance from a collaborator, a funder or a senior colleagues may leave no choice but to consider running a study with a certain potentially small sample size. What can be done in such situations?

* consider study designs involving correlated data (e.g. repeated measures, crossover or matched-pairs designs) that are usually associated with greater statistical power than those involving separate samples allocated to different treatment groups ([[https://www.sciencedirect.com/science/article/pii/S1466853X05000714 see section 2.1 here]).
* consider intervening variables or pre-intervention measurements for stratification; if not possible, one can still improve statistical power by entering these variables as covariates in the analysis (this approach has its limitations and therefore should be consulted with the statisticians)
* make sure that the most suited randomization schedule is used to control for random influences
* explore and engage all other means to minimize variation (including using properly maintained and calibrated research instruments, adequate and well controlled environmental conditions, making sure that experiments are performed by competent and adequately trained scientists)
* if a study has low power because of the given sample size, reflect this limitation in the study protocol and indicate to all stakeholders that the study cannot be run as knowledge-claiming (decision-enabling, confirmatory).
* evaluate power not only for the given sample size for also for the values around and discuss the impact of the sample size on power with the stakeholders - in some cases, it may help to lift or revise the original sample size restrictions. These discussions make sense and are justifiable only if they take place prior to the conduct of the study (i.e. not post hoc).

== C. Resources ==

Tools to sample size estimation:

* [http://www.gpower.hhu.de/ G*Power]
* [https://wise1.cgu.edu/power/index.asp WISE power tutorial]
* [http://davidmlane.com/hyperstat/power.html JAVA applets for power and sample size]
* [https://www.psychometrica.de/effect_size.html Computation of effect sizes @Psychometrica]
* [http://powerandsamplesize.com/Calculators/ Overview of sample size and power calculators]

Educational instruments and resources:

* Mayo clinical online simulator - Size matters [https://rtools.mayo.edu/size_matters/]
* Scientists talking to biostatisticians [https://www.youtube.com/watch?v=PbODigCZqL8&feature=youtu.be]

Useful literature (for non-statisticians):

* [https://stat.uiowa.edu/sites/stat.uiowa.edu/files/techrep/tr303.pdf Practical advice on sample size estimation by Russell Lenth]
* [https://www.sciencedirect.com/science/article/pii/S1466853X05000714 A primer on murky world of sample size estimation by Alan Batterham & Greg Atkinson]

Useful literature:

* [https://pdfs.semanticscholar.org/1325/24bdfe70504fcd67016b17305ccddb4bcd14.pdf Power in various ANOVA designs by Joel Levin]

* [https://www.ncbi.nlm.nih.gov/books/NBK43321/ https://www.ncbi.nlm.nih.gov/books/NBK43321/]
* [http://davidmlane.com/hyperstat/power.html http://davidmlane.com/hyperstat/power.html]
* [http://powerandsamplesize.com/Calculators/Test-1-Mean/1-Sample-Equality http://powerandsamplesize.com/Calculators]

Guidelines on reporting of sample size (in vivo research):
* [[ARRIVE 2.0]]

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Next item: [[2.1.7 Blinding]]

Spot checks

2021-08-13T12:29:08Z

Antonbespalov:

Spot check is a a close look at a few items in a group.

Within the EQIPD framework, spot check is an examination of various activities and processes to make sure that everything is being done according to the expectations.

For a spot check, records / data / experiments may be selected randomly to see how well the RU is doing by finding answers to questions such as:
- how easy is it to retrieve and reconstruct the data?
- are study protocols completed prior to the start of experiments?
- have data been generated in an unbiased fashion?
- have all results been reported?

Spot checks are typically conducted by a [https://eqipd-toolbox.paasp.net/wiki/1.5.2.3_Process_owner Process owner] or someone to whom this task is delegated. EQIPD does not require that the research unit maintains documentation on spot checks conducted and leaves it up to the Process owner and the research unit to decide how often spot checks are conducted, how the outcome is evaluated, discussed, reported or followed up. Some research units opt to build key performance indicators that quantify the outcome of the spot checks of key processes.

If a research unit undergoes an internal or external assessment, the assessors may also conduct spot checks to evaluate the overall performance and to identify processes or specific examples for an in-depth discussion with the research unit.

Spot checks

2021-08-13T12:24:53Z

Antonbespalov:

Spot check is a a close look at a few items in a group.

Within the EQIPD framework, spot check is an examination of various activities and processes to make sure that everything is being done according to the expectations.

For a spot check, data / experiments may be selected randomly to see how well the RU is doing by finding answers to questions such as:
- how easy is it to retrieve and reconstruct the data?
- are study protocols completed prior to the start of experiments?
- have data been generated in an unbiased fashion?
- have all results been reported?

Spot checks are typically conducted by a [https://eqipd-toolbox.paasp.net/wiki/1.5.2.3_Process_owner Process owner] or someone to whom this task is delegated.

If a research unit undergoes an internal or external assessment, the assessors may also conduct spot checks to evaluate the overall performance and to identify processes or specific examples for an in-depth discussion with the research unit.

Spot checks

2021-08-13T12:23:28Z

Antonbespalov:

2021-05-06T08:30:20Z

Antonbespalov: /* B. Guidance & Expectations */

== A. Background & Definitions ==
EQIPD cannot and does not intend to advise research units regarding the relevant legislation and policies.

However, EQIPD does not want to collaborate or be associated with the organizations and individuals that do not comply with applicable national and international legislation and policies.

This Toolbox item refers to one of the [[Core Requirements]] (Core Requirement 4 - "All activities must comply with relevant legislation and policies").

== B. Guidance & Expectations ==
Process Owner is advised to discuss with the research unit members what legal regulations and policies apply (e.g. related to animal care and use, handling of genetically modified organisms (GMOs), GHS labeling, unborn life, controlled substances, radioactivity, etc.).

Process Owner may want to create an overview (e.g. checklist) to facilitate regular updating and to avoid gaps in maintaining relevant documentation, providing required training and communication, etc.

In many situations, research units are nested within larger organizations that provide centralized documentation and training support. However, even in such cases, Process Owner is advised to be informed about applicable legislation and policies as well as about her/his personal responsibility in securing the compliance.

'''PLEASE DO NOT FORGET'''

** To review applicable legislation and policies when conducting the self-assessment procedure ([[4.1.2 Self assessment]]).
* If the Process Owner and the research unit are using the EQIPD Dossier in the suggested format, information about applicable legislation and policies can be saved in the folders 1.4.2.1-1.4.2.8 of the [[Dossier]].
* For research units conducting experimental work using laboratory animals in animal facilities without full accreditation by AAALAC (or equivalent), animal care and use program must be described in a standalone document prepared according to the EQIPD checklist ([3.4.1 Animal characteristics, care and use]).

== C. Resources ==
to be added

----------------
back to [[Toolbox]]

Next item: [[1.4.2.1 Biosafety]]

1.4.2 Adherence to legal and regulatory considerations

2021-05-06T08:30:04Z

Antonbespalov: /* B. Guidance & Expectations */

== A. Background & Definitions ==
EQIPD cannot and does not intend to advise research units regarding the relevant legislation and policies.

However, EQIPD does not want to collaborate or be associated with the organizations and individuals that do not comply with applicable national and international legislation and policies.

This Toolbox item refers to one of the [[Core Requirements]] (Core Requirement 4 - "All activities must comply with relevant legislation and policies").

== B. Guidance & Expectations ==
Process Owner is advised to discuss with the research unit members what legal regulations and policies apply (e.g. related to animal care and use, handling of genetically modified organisms (GMOs), GHS labeling, unborn life, controlled substances, radioactivity, etc.).

Process Owner may want to create an overview (e.g. checklist) to facilitate regular updating and to avoid gaps in maintaining relevant documentation, providing required training and communication, etc.

In many situations, research units are nested within larger organizations that provide centralized documentation and training support. However, even in such cases, Process Owner is advised to be informed about applicable legislation and policies as well as about her/his personal responsibility in securing the compliance.

'''PLEASE DO NOT FORGET'''

* To review applicable legislation and policies when conducting the self-assessment procedure ([[4.1.2 Self assessment]]).
* If the Process Owner and the research unit are using the EQIPD Dossier in the suggested format, information about applicable legislation and policies can be saved in the folders 1.4.2.1-1.4.2.8 of the [[Dossier]].
* For research units conducting experimental work using laboratory animals in animal facilities without full accreditation by AAALAC (or equivalent), animal care and use program must be described in a standalone document prepared according to the EQIPD checklist ([3.4.1 Animal characteristics, care and use]).

== C. Resources ==
to be added

----------------
back to [[Toolbox]]

Next item: [[1.4.2.1 Biosafety]]