Mais cedo não é necessariamente melhor

7 Ways Minimalism Can Help You Get Out of Debt Faster

Here are a few lifestyle changes you can adopt to spend less money, pay off your loans faster, and save for things you’ve been dreaming of for years.

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Carryover effects: What are they, why are they problematic, and what can you do about them?

Carryover effects are challenging for within-subjects research designs, that is, when the same participants are exposed to all experimental treatments and results are compared across different treatments. These designs are useful, among other reasons, because they allow a near perfect match of subject characteristics as the researcher compares measurements of the same participant. However, as the same subjects are used in all experimental treatments, there is a possibility that a previous treatment can alter behaviour in a subsequent experimental treatment. This is known as a carryover effect.

Why are carryover effects problematic?

Carryover effects will confound the effect of an independent variable and destroy the near perfect matching that is so valuable in within-subjects designs. The internal validity of the experiment is greatly reduced. In other words, because a previous treatment has altered observed behaviour in a later treatment, the extent to which the research design tests the hypothesis it is designed to test is reduced.

Imagine a study where you examine the effect of a noisy environment on the ability to solve mathematical equations. Subjects first solve mathematical equations in a noisy environment and then in a quiet environment. If you observe an improvement in performance it is possible that this is due to the lack of noise. It is, however, also possible that the improvement is due to practice; perhaps the subjects in the second treatment have “warmed up” or are more focused. Put simply, it is possible that the earlier experimental treatment (noisy environment) affects performance in the second treatment (quiet environment) through practice; and not due to lack of noise per sé.  This muddies the evaluation of how a noisy environment affects mathematical ability, as, at the very least, some of the improvement is due to carryover effects.

What are sources of carryover effects?

There are lots of possible sources for carryover effects – relevant or irrelevant depending on the particular research setting and design. Learning is a common source of carryover effects (as mentioned in the previous example). If treatments lead to fatigue, this can also create carryover effects. Other possible sources are (among others) habituation, sensitization, and adaptation.

What can you do about them?

There are a few strategies for dealing with carryover effects:

• Minimize and eliminate: Obvious, but important nonetheless. Pre-training is one example, if you’re not interested in the actual effects of learning.
• Counterbalancing: This is an important method for reducing carryover effects. The researcher assigns the experimental treatments in different orders for different subjects. You can use either complete counterbalancing (every possible treatment order is assigned to at least one subject) or partial counterbalancing (includes some of the possible treatment orders). The goal is to spread the carryover effects across the different experimental treatments, so that the average difference between the groups is unaffected by them. The idea is that the carryover effects within the different treatments will cancel each other out. In order to achieve complete counterbalancing you’ll usually need lots of subjects for statistical power, so partial counterbalancing is often your best bet.
• Making treatment order an independent variable: This is another option, where again, different subjects are exposed to different orders of treatments. However, enough subjects are allowed in each group so that a statistical analysis of the treatment order as a separate, independent variable, can be done. This allows for a measurement of the size of the carryover effects.

Carryover effects can affect outcomes and results of research, and are important to consider, particularly in the design phase of a study. They can pose a threat to the validity of results and therefore have to be minimized.

References

Bordens, K. S. & Abbott, B. B.: Research Design and Methods A process approach, 2011. McGraw-Hill. ISBN: 0072887648.

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Budgeting for Short-Term and Long-Term Goals

How to keep both short- and long-term goals in mind when organizing your savings, and different strategies for each.

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How a VR Programmer with an Economics Degree Got into the Industry

Your major doesn’t have to define your career path.

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Mais não significa necessariamente melhor

PRioRiTy II: The research project aiming to set the agenda for trials retention research

Randomised controlled trials are central to evidence-based medicine and healthcare. Thousands of randomised trials are designed and conducted around the world every year. These trials provide us with evidence about the effectiveness of medicinal products, devices or services, therefore enabling patients, researchers, healthcare professionals and policy-makers to make evidence-based decisions.

What is surprising about randomised trials is that the design and execution of the processes fundamental to their success is often not based on reliable evidence.

In a previous Students 4 Best Evidence blog post I introduced Trial Forge; an initiative aiming to make trials more efficient by generating, coordinating and spreading awareness of the evidence behind trial decisions. I focussed specifically on my work around the recruitment of potential participants to trials, which you can find out more about here.

Now, a new Trial Forge related project is focussing on how we can keep those participants in trials; the process of participant retention. PRioRiTy II is a research project in collaboration with the James Lind Alliance.

PRioRiTy II aims to identify the most important things that we currently don’t know about things that affect whether people stay involved in trials. This is important because trial teams work out the number of participants they need for a trial to produce meaningful results.  If lots of people drop-out of the trial early, then the trial results may become unreliable or unstable.

The project started with a survey which involved people across the UK and Ireland who are, or have been, involved directly in designing, running, analysing, or taking part and/or staying involved in randomised trials. This survey collected people’s suggestions for important questions about trial retention that still need answering.

Those questions have now been reduced to a shortlist of 27 questions.  Our second survey asks people to choose their top 10 questions from this shortlist.  These top 10 lists will feed into the next stage of the process, which will produce the final list of the 10 most important unanswered questions in trial retention.

We want your help in improving the future of clinical trials by improving what we know about the best ways to encourage people to stay involved in clinical trials.

The survey only takes 5 minutes, and it would enable us to include your views as we set the agenda for trials retention research. To take part in the survey, please click here.

For more information about PRioRiTy II, take a look here and also at the video below.

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4 Financial Decisions to Make Before and After Getting Married

Marriage isn’t just about loving someone enough, it’s about trusting them with your finances, and building a new life together.

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Student Loan Forgiveness Options for Teachers

There are a number of federal and state-sponsored programs that teachers can take advantage of to help make a dent in their debt.

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A Checklist for Grad School Student Loan Borrowers

A checklist of actions to take when it comes to paying for graduate school.

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Publication bias: The answer to your research question may be sitting in somebody’s file drawer

Publication bias, also known as dissemination bias, is defined as:

the publication or non-publication of research findings based on the nature and/or direction of their results (1).

Studies reporting results that are perceived as positive, statistically significant, or favouring the study hypothesis are more likely to be published. Several projects assessing the rate of publication of randomised controlled trials (RCTs) protocols registered prospectively in ClinicalTrials.gov, submitted to ethics committees, or of abstracts presented in conferences or congresses, show that publication rates range between 60 to 98% for studies with positive results and between 20 to 85% for studies with negative results (2).

Publication bias is just one of the many shapes of an overarching phenomenon: reporting bias, which also encompasses time lag bias, location bias, language bias, multiple publication bias, citation bias, and selective outcome reporting, among others (Table 1) (1,3). Reporting bias is generated from the perception that positive, statistically significant results will have a greater impact on the scientific community and in clinical practice, with the corresponding effect on researchers’ academic careers.

Table 1: Types and definitions of reporting bias (from the Cochrane handbook)

To further explore the issue of publication bias, we have contacted Dr. Hector Pardo-Hernandez (@hpardoh), researcher at the Iberoamerican Cochrane Centre, who recently defended his thesis project “Strategies to prevent and control dissemination bias of controlled clinical trials”.

• Why is publication bias a problem?

When a study is not published, its visibility and potential impact is diminished, disavowing the contribution made by patients who oftentimes participate without the certainty that they will receive a direct benefit. In addition, publication bias may result in redundant research and in a consequent waste of economic resources. As such, a 2012 study on reporting completeness and time to publication of RCT protocols in ClinicalTrials.gov found that less than half had published results; for those who did have published results, important data such as adverse events and the flow of participants was poorly described (4). Moreover, the efficacy of the research process is limited by a lack of access to essential data needed for, just to cite an example, the development or updating of systematic reviews (SRs).

• How does publication bias affect the practice of evidence-based medicine?

Publication bias generally leads to the dissemination of positive results that support the study hypothesis. As such, it translates into an overestimation of treatment effects during SRs’ development (type 1 error). Publication bias therefore undermines the validity of SRs and reduces confidence in evidence obtained from RCTs.

• Speaking of systematic reviews, what are the strategies that authors can implement to prevent or control publication bias?

The Methodological Expectations of Cochrane Interventions Reviews (MECIR) standards recommend extensive searching during systematic review development in several databases, including but not limited to Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Cochrane Specialized Registers, and databases relevant to the research question, such as CINAHL for nursing or PsycINFO for interventions in psychology. In addition, authors are advised to consult ClinicalTrials.gov and other prospective trials registers and registries, as well as grey literature and the reference lists of eligible studies.

• What about journals not found or indexed in the main databases?

There are different initiatives around the globe to tackle this shortcoming. For instance, at the Iberoamerican Cochrane Centre we lead an initiative through which references to randomized controlled trials (RCTs) are identified by checking Spanish and Latin American journal articles by hand, page by page (handsearching). Retrieved references are then submitted to CENTRAL for their potential inclusion in SRs.

• Does the problem affect just RCTs?

No, publication bias comes in many forms and shapes. Different studies have identified publication bias among SRs and qualitative research, among others (5,6,7). For instance, a 2006-2007 survey among systematic review corresponding authors found that, collectively, the 348 respondents had a total of 199 unpublished systematic reviews (with a median of 2.0 unpublished systematic reviews per author) (8).

• Gordon Guyatt and Ben Djulbegovic have cited publication bias as one of the main threats to the practice of evidence-based medicine (9). What initiatives are being implemented to tackle publication bias?

Initiatives such as “AllTrials”, which calls for prospective registration of clinical trials and for full reporting of methods and results (10), “REduce research Waste And Reward Diligence (REWARD)” which sets research priorities and looks for the complete report of any investigation (11), and the ethical standards provided by the Code of Conduct of the Committee on Publication Ethics (12)  and World Association of Medical Editors (13), have advocated for the publication of all RCTs results regardless of their direction or magnitude (9,14). In addition, one of the proposals that currently generates more interest is the deposit of anonymous individual patient data from RCTs in open access registers. Anonymous individual patient data facilitates the reproducibility of analyses and makes us less dependent on full-text articles to obtain RCT results.

Other strategies, such as open access publishing, can reduce publication bias by enhancing access to full-text articles and facilitating the dissemination of negative, not statistically significant study results. On the other hand, open access publishing may exacerbate the problem in developing countries, by imposing an economic burden for publishing that may not be assumed by unfunded researchers (15).

References

(Featured image: Photo by Sanwal Deen on Unsplash)

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Building the Mentorship Program at Earnest

We created a mentorship program to support Earnies’ professional development and overall growth.

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Med School Paid off in 10 Years? How This Couple Made It Happen

Their strategy was simple: act with discipline, live below their means, and work hard towards meaningful goals.

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