Bias (Epidemiology) - Science topic

As a result of tools such as Chat GPT, Bing and others, What would be the main risks for democratic systems when using AI like these? personalized fake news? Perpetuation of biases? Or what other elements?

How to derive a conclusion from systematic review without biases

For evaluation of quality evidence in systematic review and meta-analysis in animals, what methods are best or better indicated? GRADE pro, ARRIVE 2.0, or STAIR 2021? To assess the risk of bias, I proceeded with SYRCLE

To the question above, I know there is a RoB issue AFTER, but not sure if there is before. Another question...can per-protocol analyses be enough (for masters level dissertation) to cover RoB regardless of dropout or not? I really can't get my head around the difference in RoB analyses (per-protocol and intention-to-treat) on my papers, seems to be the same answer for everything. My tutor told me that per-protocol analyses is fine for all my studies, but I wanted to check this...I'm currently writing a Cochrane-style systematic review and meta analysis.

Thanks for your support :)

Suppose i have collected the data on Emotional Intelligence of Secondary School teachers using The Schutte Self-Report Emotional Intelligence Test (SSEIT). I am afraid there may be a brassiness in respondents scores. How can I know about and what are the method to check these biases?

Does the Criminal Justice System show bias towards the upper classes?

I am interested in predicting the occurrence of different species s {e.g. weeds, fungi, insects} at time t {e.g. month, season, year}. Presence in the t-1 period may increase the probability of presence in period t. The data is crowd-sourced presence-only observations over multiple years, and the user-base increased over time. Due to the presence-only nature, I thought of using maximum entropy. However, I'd like to take into account that the sampling bias changes over time (i.e. the increase in user base).

What ways are there to explicitly consider time-variant sampling bias?

Risk of bias assessment (sometimes called "quality assessment" or "critical appraisal") helps to establish transparency of evidence synthesis results and findings. and it is mandatory to have it in your systematic review!

if you know any tools or used ones, can you please share it/them with me?

or if you have extra information regarding the risk of basis assessments, can you share it with me?

After reading some articles and watching videos, I realized I need to draw a funnel plot for reporting bias assessment and GRADE approach for certainty evidence. But I am not getting on how to plot funnel plot and make GRADE approach.

1. How a study of high risk of bias study influences result of meta analysis?

2. Author should exclude the study of high risk of bias study or there is any other method to compensate this problem?

The data has endogeneity due to omitted variable bias as revealed by Ramsey OV test in Stata

Can anyone help us to share Bias correction codes for wind speed correction?

We have an "observed file and model file" for wind speed data.

kindly assist me

reviewers preferentially accept or reject articles based on a number of demographic factors, most especially authors country & affiliation.

I tried to apply a series of pulse voltage on the gate of MOSFET, and i used the "solve" statement,

with "sqpulse" and "trans.analy" parameter ,like:

"solve vdrain=5

log outf=mos_t.log master

solve trans.analy name=gate sqpulse tdelay=0.05 trise=0.01 PULSE.WIDTH=0.5 tfall=0.01 frequence=0.5"

But no transient solution was obtained . the warning said:"

Warning: No solution present. Performing zero carrier, zero bias

calculation for initial guess."

how can i solve it?

Greetings, all researchers! Right now, I'm looking for technical assistance with my research. I've set a goal to assess agroclimatic indicators' current and future impacts on maize crop yields using the impact modeling approach. How can I correct agroclimatic indicator biases in raster data?

I have observation data from ERA5, historical data from CMIP6 and want to forecast temperature and Precipitation which are derived from GeoMIP G6Sulfur.

I have to perform bias correction on each grid point for a period of 2020 to 2100. I have searched and find some tutorial to bias correct timeserise data but I need to perform bias correction on each grid point as well. Can I do it directly on netcdf data or is there any other way? I need to perform bias correction on each grid point and over the time period. Looking forward for your help.

Hi,

I am working on a meta-analysis on the volume measurement of pulmonary nodules by automatic software tools. I used I^2 to calculate heterogeneity between studies but I was advised to do funnel plots. The studies are either phantom based (artificial nodules) or coffee-break in vivo studies were the actual volume of the nodule does not change. This is because there is no gold standard for the in vivo volume, since after surgery the nodule is known to shrink.

to my understanding, funnel plots apply to sensitivity / specificity studies, and I am having a hard time understanding what that means in this particular case, since all nodules, do not change (I.e., no false negatives or true positives for growth).

how else could I check for bias of publication?

Greetings!

Me and my team are conducting a systematic review with meta-analysis focused on correlation between two variables. The purpose of that work is not focused on any intervention or outcome. Therefore, we do not need to assess risk of bias of e.g. allocation sequence or randomization simply because it does not influence the relationship between the two variables we are trying to analyse. However, there are certain things that could bias this correlation and we would like to incorporate those things into assessing the risk of bias of individual studies. Is it, therefore, methodologically correct to not use RoB2 tool to assess quality of RCTs, but instead use our own developed risk of bias score?

Thank you in advance,

Zbigniew

Hi all,

UKAS have asked me to include bias into my uncertainty calculation to have an expanded uncertainty.

I ran 20 control samples for a moisture that I used to set up my QC charts. I obtained the following:

Average 9.94%

Stdev: 0.31

RSD%: 3.08%

We were reporting uncertainty with k = 2 so that RSD% x 2 = 6.16 uncertainty.

UKAS were happy with this. However they wanted us to add bias from our PT data and create an expanded uncertainty. Reading different articles has left me very confused on how to do this.

Past PT data:

I calculated bias for each round of moisture sample as follows:

Bias = (obtained result - expected result)/ expected result

-4.7936

-3.8741

-2.3148

-4.2735

1.4689

0.0000

-6.474

-5.1151

I read you then had to get RMS bias for the PT data which gave me : 4.06

How to you now get the expanded uncertainty using my 20 moisture runs and including the bias from my PT???

Thank you for your help.

Fama and french calculated value weighted portfolio return while framing SMB, HML and WML factor. But why not equal weighted portfolio returns because I think equal weighted portfolio have more diversification as compared to value weighted portfolio. Because value weighted portfolio is biased by giving more weight to large cap companies so the mean return are mostly driven by large cap. I also found one study which found equal weighted portfolio outperform price - weighted or value weighted portfolio. So can someone enlighten me which one to use while framing these factors.

In research, we usually worry about endogeneity problem arising from reverse causality as it leads to biases in the estimates. So, researchers usually use the lag values of the endogenous covariate or use instruments to deal with the problem. However, these approaches are also not without próblems. So, why ís reverse causality as such a concern in research?

I am currently carrying out a meta-analysis on metabolite levels in Case vs controls and I am trying to understand which risk of Bias assessment to use. Cochrane suggest ROBINS - however this is for interventions (which I am not looking at). Then others suggest NOS however I have seen it has been critised. I wondered if anyone could give me some advice in which one to use?

Being Phd Student , i need suggestion from experts , that please guide me what innovative or challenging i can do in BIAS IN DATA DRIVEN AI. May it be research gap .

I do experiments on lipid droplet for a project and it's not my area of ​​expertise. For my experiments, I use plastic falcons, plastic cones, etc... My colleague says that the lipid droplets are adherent to plastic and using plastic instruments is a mistake. I should only use glass. I couldn't find any articles discussing this topic. Could you confirm that the use of plastic is a bias and that lipid droplets can adhere to plastic? Thank you for your answers.

Research studies including randomized controlled trials often have a time-to-event outcome as the primary outcome of interest, although competing events can precede the event of interest and thus may prevent the primary outcome from occurring - for example mortality may prevent observing cancer recurrence or may preclude need for reoperation in patients who undergo surgical repair of heart valves. Researchers often use Kaplan-Meier survival curves or the Cox proportional hazards regression model to estimate survival in the presence of censoring. These models can provide biased estimates (usually upward) of the incidence of the primary outcome over time and therefore other models which address competing risks, such as the Fine-Gray subdistribution hazards model, may be more suitable for estimating the absolute incidence of the primary outcome as well as the relative effect of treatment on the cumulative incidence function (CIF). My question is whether the Nelson-Aalen estimator is a reasonable option for estimating the hazard function and the cumulative incidence of the outcome of interest in the scenario of competing risks and if so, why is this a preferred approach over the Kaplan-Meier estimator?

During the lecture, the lecturer mentioned the properties of Frequentist. As following

Unbiasedness is only one of the frequentist properties — arguably, the most compelling from a frequentist perspective and possibly one of the easiest to verify empirically (and, often, analytically).

There are however many others, including:

1. Bias-variance trade-off: we would consider as optimal an estimator with little (or no) bias; but we would also value ones with small variance (i.e. more precision in the estimate), So when choosing between two estimators, we may prefer one with very little bias and small variance to one that is unbiased but with large variance;

2. Consistency: we would like an estimator to become more and more precise and less and less biased as we collect more data (technically, when n → ∞).

3. Efficiency: as the sample size incrases indefinitely (n → ∞), we expect an estimator to become increasingly precise (i.e. its variance to reduce to 0, in the limit).

Why Frequentist has these kinds of properties and can we prove it? I think these properties can be applied to many other statistical approach.

I am looking for publications with specific error metrics (BIAS, MAE, RMSE etc.) for allometric tree volume equations for European tree species. Preferably in comparison to destructive measurements. If anyone knows of such publications, please leave a link. Many thanks in advance.

Hi mates, im looking for a risk of bias tools, that can be used in interventional studies ( either RCT or NON RCT).

Im doing a meta analisys about exercise and inflammatory markers in the elderly. i got some studys RCT and some Non RCT, what tool do you advise ?

Thanks for any help, im a newbie, best regards Luís Silva.

We are doing a systematic review and the majority of the studies meeting our inclusion criteria are case series. We are unable to find any risk of bias assessment tool specifically for case series. Can you suggest any risk of bias tool for case series?

Thank you

Dear colleagues,

My research group and I are conducting a systematic prevalence review, and we are having difficulty understanding the tools for assessing the risk of bias.

Which would be more suitable to fulfill this role - ROBINS-I or ROBINS-E?

I would like seek your knowledge about how to reply for a biased reviewer?

Also, sometimes the author feels that the reviewer does catch the main idea of the research; how to respond to them?

A patient is taken off a treatment because the outcome value of interest dropped below value B. For whatever reason the exact outcome value is missing. I need to impute it to avoid bias and to reduce my confidence intervals.

Is multiple imputation something I can use and if yes, how should I adjust it? This is obviously Missing Not At Random. If not multiple imputation, what else can I do? Is there a standard approach? Non–random attrition should be a very common thing in RCTs.

Dear Respected Researchers,

A few months ago, I saw an article published in a very good journal (IF ~ 14) and this article used a dataset that is available in a famous data source. After reading the article, I found errors in the dataset, however, the authors made some conclusions based on this biased data. Recently, I found another article based on the same biased dataset and this new article cited the previous article. This seems that researchers are using that biased dataset without verification. I am afraid that this dataset may be used by many other researchers. So, my question is, should I write a letter to the editor/comment on those published articles and submit it to the journals for consideration OR Should I ignore those articles?

I am confused because the authors may think that I am targeting them if I write a letter to the editor or comment on those articles, however, I intend to highlight errors in the dataset so that other researchers should be careful before using this dataset.

What do you suggest? Your suggestions will be helpful for me.

I look forward to hearing from you.

Which Risk of Bias tool can we use for a Qualitative systematic review that contains either cross-sectional or case control questionnaires ?

To whom who may read this question

Data extraction (data abstracting) is a key step in writing umbrella reviews. But is there any method to confirm the validity of this step? How should we ensure that this step is done correctly?

I=II %input data

T=TT %target data

net=newff(minmax(I),[1,5,1],{'logsig','tansig','purelin'},'trainLM');%Logisig,tansig,purelin are the activation function, trainlm is for training neural network

net = init(net); % Used to initialize the network (weight and biases)

net.trainParam.show =1; % The result of error (mse) is shown at each iteration (epoch)

net.trainParam.epochs = 1000; % Maximum limit of the network training iteration process (epoch)

net.trainParam.goal =1e-12; % Stopping criterion based on error (mse) goal

net=train(net,I,T)

ERROR

Error using network/train (line 340)

Output data size does not match net.outputs{3}.size.

We are using the Elementar Analyser for carbon and nitrogen content of plant, soil and fertilisers. The carbon is in low bias (the factor is~0.89...), when it should be 0.9 to 1.1 and nitrogen is in high bias.

Is there any paticular reason for this? Could it be a calibration issue or maintenance problem?

Thanks

Regards

Adiel

It is a scientific/philosophical question that how much a paper review is reliable? To what extent is the reviewer's scientific mind influenced by non-scientific issues, such as prejudices against rival researchers or research groups, biased affiliations and institutes, and special nationalities? So let me know your opinion:

Q 1: How much do you think the reviewers of scientific papers written by non-famous people have made up their minds about rejecting them before reading the papers?

1- 100% 2- 75% 3- 50% 4- 25% 5- 0%

Regression and matching are the most common econometric tools used by scholars. In the case of regression, regression always calculate correlations, but such correlation can also be interpreted as causation when certain requirements are satisfied. As Pearl says, " ‘Correlation does not imply causation’ should give way to ‘Some correlations do imply causation.’ "

One of the most critical assumptions for making causal inferences in observational studies is that (conditional on a set of variables) the treatment and control groups are (conditional) exchangeable. Confounding and selection bias are two forms of lack of exchangeability between the treated and the untreated. Confounding is a bias resulting from the presence of common causes of treatment and outcome, often viewed as the typical shortcoming of observational studies; whereas selection bias occurs when conditioning on the common effect of treatment and outcome, and can occur in both observational studies and randomized trials.

In econometrics, the definition of confounding and selection bias is not very clear. The so-called omission variable bias (also known as selection bias, as distinct from the selection bias we mentioned above) in econometrics, in my opinion, refers to bias due to confounding. As a simple regression model Y = a + bx + ɛ, we say there is omitted variables bias when the residual term is correlated with the independent variable, that is, the regression model omits variables related to the independent variable that may affect y. In another words, the omitted variable is correlated with 1) the independent variable and 2) the outcome variable. By the above definition, the common effects of X and Y should also be controlled for, and such control is known to lead to another type of bias - selection bias. Angrist addresses this issue in his book, saying:” There’s a second, more subtle, confounding force here: bad controls create selection bias …..., the moral of the bad control story is that timing matters. Variables measured before the treatment variable was determined are generally good controls, because they can’t be changed by the treatment. By contrast, control variables that are measured later may have been determined in part by the treatment, in which case they aren’t controls at all, they are outcomes.” Now we know that variables that are measured before the treatment variable is determined are not necessarily good control variables, such as M-bias. The econometric definition is confusing, and it seems to me that omitted variable bias should be distinguished from selection bias, and omitted variable bias should be defined as the variable in the residual that causes Y also causes X.

Due to presentation problems, omitted variable bias is often mistaken as the omission of variables associated with y. We often see articles with statements such as "To mitigate the omitted variables bias of the model, we also control for ....." , followed by a long list of variables that (may) have an effect on y. However, adding a series of control variables to the regression model maybe not helpful to our assessment of causal effects, but even amplify the bias. The inclusion of control variables without consideration may trigger the issue of conditioning on a collider opens the back-door path, which was blocked when the collider was not conditioned on. Therefore, when using regression for causal inference, all we have to do is to pick a set of variables based on reliable causal diagrams.

I believe that simple regression methods should not be worthless in causal inference; what we need to do is to scrutinise our assumptions before using regression (using causal diagrams to choose the control variables using to block backdoor paths is a good way), to increase the transparency of our research, and to show the reader what assumptions our results are based on, and to what extent these assumptions are reliable. Of course, no matter how much effort we put into proving that our conclusions are reliable, there is still the inevitable threat of unobservable confounding in studies based on observational data, and regression methods that coping with this by adding control variables can only address those observable confounding. However, you cannot deny one method if you cannot clearly identify where these threats are coming from. As Robins says, the critic is not making a scientific statement, but a logical one.

These are just some of my personal views from the study, all comments are welcomed!

References.

Pearl, J., & Mackenzie, D. (2018). The book of why: the new science of cause and effect. basic books.

Hernán, M. A., & Robins, J. M. (2010). Causal inference.

Angrist, J. D., & Pischke, J. S. (2014). Mastering' metrics: The path from cause to effect. princeton university press.

Hello

For external validation of diagnostic accuracy study, is a prospective study superior to a retrospective study? What is the reason for the superiority of a prospective design? Avoidance of recall bias? or avoiding sampling bias?

Mixed Methods Study

More to behavioural biases

I am interested in doing research in the field of behavioural finance. But I am unable to select cogntive biases that are the base for the research.

Kindly help

I am writing a systematic review where I will be including RCT and also randomized trials without the control group. Which risk of bias will be best to use for both please?

I want to collect data from Spanish and UK hotel employees. I am concerned about possible survey response bias resulting from nationality of employees. Are there any questions I should insert in the questionnaire to control for this?

one way from literature is testing the vif which is lower than 3.3 is ok.

is there other way to test Common method bias in smartpls?

thanks

I did Bland-Altman test, to assesses the agreement between two methods of measuring height in children under 5 years (a manual height board and a 3D imaging phone). Results from the analysis shows:

Bias= 0.5596

SD of Bias =3.535

95% lower limit of agreement = -6.369

95% upper limit of agreement = 7.488

How do I interpret these findings? Is there an agreement or not?

When DC voltage bias is very high, plasma intensity is very low, and vice versa.

I'm not able to get deposition.

Can we ask independent variables related questions to an employee and dependent variable(employee performance) questions to the employer ? As employee answering about his performance will be self biased response. If there are any papers published with two different sets of respondents for same conceptual model please post the url for my reference.

Hi, hope you are well.

I am doing a literature review on the use of VR simulation in diagnostic imaging and 3 of my studies have the same lead author as they have created the software and use the updated version in each study and but in 2 of their study, they compare VR with role playing or a computer programme. but i was not sure if there is bias or more credibility as they have a better understanding. in addition 2 of the studies, they used the same participants. was wondering if you can give me some guidance, thank you

I've been looking around for a risk of bias assessment tool specifically made for observational studies, mouse references suggest using Cochrane's ROBINS but it doesn't seem sound enough to use for observational studies(esp since the tool itself states it's for non-randomized interventions) I'm also not comfortable with the comparability implication of using it with observational studies.

Do you have any other tools to suggest or if you suggest using ROBINS (or perhaps a modified version?)

Thank you

How does the inadequate metaphysical knowledge leads to methodological bias?

can anyone help me in doing Bias correction such as Quantile mapping using climate data in python ?

It is well known that there are three types of systematic bias in causal inference–confounding, selection, and measurement. Is reverse causation, then, a fourth source of bias independent of these three forms?

Dear all,

I've recently processed some samples for ATAC-seq. My corresponding ATAC-seq library looks different (see picture: Bio-Analyzer) than the expected profile. I was wondering if I can still sequence it or it will be too biased.

Thank you for your help

Best,

Karim

According to someone, using country-level data for study might bring the "aggregate fallacy" introduced by macro data, resulting in possible bias in estimate findings. If this is true, then why have researchers published several research articles in prestigious journals in which they analyzed country-level data? What if we conducted estimates using data from income groups? Simply said, if this is indeed a problem, what will be the solution?

I am designing a perceptual study, with repeated measures design. Participants will hear some audio stimuli and be asked to respond to a target stimulus with the question "was the stimulus early, on-time, or late". There will be several experimental conditions, and all participants will undergo all conditions. I want a dependent variable measuring how accurate the participants are on this task, with the ability to see differences between early, on-time or late stimuli.

One option is to calculate the percentage of correct responses in each category, but i believe there is a problem of response bias with this approach. E.g., if someone responds "on-time" to every stimulus it will look as if they were great at recognising the on-time stimuli, but actually they were just biased towards that option.

In the past, I have used signal detection theory on 2-choice categorical data (i.e. yes/no data), to produce measures of sensitivity (d prime) and bias. This accounts for response bias in the data.

My question is, is there a way to extend this yes/no signal detection analysis to data with three response options?

Or is there another way to account for response bias?

Thanks!

Self-reporting bias is a challenge in case of GPS data collection, in studies where participants have to manually START and STOP recording their trip, unlike studies where GPS data is passively collected (continuously in the background) without the need for user intervention.

I specifically want studies which have mentioned the existence of self-reported bias in the context of GPS data collection.

Berksonian bias occurs due to differential rate of hospital admission or cases may be similar to control(Blunting of effect)which may lead to decrease generalizability . Methods to reduce the bias?

Hi there

I have undertaken SRs in the past and I know the ideal is to have 2 reviewers and a third person to resolve any discrepancies. But is there a maximum number of reviewers for scoping reviews? Would having 4 or 5 full text reviewers actually be a good thing to minimise bias or would it be a case of too many cooks?

In survey research, it is advised that the response rate should be high to avoid self-selection bias. What methods can be used to assess if the data is affected by biases resulting from low response rate,

I have heard in videos that variation in R2 and path coefficients (before and after common method bias correction) should be <10% for unmeasured marker variable

and <30% for measured latent marker variable correction method.

Can anyone share the articles or references? Where does this cut-off value come from?

Dear colleagues,

I am currently working with a mentor to conduct a systematic review and meta-analysis for a prevalence and risk of a specific condition in a specific duration. The purpose is to evaluate cross-sectional studies. The problem is that there is plenty of different risk of bias assessments to assess the risk of bias of cross-sectional studies, and I am confused about which assessment is the best for my study. Therefore, can you guide me to solve this problem?

I am looking forward to your help. Thank you in advance.

Hi all,

I'm currently in the full text review phase of an SLR on predictors / risk factors of treatment resistant depression. I'm including observational studies and RCTs. Virtually all of these studies are non-interventional. For data synthesis, I'm looking to conduct a narrative summary of results, separated in categories such as clinical, genetic, demographic etc. I'm finding it quite difficult to source a risk of bias tool for non-interventional studies with the end product being a narrative summary. Does anyone have any suggestions regarding suitable RoB tools?

Thanks in advance!

According to Podsakoff et al. (2003), measuring some types of construct (i.e. attitude, personality .. etc.) would probably exist bias due to systematic bias - common method variance/bias (CMV/CMB). However, I found that in my research that most of constructs are perceptional or self-referencing (i.e. Work-family conflict/enrichment/balance). Is there any thesis articulating that these constructs could avoid the threat from CMV, or anyone who has experiences dealing with this issue?

I would like to calculate average field size of agricultural fields in the landscapes surrounding my study sites. Calculating field size in ArcGis Pro is easy enough, but when intersecting my landscape buffers with the field layer and then calculating average field size based on the intersected layer, some of my fields are cut off and "made smaller", which will underestimate average field size.

Any ideas on how to deal with this issue? Are there tools in ArcGIS Pro (or anopther programm) that account for/deal with this bias?

I very much appreciate your ideas and suggestions!

Through an intervention program-8 weeks, I am working on behavior change. Due to time constraints and, ethically, not letting anyone unexposed to the intervention as the targeted behavior may cause danger to health, I have chosen non-randomly selected one group for pre and post test. Post test will be repeated at 10th, 12th, and 14th week.

How can I provide sound rationale for using this design and how can i address the well-known biases associated with this design e.g. maturation, RTM, history, and pre-test etc. I will be thankful for your suggestions, please.

Hi Dear,

Was somebody able to use R Shiny web application (https://

cinema.ispm.unibe.ch/rob-men/) for Risk of Bias due to missing evidence evaluation?

A continuous error occurs when I apply for Data analysis.

I define one part of my model as a ferrite material (saturation more than zero), when we have ferrite material, we should define the magnetic bias for it, I defined it, but after simulation it does not get converged and I got the error "matrix solver exception file i/o failure" , what should I do ? How much this magnetic bias should be?

I am using NIH quality assessment tool (https://www.nhlbi.nih.gov/health-topics/study-quality-assessment-tools) to critically appraise controlled intervention studies and before-after studies as part of my Msc dissertation. I am aware that it is not advised to use a numeric score to infer quality of the study, can anybody advise if there is a best way to determine poor/ fair/ good or is it simply opinion? I am concerned of the bias introduced if I simply state I decided poor/ fair/ good.

Thanks in advance

I have a model in HFSS, I want to simulate in under two condition, the first condition is without any magnetic media, assign excitation and calculate fields. The second condition is putting a magnetic media (not exactly ferrite, but with permittivity=1, permeability = from 1 to 10, magnetic saturation = more than zero from 1 to1000, G=2, delta-H=0) the frequency is 100MHz, three terminals, one as input, and two as outputs. incident voltage = 0.5,(driven-Terminal)

under the first condition the simulation is working. But, in the second circumstance, when I define the new material with the properties that I mentioned, I got the error that I must define the magnetic bias for ferrite material (Ms>0), this magnetic bias needs the internal bias, in order to align the dipole of the ferrite, but it did not converge. My question is that why I should define the internal bias? My goal is to magnetize the media by the magentic field that is producing in the model, not by the assigned magnetic bias.

How to reduce BIAS and improve RMSE in multivariate data analysis?

There are many tools for assessing risk of bias, however, I'm confused which is most proffered for evaluation risk of bias in studies?