Bias in Clinical Studies PDF

Bias is the intentional or unintentional adjustment in the design and/or conduct of a clinical trial, and analysis and evaluation of the data that may affect the results  ERRORS Two broad types of error can affect scientific investigations and distort measu...

Bias is the intentional or unintentional adjustment in the design and/or conduct of a clinical trial, and analysis and evaluation of the data that may affect the results  ERRORS Two broad types of error can affect scientific investigations and distort measurements, random and systematic Random Error (Type 1 and Type II) Refers to the fluctuations around a true value Systematic Error (Confounders &Bias) Rothman provides a conceptual  RANDOM ERROR Type 1 Type II (False (False Positive ) Negative) Observing a difference Failing to observe a when in truth there is difference none. when there is one. Sources of Random Error 1.Biologic Variation: It refers to the fluctuation in biological processes in the same individual over time. 2.Sampling Error: The part of the total estimation error caused by random influences on who or what is selected for the study. 3.Measurement Error: The error resulting from random fluctuations in measurement. CONFOUNDERS  DEFINITION Confounders are variables that are associated with both the exposure and the outcome Not in the causal pathway between them. They influence both the independent and dependent variables.  Degree of Confounding The magnitude of the confounding effect depends on the strength of the association between the confounder and both the Confounding leads to exposure and the outcome. Strong confounders can significantly distort the results Overestimation Weaker confounders may have a minimal Underestimation impact. Reversal of true association.  CHALLENGES IN IDENTIFICATION Causal Relationships: Determining causality is challenging as it cannot be established solely from data. Researchers often need to rely on existing knowledge, expert opinion, and a combination of statistical and causal criteria to identify potential confounders. Measurement Error and Residual Confounding: Inaccuracies in measuring potential confounders can lead to residual confounding, meaning that the adjusted estimates may still be biased. Overadjustment: Adjusting for too many variables, including mediators rather than confounders, can lead to incorrect estimates of the exposure's effect and statistical issues like collinearity (when predictor variables are highly correlated)  CRITERIA FOR IDENTIFYING CONFOUNDERS A variable must meet three key criteria to be considered a confounder: Associated with the exposure A cause of the outcome Not a mediator: The confounder should not be in the causal pathway between the exposure and the outcome. If it is, it's considered a mediator, not a confounder.  MOST COMMON CONFOUNDERS IN CLINICAL TRIALS Age Sex Smoking Socioeconomic Status Lifestyle Factors Comorbidities Medication and systemic disease  CONFOUNDERS IN PERIODONTOLOGY AND IMPLANTOLOGY. Patient Characteristics: Age, sex, socioeconomic status (SES), smoking habits, oral hygiene practices, systemic diseases (e.g., diabetes), and genetic predisposition are all potential confounders in periodontology research. These factors can influence both the exposure (e.g., periodontal disease) and the outcome (e.g., tooth loss, implant failure). Lifestyle Factors: Diet, alcohol consumption, stress levels, and physical activity can also be confounders in this field. Clinical Factors: The type and severity of periodontal disease, the presence of other oral conditions (e.g., caries, Crowns which have open margin or violating biological width, oral cancer), the quality of oral health care received, and the use of certain medications (e.g., immunosuppressants) can act as confounders in both periodontology and  HOW TO CONTROL? Randomization: Randomization, particularly with large sample sizes, is the most effective method for minimizing the impact of confounders. Random assignment of participants to treatment groups helps balance both known and unknown confounders. Matching: Matching involves pairing participants based on potential confounders. For example, in a study exploring the effect of opium on mortality, researchers could match participants based on sex and age to control for these factors. However, matching becomes less practical when dealing with multiple confounders.  HOW TO CONTROL ? Regression Models: Multi-predictor regression models statistically adjust for confounders by including them in the analysis. These models estimate the exposure's effect while holding confounders constant. Regression methods are commonly used in observational studies due to their flexibility in handling multiple confounders simultaneously Restriction: This method involves limiting the study population to a specific level of a potential confounder. For instance, researchers could restrict a study on opium use and mortality to only non-smokers to eliminate confounding by tobacco use. However, restriction can significantly reduce the sample size and limit the generalizability of the results.  HOW TO CONTROL ? Stratification: is a statistical technique used to control the effects of confounders in epidemiological studies. It involves dividing the study population into subgroups, based on the levels of the potential confounder. Stratification analyzes the association between exposure and outcome within different levels of the confounder. This helps determine if the confounder influences the relationship. While it’s useful for understanding confounding, it becomes less feasible with numerous confounders.  IMPORTANCE OF CONFOUNDER CONTROL Valid Interpretation of Research: Accurately accounting for confounders enables researchers to draw reliable conclusions about the true causal effect of an exposure on an outcome. Robust Study Design: Knowledge of confounders can inform study design choices to minimize their impact, leading to more reliable research findings. Evidence-Based Decision Making: Controlling for confounders ensures that public health recommendations and interventions are based on sound and unbiased evidence.  BIAS Deviation of the truth We define bias as a systematic error, or deviation from the truth, in results. Can lead to under-estimation or over-estimation of the true intervention effect and can vary in magnitude: some are small, and some are substantial. A source of bias may even vary in direction across studies. (Jüni et al 2001).  TYPES OF BIAS Selection Bias Performance Bias Detection Bias Reporting Bias Attrition Bias Publication Bias  SELECTION BIAS Definition A systematic difference between either: Those who participate in the study and those who do not (affecting generalizability) or Those in the treatment arm of a study and those in the control group  SELECTION BIAS It can have a major impact on the internal validity of the study and the legitimacy of the conclusion That is, there are differences in the characteristics between study groups, and those characteristics are related to either the exposure or outcome under investigation. Selection bias can occur for several reasons  VOLUNTEER BIAS Definition Known as self-selection bias Happens when people choose to join a group on their own. Problem: Does not represent the general population. Can incorrectly alter a study's conclusions.  HOW TO AVOID SELECTION BIAS A rule for allocating interventions to participants must be specified, based on some chance (random) process. We call this sequence generation. steps must be taken to secure strict implementation of that schedule of random assignments by preventing foreknowledge of the forthcoming allocations. This process if often termed allocation concealment Determine proper inclusion and exclusion criteria that will define the accessible population How to avoid: Randomization Fisher first introduced the idea of randomization in a 1926 Randomization is the process of assigning participants to treatment and control groups, assuming that each participant has an equal chance of being assigned to any group.  TYPES OF RANDOMIZATION 1.Simple Randomization Used in Large sample Using a coin flip or throwing dice 2. Block Randomization Ensures a balance in sample size across groups. Used in absences of covariates (an independent variable that can influence the outcome , but which is not of direct interest). (1)a block size of 4 is chosen, 2) Possible balanced combinations with 2 C (control) and 2 T (treatment) subjects are calculated as 6 (TTCC, TCTC, TCCT, CTTC, CTCT, CCTT) 3) Blocks are randomly chosen to determine the assignment of all 40 participants 3. Stratified Randomization Used to control and balance the influence of covariates. Although stratified randomization is a relatively simple and useful technique, especially for smaller clinical trials, it becomes complicated to implement if many covariates must be controlled, resulting in small blocks. Flowchart for selecting appropriate randomization technique  Quasi-randomization One in which participants are allocated to different arms of the trial (to receive the study medicine, or placebo, for example) using a method of allocation that is not truly random Alphabetical: patients with last name A–M Tx B = patients with last name N–Z Telephone number: Tx A = last digit odd Tx B = last digit even  ALLOCATION CONCEALMENT Definition: prevents researchers from (unconsciously or otherwise) influencing which participants are assigned to a given intervention group or control group. Concealment is a technique used to prevent selection bias in Randomized Controlled Trials (RCT’s) by concealing the allocation sequence from those assigning participants to the intervention groups, until the moment of assignment.  IMPORTANT ASPECT Generation of the allocation sequence Adequate: if random numbers generated by a computer- generated number, table of random numbers, drawing of envelope , tossing a coin , shuffling cards , throwing dice. Inadequate: could be related to prognosis or introduces selection bias : case record number , date of birth , day , month or year of admission Concealment of the allocation sequence until assignment occurs Adequate If central randomization, sequentially numbered , sealed/opaque envelopes, coded drug containers of identical app. Prepared in an independent pharmacy Inadequate : If alteration , unsealed or non opaque envelopes  Randomizing participants using sequentially numbered, opaque sealed envelopes (SNOSE) is the most accessible and straightforward method of maintaining allocation concealment and does not require the use of specialized technology Why SNOSE ? that is simple, cheap, and effective. the Cochrane handbook of systematic reviews says that trials that use SNOSE have a low risk of bias  EXCLUSION BIAS Definition Exclusion bias refers to the systematic error that occurs when certain individuals or groups are excluded out of a data sample, leading to results that may not accurately represent the entire population How to avoid: Using careful research design and sampling Define a target population and a sampling frame Set clear objectives  PERFORMANCE BIAS Systematic differences between groups in the care that is provided, or in exposure to factors other than the interventions of interest. After enrollment into the study, blinding (or masking) of study participants and personnel may reduce the risk that knowledge of which intervention was received, rather than the intervention itself, affects outcomes.  HAWTHORNE EFFECT People in an experimental Studies adjust and improve their behavior due to their awareness of being observed  BLINDING Blinding is the process by which trial participants and their relatives, care-givers, data collectors and/or those assessing outcomes are unaware of which treatment is being given to the individual participants Prevents clinicians from consciously or subconsciously treating patients differently based on treatment allocation (e.g. detection bias) Prevents data collectors from introducing bias when there is a subjective assessment to be made for eg “pain score” (e.g. observer bias) Prevents outcome assessors from introducing bias when there is a subjective outcome assessment to be made (e.g. Glasgow outcome Score) (e.g. observer bias) Prevents patients from introducing bias when being treated (performance bias)  1. Single blind trial: Participant is not aware. If the trial involves 2 similar procedures, trialists may incorporate blinding by simply not informing patients of their treatment allocation. However, if researchers are comparing surgical therapy to nonoperative management, patients can only be blinded with ethically questionable methods like sham surgery.  WHY ? knowledge of group assignment may affect their behaviour in the trial and their responses to subjective outcome measures. For example, a participant who is aware that he is not receiving active treatment may be less likely to comply with the trial protocol, more likely to seek additional treatment outside of the trial and more likely to leave the trial without providing outcome data. Those aware that they are receiving or not receiving therapy are more likely to provide biased assessments of the effectiveness of the intervention  PLACEBO EFFECT This is a psychological phenomenon where a patient experiences an improvement in symptoms due to the belief that they are receiving treatment. This can inadvertently distort results of clinical trials where a 'placebo group' believes they are receiving the treatment under study.  2. Double blind trial: Neither investigator nor participant are aware. Pelekos G, Ho SN, Acharya A, Leung WK, McGrath C. A double-blind, paralleled-arm, placebo- controlled and randomized clinical trial of the effectiveness of probiotics as an adjunct in periodontal care. J Clin Periodontol. 2019; 46: 1217–1227. https://doi.org/10.1111/jcpe.13191  WHY ? blinded investigator are much less likely to unconsciously influence the treatment or outcomes through their behavior, assessment, or data collection methods. This helps ensure that the results are not swayed by the expectations of the investigators  DETECTION BIAS Detection bias refers to systematic differences between groups in how outcomes are determined. When one population is more likely to have the disease or condition detected than another because of increased testing, screening or surveillance in general. Or, more plainly: When you look for more, you find more. Blinding (or masking) of outcome assessors may reduce the risk that knowledge of which intervention was received. And can be especially important for assessment of subjective outcomes, such as degree of postoperative pain Triple blind trial : Investigator, participant and the outcome assessor are not aware. Das R, Deshmukh J, Asif K, Sindhura H, Devarathanamma MV, Jyothi L. Comparative evaluation of analgesic and anti-inflammatory efficacy of ibuprofen and traumeel after periodontal flap surgery: A randomized triple- blind clinical trial. J Indian Soc Periodontol. 2019 Nov-Dec;23(6):549-553. doi: 10.4103/jisp.jisp_85_19. PMID:  Blinding is not always possible, however. For example, it is usually impossible to blind people to whether major surgery has been undertaken. Blinding is not an all-or-nothing phenomenon; researchers may blind any of the involved groups. Furthermore, even within one of the groups (such as outcome adjudicators), some individuals may be blinded while others are aware of group allocation  The 2010 CONSORT (Consolidated Standards of Reporting Trials) Statement specifies that authors and editors should not use the terms “single-blind,” “double-blind,” and “triple-blind” as the terms are ambiguous. Instead reports of blinded RCT should discuss “If done, who was blinded after assignment to interventions (for example, participants, care providers, those assessing outcomes) and how  REPORTING BIAS Systematic differences between reported and unreported findings. Within a published report those analyses with statistically significant differences between intervention groups are more likely to be reported than non-significant differences. (Chan 2005).  REPORTING BIAS 1.Pre-register the Study Protocol: Register the trial protocol in a public registry (e.g., ClinicalTrials.gov). Pre-registration ensures that the study's methodology, outcomes, and analyses are predefined, which can reduce selective reporting. 2. Specify Primary and Secondary Outcomes: Clearly distinguish between primary and secondary outcomes in the study design. This prevents “outcome switching” (where new outcomes are reported as if they were the primary interest). REPORTING BIAS  ATTRITION BIAS systematic differences between study groups in the number and the way participants are lost from a study. It may lead to overestimation or underestimation of effectiveness and affects validity and generalizability Over-recruitment beyond the numbers originally calculated may be helpful.  PUBLICATION BIAS This happens when journals selectively choose to publish studies with positive results or better-quality study designs. This usually happens with systematic reviews and meta- analyses which lead to overly optimistic results  BIAS IN OBSERVATIONAL STUDIES Mostly related to collecting information from the subjects Information bias: recall bias, interviewer bias Checked by STARD  RECALL BIAS : A type of bias that occurs Research tells us that 20% when participants in a of critical details of a research study or clinical can be introduced in the recognized event are trial do not accurately data collection stage of irretrievable after one year remember a past event or investigation. from its occurrence and 50% experience or leave out are irretrievable after 5 details when reporting about years them The accuracy of recall in (RCTs) with subjective humans significantly outcomes may also be depends on the time interval contaminated by recall bias between the event and the if patients enrolled in the time of its assessment: the trial were not blinded to longer the interval, the their treatment higher the probability of allocation incorrect recalls  TOOLS FOR CHECKING BIAS ROB-2 (Risk of Bias in randomized trials) ROBINS-I tool (Risk Of Bias in Non-randomized Studies - of Interventions) ROB-2 is a tool provided by Cochrane. It is used to check for the risk of bias in randomized studies specifically. There are also several other tools for each type of study.  ROB-2 1. Selecting which results to assess within the review Authors will need to select which specific results from the included trials to assess. Because trials usually contribute multiple results to a systematic review 2. Assess the risk of bias in each bias domain by using signaling questions which are answered as follows: Yes Probably yes Probably no No No information.  YOU SHOULD RECORD IMPORTANT CHARACTERISTIC S OF THE ASSESSMENT BEFORE STARTING TO ASSESS YOUR TRIAL  Bias Domains: 1.Bias arising from the randomization process 2.Bias due to deviations from intended interventions 3.Bias due to missing outcome data 4.Bias in measurement of the outcome 5.Bias in selection of the reported result.  BIAS ARISING FROM THE RANDOMIZATION PROCESS BIAS DUE TO DEVIATIONS FROM INTENDED INTERVENTIONS  BIAS DUE TO MISSING OUTCOME DATA Possible reasons for missing outcome data include participants withdraw from the study or cannot be located (‘loss to follow-up’ or ‘dropout’) participants do not attend a study visit at which outcomes should have been measured data or records are lost or are unavailable for other reasons participants can no longer experience the outcome, for example because they have died. BIAS IN MEASUREMENT OF THE OUTCOME BIAS IN SELECTION OF THE REPORTED RESULT  Once the signaling questions are answered, the next step is to reach a risk-of-bias judgement, and assign one of three levels to eachrisk-of-bias Overall domain: Criteria judgement Low risk of bias The study is judged to be at low risk of bias for all domains for this result. Some concerns The study is judged to raise some concerns in at least one domain for this result, but not to be at high risk of bias for any domain. High risk of bias The study is judged to be at high risk of bias in at least one domain for this result. Or The study is judged to have some concerns for multiple domains in a way that substantially lowers  TO WRAP UP Selection Random Sequence Generation Allocation Concealment Performance Blinding of participant , assessor Detection Blinding of outcome assessor Attrition Sample Size (Incomplete Outcome Data) Reporting (Selective Reporting ) Blinding  BIAS ERROR During Study Design Selective Bias During Data Collection Information Bias During Data Analysis Confounding  REFERENCES Sica, G. T. (2006). Bias in Research Studies. 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