4 BiasHaving a preference to one particular person / group / point of view - “one-sided inclination”Prejudice – negative biasIn statistics, if a bias exists it means that the processes involved are not uniformly random and one outcome is favoured over others.
5 Factors Influencing CT Results Factors other than the intervention under study can influence results on the studyRandom Error Natural variationSystematic Error BiasAll efforts are made to reduce both types of error
6 Random ErrorError that occurs due to natural / biological / random variation in the processMay be on either side of true value
7 How to deal with Random Error Sample size large enough to detect clinically meaningful differenceRepeated sampling
8 Bias or Systematic Error Difference between the true value and observed value due to all causes other than random variabilityA flaw in either the study design or data analysisLeads to an erroneous resultIntentional or unintentional
10 Bias in Clinical Trials The control and intervention groups must be similar enough so that any differences detected in patient outcomes can reasonably be attributed only to the intervention under study.If systematic differences exist between the control and intervention groups, then it is possible that the results of the study are biased.
12 Sampling BiasSystematic error due to study of a non-random sample of a populationSample is not a random sample - some individuals are more likely than others to be chosenFor example, if you are asking college students how much they study, going to the library and randomly selecting people there to ask would introduce obvious bias: People who spend more time in the library are more likely to be chosen, and presumably report spending more time studying. Going to the Campus canteen at mealtime is subtler.
13 Sampling BiasA special kind of sampling bias of particular significance is non-response bias.This occurs when individuals have a choice of whether or not to respond. If significant numbers of individuals choose not to respond, you very likely have response bias, because it is likely that those who refuse have different answers than those that agree.This is a serious problem with modern polls, because large percentages of people refuse to cooperate with pollsters. These end up being a sampling of the most passionate people, who's views are generally dramatically different from the broad middle.
14 Sources of Sampling Bias Failure to adhere to the random sampling procedures.Omission of specific subgroups of the population from the sampling frame and therefore from the sample.Faulty measuring devices (this may be in terms of the specific questions used in a questionnaire, and may also arise in a survey that involves taking physical measurements, when the measuring device is incorrect, e.g., using a defective BP machine, so that all measurements are low / high).Non-response to a survey by specific subgroups of the population that are relevant to the measures of concern in the survey.
15 Preventing Sampling Bias Random samplingSampling all subgroups (representative sampling)Accurate measurementsTaking into account non-responders in a survey
18 Comparator BiasNot using control treatment known to be beneficial / standardFor example, even though the effectiveness of erythropoietin in preventing anemia in cancer patients had been convincingly demonstrated by a number of controlled trials, some researchers continued to compare their drug with placebos.Comparator biases will be introduced when patients are denied effective treatments, and the active treatments studied in the trial will be given an unfair advantage.
19 Comparator Bias Giving an inappropriately low dose of a treatment This has occurred in comparisons of new non-steroidal anti-inflammatory agents used for arthritis with older drugs in the same class (Rochon et al. 1994).Inappropriately low doses can also result from giving a treatment by an inappropriate route, for example, by comparing intravenous administration of a drug with oral administration of a drug that is poorly absorbed from the gastro-intestinal tract (Johanson and Gøtzsche 1999).
20 Comparator Bias Giving an inappropriately high dose of a treatment Some of the newer drugs for treating schizophrenia, for example, have been shown to be preferable to established drugs for this reason. However, this apparent advantage may be because the newer agents have been compared with inappropriately high doses of the older, comparator drug (Waraich et al. 2004).The net usefulness of treatments often requires trade-offs between wanted and unwanted effects. Treatments may be of real value if, although their beneficial effects are no better than alternatives, they have fewer adverse effects.
21 How to Reduce Comparator Bias Appropriate choice of comparator group by systematic review of existing evidenceUse of placebo only when essentialUsing appropriate dose & route of administration of comparator drugEvaluation of net effects of treatments (benefits vs risks)
24 Selection BiasSelecting & allocating participants to treatment groups depending upon investigator’s beliefs about efficacy / safety of treatments or other subjective reasonsResults in ‘dissimilar’ groups
25 Ways to minimize Selection Bias Randomization – single most effective way to reduce selection / allocation biasEvery subject has equal chance of receiving test / comparator treatmentResults in similar ‘intervention’ & ‘control’ groupsProvides basis for statistical inference
26 Randomization Alternate allocation to groups Tossing a coin Randomization TablesComputarized randomization (all patients, blocked, stratified)Methods for concealed randomization
28 Expectation BiasBoth the patient’s and therapist’s expectations can influence the results of a clinical trial (even after randomization)Not let one / both know which treatment is being given - BlindingBlinding reduces expectation bias
33 Bias in AnalysisAnalyzing only select group of subjects for showing positive outcomeNot including drop-outs, withdrawn subjects in analysisMultiple subgroup analysis (not pre-planned) to find some favourable outcomesConfounding variables - factors other than intervention (e.g. age, degree of severity of disease, previous treatment) that may influence outcome
34 Ways to minimize bias in analysis Have a statistical analysis plan before the study & include in protocolStratified design for significant variablesIntention-to-treat analysisSeparate subgroup analysis for significant variablesStratified or multivariate analysis for confounding variables
38 Ways to Minimize Reporting Bias Clinical Trial Registry – registering all clinical trials on new drugsCompulsion to submit results of all studies to regulatory authorityPublishing results of all clinical trials on websitesPublishing significant negative / no-difference studies on new treatments in well recognized journals
Your consent to our cookies if you continue to use this website.