{"id":57693,"date":"2023-12-08T05:56:12","date_gmt":"2023-12-08T05:56:12","guid":{"rendered":"https:\/\/www.homeobook.com\/?p=57693"},"modified":"2023-12-08T05:56:12","modified_gmt":"2023-12-08T05:56:12","slug":"bias-in-homeopathic-clinical-trial","status":"publish","type":"post","link":"https:\/\/www.homeobook.com\/bias-in-homeopathic-clinical-trial\/","title":{"rendered":"Bias in Homeopathic Clinical Trial"},"content":{"rendered":"
Dr. Pulkesh P. Chothani ABSTRACT Introduction Example like this<\/p>\n That samples vary from one another and thus results also vary. If we take a random sample of 20 students in same class and measure their weight, the mean would be different from the mean of another sample of 20 students in same class. Variation is an essential feature of human beings.<\/p>\n Key words : <\/strong>Bias, Control and Experimental group, Odd Ratio, Random<\/p>\n Biases<\/strong><\/p>\n Definition Bias: <\/strong>Bias\u00a0is a disproportionate weight\u00a0in favor of<\/em>\u00a0or\u00a0against<\/em>\u00a0an idea or thing, usually in a way that is\u00a0closed-minded,\u00a0prejudicial, or unfair. Biases can be innate or learned. People may develop biases for or against an individual, a group, or a belief.\u00a0In science and engineering, a bias is a\u00a0systematic error.\u00a0Statistical bias\u00a0results from an unfair\u00a0sampling\u00a0of a population, or from an\u00a0estimation\u00a0process that does not give accurate results on average.<\/p>\n Bias occurs when the results of a study are systematically different from \u2018truth\u2019. For example, if the objective of the study is to estimate the risk of disease associated with an exposure, and the result from the study consistently overestimates the risk, the result is said to be biased. Bias should be distinguished from random error, in that random error cannot be associated with a particular cause and tends to \u2018average out\u2019 in repeated sampling. Bias, on the other hand, would repeat the same direction of error in repeated sampling with the same design. Bias results from faulty design. There may be many reasons for bias, and care has to be taken to minimize bias when designing the study, since it is often difficult to separate the true effects from bias. Simply increasing the sample size, on the other hand, can minimize the effect of random error.<\/p>\n Nonresponse has two types of adverse impacts on the results. The first is that the ultimate sample size available to draw conclusions reduces, and this affects the reliability of the results. This deficiency can be remedied by increasing the sample size corresponding to the anticipated nonresponse. The second is more serious. Suppose you select a sample of 3000 out of one million. But if only 250 respond out of 3000, your survey could be severely biased. These responders could be those who are favourable or those with strong views.<\/p>\n This bias occurs when the case group and control group are not equivalent at baseline, and differentials in factors affecting the results are not properly accounted for at the time of analysis.<\/p>\n The subjects included in the study may not truly represent the target population. This can happen either because the sampling was not random, or because the sample size is too small to represent the entire spectrum of subjects in the target population. Studies on volunteers always have this kind of bias. Selection bias can also occur because the serious cases have already died and are not available with the same frequency as the mild cases (survival bias).<\/p>\n Selected patients may suffer from other apparently unrelated conditions but their response might differ either because of the condition itself or because of medication given concurrently for that condition.<\/p>\n Error can occur in diagnostic or screening criteria. For example, a laboratory investigation done properly in a hospital setting is less error prone compared to one carried out in a field setting where the study is actually done. In a prostate cancer detection study, if prostate biopsies are not performed in men with normal results after screening, true sensitivity and specificity of the test cannot be determined.<\/p>\n All cases are not detected at the same stage of the disease. With regard to cancers, some may be detected at the time of screening, for example by pap smear, and some may be detected when the patients start complaining. But the follow-up is generally from the time of detection. This difference in \u201clead time\u201d can cause systematic error in the results.<\/p>\n Control subjects are generally those that receive placebo or existing therapy. If these subjects are in their homes, it is difficult to know if they have received some other therapy that can affect their status as controls.<\/p>\n Interviewer bias occurs when one is able to get better responses from one group of patients (say, those who are educated) relative to the other kind (such as illiterates). Observer bias occurs when the observer unwittingly (or even intentionally) exercises more care about one type of responses or measurements such as those supporting a particular hypothesis than those opposing the hypothesis<\/p>\n This occurs when the measuring instrument is not properly calibrated. A scale may be biased to give a higher reading than the actual or lower than the actual such as a mercury column of a blood pressure instrument not being empty in the resting position.<\/p>\n If subjects know that they are being observed or being investigated, their behaviour and response can change. In fact, this is the basis for including a placebo group in a trial. Usual responses of subjects are not the same as when under a scanner<\/p>\n There are two types of recall bias. One such bias arises from better recall of recent events than those that occurred a long time ago. Also, serious illnesses are easier to recall than mild illnesses. The second type of bias arises when cases suffering from a disease are able to recall events much more easily than the controls if they are apparently healthy subjects.<\/p>\n Sometimes the subjects after enrolment have to be excluded if they develop an unrelated condition such as an injury, or become so serious that their continuation in the trial is no longer in the interest of the patient. If a new facility such as a health centre is started or closed for the population being observed for a study, the response may alter. If two independent trials are going on in the same population, one may contaminate the other. An unexpected intervention such as a disease outbreak can alter the response of those who are not affected.<\/p>\n Many diseases are self-limiting. Improvement over time occurs irrespective of the intervention, and it may be partially or fully unnecessarily ascribed to the intervention. Diseases such as arthritis and asthma have natural periods of remission that may look like the effect of therapy.<\/p>\n It is well known that almost all of us have a special love for digits zero and five. Measurements are more frequently recorded ending with these digits. A person aged 69 or 71 is very likely to report one\u2019s age as 70 years. Another manifestation of digit preference is in forming intervals for quantitative data. Blood glucose level categories would be 70\u201379, 80\u201389, 90\u201399, etc., and not 64\u201371, 72\u201379, etc. If digit zero is preferred, 88, 89, 90, 91, and 92 can be recorded as 90. Thus, intervals such as 88\u201392, 93\u201397, and 98\u2013102, are better to ameliorate the effect of digit preference, and not the conventional 85\u201389, 90\u201394, 95\u201399, etc.<\/p>\n The pattern of nonresponse can differ from one group to the other in the sense that in one group more severe cases drop out, whereas in another group mostly mild cases drop out.<\/p>\n Two types of errors can occur in recording. The first arises due to the inability to properly decipher the writing on case sheets. Physicians are notorious for illegible writing. This can happen particularly with similar looking digits such as 1 and 7, and 3 and 5. Thus the entry of data may be in error. The second arises due to the carelessness of the investigator. A diastolic level of 87 can be wrongly recorded as 78, or a code 4 entered as 6 when memory is relied upon, which can fail to recall the correct code. Wrongly pressing adjacent keys on the computer keyboard is not uncommon either.<\/p>\n This again can be of two types. The first occurs when gearing the analysis to support a particular hypothesis. For example, while comparing pre- and post-values, for example, hemoglobin (Hb) levels before and after weekly supplementation of iron, the increase may be small that will not be detected by comparison of means. But it may be detected when evaluated as a proportion of subjects with levels\u00a0 <10 mg\/dl before and after iron supplementation. The second can arise due to differential interpretation of p-values. When p = 0.055, one researcher may refuse to say that it is significant at 0.05 level and the other may say that it is marginally significant. Some researchers may change the level of significance from 5% to 10% if the result is to their liking.<\/p>\n This arises from the tendency among some research workers to interpret the results in favour of a particular hypothesis ignoring the opposite evidence. This can be intentional or unintentional.<\/p>\n Scales in graphs can be chosen such that a small change looks like a big change or vice versa. The second is that the researcher may merely state the inconvenient findings that contradict the main conclusion but does not highlight them in the same way as the favourable findings are done.<\/p>\n Many journals are much too keen to publish reports that give a positive result regarding efficacy of a new regimen compared with the negative trials that did not find any difference. If a vote count is done on the basis of the published reports, positive results would hugely outnumber than negative results, although the fact may be just the reverse.<\/p>\n Steps for Minimising Bias Specify the trial in full detail.<\/strong><\/p>\n Conclusion Reference <\/strong><\/p>\n Dr. Pulkesh P. Chothani <\/strong>(MD Homeopath), Government Homeopathic Medical College, Dethali, Siddhpur. Guajrat. Dr. Pulkesh P. Chothani Dr. Pratiksha G. Rangani ABSTRACT Case taking in homeopathic science is based on unprejudiced method. Unprejudiced is indicated without bias for selection of similimum medicine. Every research requires unbiased and unprejudiced […]<\/a><\/p>\n<\/div>","protected":false},"author":1,"featured_media":42204,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[41],"tags":[12490,12491],"class_list":{"0":"post-57693","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-research-homoeopathy","8":"tag-bias","9":"tag-homeopathic-clinical-trial"},"yoast_head":"\n
\nDr. Pratiksha G. Rangani<\/strong><\/p>\n
\n<\/strong>Case taking in homeopathic science is based on unprejudiced method. Unprejudiced is indicated without bias for selection of similimum medicine. Every research requires unbiased and unprejudiced method.\u00a0 Various bias occurs in clinical trial research in homeopathy.<\/p>\n
\n<\/strong>Clinical trials are experiment on human being conducted in a scientific way. The objective is to find out whether, when we do something to a group of people, it gives you the desire result in many of them or not? Some newly devised or modified regimen is intentionally applied on people with or without disease to find out the efficacy and safety of this regimen. Therefore, in homeopathy, clinical trials as the scientific investigation that examine and evaluate safety and efficacy of medicine therapies in human subjects are randomly allocated to two group, known as the \u201cExperiment\u201d and the \u201cControl\u201d group.\u00a0 The experimental group is given the medicine being tested and the control group is given the placebo, an inert substance in sugar pill.<\/p>\n\n
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\n<\/strong>The purpose of describing various types of biases in so much detail is to create awareness to avoid or at least minimise them. Everything possible should be done to keep them under control. The following steps can be suggested to minimise bias in the results in a research setup. All steps do not apply to all the situations.<\/p>\n\n
\n<\/strong>Bias with prejudice mind in research result definitely will come wrong.<\/p>\n\n
\nDr. Pratiksha G. Rangani <\/strong>(P.G Scholar), Tantia Homeopathic Medical College, Shri Ganganagar, Rajasthan.
\nPh: 9825230997
\nEmail- pulkesh1983@gmail.com<\/p>\n","protected":false},"excerpt":{"rendered":"