Clinical epidemiology. Modern principles and rules
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Clinical epidemiology. Modern principles and rules
- 1. MINISTRY OF HEALTH OF UKRAINE VDNZU "UKRAINIAN MEDICAL DENTAL ACADEMY" Department of Social Medicine, organization, economy Health of biostatistics and medical jurisprudence Lectures: "Clinical Epidemiology. Modern principles and rules of conducting of clinical research“
- 2. Epidemiology is the study of the distribution of health-related states or events in specified populations and the application of this study to the control of health problems (CDC). The subjects of Epidemiology are: The process of the emergence and spread of any pathological conditions in humans (in the population); Health status (inability emergence and spread of pathological conditions).
- 3. Epidemiology: "Epi" - upon, "demos" - the people, "logos" - study of. The logical, systematic approach to understanding the complexities of disease (Torrence, 1997). The logic of observation and the methods to quantify these observations in populations (groups) of individuals. Epidemiology is the study of the distribution of health-related states or events in specified populations and the application of this study to the control of health problems (CDC).
- 4. Epidemiology includes 1) the methods for measuring the health of groups and for determining the attributes and exposures that influence health; 2) the study of the occurrence of disease in its natural habitat rather than the controlled environment of the laboratory; and 3) the methods for the quantitative study of the distribution, variation, and determinants of health-related outcomes in specific groups (populations) of individuals, and the application of this study to the diagnosis, treatment, and prevention of these states or events. (Last, 1995)
- 5. The purpose of epidemiological studies To answer the questions: 1.Descriptive (Observational) Epidemiology: The most basic form of epidemiology, which is the description of the patterns of occurrence of health-related states or events in groups; answering the questions of "Who?", "What?" "Where?", and "When?". Descriptive epidemiology is usually one of the first things done at the scene of any disease outbreak. 2. Analytical Epidemiology: The design, execution and analysis of studies in groups to evaluate potential associations between risk factors and health outcomes to answer the question "Why?".
- 6. Brief summaries illustrating the contributions of epidemiology to public health Topic Topic Brief summary Brief summary Childhood cancers Studies analysed risk factors for childhood cancers. One prominent finding was a strong association between abdominal X-rays of pregnant women and leukaemia in children. Heart disease Community-based longitudinal study in Framingham, Massachusetts, USA showed the association of ischaemic heart disease with hypertension, level of serum cholesterol and other risk factors
- 7. Some of the variables used to discribe the distribution of disease in descriptive epidemiology People Age, sex, marital status Race, ethnic group, religion Occupation, education, cocioeconomic status, Personal habits – use of alcohol and tobacco Place Climatic zones, Country, region, state, district, urban or rural, local community, city wards, precise location an in institution Time Year, season, day, secular trends, periodic changes, seasonal variations and other cyclical fluctuations
- 8. The main types of epidemiological observations 1. Observational study (study without the intentional intervention by the researcher) Descriptive. Analytical (the most famous are cross-sectional, cohort and studies of "case-control"). 2 Experimental research - a comparative study with the intentional intervention in one of the studied groups (randomized clinical studies).
- 9. The basic principle of clinical epidemiology is that the subject of evaluation is the definite clinical results disease discomfort disability death Clinical Epidemiology: The application of the logical and quantitative concepts and methods of epidemiology to problems (diagnostic, prognostic, therapeutic, and preventive) encountered in the clinical delivery of care to individual patients. The population aspect of epidemiology is present because these individual patients are members of conceptual populations. "A basic science for clinical medicine" (Sackett et al.).
- 10. Design of clinical research - plan for its implementation The choice of research method Clinical studies in the same group (everyone gets one experimental treatment) - the aim is to compare the previous state A clinical trial in parallel groups (different groups receiving different treatments) Choice Contingent (object of study)
- 11. Population study This study is conducted in the volume of the entire population (General totality), which is often referred to in Epidemiology term population study (Morbidity on the Earth)
- 12. Sample study Sample size (sample) - part of a population that is representative illustration (reduced model) of the population (general totality). The goal of sample surveys to get representative information that can be extrapolated to the entire population.
- 13. Sampling Frame > Sampling Unit > Sampling Fraction In statistics, a sample group can be defined as a subset of a population. The population, or target population, is the total population about which information is required.
- 14. Types of Sampling 1. Non-Probability Sampling 2. Probability Sampling
- 15. Randomization is a sampling method used in scientific experiments. It is commonly used in randomized controlled trials in experimental research.: In medical research, randomization and control of trials is used to test the efficacy or effectiveness of healthcare services or health technologies like medicines, medical devices or surgery.
- 16. Probability Sampling and Randomization Probability sampling is a sampling technique wherein the samples are gathered in a process that gives all the individuals in the population equal chances of being selected. In this sampling technique, the researcher must guarantee that every individual has an equal opportunity for selection and this can be achieved if the researcher utilizes randomization.
- 17. Types of Probability Sampling Simple Random Sampling Stratified Random Sampling Systematic Random Sampling Cluster Random Sampling Mixed/Multi-Stage Random Sampling
- 18. The sample size of a statistical Sampling is the number of observations that constitute it. 2 n= t . p.q 2 ∆ t ⋅δ ⋅ N n= 2 2 2 ∆ ⋅ N + t ⋅δ 2 2 n – number of observations N - number of the general population (totality) t - test the reliability δ - standard deviation p - the percentage of events that occurs q = 100 - p Δ2 - Boundary bias . Boundary bias is usually taken equal to 5% (0.05).
- 19. Analytical epidemiology Two types of study are employed: Case-control studies (retrospective studies or case-history studies) Cohort studies
- 20. Case Study A case control study is a method extensively used by the medical profession, as an easy and quick way of comparing treatments, or investigating the causes of disease.
- 21. A cohort study is a research program investigating a particular group with a certain trait, and observes over a period of time. Retrospective Cohort Study Prospective Cohort Study Ambidirectional Cohort Study
- 22. Description of methods General totality Group control Evaluation of reaction Comparison of the results Sampling Observer Group Evaluation of reaction
- 23. Analysis of case-control studies Exposure Exposed Not (+) exposed (-) Incidence Disease (+) A B A/(A+B) No disease (-) C D C/(С+D)
- 24. Analysis of cohort study Disease Incidence Present(+) Absent() Exposed (+) A B A/(A+B) Not Exposed( -) C D C/(С+D)
- 25. The reliability of the research results Estimate the reliability of the research results - means to establish the probability prediction, ie, to determine with what probability may transfer the results obtained on the sample, the entire general population or other studies.
- 27. Validity and Reliability The principles of validity and reliability are fundamental cornerstones of the scientific method. Together, they are at the core of what is accepted as scientific proof, by scientist and philosopher alike. By following a few basic principles, anyexperimental design will stand up to rigorous questioning and skepticism. What is Reliability? The idea behind reliability is that anysignificant results must be more than a one-off finding and be inherentlyrepeatable. What is Validity? Validity encompasses the entire experimental concept and establishes whether the results obtained meet all of the requirements of the scientific research method.
- 28. Any research can be affected by different kinds of factors which, while extraneous to the concerns of the research, can invalidate the findings" (Seliger & Shohamy 1989, 95).
- 29. Склад оцінки результатів дослідження Визначення помилки репрезентативності (середніх помилок середніх арифметичних і відносних величин). m = n р•q m = n Визначення довірчих меж середніх (або відносних) величин. σ Показник вважається достовірним, якщо він перевищує свою помилку в 3 рази Pген = Pвыб ± tm (для относительных показателей), Мген = Мвыб ± tm (для средних величин), М где Рген и Мген - искомые генеральные параметры частоты и среднего уровня, Рвыб и Мвыб – найденные выборочные показатели, m – ошибка представительности, t – доверительный критерий.
- 30. Приклад Визначити ефективність щеплення вакциною А. Вакциновано 400 чоловік, з них 12 захворіло (3%) m = 3 • (100 − 3) = 0,85% 400 Прийнята ймовірність 95,5% (t =2) або 99,7 (t=3) P ген. сукуп. = Р виборки ± t * m Висновок: При застосуванні для щеплення вакцини А. захворюваність для генеральної сукупності буде складати Р = 3,0 ± 2 * 0,85% = ±1,7 (от 1,3% до 4,7%)
- 31. Порівняння різних сукупностей з використанням параметричних методів tср = Х1 − Х 2 m +m 2 1 2 2 tотн = Р1 − Р2 m +m 2 1 2 2 ВИКОРИСТОВУЮТЬ при нормальному розподілі варіант у сукупності t - критерий точности, Стьюдента (Вильямса Госсета) t ≥ 2 вероятность безошибочного прогноза 95% и более (р <0,05) t < 2 вероятность безошибочного прогноза менее 95% (р >0,05)
- 32. Порівняння різних сукупностей з використанням непараметричних методів Різниця між пов'язаними групами 1. Критерій знаків для порівняння груп с попарно пов'язаними варіантами 2. Критерий Вілкоксона Різниця між непов'язаними групами 1. Критерий Манна— Уитни 2. Критерий КолмогороваСмирнова Для порівнянні більше двох показників, для оцінки якісних ознак : Використовують критерій відповідності - Пирсона (х2) – хи квадрат
- 33. При перевірці статистичних гіпотез використовуються два поняття Нульова гіпотеза (гіпотеза про схожість відмінностей) - Передбачаємо, що відмінностей немає - Збираємо данні та оцінюємо - якщо ймовірність мала – гіпотеза відкидається Альтернативна гіпотеза (гіпотеза про наявність відмінностей)
- 34. Критерій знаків для порівняння груп с попарно пов'язаними варіантами 1 день 10 день Спрямо ваність різниці 1 13 23 + 2 22 15 - 3 16 18 + 4 20 14 - 5 19 11 - 6 25 13 - 7 23 12 - 8 20 13 - 9 17 18 + 10 18 18 = № п/п 1. Розрахунок 2. Порівняння з граничними табличними даними 3. Висновок: Не можна зробити висновок про суттєвість динаміки (р≥0,05) Можна зробити висновок про суттєвість динаміки (р ≤0,05) ШОЕ Z=3
- 35. В епідеміологічних дослідженнях найчастіше виявляють причино наслідкову залежність захворюваності та передбачуваної причини Оцінка взаємозв'язку ознак у статистичній сукупності 1.Оцінка напрямку і сили зв'язку між ознаками 2.Оцінка кількісної залежності величини однієї ознаки від зміни величини іншої
- 36. Форми прояву кількісних зв'язків Функціональна При якій значенню одної з ознак відповідає суворо певне значення іншої (для фізико-хімічних процесів) Кореляційна Значенню кожній величини ознаки відповідає декілька значень іншого пов'язаного признака
- 37. Кореляційний зв’язок Оцінка напрямку і сили зв'язку між ознаками Сила зв'язку Напрямок зв'язку Пряма (+) Зворотна (-) Слабка (низька) Від 0 до + 0,29 Від 0 до - 0,29 Середня Від + 0,3 до + 0,69 Від – 0,3 до – 0,69 Сильна (висока) Від + 0,7 до +0,99 Від – 0,7 до – 0,99 Повний зв'язок +1 -1
Editor's Notes
- A research population is generally a large collection of individuals or objects that is the main focus of a scientific query. It is for the benefit of the population that researches are done. However, due to the large sizes of populations, researchers often cannot test every individual in the population because it is too expensive and time-consuming. This is the reason why researchers rely on sampling techniques. A research population is also known as a well-defined collection of individuals or objects known to have similar characteristics. All individuals or objects within a certain population usually have a common, binding characteristic or trait. Usually, the description of the population and the common binding characteristic of its members are the same. &quot;Government officials&quot; is a well-defined group of individuals which can be considered as a population and all the members of this population are indeed officials of the government.
- Ideally, this is a population at risk. The &quot;study population&quot; is the population from which sample is to be drawn. Commonly, the population is found to be very large and in any research study, studying all population is often impractical or impossible. Therefore, sample unit gives researchers a manageable and representative subset of population. Before a sample is taken, members of study population need to be identified by constructing a list called a sampling frame. Each member of sampling frame is called sampling unit. For example, someone may want to know details about shopping trends of people coming to a particular grocery store on Sundays. So people coming to that grocery store on Sunday forms a sampling frame and each customer is a sampling unit. The sampling fraction is the ratio of sample size to study population size. For example if you choose 10 customers out of total 1000 coming to that grocery store, than the sampling fraction would be 1%. The sampling units may be individuals or they may be in groups. For example, in a particular study involving animals, one can select individual animals or groups of animals like in herds, farms, or administrative regions.
- Types of Sampling Now how to get our desired sample group? Well, there are two types ofsampling: 1. Non-Probability Sampling In non-probability sampling, the choice of sample group is left to the researcher and thus element of bias always shows up in such studies. 2. Probability Sampling In probability sampling, the selection of the sample is made using deliberate, unbiased process, so that each sample unit in a group has an equal chance of being selected. This forms the basis of random sampling. Probability sampling is most commonly used in experimental research. Randomization is performed to choose samples providing each sample an equal chance of being selected and thus minimizing or eliminating biasaltogether.
- What is Randomization? So what is randomization? Let&apos;s suppose you have five chocolates bars and total 8 friends to distribute these 5 chocolates to. Now how you are going to do this so the whole distribution process is with a minimum of bias? You may write down names of each of your friends on a separate small piece of paper, fold all small pieces of papers so no one know what name is on any paper. Then you ask someone to pick 5 names and give chocolates to first 5 names. This will remove the bias without hurting any of your friend&apos;s feelings. The way you did this is what we call randomization. In randomized controlled trials, the research participants are assigned by chance, rather than by choice, to either the experimental group or the control group. Randomization reduces bias as much as possible. Randomization is designed to &quot;control&quot; (reduce or eliminate if possible) bias by all means. The fundamental goal of randomization is to certain that each treatment is equally likely to be assigned to any given experimental unit. How Randomization Actually Works? How to achieve randomization in randomized controlled trials? Well, there are different options used by researchers to perform randomization. It can be achieved by use of random number tables given in most statistical textbooks or computers can also be used to generate random numbers for us. If neither of these available, you can devise your own plan to perform randomization. For example, you can select the last digit of phone numbers given in a telephone directory. For example you have different varieties of rice grown in10 total small plots in a greenhouse and you want to evaluate certain fertilizer on 9 varieties of rice plants keeping one plot as a control. You can number each of the small plots up to 9 and then you can use series of numbers like 8 6 3 1 6 2 9 3 5 6 7 5 5 3 1 and so on You can then allocate each of three doses of fertilizer treatment (call them doses A, B, C). Now you can apply dose A to plot number 8, B to 6, and C to 3. Then you apply dose A to 1, B to 2 because dose B is already used on plot 6 and so on. Blinding: An Excellent Tool to Eliminate Bias in Randomized Controlled Trials
- The advantage of using a random sample is the absence of both systematic andsampling bias. If random selection was done properly, the sample is therefore representative of the entire population. The effect of this is a minimal or absent systematic bias which is the difference between the results from the sample and the results from the population. Sampling bias is also eliminated since the subjects are randomly chosen.
- Types of Probability Sampling Simple Random Sampling Simple random sampling is the easiest form of probability sampling. All the researcher needs to do is assure that all the members of the population are included in the list and then randomly select the desired number of subjects. There are a lot of methods to do this. It can be as mechanical as picking strips of paper with names written on it from a hat while the researcher is blindfolded or it can be as easy as using a computer software to do the random selection for you. Stratified Random Sampling Stratified random sampling is also known as proportional random sampling. This is a probability sampling technique wherein the subjects are initially grouped into different classifications such as age, socioeconomic status or gender. Then, the researcher randomly selects the final list of subjects from the different strata. It is important to note that all the strata must have no overlaps. Researchers usually use stratified random sampling if they want to study a particular subgroup within the population. It is also preferred over the simple random sampling because it warrants more precise statistical outcomes. Systematic Random Sampling Systematic random sampling can be likened to an arithmetic progression wherein the difference between any two consecutive numbers is the same. Say for example you are in a clinic and you have 100 patients. The first thing you do is pick an integer that is less than the total number of the population; this will be your first subject e.g. (3). Select another integer which will be the number of individuals between subjects e.g. (5). You subjects will be patients 3, 8, 13, 18, 23, and so on. There is no clear advantage when using this technique. Cluster Random Sampling Cluster random sampling is done when simple random sampling is almost impossible because of the size of the population. Just imagine doing a simple random sampling when the population in question is the entire population of Asia. In cluster sampling, the research first identifies boundaries, in case of our example; it can be countries within Asia. The researcher randomly selects a number of identified areas. It is important that all areas (countries) within the population be given equal chances of being selected. The researcher can either include all the individuals within the selected areas or he can randomly select subjects from the identified areas. Mixed/Multi-Stage Random Sampling This probability sampling technique involves a combination of two or more sampling techniques enumerated above. In most of the complex researches done in the field or in the lab, it is not suited to use just a single type of probability sampling. Most of the researches are done in different stages with each stage applying a different random sampling technique.
- The sample size is typically denoted by n and it is always a positive integer. No exact sample size can be mentioned here and it can vary in different research settings. However, all else being equal, large sized sample leads to increased precision in estimates of various properties of the population. What Should Be the Sample Size? Determining the sample size to be selected is an important step in any research study. For example let us suppose that some researcher wants to determine prevalence of eye problems in school children and wants to conduct asurvey. The important question that should be answered in all sample surveys is &quot;How many participants should be chosen for a survey&quot;? However, the answer cannot be given without considering the objectives and circumstances of investigations. The choosing of sample size depends on non-statistical considerations and statistical considerations. The non-statistical considerations may include availability of resources, manpower, budget, ethics and sampling frame. The statistical considerations will include the desired precision of the estimate of prevalence and the expected prevalence of eye problems in school children. Following three criteria need to be specified to determine the appropriate samples size: 1. The Level of Precision Also called sampling error, the level of precision, is the range in which the true value of the population is estimated to be. This is range is expressed in percentage points. Thus, if a researcher finds that 70% of farmers in the sample have adopted a recommend technology with a precision rate of ±5%, then the researcher can conclude that between 65% and 75% of farmers in thepopulation have adopted the new technology. 2. The Confidence Level The confidence interval is the statistical measure of the number of times out of 100 that results can be expected to be within a specified range. For example, a confidence interval of 90% means that results of an action will probably meet expectations 90% of the time. The basic idea described in Central Limit Theorem is that when a population is repeatedly sampled, the average value of an attribute obtained is equal to the true population value. In other words, if a confidence interval is 95%, it means 95 out of 100 samples will have the true population value within range of precision. 3. Degree of Variability Depending upon the target population and attributes under consideration, the degree of variability varies considerably. The more heterogeneous a population is, the larger the sample size is required to get an optimum level of precision. Note that a proportion of 55% indicates a high level of variability than either 10% or 80%. This is because 10% and 80% means that a large majority does not or does, respectively, have the attribute under consideration. There are number of approaches to determine the sample size including: using a census for smaller populations, using published tables, imitating a sample size of similar studies, and applying formulas to calculate a sample size.
- The case control study uses groups of patients stricken with a disease and compares them with a control group of patients not suffering symptoms. Medical records and interviews are used to try to build up a historical picture of the patient&apos;s life, allowing cross-reference between patients and statistical analysis. Any trends can then be highlighted and action can be taken. Statistical analysis allows the researcher to draw a conclusion about whether a certain situation or exposure led to the medical condition. For example, a scientist could compare a group of coal miners suffering from lung cancer with those clear of the disease, and try to establish the underlying cause. If the majority of the cases arose in collieries owned by one company, it might indicate that the company&apos;s safety equipment and procedures were at fault. Possibly the most famous case control study using this method was a study into whether bicycle helmets reduce the chance of cyclists receiving bad head injuries in an accident. Obviously, the researcher could not use standardexperimentation and compare a control group of non-helmet wearers with helmet wearers, to measure the chances of head injury, as this would be unethical. A case study control was utilized, and the researchers looked at medical records, comparing the number of head injury sufferers wearing helmets against those without. This generated a statistical result, showing that wearing a cycle helmet made it 88% less likely that head injury would be suffered in an accident. The main weakness of the case control study is that it is very poor at determining cause and effect relationships. Исследование случай-контроль использует группы пациентов , заболевших болезнью и сравнивает их с контрольной группой пациентов , не страдающих симптомы. Медицинские отчеты и интервью используются , чтобы попытаться построить историческую картину жизни пациента , что позволяет перекрестных ссылок между пациентами и статистического анализа. Любые тенденции , то можно выделить и меры могут быть приняты . Статистический анализ позволяет исследователю сделать вывод о том определенная ситуация или воздействие привело к медицинским показаниям. Например , ученый мог сравнить группу шахтеров , страдающих от рака легких с теми, подальше от этой болезни , и попытаться установить первопричину . Если большинство случаев возникло в угольных шахтах , принадлежащих одной компании , это может означать, что оборудование для обеспечения безопасности и процедуры компании были виноваты . Возможно, наиболее известное исследование случай-контроль с помощью этого метода было изучение в ли велосипедные шлемы уменьшить вероятность велосипедистов , получающих плохие травмы головы в результате несчастного случая . Очевидно , исследователь не могли использовать стандартный эксперименты и сравните контрольная группа , не шлем , кто носит с шлем владельцев , чтобы измерить шансы травмы головы , так как это было бы неэтично . Случай-контроль исследование был использован , и ученые изучили медицинские записи , сравнивая число травм головы страдальцев шлемах против тех, кто не . Это вызвало статистический результат, показывающий , что в шлеме цикла сделал это 88% менее вероятно, что черепно-мозговая травма будет пострадали в результате несчастного случая . Главная слабость исследования случай-контроль является то, что это очень бедно на определение причинно-следственных связей .
- Some examples of cohorts may be people who have taken a certain medication, or have a medical condition. Outside medicine, it may be a population of animals that has lived near a certain pollutant or a sociological study of poverty. A cohort study can delve even further and divide a cohort into sub-groups, for example, a cohort of smokers could be sub-divided, with one group suffering from obesity. In this respect, a cohort study is often interchangeable with the term naturalistic observation. There are two main sub-types of cohort study, the retrospective and the prospective cohort study. The major difference between the two is that the retrospective looks at phenomena that have already happened, whilst the prospective type starts from the present. Retrospective Cohort Study The retrospective case study is historical in nature. Whilst still beginning with the division into cohorts, the researcher looks at historical data to judge the effects of the variable. For example, it might compare the incidence of bowel cancer over time in vegetarians and meat eaters, by comparing the medical histories. It is a lot easier than the prospective, but there is no control, and confounding variables can be a problem, as the researcher cannot easily assess the lifestyle of the subject. A retrospective study is a very cheap and effective way of studying health risks or the effects of exposure to pollutants and toxins. It gives results quickly, at the cost of validity, because it is impossible to eliminate all of the potentially confounding variables from historical records and interviews alone. Prospective Cohort Study In a prospective cohort study, the effects of a certain variable are plotted over time, and the study becomes an ongoing process. To maintain validity, all of the subjects must be initially free of the condition tested for. For example, an investigation, over time, into the effects of smoking upon lung cancer must ensure that all of the subjects are free of the disease. It is also possible to subgroup and try to control variables, such as weight, occupation type or social status. They are preferable to a retrospective study, but are expensive and usually require a long period of time to generate useful results, so are very expensive and difficult. Some studies have been running for decades, but are generating excellent data about underlying trends in a population. The prospective cohort study is a great way to study long-term trends, allowing the researcher to measure any potential confounding variables, but the potential cost of error is high, so pilot studies are often used to ensure that the study runs smoothly. Ambidirectional Cohort Study The ambidirectional cohort study is the ultimate method, combining retrospective and prospective aspects. The researcher studies and analyzes the previous history of the cohorts and then continues the research in a prospective manner. This gives the most accurate results, but is an extremely arduous undertaking, costing time and a great deal of money. The ambidirectional study shares one major drawback with the prospective study, in that it is impossible to guarantee that any data can be followed up, as participants may decline to participate or die prematurely. These studies need to look at very large samples to ensure that any attributional losses can be absorbed by the statistics. Некоторые примеры когорт могут быть люди , которые взяли на определенные лекарства или есть заболевание . Вне медицины , это может быть население животных , который жил вблизи некоторой загрязнителя или социологического исследования бедности. Когорта исследования могут углубиться еще дальше и разделить когорту на подгруппы , например, когорта курильщиков может быть подразделена , с одной группы страдают от ожирения . В этом отношенииисследование когорты часто взаимозаменяемы с термином наблюдением в естественных условиях . Существуют два основных подтипа изучении когорты , ретроспективы и проспективного исследования когорты . Основное различие между ними состоит в том , что ретроспективные взгляды на явления, которые уже произошли , в то время как предполагаемый тип начинается от настоящего. Ретроспективный когортное исследование Ретроспективное исследование случай исторический характер . В то время как до сих пор , начиная с делением на когорт , исследователь смотрит на исторических данных , чтобы судить эффекты переменной . Например, это может сравнить заболеваемость раком кишечника в течение долгого времени в вегетарианцев и мясоедов , сравнивая истории болезни . Это намного проще, чем предполагаемый , но нет никакого контроля , и вмешивающиеся переменные могут быть проблемой , так как исследователь не может легко оценить образ жизни субъекта. В ретроспективном исследовании является очень дешевым и эффективным способом изучения рисков для здоровья или последствия воздействия загрязняющих веществ и токсинов . Это дает результаты быстро , за счет действия , потому что это невозможно устранить все потенциально вмешивающихся факторов из исторических записей и интервью в одиночку. Предполагаемый когортное исследование В проспективном исследовании когорты , последствия определенной переменной нанесены в течение долгого времени , и изучение становится непрерывным процессом. Для поддержания достоверности , все субъекты должны быть первоначально свободной от испытываемого состояния для . Например , исследование , с течением времени, в последствиях курения при раке легких должны убедиться, что все субъекты свободны от этой болезни. Кроме того, можно к подгруппе и пытаться контролировать переменные, такие как вес, тип профессии или социального статуса. Они предпочтительнее ретроспективном исследовании , но дороги и , как правило, требуют длительного периода времени , чтобы генерировать полезные результаты , так что очень дорого и сложно . Некоторые исследования уже на протяжении десятилетий, но создают отличные данные о основных тенденций в популяции . Проспективное исследование когорты является отличным способом для изучения долгосрочных тенденций , что позволяет исследователю измерять любые потенциальные вмешивающиеся переменные , но потенциал цена ошибки высока , поэтому экспериментальные исследования часто используются для того, чтобы исследование проходит гладко. Ambidirectional когортное исследование Ambidirectional групповое исследование является конечной метод , сочетающий ретроспективные и перспективные аспекты . Исследователь изучает и анализирует предыдущую историю когорт , а затем продолжает исследования в предполагаемом порядке . Это дает наиболее точные результаты , но является чрезвычайно трудным делом , стоимостью время и много денег . Ambidirectional исследование разделяет один существенный недостаток с проспективного исследования , в том, что невозможно гарантировать , что любые данные можно проследить , как участники могут отказаться от участия или умереть преждевременно. Эти исследования должны смотреть на очень больших выборках , чтобы гарантировать, что любые атрибутивные потери могут быть поглощены статистике .