3 types of epidemiology studies the relationship

3 types of epidemiology studies the relationship

Epidemiologic studies fall into two categories: experimental and observational. enrollees to one of three groups — placebo, an anti-diabetes drug, in epidemiology is to identify and quantify the relationship between an. Types of epidemiological studies. Epidemiological studies generally fall into four broad categories: cross-sectional studies; case-control studies; cohort. It should first be emphasized that all epidemiological studies are (or should be) then the different epidemiological study designs differ primarily in the manner . Table 3 shows the data from a hypothetical incidence case–control study of all .. Relationship of oral contraceptives to cervical carcinogenesis.

Then identify a control group who do not have brain cancer. In fact, great care is needed in choosing the control group: Again, you might be able to collect this "exposure" information from their phone bills. The hypothesis would be that phone usage would be significantly higher in the cancer group than the control group; after collecting the data you can test how well the data fit this hypothesis using a statistical test. The advantages are that a case-control study can be done faster and more cheaply than a cohort study.

However, it may be difficult to collect the information you require on past exposures, and there may be other ways in which the cases and controls differ, not just the cell phone use, which could also be causing the cancer.

Sometimes you also have difficulty in being sure which came first: Note that with a case-control design you can not calculate incidence of cancer because the cases already had cancer when you began and this weakens the analysis.

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Nor can you calculate prevalence, because it was you who decided how many cases and how many controls to choose, and this determined the apparent prevalence in the study.

Instead of the relative risk, you have to use a calculation called an " odds ratio " to estimate the association between phone use and cancer. But, because case-control studies are much more practical for studying the causes of many chronic diseases, they are used very commonly. You may come across references to "matched" and "unmatched" case-control studies. A problem with case-control studies is that the cases and controls may differ on a number of factors, including characteristics such as age, or sex, or wealth that you are not considering as potential causes.

To ensure greater comparability between the two groups, and thereby avoid confounding, the controls could be matched for sex and age to the cases. Link to ppt diagram of a case-control study Link to more on confounding. Click for an answer You cannot estimate prevalence from a case-control study because the prevalence depends entirely on how you design your study. For example, you might choose say 50 cases and 50 controls.

Or, if you believe there will be considerable variability among the controls they may have a range of other medical conditions, but not the disease you're interested inyou could choose more controls say to your 50 cases. So, what's the prevalence? So, the apparent prevalence depends entirely on the way you set the study up: The mainstay of experimental medical studies, normally used in testing new drugs and treatments. A sample of patients with the condition, and who meet other selection criteria, are randomly allocated to receive either the experimental treatment, or the control treatment commonly the standard treatment for the condition.

Occasionally, a placebo or sham treatment will be used in the control group, but where there is already an accepted treatment, it is unlikely to be ethical to use a placebo.

The experimental and control groups are then followed for a set time, and relevant measurements are taken to indicate the results or 'outcomes' in each group. Some more subtle points: The "random" refers to random allocation to either experimental or control group; it does not refer to random selection or sampling of the patients to include in the trial. Random allocation means allocating them by chance e. As long as you have relatively large groups 50 or more people in eachThis means that the two study groups will end up equivalent comparable in terms of factors such as age, sex, and even other things that you do not even know about such as their reaction to the medication.

Do not confuse random allocation to experimental and control groups with random selection of a sample. Random selection of a sample ensures that the sample is representative of the broad population; it is typically used in a survey i.

Random allocation ensures the experimental and control groups are equivalent, but does not ensure they are representative of the broad population. Indeed, they are most likely not, as they all have the disease being studied. Why do we use random allocation? This is mainly to avoid confounding. Confounding refers to confusing the effects of two or more variables — here the treatment you want to study and some other factor, such as age or sex, on which the 2 groups might differ.

To make sure that any differences in the final outcome measurements were due to your experimental treatment and not to something else, you want the two groups to be comparable on all other factors in other jargon, you want to control all other factors. In theory, if randomly allocated groups are sufficiently large, they will be equivalent so, directly comparable on any variable you care to measure.

Of course, if you know about a confounder before beginning the experiment, you could match the two groups on it e. However, matching would not remove the effects of a confounder that you do not know about, such as a biochemical parameter that modifies the action of the drug. Herein lies the genius of random allocation: This is very convenient: Because of natural biological variations, the two groups even though randomly allocated will not be absolutely, perfectly identical.

Therefore, a statistical test is used to indicate whether any difference you observe in the outcomes for the two groups may just have reflected natural variability "been due to chance alone"or whether it seems to represent a "statistically significant" difference.

Which means that it was very, very unlikely to have been due to chance differences between the 2 groups you compared. More on statistical tests. RCTs can be used to test preventive interventions. Here, analyses can record several statistics: This indicates the number of patients you need to treat to get one 'cure'. RCT True or False? A randomized controlled trial begins with a random i.

True Sorry, this is not correct. The "random" in RCT refers not to the way the sample is drawn, but to the allocation of people to either experimental or control group.

Study Designs in Epidemiology

It's not a random sample, but a random allocation to either experimental or control group. The actual sample used in the experiment may be selected in any way the experimenter chooses, and may not be representative at all — although that will limit the value of the study as the results may not be generalizable to other groups.

This field can develop only if epidemiologists and molecular biologists collaborate in the design and conduct of such studies. In the absence of adequate implementation of both aspects, the term molecular epidemiology should not be used. Considerations of the Power of Study Designs Before any study is undertaken, sound epidemiologic practice requires careful consideration of statistical power, that is, the probability that a given research study will be able to detect a true positive effect if it exists.

A study's power depends on many factors, including the increases in risk of exposed persons for the outcome under study, the size of the population to be surveyed, and, for cohort studies, the duration of followup. The higher the expected relative risk RRthe smaller the population that needs to be surveyed. Conversely, the larger the population studied, the smaller the RR that can be detected.

3 types of epidemiology studies the relationship

Thus, an environmental pollutant is likely to be associated with relatively small risks, though it could affect large numbers of people. At any given level of statistical significance, there is a relation among study power, sample size, prevalence of exposure, and expected rate of a given outcome.

In general, studies of larger numbers of persons over longer periods are more likely to yield positive results than those involving smaller populations for shorter periods. However, even large studies with long followup will result in uncertain findings if exposure is poorly measured or misclassified see chapter 3. The sample size needed to achieve a given study power is also related to whether exposure is measured as a dichotomous or continuous variable, to the variability in distribution of the exposure, and to the effects of confounders and errors in the measure of exposure.

3 types of epidemiology studies the relationship

In general, larger samples are needed when exposure measures are not continuous, when the effects of confounders and errors of measurement cannot be taken into account, and when the adverse outcome is a rare event Greenland, ; McKeown-Eyssen and Thomas, ; Lubin et al. Finally, all statistical-power calculations depend on the critical assumption that bias in both exposure and outcome can be ignored; this assumption may be rarely true in practice.

Statistical-significance testing is used to assess the likelihood that positive results of any given study represent a "real" association. However, no matter which statistical tests are employed, common research designs all produce studies with fixed, known chances of making a type I error, that is, of finding a positive result when one does not really exist.

This probability is called alpha and is generally determined by a statistician at the time the protocol is drafted. Of equal importance for environmental epidemiology is a consideration of the probability that a failure to find an effect is a false negative, or type II error.

This often occurs when small numbers of persons are studied and when relatively low risks are involved. Statistical tests cannot determine whether or not an error has been made but can indicate the probability that an error could occur, called beta, if the effect is of some hypothetical size specified by the investigator. The power to detect an effect of that size, defined as 1-beta, depends on the alpha level of significance testing and the unknown relative risk.

Tables have been devised to help determine the number of observations required to have specified power to detect an effect of specified size if an association exists Fleiss, For any specific size of effect, the power of a study increases as the study size increases.

3 types of epidemiology studies the relationship

Many episodes of environmental contamination involve low relative risks and small numbers of people, so environmental-epidemiology stud- Page 22 Share Cite Suggested Citation: This makes the development of innovative techniques to combine results an important priority for the field.

P values are measures of random uncertainty alone and are dominated by sample size and other power considerations. In observational epidemiology, the primary sources of uncertainty about whether an effect is present are confounding, selection bias, and similar problems. In contrast, measures of the size of a possible effect, such as regression coefficients or odds ratios, may be less sensitive to sample size. If associations are due primarily to confounding, investigators may report considerable variation in measures of effect across different studies and populations.

Hence, in modern epidemiology these measures of effect, and confidence intervals for them, are given greater attention than P values. Consistency in these measures across studies with differences in exposures to potential confounders can provide valuable clues about whether observed associations indicate cause-effect relationships.

A very severe problem in environmental epidemiology is known as ''multiple comparisons. While statistical methods exist to remove this effect, they have an unintended and often devastating effect on statistical power. This matter is dealt with in many statistical texts, so we do not expand on it here. Causal Inference in Epidemiology The previous volume elaborated on criteria relevant to drawing inferences from epidemiologic studies see NRC,for general guidance on these studies.

They are summarized here as follows. Strength of Association The strength of association measures the size of the risk that is correlated with a causal agent exposure.

It is typically expressed as the risk of an exposed person's incurring a disease compared with that of a non-exposed person. The larger the ratio SMR, OR, or RRthe stronger the association between the inferred link of exposure to disease for exposed individuals.

For example, an RR of 1.

3 types of epidemiology studies the relationship

For example, an RR of 4 that affects a small population may have a much smaller public-health impact than does an RR of 1. Epidemiologists are sometimes concerned with attributable risk, which is a measure of the rate of disease above the background rate that can be attributed to exposure.

This is more difficult to detect, study, and estimate in environmental epidemiology because it is difficult to determine a baseline rate. Problems with using strength of association as the principal criterion for causality include the fact that misclassification and other biases can profoundly change the strength of association. Specificity of Association Specificity suggests that the suspected causal agent induces a single disease. While this may apply to a few associations between exposure and disease e.

The Relationship Between Incidence and Prevalence

Specificity can be diminished by inappropriate or inaccurate grouping of diseases in a way that obscures a real effect e. Consistency of Association The observed relation between exposure and disease is seen rather regularly in independently conducted studies; the value of consistency is enhanced if the studies are of different types and in different populations. For example, a study of the association between lung cancer and passive smoking may produce an RR of only 2. Because of the variety in study protocols and populations, claims of bias in all the studies have little credibility.

Studies not having statistically significant results can be combined with similar studies, as long as they all use sound methods. Studies that meet the standards for good epidemiologic practice can be grouped for meta-analysis, which allows for statistical pooling of different studies.

Epidemiology - Wikipedia

Temporality The exposure should precede the development of symptoms or diseases of interest by an appropriate interval. For example, tobacco typically causes lung cancer 25 years or more after the beginning of regular exposure, though a few cases have been observed within 10 years of first exposure Doll and Peto, Biologic Gradient of Relation Between Estimated Exposure and Disease In general, a greater exposure should cause a stronger though not always proportional effect.

For example, smoking more cigarettes increases the risk of lung cancer. Typically, dose equals the concentration integrated over time. In some cases, however, dosing patterns can be more important than the overall dose in the relation between dose and response.

Also, the timing of the exposure can be critical in the dose-response relation. Effects of Removal of a Suspected Cause If a causal relation exists, removing the causal agent should reduce or eliminate the effect; if the effect is irreversible in individuals already exposed, this reduction may not be apparent until the exposed generation is largely removed from the study population by death or in some other way e. If different causes are related to a single disease, then the principle applies only to the specific causal factor removed.

Biologic Plausibility The relation between the suspected causal agent and suspected effect should make sense, given the current understanding of human biology. Animal studies or other experimental evidence can strengthen or weaken the biologic plausibility of the relation by demonstrating mechanisms of disease or determining whether the association between exposure and disease holds in experimental situations.

However, lack of a known mechanism does not invalidate a causal association. For many diseases, the underlying mechanisms are unknown. Chronological trend in blood lead levels between and Page 25 Share Cite Suggested Citation: The decreasing incidence of endometrial cancer: Domestic water and dental caries.

Additional studies of the relation of fluoride domestic waters to dental caries experiences in white children aged years, of 13 cities in 4 states.