Epidemiological Concepts

Epidemiological Concepts Causal Concepts Inductive reasoning: The process of making generalized inferences about ‘causation’ based on repeated observations. Deductive reasoning: The process of inferring that a general ‘law of nature’ exists and has application in a specific, or local, instance. Cause: Any factor that produces a change in severity or frequency of the outcome. Necessary cause: One without which the disease cannot occur. Sufficient cause: Produces the disease if the factor is present. Component-cause: One of a number of factors that, in combination, con Target Population: The population to which it might be possible to extrapolate results from a study. Source Population: The population from which the study subjects are drawn. Study Sample/Group: Consists of the individuals (animals or groups of animals) that end up in the study. Internal validity: The study results are valid for members of the source population. External validity: The study results are valid for the source population, target population, and beyond. Outcomes and data analysis Continuous // dichotomous // nominal // count // time to event Animals Herds causal Areas inferences Causal-web model: Consists of multiple indirect and direct causes. The following is an example of a Causal-web model. Sampling Non-probability sampling: individual’s probability of selection is not determined (Judgment, Convenience, Purposive) Probability sampling: every element has a known non-zero probability of being included in the sample Simple random sample: Every study subject in the source population has an equal probability of being included. Systematic random sample: A complete list of the population to be sampled is not required provided an estimate of the total number of animals is available and all the animals are sequentially available. Stratified random sample: Prior to sampling, the population is divided into mutually exclusive strata based on factors likely to affect the outcome. Cluster sampling: Every study subject within the cluster (collection of subjects with 1 or more common characteristics) is included in the sample and the primary sampling unit is larger than the unit of concern. Multistage sampling: After the primary sampling unit is chosen, then a sample of secondary sampling units is selected. Targeted (risk-based) sampling: Animals are assigned point values based on the probability of them having the disease of interest and sampling is proportional to that estimate of risk. Indirect Cause Direct cause Outcome Direct Cause (Exposure) Sampling frame: List of all sampling units in the source population Type I (α) error: Concluding that the outcomes in the groups being compared are different (association exists) when they are not. Type II (β) error: Concluding that the outcomes are not different (no association) when they are Power: Probability that you will find a statistically significant difference when it exists and is of a certain magnitude (i.e. power = 1-β) Sampling Equations 1) n= total sample size To estimate a sample proportion with a desired precision: To estimate a sample mean with a desired precision: 2) (n=sample size per group) To compare 2 proportions: Where p=(p1+p2)/2 and q=1-p — Types of Error: True state of nature Conclusion of stat. analysis Effect present Effect absent Type I (α)error Effect present (reject Correct (power) (p-value) null) Effect absent (accept Type II (β) ernull) ror Correct For adjusting the sample size (n) for clusFor continuous and binary covariates, new tering, the size of new n(n’) depends on n (n’) (VIF= Variance Inflation Factor): intra-cluster correlation (ρ) and number of individuals sampled per cluster (m): 4) Sampling to confirm disease absence From finite population <1000: 6) General formula for the width of CI of a parameter Parameter ± Z*SE(parameter), where for - Estimating a mean in a single sample – To compare 2 means: From a large (infinite) population: - Comparisons of means from 2 samples – If sampling from a finite population in descriptive studies, the required sample size (n’) can be adjusted using FPC formula: 5) Adjustment of sample size (n) in multivariable studies: For k continuous covariates, new n (n’) ρce =average correlation between exposure and confounders - If expected interaction between two dichotomous variables Focus Groups: Normally a group of 6-12 people that provide opportunity for a structured form of consultation with members of the intended study population, the end users and/or the interviewers. Quantitative: ’Structured’ questionnaires designed to capture information about study subjects and their environment Questionnaires Questionnaire: A data-collection tool that can be used in a wide variety of clinical and epidemiological research settings. Survey: An observational study designed to collect descriptive information about an animal population (such as prevalence of disease, level of production etc.) Qualitative: ‘Explorative’ questionnaires consisting mainly of open questions. Open Question: There are no restrictions on the types of responses expected. Closed Question: The response has to be selected from a pre-set list of answers. Measures of Disease Frequency Study Period: Period of time over which the study is conducted. Risk period: Time during which the individual could develop the disease of interest Count: The number of cases of disease or number of animals affected with a condition in a given population Incidence (I): The number of new events in a defined population within a specific period of time Absolute rates: Number of cases of disease related to the time period of observation -Incidence times: Times which incident cases occur Closed Population: No additions to the population for the duration of the study (nor losses) -Incidence count: Count of number of cases of disease observed in a population Proportion: Ratio in which the numerator is a subset of the denominator -Incidence risk: Probability an animal will develop a disease in a defined time Odds: Ratio in which the numerator is not a subset of the denominator. -Incidence rate: Number of new cases of disease in a population per unit of animal time during a given time period Rate: Ratio in which the denominator is the number of animal-time units at risk Open Population: Animals are leaving and entering the population Prevalence (P): Cases of disease existing at a specific point in time rather than new cases occurring over a period of time (D=mean duration of disease) Measures of Association Measure of association (MA): Assesses the magnitude of the relationship between an exposure to a disease and a disease Attributable fraction (Afe): Proportion of diseases in exposed that is due to the exposure Approaches for hypothesis testing include: - Estimating standard error (SE) of the parameter as a measure of precision of the point estimate (uncertainty) - Compute test statistic and from the expected distribution of this test statistic determine p-value - Compute confidence interval (CI) for the point estimate. CI reflect the level of uncertainty in point estimates and indicate the range of values that a parameter might have (with values closer to the center being more likely than those at the ends of the range). Interpretation of Risk ratio (RR), Rate ratio (IR), and Odds ratio (OR): <1 exposure is protective, =1 no effect, and >1 exposure is positively associated with disease Interpretation of Risk difference (RD) and Incidence difference (IR): <0 exposure is protective, =0 no effect, and >0 exposure is positively associated with disease The range for AFe: Values from 0 (risks equal regardless of exposure) to 1 (no disease in non-exposedà i.e. all disease is due to exposure). Vaccine efficacy is a form of AFe. Diagnostic Tests Accuracy: Average is close to true value Multiple Tests Interpretation: True prevalence: The true state of nature. Precision: The amount of variability among test results. - Series: Result is considered positive only if both tests are positive Coefficient of variation (CV): Standard Devation/Mean (for repeat runs on same sample) - Parallel: result is considered positive if either test is positive Apparent prevalence: The result in the study due to imperfections in the diagnostic tests. Pearson correlation coefficient (PCC): Ignores the scales of the 2 sets of results Concordance correlation coefficient (CCC): Takes into account data position from equality line. Kappa Statistic: Measure of agreement for tests with qualitative outcomes. Ranges from 0 (poor agreement) to 1 (perfect agreement. Agreement: How well 2 different tests agree on the same sample. Sensitivity (Se): proportion of diseased animals that test positive (TP): p(T+|D+) Specificity (Sp): proportion of nondiseased animals that test negative (TN): p(T-|D-) Predictive Values: The probability that the animal has or does not have the disease, given the test result. —PV(+) = p(D+|T+) —PV(-) = p(D-|T-) Define cutoff: Sp increases, Se decreases. See graph below. Study Designs Descriptive Study: Describe the nature of the disease. - Case report: Based on individual - Case series: Based on group - Survey: Based on population Explanatory Study: Objective is to identify associations between factors (exposures) and disease status. (Experimental and Observational Studies) Experimental Study: Objective is to identify the effect of an exposure that is easy to manipulate (E.g. vaccine, drug) Observational Study: Objective is to study effect of complex exposures in natural state. Types: Cross-sectional, Cohort, and Case-control study Retrospective: Disease occurred when the study began. Prospective: The cases do not develop until after the study begins and the cases are enrolled in the study over time. Cross-sectional Study: Objective is to estimate some sort of population parameter. The outcome frequency of measure is prevalence since this study looks only a snip of time. Controlled trial: Planned experiment carried out on subjects in their usual environment (clinical trail in a clinical setting) •Phase I: (formulation trials): Trials in healthy animals to evaluate safety of the drug (dose, adverse reactions…) Cohort Study: A cohort is a group of subjects with common exposure, and the objective of a cohort study is to evaluate causal association between specific exposures and outcome. Most often prospective. •Phase II: Trials in a small number of animals from the target population (e.g., sick animals) to document the activity of the drug. Might involve before/after comparisons and often without controls. Case-control Study: Objective is to evaluate association(s) between exposure(s) and outcome. Most often retrospective and determine cause. •Phase III: Large-scale experimental studies to determine the efficacy of a drug in a typical clinical population, to monitor side effects and compare the drug with other available treatments. Should be based on randomized controlled trials! •Phase IV: Post-registration trials designed to evaluate the most effective way of using a product. Also, should be carried out as randomized Study base: Population from which the cases and controls are obtained. Bias Selection bias: Composition of the study group(s) differs from that in the source population (and target population). Confounding: Due to effects of factors other than the exposure of interest on the observed measure of association. Information bias: Incorrectly measured/ classified subject’s exposure, outcome, extraneous factors Confounding control at the study design stage includes: - Exclusion (Restricted sampling) - Matching: Involves making distribution of the extraneous factor(s) in the groups being compared the same. Prevents confounding and may increase power of the study. Misclassification: Rearrangement of study individuals into incorrect categories because of errors in classifying exposure, outcome or both Non-differential Misclassification: If misclassification of the exposure and the outcome “disease” are independent. Will bias the measures of association toward the null. Differential Misclassification: If the errors in exposure classification are related to the status of the outcome under study. Resulting bias in the measure of association might be in any direction Measurement error: Errors in measuring quantitative factors can lead to biased measures of association. Confounding control during analysis: The Mantel-Haenszel (MH) estimator for Categorical data with dichotomous exposure. Will need: 1) to stratify data according to the combination of levels of the confounding variables; 2) examine stratum specific measures; 3) assure that stratum specific measures are equal using a homogeneity test; 4)calculate a pooled weighted (adjusted) estimate of association Interaction: Stratum specific measures different (based on the homogeneity test) providing a more detailed description of the relationship between exposure and disease. Needs to be measured on either the additive or multiplicative scale. No interaction on an Additive scale: (RR11 ‑ 1) = (RR10 ‑ 1) + (RR01 ‑ 1) No interaction on a Multiplicative scale: RR11 = RR10 * RR01 R11 = Pr(D|A1B1) R10 = Pr(D|A1B0) R01 = Pr(D|A0B1) R00 = Pr(D|A0B0) RR11 =R11/R00, RR10 =R10/R00, RR01 =R01/R00