Trial Designs. Professor Peter Cameron

Transcription

1 Trial Designs Professor Peter Cameron OVERVIEW Review of Observational methods Principles of experimental design applied to observational studies Population Selection Looking for bias Inference Analysis

2 Observation Methods Selected Units: individuals, groups Study Populations: cross-sectional, longitudinal Data collection timing: prospectively, retrospectively, combination Study populations Cross-sectional: where only ONE set of observations is collected for every unit in the study, at a certain point in time, disregarding the length of time of the study as a whole Longitudinal: where TWO or MORE sets of observations are collected for every unit in the study, i.e. follow-up is involved in order to allow monitoring of a certain population (cohort) over a specified period of time. Such populations are AT RISK (disease-free) at the start of the study.

3 Case-series: Clinical case series Clinical case-series: usually a coherent and consecutive set of cases of a disease (or similar problem) which derive from either the practice of one or more health care professionals or a defined health care setting, e.g. a hospital or family practice. A case-series is, effectively, a register of cases. Analyse cases together to learn about the disease. Clinical case-series are of value in epidemiology for: Studying symptoms and signs Creating case definitions Clinical education, audit and research Case series: Population based When a clinical case-series is complete for a defined geographical area for which the population is known, it is, effectively, a population based case-series consisting of a population register of cases. Epidemiologically the most important case-series are registers of serious diseases or deaths and of health service utilisation, e.g. hospital admissions. Often compiled for administrative and legal reasons. In Australia now Clinical Quality Registries.

4 Cross-sectional Studies (Community health studies, surveys) Characteristics: detects point prevalence; relative conditions; allows for stratification Merits: feasible; quick; economic; allows study of several diseases / exposures; useful for estimation of the population burden, health planning and priority setting of health problems Limitations: temporal ambiguity (cannot determine whether the exposure preceded outcome); possible measurement error; not suitable for rare conditions; liable to survivor bias Effect measure: Odds Ratio Overlap in the conceptual basis of quantitative study designs The cross-sectional study can be repeated If the same sample is studied for a second time i.e. it is followed up, the original cross-sectional study now becomes a cohort study. If, during a cohort study, possibly in a subgroup, the investigator imposes an intervention, a trial begins. Cohort study also gives birth to case-control studies, using incident cases (nested case control study). Cases in a case-series, particularly a population based one, may be the starting point of a case-control study or a trial. Not every epidemiological study fits neatly into one of the basic designs.

5 Study Design Life s a journey... In individuals, the only way to know if a risk factor caused disease would be to find an exact double, living in a parallel universe, identical in every way to the exposed subject--except for the exposure. Principles of experimental studies applied to observational cohort studies Randomization of treatment so groups are comparable on known and unknown confounders. Can't randomize in an observational study so select a comparison group as alike as possible to the exposed group

6 Principles of experimental studies applied to observational cohort studies Use placebo in order to reduce bias. Can t use placebo in observational studies so you must make the groups as comparable as possible. Principles of experimental studies applied to observational cohort studies Blinding to avoid bias in outcome ascertainment. In a cohort study, it is crucial to have high follow-up rates and comparable ascertainment of outcomes in the exposed and comparison groups. You can blind the investigators conducting follow up and confirming the outcomes.

7 Issues in design of cohort studies Selection of exposed population Choice depends upon hypothesis under study and feasibility considerations Issues in design of cohort studies Examples of exposed populations: Occupational groups Groups undergoing particular medical treatment Groups with unusual dietary or life style factors Professional groups (nurses, doctors) Students or alumni of colleges Geographically defined areas (e.g. Framingham)

8 Issues in design of cohort studies For rare exposures, you need to assemble special cohorts (occupational groups, groups with unusual diets etc.) Example of special cohort study Rubber workers in Akron, Ohio Exposure: industrial solvent Outcomes: cancer Issues in design of cohort studies Selection of comparison (unexposed) group Principle: You want the comparison (unexposed) group to be as similar as possible to the exposed group with respect to all other factors except the exposure. If the exposure has no effect on disease occurrence, then the rate of disease in the exposed and comparison groups will be the same.

9 Issues in design of cohort studies Selection of comparison (unexposed) group (cont d) Counterfactual ideal: The ideal comparison group consists of exactly the same individuals in the exposed group had they not been exposed. Since it is impossible for the same person to be exposed and unexposed simultaneously, epidemiologists must select different sets of people who are as similar as possible. Figure 2 Total Population Reference Population Cases Controls

10 Methodologic Issues in observational studies Handling potential confounding factors a) In the process of selecting controls: Matching The process of selecting controls so that they are similar to the cases in regard to certain characteristics such as age, sex and race. (i) Group matching (frequency matching, stratification) (ii) Individual matching (matched pairs) Methodologic Issues Handling potential confounding factors in matching: (iii) Problems with matching: - Matching on many variables may make it difficult or impossible to find an appropriate control. - Cannot explore possible association of disease with any variable on which cases and controls have been matched.

11 Methodologic Issues Evaluating Information on Exposure a) Problems of recall in case-control studies (i) Limitations in human ability to recall (ii) Recall bias (cases may remember their exposure with a higher or lower accuracy than controls do) 2. Evaluating Information on Exposure b) Other biases (i) Selection bias (ii) Information bias (iii) Non-response bias (iv) Analysis bias c) Validity testing (reliability, sensitivity and specificity)

12 SAMPLING Whole population of Framingham Target population All contactable adult people Inclusion criteria Sampling Every second person was invited However 1 in 3 refused Replaced with volunteers Accessible Population Study Sample Generalizability Framingham Framingham

13 INFERENCE Inference is the act or process of deriving logical conclusions from premises known or assumed to be true. The conclusion drawn is also called an idiomatic. The laws of valid inference are studied in the field of logic.

14 Fairly secure Fairly secure Average security STUDY FINDINGS Actual subjects TRUTH IN THE STUDY Intended study sample ACCESSIBLE POPULATION TARGET POPULATION Internal validity External validity External validity WIDER INFERENCE Poor security Analysis of cohort studies Basic analysis involves calculation of incidence of disease among exposed and unexposed groups. Depending on available data, you can calculate cumulative incidence or incidence rates. Recall set up of 2 x 2 tables.

15 Analysis of cohort studies Example: Tuberculosis treatment and breast cancer study Followed 1,047 women who were treated with air collapse therapy and exposed to numerous fluoroscopic examinations (radiation) and 717 who received other treatments. A total of 47,036 woman-years of follow-up were accumulated during which 56 breast cancer cases occurred. Analysis of cohort studies Breast Cancer Cases No breast cancer Exposed a b Unexposed c d Total A+C B+D OR = AD/BC

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