Development of Analytical and Biometric Procedures
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Development of Analytical and Biometric Procedures
M E D I C I N E
material properties, as well as the development of analytical and biometric procedures.
Clinical research includes interventional and non- interventional studies. The objective of interventional clinical studies (clinical trials) is “to study or demon- strate the clinical or pharmacological activities of drugs” and “to provide convincing evidence of the safety or efficacy of drugs” (AMG, German Drugs Act §4) (5). In clinical studies, patients are randomly assigned to treatment groups. In contrast, noninter- ventional clinical studies are observational studies, in which patients are given an individually specified treatment (6, 7).
Epidemiological research studies the distribution and changes with time of the frequency of diseases and of their causes. Experimental studies are distin- guished from observational studies (7, 8). Interventional studies (such as vaccination, addition of food additives, fluoride addition to drinking water) are of experimental character. Examples of observational epidemiological studies include cohort studies, case control studies, cross-sectional studies, and ecological studies.
A subsequent article will discuss the different study types in detail.
Unit of analysis The unit of analysis (investigational unit) must be specified before starting a medical study. In a typical clinical study, the patient is the unit of analysis. However, the unit of analysis may also be a technical model, hereditary information, a cell, a cellular structure, an organ, an organ system, a single test individual (animal or man), or specified subgroup or the population
of a region or of a country. In systematic reviews, the unit of analysis is a single study. The sample then includes the total of all units of analysis. The interesting information or data (observations, variables, characteristics) are recorded for the statistical units. For example, if the heart is being investigated in a pa- tient (the unit of analysis), the heart rate may be mea- sured as a characteristic of performance.
The selection of the unit of analysis influences the interpretation of the study results. It is therefore important for statistical reasons to know whether the units of analysis are dependent or independent of each other with respect to the outcome parameter. This distinction is not always easy. For example, if the teeth of test persons are the unit of analysis, it must be clarified whether these are independent with respect to the question to be answered (i.e. from different test persons) or dependent (i.e. from the same test person). Teeth in the mouth of a single test person are generally dependent, as specific factors, such as nutrition and teeth cleaning habits, act on all teeth in the mouth in the same way. On the other hand, extracted teeth are generally independent study objects, as there are no longer any shared factors which influence them. This is particularly the case when the teeth are subject to additional preparation, for example, cutting or grind- ing. On the other hand, if the observations are on tooth characteristics developed before extraction, these characteristics must be regarded as dependent.
Measuring technique The term “measuring technique” includes the use of measuring instruments and the method of measure- ment.
Use of measuring instruments Measuring instruments include instruments which specifically record measuring data (such as blood pressure or laboratory parameters), as well as data collection with standardized or self-designed question- naires (for example, quality of life, depression, or satisfaction).
During the validation of a measuring instrument, its quality and practicability are evaluated using statis- tical parameters. Unfortunately, the nomenclature is not fully standardized and also depends on the special area (for example, chemical analysis, psychological studies with questionnaires, or diagnostic studies). It is always the case that a measuring instrument of high quality should be of high precision and validity.
Precision describes the extent to which a measuring technique consistently provides the same results if the measurement is repeated (9). The reliability (or preci- sion) provides information on the precision or the occurrence of random errors. If the precision is low, the correlation coefficients are low, measurements are imprecise and a larger sample size is needed (9). On the other hand, the validity (accuracy of the mean or trueness) of a measuring instrument is high if it mea- sures exactly what it is supposed to measure. Thus the
FIGURE 2Portrayal of the terms reliability (precision) and
validity (trueness) using a target
Deutsches Ärzteblatt International⏐⏐Dtsch Arztebl Int 2009; 106(11): 184–9 187
M E D I C I N E
validity provides information on the occurrence of systematic errors (10). Whereas the precision describes the difference (variance) between repeated measure- ments, the validity reflects the difference between the measured and true parameter (10). Figure 2 portrays the terms, using a target as a model.
Reliability and validity are subsumed in the term accuracy (11, 12). The accuracy is only high when both the precision and the validity are high. Table 1 summarizes the important terms to validate a mea- surement method.
The problem is not only that the measurements may be invalid or false, but also that the measurements may lead to erroneous conclusions. External and inter- nal validity can be distinguished (13). External validity means the possibility of generalizing the study results for the study population to the target population. The in- ternal validity is the validity of a result for the actual question to be answered. This can be optimized by detailed planning, defined inclusion and exclusion criteria, and reduction of external interfering factors.
Measurement plan The measurement plan describes the number and time points of the measurements to be performed. To obtain comparable and objective measurements, the measurement conditions must be standardized. For example, clinical study measurements such as blood pressure must always be performed at the same time, in the same room, in the same position, with the same instrument, and by the same person. If there are differ- ences, for example in the investigator, measuring instrument, analytical laboratory or recording time, it must be established that the measurements are in agree- ment (10, 13).
The type of scale used for the recorded parameter is also of decisive importance. Putting it simply, metric scales are superior to ordinal scales, which are superior to nominal scales. The type of scale is so important, as both descriptive statistics and statistical test procedures depend on it. Transformation from a higher to a lower scale type is in principle possible, although the con- verse is impossible. For example, the hemoglobin content may be determined with a metric scale (e.g. as g/dL). It can then be transformed to an ordinal scale (e.g. low, normal and high hemoglobin status), but not conversely.
Calculation of sample size Whatever the study design, a calculation must be per- formed before the start of the study to estimate the necessary number of units of analysis (for example, patients) to answer the main study question (14–16). This requires calculation of sample size, exploiting knowledge of the expected effect (for example, the clinically relevant difference) and its scatter (for example, standard deviation). These may be determined in preliminary studies or from published information. It is generally true that a large sample is required to discover a small difference. The sample must also be
large if the scatter of the outcome parameter is large in the study groups. Sample size planning helps to ensure that the study is large enough, but not excessively large. The sample size is often restricted by the available time and/or by the budget. This is not in accordance with good scientific practice. If the sample is small, the power will also be low, bringing the risk that real dif- ferences will not be identified (16, 17). There are both ethical problems—stress to patients, possibly random allocation of therapy—and economic problems— financial, structural, and with regard to personnel— which make it difficult to justify a study which is eit- her too large or not large enough (16–19). The research worker has to consider whether alternative procedures might be possible, such as increasing the time available, the personnel or the funding, or whether a multicenter study should be performed in collaboration with col- leagues.
Discussion Planning, performance, documentation, analysis, and publication are the component parts of medical studies (1, 2). Study design is of decisive importance in plan- ning. This not only lays down the statistical analysis, but also ultimately the reliability of the conclusions and the significance and implementation of the study results (2). A six point checklist can be used for the rapid evaluation of the study design (table 2).
According to Sackett, about two thirds of 56 typical errors in studies are connected to errors in design and performance (20). This cannot be corrected once the data have been collected. This makes the study less con- vincing. As a consequence, the design must be precise- ly planned before starting the study and this must be laid down in the study protocol. This requires a great deal of time.
In the final analysis, studies with poor design are unethical. Test persons (or animals) are subjected to unnecessary stress and research capacity is wasted (21, 22). Medical studies must consider both individual ethics (protection of the individual) and collective ethics (benefit for society) (22). The size of medical studies is often too small, so that the power is also too small (23). For this reason, a real difference—for example, between the activity of two therapies—is either unidentified or only described imprecisely (24). Low power is the result if the study is too small, the
Development of Analytical and Biometric Procedures
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