![]() Data are from a clinical trial on the treatment of acute (1) EAR.ZIP: Otitis Media Clinical Trial (Source of data: Rosner, 1990, p. To achieveĨ0% power to detect a RR of 3, we need n 1 = n 2 = 72. Subjects, and an expected incidence in the non-exposed group of 10%, with 1 - a =. To achieve 80% power to detect a RR of 2 in a study with an allocation ratio of 1:1 non-exposed to exposed Size > Cohort Study for sample size requirement calculations. We will use the program EpiTable > Sample > Sample Sample size calculations can be viewed as "power calculations in reverse." Here, we specify the required power (or precision) to deriveĪ reasonable estimate of the sample size required for a given study. Given no information, the investigator has no basis forĭesigning the study intelligently and would be hard put to justify designing it at all (paraphrased from Fleiss, 1981, p. Some information, the investigator can, using his or her imagination and expertise, come up with an estimate of a differenceīetween two proportions that is scientifically or clinically important. This knowledge might come from previous research, from an accumulation of clinicalĮxperience, from small-scale pilot work, or from readily available sources of statistics (e.g., morbidity surveys). (Power should be at least 80%, preferably 90%.) Comment : In determining sample size requirements, the investigator must have some idea of the order of magnitude of Based on these assumptions, EpiTableĬalculates power = 42.4%. Incidence of 10% in the unexposed group, an a level of 0.05, and an expected RR of 2. A study has 100 exposed subjects, 100 unexposed subjects (allocation ratio = 100/100 = 1), an expected Power computations (method based on Fleiss, 1981, pp. ![]() ![]() The risk ratio = 1.7% / 0.0% = undefined, with aĮpiInfo is unable to calculate a confidence interval for these data but tests H 0: RR = 1 with Fisher's test:įisher exact: 1-tailed P-value: 0.0141750 Sample > Power calculation > Cohort Study to perform In contrast, 0 of 862 non-exposed patientsĮxperienced colonic necrosis. Results show 2 of the 117 Kayexelate-exposed patients experienced colonic necrosis. RISK RATIO(RR)(Outcome:TOX=1 Exposure:GENERIC=1) 4.99ĩ5% confidence limits for RR 1.55 READ KX-NECRO Risk ratio estimates are printed in the output below the 2-by-2 table. Thus, the incidence of toxicity in the exposed group ( p 1) = 11 / 25 = 0.440, the incidence in the unexposed group ( p 2) = 3 / 34 =Ġ.088, and rr = 0.440 / 0.088 = 4.99 5.0, indicating that toxicity was 5 times more frequent in the exposed group than in the Where and represent the names of the exposure and disease variables, respectively.įor example, to cross-tabulate the current data issue the command: Records and last record of the data set are:ĭata are cross-tabulated with the command: The disease information denotes cerebellar toxicity as stored in the variable TOX ![]() Exposure information is stored in the variable GENERIC (exposed: 1 = yes, 2 = no). Generic drug while the other group uses the innovator manufacturer's product (and are thus One group is exposed to (i.e., treated with) a As an example we consider a cohort of cancer patients undergoing bone marrow ablation with theĭrug cytarabine (Jolson, et al, 1992). Thus, rr represents the risk ratio estimate and RR Notation: Lower case acronyms denote estimators, while upper case Proportion ratio - is often referred to as the risk ratio or relative risk: Group ( p 1 = a / n 1) and p 2 represent the proportion in the non-exposed group ( p 2 = c / n 2). Let p 1 represent the proportion in the exposed Each individual is classified as diseased or notĭiseased according to defined criteria. We consider two independent groups derived by either a cohort orĬross-sectional sample. | p Values | Power and Sample Size | Exercises Introduction Gerstman Binary Outcome, Cohort and Cross-Sectional Samples (RRs)
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