Jebeile H, Lister NB, Libesman S, Hunter KE, McMaster CM, Johnson BJ, Baur LA, Paxton SJ, Garnett SP, Ahern AL, Wilfley DE, Maguire S, Sainsbury A, Steinbeck K, Askie L, Braet C, Hill AJ, Nicholls D, Jones RA, Dammery G, Grunseit AM, Cooper K, Kyle TK, Heeren FA, Quigley F, Barnes RD, Bean MK, Beaulieu K, Bonham M, Boutelle KN, Branco BHM, Calugi S, Cardel MI, Carpenter K, Cheng HL, Dalle Grave R, Danielsen YS, Demarzo M, Dordevic A, Eichen DM, Goldschmidt AB, Hilbert A, Houben K, Lofrano do Prado M, Martin CK, McTiernan A, Mensinger JL, Pacanowski C, do Prado WL, Ramalho SM, Raynor HA, Rieger E, Robinson E, Salvo V, Sherwood NE, Simpson SA, Skjakodegard HF, Smith E, Partridge S, Tanofsky-Kraff M, Taylor RW, Van Eyck A, Varady KA, Vidmar AP, Whitelock V, Yanovski J, Seidler AL Eating Disorders In weight-related Therapy (EDIT) Collaboration. (2002) Sample Size Slippages in Randomised Trials Exclusions and the Lost and Wayward. Investigators and readers, however, need to grasp that the estimated treatment effects are prone to exaggeration, a random high, with early stopping. Implementing a trial under these stopping rules resembles a conventional trial, with the exception that it can be terminated early should a treatment prove greatly superior. PMID: 11830217 DOI: 10.1016/S0140-6736(02)07500-1 Abstract A cohort study tracks two or more groups forward from exposure to outcome. Both adopt stringent criteria (low nominal p values) during the interim analyses. Affiliation 1 Family Health International, PO Box 13950, Research Triangle Park, NC 27709, USA. The O'Brien-Fleming and Peto group sequential stopping methods are easily implemented and preserve the intended alpha level and power. Statistical stopping methods must be used. Owens joined Stroock as a partner in its Financial Services. However, repeatedly testing at every interim raises multiplicity concerns, and not accounting for multiplicity escalates the false-positive error. attorney for the Western District of Pennsylvania. Investigators cannot avoid interim analyses when data monitoring is indicated. However, if they are necessary, researchers should do statistical tests of interaction, rather than analyse every separate subgroup. In general, we discourage subgroup analyses. Investigators might undertake many analyses but only report the significant effects, distorting the medical literature. By testing enough subgroups, a false-positive result will probably emerge by chance alone. Better training and more circumspection on the part of investigators, tougher editorial standards on the part of journals, and hefty skepticism on the part of referees and readers are necessary to avoid the dangers of false alarms, pseudo-epidemics, and their unfortunate consequences.Subgroup analyses can pose serious multiplicity concerns. Only in a properly performed randomized controlled trial, free of bias, should small associations merit attention. PMID: 11853818 DOI: 10.1016/S0140-6736(02)07683-3 Abstract The randomised controlled trial sets the gold standard of clinical research. For each patient, they assemble clinical clues to establish causes of signs and symptoms. Clinicians are medical detectives by training. This practical guide speaks to two audiences: those who read and those who conduct research. However, these guidelines are not foolproof: strong (yet spurious) associations can result when large amounts of bias are present. Affiliation 1 Family Health International, PO Box 13950, Research Triangle Park, NC 27709, USA. Elsevier Health Sciences, Medical - 272 pages. In general, unless RRs in cohort studies exceed 2 to 3 or ORs in case-control studies exceed 3 or 4, associations in observational research findings should not be considered credible. Hence, detection of small associations falls below the discriminatory ability of observational studies. All observational research has bias (which can include selection, information, and confounding bias). Such associations, commonly reported in the medical literature, are more likely to be attributable to bias than to causal association. Without it, even properly developed random allocation sequences can be subverted. An allocation concealment process keeps clinicians and participants unaware of upcoming assignments. This issue is especially problematic for weak associations, variably defined as relative risks (RRs) or odds ratios (ORs) less than 4. 11867132 DOI: 10.1016/S0140-6736(02)07750-4 Abstract Proper randomisation rests on adequate allocation concealment. This credibility problem has many causes, including the failure of authors, reviewers, and editors to recognize the inherent limitations of these studies. Most reported associations in observational clinical research are false, and the minority of associations that are true are often exaggerated.
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