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Kenneth F Schulz

Publications and source records attributed to Kenneth F Schulz.

44 records · Page 3Linked to original sources
PubMedDate not supplied

Sample size calculations in randomised trials: mandatory and mystical.

Investigators should properly calculate sample sizes before the start of their randomised trials and adequately describe the details in their published report. In these a-priori calculations, determining the effect size to detect--eg, event rates in treatment and control groups--reflects inherently subjective clinical judgments. Furthermore, these judgments greatly affect sample size calculations. We question the branding of trials as unethical on the basis of an imprecise sample size calculation process. So-called underpowered trials might be acceptable if investigators use methodological rigor to eliminate bias, properly report to avoid misinterpretation, and always publish results to avert publication bias. Some shift of emphasis from a fixation on sample size to a focus on methodological quality would yield more trials with less bias. Unbiased trials with imprecise results trump no results at all. Clinicians and patients deserve guidance now.

Randomized Controlled Trials as Topic↗
PubMedDate not supplied

Compared to what? Finding controls for case-control studies.

Use of control (comparison) groups is a powerful research tool. In case-control studies, controls estimate the frequency of an exposure in the population under study. Controls can be taken from known or unknown study populations. A known group consists of a defined population observed over a period, such as passengers on a cruise ship. When the study group is known, a sample of the population can be used as controls. If no population roster exists, then techniques such as random-digit dialling can be used. Sometimes, however, the study group is unknown, for example, motor-vehicle crash victims brought to an emergency department, who may come from far away. In this situation, hospital controls, neighbourhood controls, and friend, associate, or relative controls can be used. In general, one well-selected control group is better than two or more. When the number of cases is small, the ratio of controls to cases can be raised to improve the ability to find important differences. Although no ideal control group exists, readers need to think carefully about how representative the controls are. Poor choice of controls can lead to both wrong results and possible medical harm.

Case-Control Studies↗
PubMedDate not supplied

Refining clinical diagnosis with likelihood ratios.

Likelihood ratios can refine clinical diagnosis on the basis of signs and symptoms; however, they are underused for patients' care. A likelihood ratio is the percentage of ill people with a given test result divided by the percentage of well individuals with the same result. Ideally, abnormal test results should be much more typical in ill individuals than in those who are well (high likelihood ratio) and normal test results should be most frequent in well people than in sick people (low likelihood ratio). Likelihood ratios near unity have little effect on decision-making; by contrast, high or low ratios can greatly shift the clinician's estimate of the probability of disease. Likelihood ratios can be calculated not only for dichotomous (positive or negative) tests but also for tests with multiple levels of results, such as creatine kinase or ventilation-perfusion scans. When combined with an accurate clinical diagnosis, likelihood ratios from ancillary tests improve diagnostic accuracy in a synergistic manner.

Diagnosis↗
PubMedDate not supplied

Multiplicity in randomised trials I: endpoints and treatments.

Multiplicity problems emerge from investigators looking at many additional endpoints and treatment group comparisons. Thousands of potential comparisons can emanate from one trial. Investigators might only report the significant comparisons, an unscientific practice if unwitting, and fraudulent if intentional. Researchers must report all the endpoints analysed and treatments compared. Some statisticians propose statistical adjustments to account for multiplicity. Simply defined, they test for no effects in all the primary endpoints undertaken versus an effect in one or more of those endpoints. In general, statistical adjustments for multiplicity provide crude answers to an irrelevant question. However, investigators should use adjustments when the clinical decision-making argument rests solely on one or more of the primary endpoints being significant. In these cases, adjustments somewhat rescue scattershot analyses. Readers need to be aware of the potential for under-reporting of analyses.

Data Interpretation, Statistical↗
PubMedDate not supplied

Multiplicity in randomised trials II: subgroup and interim analyses.

Subgroup analyses can pose serious multiplicity concerns. By testing enough subgroups, a false-positive result will probably emerge by chance alone. Investigators might undertake many analyses but only report the significant effects, distorting the medical literature. In general, we discourage subgroup analyses. However, if they are necessary, researchers should do statistical tests of interaction, rather than analyse every separate subgroup. Investigators cannot avoid interim analyses when data monitoring is indicated. However, repeatedly testing at every interim raises multiplicity concerns, and not accounting for multiplicity escalates the false-positive error. Statistical stopping methods must be used. The O'Brien-Fleming and Peto group sequential stopping methods are easily implemented and preserve the intended alpha level and power. Both adopt stringent criteria (low nominal p values) during the interim analyses. 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. Investigators and readers, however, need to grasp that the estimated treatment effects are prone to exaggeration, a random high, with early stopping.

Data Interpretation, Statistical↗