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PubMedDate not supplied

How much of the placebo 'effect' is really statistical regression?

Statistical regression to the mean predicts that patients selected for abnormalcy will, on the average, tend to improve. We argue that most improvements attributed to the placebo effect are actually instances of statistical regression. First, whereas older clinical trials susceptible to regression resulted in a marked improvement in placebo-treated patients, in a modern series of clinical trials whose design tended to protect against regression, we found no significant improvement (median change 0.3 per cent, p greater than 0.05) in placebo-treated patients. Secondly, regression can yield sizeable improvements, even among biochemical tests. Among a series of 15 biochemical tests, theoretical estimates of the improvement due to regression by selection of patients as high abnormals (i.e. 3 standard deviations above the mean) ranged from 2.5 per cent for serum sodium to 26 per cent for serum lactate dehydrogenase (median 10 per cent); empirical estimates ranged from 3.8 per cent for serum chloride to 37.3 per cent for serum phosphorus (median 9.5 per cent). Thus, we urge caution in interpreting patient improvements as causal effects of our actions and should avoid the conceit of assuming that our personal presence has strong healing powers.

Clinical Trials as Topic↗
PubMed2006

[Possibilities and limitations of statistical regression models for the calculation of threshold values for minimum provider volumes].

Inadequate statistical procedures are often applied for the derivation of threshold values in various medical research areas. The frequently applied method to establish threshold values on the basis of simple comparisons between arbitrarily defined low-volume and high-volume groups may be misleading because the result depends on the preceding classification. In this paper, the possibilities and limitations of statistical regression models for the calculation of threshold values are described. The features of these models for the selection of minimum volumes for hospitals or physicians are discussed. Simulated data examples are used to demonstrate that the definition of a useful minimum provider volume should not be based upon a calculated value of purely mathematical meaning without clinically assessing the risk curve. In the application of statistical regression models to retrospective observational data it should be noticed that calculated threshold values are only of a hypothesis-generating character. In order to verify that a minimum provider volume leads to the expected quality improvement, a prospective intervention study is required.

Data Interpretation, Statistical↗
PubMed2002

Unskilled, unaware, or both? The better-than-average heuristic and statistical regression predict errors in estimates of own performance.

People who score low on a performance test overestimate their own performance relative to others, whereas high scorers slightly underestimate their own performance. J. Kruger and D. Dunning (1999) attributed these asymmetric errors to differences in metacognitive skill. A replication study showed no evidence for mediation effects for any of several candidate variables. Asymmetric errors were expected because of statistical regression and the general better-than-average (BTA) heuristic. Consistent with this parsimonious model, errors were no longer asymmetric when either regression or the BTA effect was statistically removed. In fact, high rather than low performers were more error prone in that they were more likely to neglect their own estimates of the performance of others when predicting how they themselves performed relative to the group.

Adult↗
PubMed2006

Predicting germination response to temperature. II. Three-dimensional regression, statistical gridding and iterative-probit optimization using measured and interpolated-subpopulation data.

BACKGROUND AND AIMS: Most current thermal-germination models are parameterized with subpopulation-specific rate data, interpolated from cumulative-germination-response curves. The purpose of this study was to evaluate the relative accuracy of three-dimensional models for predicting cumulative germination response to temperature. Three-dimensional models are relatively more efficient to implement than two-dimensional models and can be parameterized directly with measured data. METHODS: Seeds of four rangeland grass species were germinated over the constant-temperature range of 3 to 38 degrees C and monitored for subpopulation variability in germination-rate response. Models for estimating subpopulation germination rate were generated as a function of temperature using three-dimensional regression, statistical gridding and iterative-probit optimization using both measured and interpolated-subpopulation data as model inputs. KEY RESULTS: Statistical gridding is more accurate than three-dimensional regression and iterative-probit optimization for modelling germination rate and germination time as a function of temperature and subpopulation. Optimization of the iterative-probit model lowers base-temperature estimates, relative to two-dimensional cardinal-temperature models, and results in an inability to resolve optimal-temperature coefficients as a function of subpopulation. Residual model error for the three-dimensional model was extremely high when parameterized with measured-subpopulation data. Use of measured data for model evaluation provided a more realistic estimate of predictive error than did evaluation of the larger set of interpolated-subpopulation data. CONCLUSIONS: Statistical-gridding techniques may provide a relatively efficient method for estimating germination response in situations where the primary objective is to estimate germination time. This methodology allows for direct use of germination data for model parameterization and automates the significant computational requirements of a two-dimensional piece-wise-linear model, previously shown to produce the most accurate estimates of germination time.

Elymus↗
PubMed1991

Influence of the test medium on azithromycin and erythromycin regression statistics.

Azithromycin and erythromycin disk test results were compared to MIC values obtained in six different media. One hundred isolates were tested in triplicate, and geometric mean MICs were plotted against arithmetic mean zone diameters and regression statistics calculated. The test media evaluated did not markedly influence MIC values, but incubation in 5-7% CO2 resulted in a two- to four-fold decrease in the activity of both drugs. For testing Haemophilus influenzae and other species that need to be tested in 5-7% CO2, interpretive breakpoints for the macrolides and azalides should be modified to compensate for the anticipated decrease in activity.

Azithromycin↗
PubMed1982

[Statistical regression analysis for four new cephalosporins (author's transl)].

The MIC (agar dilution method) and the zone diameter (disk diffusion) were determined for two hundred and sixteen of the most frequently bacterial strains found in hospitals in order to establish the regression curves for four new cephalosporins : cefotaxime, cefoperazone, cefotiam, moxalactam. The dta (MIC, zone size) were represented by a series of points (Mi (Xi Yi) in semilogarithmic rectangular co-ordinates expressed as : Xi = log2 MIC + 7 ; Yi = zone diameter. Firstly, we tried to fit a regression polynomial to this series of points progressing from degree 1, simple equation (standard regression line) 2, 3, etc. in order to reduce if need be, the deviation between the model (polynomial) and the empirical data. For these four cephalosporins a regression polynomial was obtained. Secondly, the global series was divided into three series of points Mi corresponding to three ranges of MIC : MIC less than or equal to 4 microgram/ml ; 4 less than or equal to 16 micrograms/ml; MIC greater than or equal to 16 microgram/ml. A regression line was obtained for each series and compared with each other and each one with the global regression line (they do not coincide statistically). Concerning these antibiotics, it is better to use a regression polynomial rather than apply a standard regression line.

Bacteria↗
PubMed1994

Artificial intelligence versus logistic regression statistical modelling to predict cardiac complications after noncardiac surgery.

The traditional approach to developing models predictive of cardiac events has been to perform logistic regression (LR) analysis on a variety of potential predictors. An alternative to use an artificial intelligence system called a neural network (NN) which simulates biological intelligence. To evaluate the potential applicability of the latter method, we compared the ability of LR and NN techniques to predict cardiac events after noncardiac surgery. A total of 200 patients (training group) underwent cardiac risk assessment before major noncardiac surgery using 17 clinical parameters and 7 quantitative indices based on dipyridamole-thallium imaging. There were 21 post-operative myocardial infarctions and/or cardiac deaths. Data from the training group were used to develop two predictive models: one based on backward stepwise LR multivariate statistical analysis and the other one using a neural network. Both models were then validated on a second group of 160 consecutive patients also referred for preoperative risk stratification (validation group). The NN consisted of 14 input, 29 hidden, and 1 output neurons and used a back-propagation algorithm (learning rate 0.2, training tolerance 0.5, sigmoid transfer function). The sensitivity, specificity, positive and negative predictive accuracies for the prediction of postoperative events in the validation group of 160 patients were, respectively, 67% (6/9), 82% (124/151), 18% (6/33), and 98% (124/127) for LR, and 67% (6/9), 96% (145/151), 50% (6/12), and 98% (145/148) for the NN, with a difference in specificity which attained statistical significance (p < 0.01). Artificial intelligence may provide a useful alternative to conventional LR statistical analysis for the purpose of preoperative cardiac risk assessment.

Artificial Intelligence↗
PubMed1994

Prediction of occult neck disease in laryngeal cancer by means of a logistic regression statistical model.

The ability to accurately predict the presence of subclinical metastatic neck disease in clinically N0 patients with primary epidermoid cancer of the larynx would be of great value in determining whether to perform an elective neck dissection. We describe a statistical approach to estimating the probability of occult neck disease given pretreatment clinical parameters. A retrospective study was performed involving 736 clinically N0 patients with primary laryngeal cancer who were treated surgically with primary resection and ipsilateral neck dissection. Nodal involvement was determined histologically after surgical lymphadenectomy. A logistic regression model was used to derive an equation that calculated the probability of occult neck metastasis based on pretreatment T stage, tumor location, and histologic grade. The model has a sensitivity of 74%, a specificity of 87%, and can be entered into a programmable calculator.

Adult↗
PubMed1995

PRESS-related statistics: regression tools for cross-validation and case diagnostics.

In the health science literature, a common approach of validating a regression equation is data-splitting, where a portion of the data fits the model (fitting sample) and the remainder (validation sample) estimates future performance. The R2 and SEE obtained by predicting the validation sample with the fitting sample equation is a proper estimate of future performance, tending to correct for the natural upward bias of the R2 and SEE obtained from fitting sample alone. Data-splitting has several disadvantages, however. These include: 1) difficulty, arbitrariness, and inconvenience of matching samples; 2) the need to report two sets of statistics to determine homogeneity; and 3) the lack of equation stability due to diluted sample size. The PRESS statistic and associated residuals do not require the data to be split, yield alternative unbiased estimates of R2 and SEE, and provide useful case diagnostics. This procedure is easy to use, is widely available in modern statistical packages, but is rarely utilized. The two methods are contrasted here using a simulation from original data for predicting body density from anthropometric measurements of a group of 117 women. The PRESS approach is particularly appropriate for smaller datasets; methods of reporting these statistics are recommended.

Adult↗
PubMed1991

A brief introduction to influence diagnostics in regression.

Statistical analyses that involve regression methods often encounter data in which some observations have substantial influence. This article presents a nontechnical discussion of influence and of two techniques, based on leaving out each individual observation in turn, for diagnosing influential data. An example illustrates the techniques in an analysis of recurrence rates in endoscopic treatment of bleeding peptic ulcers.

Endoscopy↗
PubMed2004

Fuzzy QSARs for predicting logKoc of persistent organic pollutants.

Fuzzy regression methodology has been employed in this study to develop a relationship for logKoc for persistent organic pollutants (POPs) using other property and molecular descriptors. Fuzzy regression is distinct from statistical regression and is used to characterize the imprecision arising from limited data and/or incomplete model descriptions. The study is based on the premise that statistically based QSARs do not fully account for all the sorbate-sorbent interactions pertinent to the partitioning of POPs and as such these relationships have inherent fuzziness associated with them. A comparison between the statistical and fuzzy logKow-logKoc relationship indicated that the fuzzy regression model enveloped all scatter in the data and provided a tighter fit around the mid-point values (least-square estimates). In addition, fuzzy regression was also employed to characterize imprecision associated with a three parameter QSAR that employs molecular connectivity indicies. A comparison between fuzzy and statistical regression analysis indicated that the fuzziness in this model was primarily associated with characterization of local (atomic) scale interactions while statistical randomness manifested at both local and global (molecular) scales. Experimental and estimation artifacts appear to have a higher impact on statistical regression than fuzzy regression. However, the superiority of the fuzzy regression seems to diminish with increasing correlation between the inputs and the output variable.

Environmental Pollutants↗