On ignoring scientific evidence: The bumpy road to enlightenment

It is well accepted that people resist evidence that contradicts their beliefs.Moreover, despite their training, many scientists reject results that are inconsistent withtheir theories. This phenomenon is discussed in relation to the field of judgment anddecision making by describing four case studies. These concern findings that clinical judgment is less predictive than actuarial models; simple methods have proven superiorto more theoretically correct methods in times series forecasting; equal weighting ofvariables is often more accurate than using differential weights; and decisions cansometimes be improved by discarding relevant information. All findings relate to theapparently difficult-to-accept idea that simple models can predict complex phenomenabetter than complex ones. It is true that there is a scientific market place for ideas.However, like its economic counterpart, it is subject to inefficiencies (e.g., thinness,asymmetric information, and speculative bubbles). Unfortunately, the market is only correct in the long-run. The road to enlightenment is bumpy.

Ignoring information in binary choice with continuous variables: When is less 'more'?

When can a single variable be more accurate in binary choice than multiple sources of information? We derive analytically the probability that a single variable (SV) will correctly predict one of two choices when both criterion and predictor are continuous variables. We further provide analogous derivations for multiple regression (MR) and equal weighting (EW) and specify the conditions under which the models differ in expected predictive ability. Key factors include variability in cue validities, intercorrelation between predictors, and the ratio of predictors to observations in MR. Theory and simulations are used to illustrate the differential effects of these factors. Results directly address why and when one-reason decision making can be more effective than analyses that use more information. We thus provide analytical backing to intriguing empirical results that, to date, have lacked theoretical justification. There are predictable conditions for which one should expect less to be more.

Bias from ignoring price dispersion in demand estimation

Consumers often pay different prices for the same product bought in the same store at the same time. However, the demand estimation literature has ignored that fact using, instead, aggregate measures such as the “list” or average price. In this paper we show that this will lead to biased price coefficients. Furthermore, we perform simple comparative statics simulation exercises for the logit and random coefficient models. In the “list” price case we find that the bias is larger when discounts are higher, proportion of consumers facing discount prices is higher and when consumers are more unwilling to buy the product so that they almost only do it when facing discount. In the average price case we find that the bias is larger when discounts are higher, proportion of consumers that have access to discount are similar to the ones that do not have access and when consumers willingness to buy is very dependent on idiosyncratic shocks. Also bias is less problematic in the average price case in markets with a lot of bargain deals, so that prices are as good as individual. We conclude by proposing ways that the econometrician can reduce this bias using different information that he may have available.

Ignoring non-specific abdominal pain in emergency department patients may be related to decreased quality of life. A follow up of an underestimated problem

Patients suffering from chronic pain have a high prevalence of depression, resulting in a significant impact on overall quality of life. Our aim was to investigate how long term acute non-specific abdominal pain (NSAP) affected overall physical and mental well-being in patients admitted to our emergency department (ED).

Blinded by PRISMA: are systematic reviewers focusing on PRISMA and ignoring other guidelines?

BACKGROUND PRISMA guidelines have been developed to improve the reporting of systematic reviews (SRs). Other reporting guidelines and techniques to assess methodological quality of SRs have been developed. We aimed to assess the frequency of the use of reporting and other guidelines in SRs to assess whether PRISMA is being used inappropriately as a substitute for other relevant guidelines. METHODS Web of Knowledge was searched to identify articles citing the PRISMA guidelines over a 12-month period. The use of reporting guidelines (including PRISMA and MOOSE) and tools for assessing methodological quality (including QUADAS) was assessed. Factors associated with appropriate use of guidelines including review type, field of publication and involvement of a methodologist were investigated. RESULTS Over the 12-month period, 701 SRs were identified. MOOSE guidelines were cited in just 17% of epidemiologic reviews; QUADAS or QUADAS-2 was referred to in just 40% of diagnostic SRs. In the multivariable analysis, medical field of publication and methodologist involvement (OR = 1.97, 95% CI: 1.37, 2.83) were significant predictors of appropriate use of guidelines. Inclusion of a meta-analysis resulted in 73% higher odds of appropriate usage of systematic review guidelines (OR = 1.73, 95% CI: 1.22, 2.35). CONCLUSIONS Usage of SR reporting guidelines and tools for assessment of methodological quality other than PRISMA may be under-utilized with negative implications both for the reporting and methodological quality of systematic reviews.

Ignoring heterozygous sites biases phylogenomic estimates of divergence times: implications for the evolutionary history of Microtus voles

Phylogenetic reconstruction of the evolutionary history of closely related organisms may be difficult because of the presence of unsorted lineages and of a relatively high proportion of heterozygous sites that are usually not handled well by phylogenetic programs. Genomic data may provide enough fixed polymorphisms to resolve phylogenetic trees, but the diploid nature of sequence data remains analytically challenging. Here, we performed a phylogenomic reconstruction of the evolutionary history of the common vole (Microtus arvalis) with a focus on the influence of heterozygosity on the estimation of intraspecific divergence times. We used genome-wide sequence information from 15 voles distributed across the European range. We provide a novel approach to integrate heterozygous information in existing phylogenetic programs by repeated random haplotype sampling from sequences with multiple unphased heterozygous sites. We evaluated the impact of the use of full, partial, or no heterozygous information for tree reconstructions on divergence time estimates. All results consistently showed four deep and strongly supported evolutionary lineages in the vole data. These lineages undergoing divergence processes split only at the end or after the last glacial maximum based on calibration with radiocarbon-dated paleontological material. However, the incorporation of information from heterozygous sites had a significant impact on absolute and relative branch length estimations. Ignoring heterozygous information led to an overestimation of divergence times between the evolutionary lineages of M. arvalis. We conclude that the exclusion of heterozygous sites from evolutionary analyses may cause biased and misleading divergence time estimates in closely related taxa.

Survival Extrapolation In The Presence Of Cause Specific Hazards.

Health economic evaluations require estimates of expected survival from patients receiving different interventions, often over a lifetime. However, data on the patients of interest are typically only available for a much shorter follow-up time, from randomised trials or cohorts. Previous work showed how to use general population mortality to improve extrapolations of the short-term data, assuming a constant additive or multiplicative effect on the hazards for all-cause mortality for study patients relative to the general population. A more plausible assumption may be a constant effect on the hazard for the specific cause of death targeted by the treatments. To address this problem, we use independent parametric survival models for cause-specific mortality among the general population. Because causes of death are unobserved for the patients of interest, a polyhazard model is used to express their all-cause mortality as a sum of latent cause-specific hazards. Assuming proportional cause-specific hazards between the general and study populations then allows us to extrapolate mortality of the patients of interest to the long term. A Bayesian framework is used to jointly model all sources of data. By simulation, we show that ignoring cause-specific hazards leads to biased estimates of mean survival when the proportion of deaths due to the cause of interest changes through time. The methods are applied to an evaluation of implantable cardioverter defibrillators for the prevention of sudden cardiac death among patients with cardiac arrhythmia. After accounting for cause-specific mortality, substantial differences are seen in estimates of life years gained from implantable cardioverter defibrillators.

Levotiroxina e o problema da permutabilidade de drogas de estreito intervalo terapêutico

The exchange of a prescribed drug by other similar, by generic products and even by custom products has become common practice in our country, often ignoring basic tenets of bioequivalence, interchangeability, stability and characteristics of the pharmaceutical compounds. In the case of drugs of narrow therapeutic index, such as levothyroxine, these problems are intensified, putting the effectiveness of treatment and patient health at serious risk. We review the pertinent legislation, emphasizing the characteristics of levothyroxine and adverse effects that limit the interchangeability of the compound.