Explore the vast range of titles available.

Research summary: The use of Heckman models by strategy scholars to resolve sample selection bias has increased by more than 700 percent over the last decade, yet significant inconsistencies exist in how they have applied and interpreted these models. In view of these differences, we explore the drivers of sample selection bias and review how Heckman models alleviate it. We demonstrate three important findings for scholars seeking to use Heckman models: First, the independent variable of interest must be a significant predictor in the first stage of a model for sample selection bias to exist. Second, the significance of lambda alone does not indicate sample selection bias. Finally, Heckman models account for sample-induced endogeneity, but are not effective when other sources of endogeneity are present. Managerial summary: When nonrandom samples are used to test statistical relationships, sample selection bias can lead researchers to flawed conclusions that can, in turn, negatively impact managerial decision-making. We examine the use of Heckman models, which were designed to resolve sample selection bias, in strategic management research and highlight conditions when sample selection bias is present as well as when it is not. We also distinguish sample selection bias, a form of omitted variable (OV) bias, from more traditional OV bias, emphasizing that it is possible for models to have sample selection bias, traditional OV bias, or both. Accurately identifying the type(s) of OV bias present is essential to effectively correcting it. We close with several recommendations to improve practice surrounding the use of Heckman models.

It has been argued that survival bias may distort results in Mendelian randomization studies in older populations. Through simulations of a simple causal structure we investigate the degree to which instrumental variable (IV)-estimators may become biased in the context of exposures that affect survival. We observed that selecting on survival decreased instrument strength and, for exposures with directionally concordant effects on survival (and outcome), introduced downward bias of the IV-estimator when the exposures reduced the probability of survival till study inclusion. Higher ages at study inclusion generally increased this bias, particularly when the true causal effect was not equal to null. Moreover, the bias in the estimated exposure-outcome relation depended on whether the estimation was conducted in the one- or two-sample setting. Finally, we briefly discuss which statistical approaches might help to alleviate this and other types of selection bias. See video abstract at, http://links.lww.com/EDE/B589.

Most methods for modeling species distributions from occurrence records require additional data representing the range of environmental conditions in the modeled region. These data, called background or pseudo-absence data, are usually drawn at random from the entire region, whereas occurrence collection is often spatially biased toward easily accessed areas. Since the spatial bias generally results in environmental bias, the difference between occurrence collection and background sampling may lead to inaccurate models. To correct the estimation, we propose choosing background data with the same bias as occurrence data. We investigate theoretical and practical implications of this approach. Accurate information about spatial bias is usually lacking, so explicit biased sampling of background sites may not be possible. However, it is likely that an entire target group of species observed by similar methods will share similar bias. We therefore explore the use of all occurrences within a target group as biased background data. We compare model performance using target-group background and randomly sampled background on a comprehensive collection of data for 226 species from diverse regions of the world. We find that target-group background improves average performance for all the modeling methods we consider, with the choice of background data having as large an effect on predictive performance as the choice of modeling method. The performance improvement due to target-group background is greatest when there is strong bias in the target-group presence records. Our approach applies to regression-based modeling methods that have been adapted for use with occurrence data, such as generalized linear or additive models and boosted regression trees, and to Maxent, a probability density estimation method. We argue that increased awareness of the implications of spatial bias in surveys, and possible modeling remedies, will substantially improve predictions of species distributions.

Objectives Longitudinal data offer many advantages to criminological research yet suffer from attrition, namely in the form of sample selection bias. Attrition may undermine reaching valid inferences by introducing systematic differences between the retained and attrited samples. We explored (1) if attrition biases correlates of recidivism, (2) the magnitude of bias, and (3) how well methods of correction account for such bias. Methods Using data from the LoneStar Project, a representative longitudinal sample of reentering men in Texas, we examined correlates of recidivism using official measures of recidivism under four sample conditions: full sample, listwise deleted sample, multiply imputed sample, and two-stage corrected sample. We compare and contrast the results regressing rearrest on a range of covariates derived from a pre-release baseline interview across the four sample conditions. Results Attrition bias was present in 44% of variables and null hypothesis significance tests differed for the correlates of recidivism in the full and retained samples. The bias was substantial, altering effect sizes for recidivism by a factor as large as 1.6. Neither the Heckman correction nor multiple imputation adequately corrected for bias. Instead, results from listwise deletion most closely mirrored the results of the full sample with 89% concordance. Conclusions It is vital that researchers examine attrition-based selection bias and recognize the implications it has on their data when generating evidence of theoretical, policy, or practical significance. We outline best practices for examining the magnitude of attrition and analyzing longitudinal data affected by sample selection.

Rising costs of survey data collection and declining response rates have caused researchers to turn to non-probability samples to make descriptive statements about populations. However, unlike probability samples, non-probability samples may produce severely biased descriptive estimates due to selection bias. The paper develops and evaluates a simple model-based index of the potential selection bias in estimates of population proportions due to non-ignorable selection mechanisms. The index depends on an inestimable parameter ranging from 0 to 1 that captures the amount of deviation from selection at random and is thus well suited to a sensitivity analysis. We describe modified maximum likelihood and Bayesian estimation approaches and provide new and easy-to-use R functions for their implementation. We use simulation studies to evaluate the ability of the proposed index to reflect selection bias in non-probability samples and show how the index outperforms a previously proposed index that relies on an underlying normality assumption. We demonstrate the use of the index in practice with real data from the National Survey of Family Growth.

Background Propensity score matching has become a popular method for estimating causal treatment effects in non-randomized studies. However, for time-to-event outcomes, the estimation of hazard ratios based on propensity scores can be challenging if omitted or unobserved covariates are present. Not accounting for such covariates could lead to treatment estimates, differing from the estimate of interest. However, researchers often do not know whether (and, if so, which) covariates will cause this divergence. Methods To address this issue, we extended a previously described method, Dynamic Landmarking , which was originally developed for randomized trials. The method is based on successively deletion of sorted observations and gradually fitting univariable Cox models. In addition, the balance of observed, but omitted covariates can be measured by the sum of squared z-differences. Results By simulation we show, that Dynamic Landmarking provides a good visual tool for detecting and distinguishing treatment effect estimates underlying built-in selection or confounding bias. We illustrate the approach with a data set from cardiac surgery and provide some recommendations on how to use and interpret Dynamic Landmarking in propensity score matched studies. Conclusion Dynamic Landmarking is a useful post-hoc diagnosis tool for visualizing whether an estimated hazard ratio could be distorted by confounding or built-in selection bias.

Research summary Patent data is a valued source of information for strategy research. However, patent‐based studies may suffer from sample selection bias given that patents result from within‐firm selection processes and hence do not represent the full population of inventions. We assess how incidental and nonincidental data truncation resulting from firm‐level and inventor‐level selection processes may result in sample selection bias using a quasi‐replication approach, drawing on rich qualitative data and a novel, proprietary dataset of all 40,000 invention disclosures within a large multinational firm. We find that accounting for selection both reaffirms and challenges past work, and discuss the implications of our findings for work on the microfoundations of exploratory innovation activities and for strategy research drawing on patent data. Managerial summary Much of what is known about innovation in general, and in particular about what makes inventors prolific, comes from studies that use patent data. However, many ideas are never patented, meaning that these studies may not in reality talk about ideas or inventions, but only about patents. In this paper, we examine the question of whether patent data can accurately be used to represent inventions by using data on all inventions generated within a large multinational firm to explore how and to what degree the selection processes behind firms' patenting decisions may lead to important differences between the two. We find that accounting for selection changes many previously given managerial implications; for example, we show how junior inventors may often not get the credit they deserve.

BACKGROUND:Structural selection bias and confounding are key threats to validity of causal effect estimation. Here, we consider M-bias, a type of selection bias, described by Hernán et al as a situation wherein bias is caused by selecting on a variable that is caused by two other variables, one a cause of the exposure, the other a cause of the outcome. Our goals are to derive a bound for (the maximum) M-bias, explore through examples the magnitude of M-bias, illustrate how to apply the bound for other types of selection bias, and provide a program for directly calculating M-bias and the bound. METHODS:We derive a bound for selection bias assuming specific, causal relationships that characterize M-bias and further evaluate it using simulations. RESULTS:Through examples, we show that, in many plausible situations, M-bias will tend to be small. In some examples, the bias is not small–but plausibility of the examples, ultimately to be judged by the researcher, may be low. The examples also show how the M-bias bound yields bounds for other types of selection bias and also for confounding. The latter illustrates how Lee’s bound for confounding can arise as a limiting case of ours. CONCLUSIONS:We have derived a new bound for M-bias. Examples illustrate how to apply it with other types of selection bias. They also show that it can yield tighter bounds in certain situations than a previously published bound for M-bias. Our examples suggest that M-bias may often, but not uniformly, be small.

Abstract BACKGROUND No guidelines have been published regarding stereotactic radiosurgery (SRS) in the management of Spetzler-Martin grade I and II arteriovenous malformations (AVMs). OBJECTIVE To establish SRS practice guidelines for grade I-II AVMs on the basis of a systematic literature review. METHODS Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-compliant search of Medline, Embase, and Scopus, 1986-2018, for publications reporting post-SRS outcomes in ≥10 grade I-II AVMs with a follow-up of ≥24 mo. Primary endpoints were obliteration and hemorrhage; secondary outcomes included Spetzler-Martin parameters, dosimetric variables, and “excellent” outcomes (defined as total obliteration without new post-SRS deficit). RESULTS Of 447 abstracts screened, 8 were included (n = 1, level 2 evidence; n = 7, level 4 evidence), representing 1102 AVMs, of which 836 (76%) were grade II. Obliteration was achieved in 884 (80%) at a median of 37 mo; 66 hemorrhages (6%) occurred during a median follow-up of 68 mo. Total obliteration without hemorrhage was achieved in 78%. Of 836 grade II AVMs, Spetzler-Martin parameters were reported in 680: 377 were eloquent brain and 178 had deep venous drainage, totaling 555/680 (82%) high-risk SRS-treated grade II AVMs. CONCLUSION The literature regarding SRS for grade I-II AVM is low quality, limiting interpretation. Cautiously, we observed that SRS appears to be a safe, effective treatment for grade I-II AVM and may be considered a front-line treatment, particularly for lesions in deep or eloquent locations. Preceding publications may be influenced by selection bias, with favorable AVMs undergoing resection, whereas those at increased risk of complications and nonobliteration are disproportionately referred for SRS.

Background The hazard ratio of the Cox proportional hazards model is widely used in randomized controlled trials to assess treatment effects. However, two properties of the hazard ratio including the non-collapsibility and built-in selection bias need to be further investigated. Methods We conduct simulations to differentiate the non-collapsibility effect and built-in selection bias from the difference between the marginal and the conditional hazard ratio. Meanwhile, we explore the performance of the Cox model with inverse probability of treatment weighting for covariate adjustment when estimating the marginal hazard ratio. The built-in selection bias is further assessed in the period-specific hazard ratio. Results The conditional hazard ratio is a biased estimate of the marginal effect due to the non-collapsibility property. In contrast, the hazard ratio estimated from the inverse probability of treatment weighting Cox model provides an unbiased estimate of the true marginal hazard ratio. The built-in selection bias only manifests in the period-specific hazard ratios even when the proportional hazards assumption is satisfied. The Cox model with inverse probability of treatment weighting can be used to account for confounding bias and provide an unbiased effect under the randomized controlled trials setting when the parameter of interest is the marginal effect. Conclusions We propose that the period-specific hazard ratios should always be avoided due to the profound effects of built-in selection bias.