Statistical Interpretation including the APPROPRIATE Statistical Tests

Outline: Evaluation of treatment’s therapeutic potential after experimental stroke. Post-stroke behavioral testing and functional recovery

Inter-Relationships Linking Probability of Becoming a Case of Nicotine Dependence With Frequency of Tobacco Cigarette Smoking

INTRODUCTION: Once smoking starts, some tobacco cigarette smokers (TCS) can make very rapid transitions into tobacco dependence syndromes (TCD). With adjustment for smoking frequency, we posit female excess risk for this rapid-onset TCD. In a novel application of functional analysis for tobacco research, we estimate four Hill function parameters and plot TCD risk against a gradient of smoking frequency, as observed quite soon after smoking onset. METHODS: In aggregate, the National Surveys of Drug Use and Health, 2004-2013, identified 1546 newly incident TCS in cross-sectional research, each with standardized TCD assessment. RESULTS: Hill function estimates contradict our apparently over-simplistic hypothesis. Among newly incident TCS males with only 1-3 recent smoking days, an estimated 1%-3% had become rapid-onset TCD cases; non-overlapping confidence intervals show lower TCD risk for females. In contrast, among daily smokers, closer to 50% of female TCS showed rapid-onset TCD, versus under 20% of male TCS, but a larger sample will be needed to confirm the apparent female excess risk at the daily smoking frequency level. CONCLUSIONS: Smoking frequency and TCD onset become inter-dependent quite soon after TCS onset. Feedback loops are expected, and might explain a potential reversal of male-female differences across smoking frequency gradients. These novel epidemiological estimates prompt new thinking and questions about interventions. IMPLICATIONS: In this large sample epidemiological study, with a nationally representative sample of newly incident TCS assessed cross-sectionally, we see a quite rapid onset of tobacco dependence, with an early male excess that fades out at higher levels of smoking frequency. Next steps include development of outreach and intervention for this very rapid-onset tobacco dependence

Estimated Probability of Becoming a Case of Drug Dependence in Relation to Duration of Drug-Taking Experience: A Functional Analysis Approach

Measured as elapsed time from first use to dependence syndrome onset, the estimated induction interval for cocaine is thought to be short relative to the cannabis interval, but little is known about risk of becoming dependent during first months after onset of use. Virtually all published estimates for this facet of drug dependence epidemiology are from life histories elicited years after first use. To improve estimation, we turn to new month-wise data from nationally representative samples of newly incident drug users identified via probability sampling and confidential computer-assisted self-interviews for the United States National Surveys on Drug Use and Health, 2004-2013. Standardized modules assessed first and most recent use, and dependence syndromes, for each drug subtype. A four-parameter Hill function depicts the drug dependence transition for subgroups defined by units of elapsed time from first to most recent use, with an expectation of greater cocaine dependence transitions for cocaine versus cannabis. This study\u27s novel estimates for cocaine users one month after first use show 2-4% with cocaine dependence; 12-17% are dependent when use has persisted. Corresponding cannabis estimates are 0-1% after one month, but 10-23% when use persists. Duration or persistence of cannabis smoking beyond an initial interval of a few months of use seems to be a signal of noteworthy risk for, or co-occurrence of, rapid-onset cannabis dependence, not too distant from cocaine estimates, when we sort newly incident users into subgroups defined by elapsed time from first to most recent use

Estimated Probability of Becoming Alcohol Dependent: Extending a Multiparametric Approach

Background: United States (US) epidemiological studies suggest that for every 5-8 who start drinking alcoholic beverages, at least one drinker will develop an alcohol dependence (AD) syndrome within the first 10 years after onset of drinking (Lopez-Quintero et al., 2011; Wagner & Anthony, 2002). Recently, we described a multiparametric functional analysis approach for new research to estimate these transition probabilities with a one-dimensional function (1D; Vsevolozhskaya & Anthony, 2015). Here, we demonstrate extension of this analysis to two-dimensional (2D) functions that combine information about number of recent drinking days and number of drinks on the typical drinking day. Methods: Data are from the United States National Survey on Drug Use and Health (NSDUH) Restricted-use Data Analysis System, 2002-2011, with nationally representative samples of newly incident drinkers and rapid-onset AD syndromes ascertained via standardized audio computer self interviews, completed for surveys of non-institutionalized civilian US citizens, age 12 years and older. Drinking history, including DSM-IV AD status, were assessed via the standardized computer-assisted interview assessments. The 2D functional estimates are based on a non-linear parametric Hill equation evaluated for (1) number of drinking days in 30 days just before NSDUH assessment, and (2) typical number of drinks on recent drinking days. Results: Among newly incident drinkers with just one drink per drinking day, the estimated AD risk ranges from more or less 1% among infrequent drinkers with a single drinking day per month (95% bootstrap confidence interval, CI: 0.7, 1.0), upward to about 3% among daily drinkers (95% CI: 1.4, 3.7). Among newly incident drinkers with ~2 drinks per drinking day, estimated AD risk is much larger among daily drinkers (21.4%; 95% CI = 5, 21). Across subgroups defined by 3, 4, and 5 or more drinks per day, the estimated AD risk is larger, as can be seen clearly for those who have progressed to daily drinking: 31% for 3 drinks, 84% for 4 drinks, 90% for 5+ drinks, respectively, with some degree of CI overlap. However, among infrequent drinkers, with no more than one drinking day per month, the estimated AD risk does not appreciably differ from 1% irrespective of the number of drinks consumed per typical drinking day. Conclusions: Via the multiparametric functional analysis approach extended beyond the number of drinks per typical drinking day, this evidence helps clarify that AD risk apparently is relatively constant and quite limited when newly incident drinking is limited to no more than one drinking day per month. When newly incident drinkers are observed within 12 months after drinking onset, there is substantial increase in AD risk among daily drinkers, provided the typical number of drinks per day increases from 1 to 5+ drinks. This study is novel in its focus on newly incident drinkers and variations in risk of developing alcohol dependence soon after drinking onset. A new agenda for research AD risk among newly incident drinkers can be built upon this initial platform of new evidence, particularly if family history and individual-level genomic characteristics can be assessed and brought into play in future national surveys of this type

Bayesian Prediction Intervals for Assessing \u3cem\u3eP\u3c/em\u3e-Value Variability in Prospective Replication Studies

Increased availability of data and accessibility of computational tools in recent years have created an unprecedented upsurge of scientific studies driven by statistical analysis. Limitations inherent to statistics impose constraints on the reliability of conclusions drawn from data, so misuse of statistical methods is a growing concern. Hypothesis and significance testing, and the accompanying P-values are being scrutinized as representing the most widely applied and abused practices. One line of critique is that P-values are inherently unfit to fulfill their ostensible role as measures of credibility for scientific hypotheses. It has also been suggested that while P-values may have their role as summary measures of effect, researchers underappreciate the degree of randomness in the P-value. High variability of P-values would suggest that having obtained a small P-value in one study, one is, nevertheless, still likely to obtain a much larger P-value in a similarly powered replication study. Thus, “replicability of P-value” is in itself questionable. To characterize P-value variability, one can use prediction intervals whose endpoints reflect the likely spread of P-values that could have been obtained by a replication study. Unfortunately, the intervals currently in use, the frequentist P-intervals, are based on unrealistic implicit assumptions. Namely, P-intervals are constructed with the assumptions that imply substantial chances of encountering large values of effect size in an observational study, which leads to bias. The long-run frequentist probability provided by P-intervals is similar in interpretation to that of the classical confidence intervals, but the endpoints of any particular interval lack interpretation as probabilistic bounds for the possible spread of future P-values that may have been obtained in replication studies. Along with classical frequentist intervals, there exists a Bayesian viewpoint toward interval construction in which the endpoints of an interval have a meaningful probabilistic interpretation. We propose Bayesian intervals for prediction of P-value variability in prospective replication studies. Contingent upon approximate prior knowledge of the effect size distribution, our proposed Bayesian intervals have endpoints that are directly interpretable as probabilistic bounds for replication P-values, and they are resistant to selection bias. We showcase our approach by its application to P-values reported for five psychiatric disorders by the Psychiatric Genomics Consortium group

Very Rapid Onset Cannabis Dependence Risk in Relation to Co-Occurring Use of Other Psychoactive Drugs

Background: Epidemiological estimates for lifetime cumulative incidence indicate that for every 9-11 who start using cannabis, one becomes a case of the cannabis dependence syndrome (CDS) – i.e., roughly 9%-11%. More recent estimates clarify that CDS risk might be much lower among ’cannabis only’ users, due in part to the fact that many ’cannabis only’ users try the drug a few times and never again. We turned to Hill functional analysis in order to study CDS probability soon after 1st cannabis use, estimated across strata defined by the number of recent days of cannabis use, with an acknowledgment that a persistence of cannabis use beyond a few trials (may signify a potentially higher risk subgroup). Methods: United States National Surveys on Drug Use and Health (NSDUH), 2004-2014, sampled and assessed more than 500,000 participants, yielding a nationally representative probability sample of 13,874 newly incident cannabis users, with CDS assessment no more than 12 months since 1st use. For this analysis, we focused on the subgroup of 4,934 subjects with persistence of cannabis use into the 30 days prior to assessment. For this subgroup, we used Hill functions to estimate variations in CDS probability across strata defined by cannabis-using days during the 30 days prior to assessment, and by history of using other psychoactive drug compounds. Results: Our preliminary results show that among ’cannabis only’ users (n=1,811) the probability of CDS starts at about 1% for occasional users (95% bootstrap confidence interval, CI: 0, 2), rising to about 9% for daily users (95% CI: 4.5, 23). However, estimated probability of CDS for daily users is greater when cannabis plus ethanol (but no other drugs) is being used (n=1,753): 63% (95% CI: 47, 84); here, use on same day is not required. Our presentation will show additional Hill function estimates for other cannabis and drug combinations (e.g., cannabis and tobacco, cannabis and alcohol and tobacco). Conclusions: Notwithstanding NSDUH self-report methods and other limitations, the main finding is that probability of observing cannabis dependence is greater when cannabis use co-occurs with other psychoactive drug use. CDS probability is relatively low for ’cannabis only’ users even when ’trial’ users are excluded. These epidemiological estimates are consistent with a re-appraisal of cannabis dependence risk for ’cannabis only’ users