Comparing breast cancer mortality rates before-and-after a change in availability of screening in different regions: Extension of the paired availability design

BACKGROUND: In recent years there has been increased interest in evaluating breast cancer screening using data from before-and-after studies in multiple geographic regions. One approach, not previously mentioned, is the paired availability design. The paired availability design was developed to evaluate the effect of medical interventions by comparing changes in outcomes before and after a change in the availability of an intervention in various locations. A simple potential outcomes model yields estimates of efficacy, the effect of receiving the intervention, as opposed to effectiveness, the effect of changing the availability of the intervention. By combining estimates of efficacy rather than effectiveness, the paired availability design avoids confounding due to different fractions of subjects receiving the interventions at different locations. The original formulation involved short-term outcomes; the challenge here is accommodating long-term outcomes. METHODS: The outcome is incident breast cancer deaths in a time period, which are breast cancer deaths that were diagnosed in the same time period. We considered the plausibility of the basic five assumptions of the paired availability design and propose a novel analysis to accommodate likely violations of the assumption of stable screening effects. RESULTS: We applied the paired availability design to data on breast cancer screening from six counties in Sweden. The estimated yearly change in incident breast cancer deaths per 100,000 persons ages 40–69 (in most counties) due to receipt of screening (among the relevant type of subject in the potential outcomes model) was -9 with 95% confidence interval (-14, -4) or (-14, -5), depending on the sensitivity analysis. CONCLUSION: In a realistic application, the extended paired availability design yielded reasonably precise confidence intervals for the effect of receiving screening on the rate of incident breast cancer death. Although the assumption of stable preferences may be questionable, its impact will be small if there is little screening in the first time period. However, estimates may be substantially confounded by improvements in systemic therapy over time. Therefore the results should be interpreted with care

Randomized trials, generalizability, and meta-analysis: Graphical insights for binary outcomes

BACKGROUND: Randomized trials stochastically answer the question. "What would be the effect of treatment on outcome if one turned back the clock and switched treatments in the given population?" Generalizations to other subjects are reliable only if the particular trial is performed on a random sample of the target population. By considering an unobserved binary variable, we graphically investigate how randomized trials can also stochastically answer the question, "What would be the effect of treatment on outcome in a population with a possibly different distribution of an unobserved binary baseline variable that does not interact with treatment in its effect on outcome?" METHOD: For three different outcome measures, absolute difference (DIF), relative risk (RR), and odds ratio (OR), we constructed a modified BK-Plot under the assumption that treatment has the same effect on outcome if either all or no subjects had a given level of the unobserved binary variable. (A BK-Plot shows the effect of an unobserved binary covariate on a binary outcome in two treatment groups; it was originally developed to explain Simpsons's paradox.) RESULTS: For DIF and RR, but not OR, the BK-Plot shows that the estimated treatment effect is invariant to the fraction of subjects with an unobserved binary variable at a given level. CONCLUSION: The BK-Plot provides a simple method to understand generalizability in randomized trials. Meta-analyses of randomized trials with a binary outcome that are based on DIF or RR, but not OR, will avoid bias from an unobserved covariate that does not interact with treatment in its effect on outcome

The Paired Availability Design for Historical Controls

BACKGROUND: Although a randomized trial represents the most rigorous method of evaluating a medical intervention, some interventions would be extremely difficult to evaluate using this study design. One alternative, an observational cohort study, can give biased results if it is not possible to adjust for all relevant risk factors. METHODS: A recently developed and less well-known alternative is the paired availability design for historical controls. The paired availability design requires at least 10 hospitals or medical centers in which there is a change in the availability of the medical intervention. The statistical analysis involves a weighted average of a simple "before" versus "after" comparison from each hospital or medical center that adjusts for the change in availability. RESULTS: We expanded requirements for the paired availability design to yield valid inference. (1) The hospitals or medical centers serve a stable population. (2) Other aspects of patient management remain constant over time. (3) Criteria for outcome evaluation are constant over time. (4) Patient preferences for the medical intervention are constant over time. (5) For hospitals where the intervention was available in the "before" group, a change in availability in the "after group" does not change the effect of the intervention on outcome. CONCLUSION: The paired availability design has promise for evaluating medical versus surgical interventions, in which it is difficult to recruit patients to a randomized trial

The fallacy of enrolling only high-risk subjects in cancer prevention trials: Is there a "free lunch"?

BACKGROUND: There is a common belief that most cancer prevention trials should be restricted to high-risk subjects in order to increase statistical power. This strategy is appropriate if the ultimate target population is subjects at the same high-risk. However if the target population is the general population, three assumptions may underlie the decision to enroll high-risk subject instead of average-risk subjects from the general population: higher statistical power for the same sample size, lower costs for the same power and type I error, and a correct ratio of benefits to harms. We critically investigate the plausibility of these assumptions. METHODS: We considered each assumption in the context of a simple example. We investigated statistical power for fixed sample size when the investigators assume that relative risk is invariant over risk group, but when, in reality, risk difference is invariant over risk groups. We investigated possible costs when a trial of high-risk subjects has the same power and type I error as a larger trial of average-risk subjects from the general population. We investigated the ratios of benefit to harms when extrapolating from high-risk to average-risk subjects. RESULTS: Appearances here are misleading. First, the increase in statistical power with a trial of high-risk subjects rather than the same number of average-risk subjects from the general population assumes that the relative risk is the same for high-risk and average-risk subjects. However, if the absolute risk difference rather than the relative risk were the same, the power can be less with the high-risk subjects. In the analysis of data from a cancer prevention trial, we found that invariance of absolute risk difference over risk groups was nearly as plausible as invariance of relative risk over risk groups. Therefore a priori assumptions of constant relative risk across risk groups are not robust, limiting extrapolation of estimates of benefit to the general population. Second, a trial of high-risk subjects may cost more than a larger trial of average risk subjects with the same power and type I error because of additional recruitment and diagnostic testing to identify high-risk subjects. Third, the ratio of benefits to harms may be more favorable in high-risk persons than in average-risk persons in the general population, which means that extrapolating this ratio to the general population would be misleading. Thus there is no free lunch when using a trial of high-risk subjects to extrapolate results to the general population. CONCLUSION: Unless the intervention is targeted to only high-risk subjects, cancer prevention trials should be implemented in the general population

Using observational data to estimate an upper bound on the reduction in cancer mortality due to periodic screening

BACKGROUND: Because randomized cancer screening trials are very expensive, observational cancer screening studies can play an important role in the early phases of screening evaluation. Periodic screening evaluation (PSE) is a methodology for estimating the reduction in population cancer mortality from data on subjects who receive regularly scheduled screens. Although PSE does not require assumptions about natural history of cancer it requires other assumptions, particularly progressive detection – the assumption that once a cancer is detected by a screening test, it will always be detected by the screening test. METHODS: We formulate a simple version of PSE and show that it leads to an upper bound on screening efficacy if the progressive detection assumption does not hold (and any effect of birth cohort is minimal) To determine if the upper bound is reasonable, for three randomized screening trials, we compared PSE estimates based only on screened subjects with PSE estimates based on all subjects. RESULTS: In the three randomized screening trials, PSE estimates based on screened subjects gave fairly close results to PSE estimates based on all subjects. CONCLUSION: PSE has promise for obtaining an upper bound on the reduction in population cancer mortality rates based on observational screening data. If the upper bound estimate is found to be small and any birth cohort effects are likely minimal, then a definitive randomized trial would not be warranted

Selecting patients for randomized trials: a systematic approach based on risk group

BACKGROUND: A key aspect of randomized trial design is the choice of risk group. Some trials include patients from the entire at-risk population, others accrue only patients deemed to be at increased risk. We present a simple statistical approach for choosing between these approaches. The method is easily adapted to determine which of several competing definitions of high risk is optimal. METHOD: We treat eligibility criteria for a trial, such as a smoking history, as a prediction rule associated with a certain sensitivity (the number of patients who have the event and who are classified as high risk divided by the total number patients who have an event) and specificity (the number of patients who do not have an event and who do not meet criteria for high risk divided by the total number of patients who do not have an event). We then derive simple formulae to determine the proportion of patients receiving intervention, and the proportion who experience an event, where either all patients or only those at high risk are treated. We assume that the relative risk associated with intervention is the same over all choices of risk group. The proportion of events and interventions are combined using a net benefit approach and net benefit compared between strategies. RESULTS: We applied our method to design a trial of adjuvant therapy after prostatectomy. We were able to demonstrate that treating a high risk group was superior to treating all patients; choose the optimal definition of high risk; test the robustness of our results by sensitivity analysis. Our results had a ready clinical interpretation that could immediately aid trial design. CONCLUSION: The choice of risk group in randomized trials is usually based on rather informal methods. Our simple method demonstrates that this decision can be informed by simple statistical analyses

Paliperidone ER and oral risperidone in patients with schizophrenia: a comparative database analysis

<p>Abstract</p> <p>Background</p> <p>To compare the efficacy and tolerability of paliperidone extended-release (ER) with risperidone immediate-release using propensity score methodology.</p> <p>Methods</p> <p>Six double-blind, randomized, placebo-controlled, short-term clinical trials for acute schizophrenia with availability of individual patient-level data were identified (3 per compound). Propensity score pairwise matching was used to balance observed covariates between the paliperidone ER and risperidone patient populations. Scores were generated using logistic regression models, with age, body mass index, race, sex, baseline Positive and Negative Syndrome Scale (PANSS) total score and baseline Clinical Global Impressions–Severity (CGI-S) score as factors. The dosage range of paliperidone ER (6-12 mg/day) was compared with 2 risperidone dosage ranges: 2-4 and 4-6 mg/day. The primary efficacy measure was change in PANSS total score at week 6 end point. Tolerability end points included adverse event (AE) reports and weight. AEs with rates ≥5% and with a ≥2% difference between paliperidone ER and risperidone were identified.</p> <p>Results</p> <p>Completion rates for placebo-treated subjects in paliperidone ER trials (n = 95) and risperidone trials (n = 122) groups were 36.8% and 51.6%, respectively; end point changes on PANSS total scores were similar (p = 0.768). Completion rates for subjects receiving paliperidone ER 6-12 mg/day (n = 179), risperidone 2-4 mg/day (n = 113) or risperidone 4-6 mg/day (n = 129) were 64.8%, 54.0% and 66.7%, respectively (placebo-adjusted rates: paliperidone ER vs risperidone 2-4 mg/day, p = 0.005; paliperidone ER vs risperidone 4-6 mg/day, p = 0.159). PANSS total score improvement with paliperidone ER was greater than with risperidone 2-4 mg/day (difference in mean change score, -6.7; p < 0.05) and similar to risperidone 4-6 mg/day (0.2; p = 0.927). Placebo-adjusted AEs more common with paliperidone ER were insomnia, sinus tachycardia and tachycardia; more common with risperidone were somnolence, restlessness, nausea, anxiety, salivary hypersecretion, akathisia, dizziness and nasal congestion. Weight changes with paliperidone ER and risperidone were similar (paliperidone ER vs risperidone 2-4 mg/day, p = 0.489; paliperidone ER vs risperidone 4-6 mg/day, p = 0.236).</p> <p>Conclusions</p> <p>This indirect database analysis suggested that paliperidone ER 6-12 mg/day may be more efficacious than risperidone 2-4 mg/day and as efficacious as risperidone 4-6 mg/day. The AE-adjusted incidence rates suggest differences between treatments that may be relevant for individual patients. Additional randomized, direct, head-to-head clinical trials are needed to confirm these findings.</p

Markers for early detection of cancer: Statistical guidelines for nested case-control studies

BACKGROUND: Recently many long-term prospective studies have involved serial collection and storage of blood or tissue specimens. This has spurred nested case-control studies that involve testing some specimens for various markers that might predict cancer. Until now there has been little guidance in statistical design and analysis of these studies. METHODS: To develop statistical guidelines, we considered the purpose, the types of biases, and the opportunities for extracting additional information. RESULTS: The following guidelines: (1) For the clearest interpretation, statistics should be based on false and true positive rates – not odds ratios or relative risks (2) To avoid overdiagnosis bias, cases should be diagnosed as a result of symptoms rather than on screening. (3) To minimize selection bias, the spectrum of control conditions should be the same in study and target screening populations. (4) To extract additional information, criteria for a positive test should be based on combinations of individual markers and changes in marker levels over time. (5) To avoid overfitting, the criteria for a positive marker combination developed in a training sample should be evaluated in a random test sample from the same study and, if possible, a validation sample from another study. (6) To identify biomarkers with true and false positive rates similar to mammography, the training, test, and validation samples should each include at least 110 randomly selected subjects without cancer and 70 subjects with cancer. CONCLUSION: These guidelines ensure good practice in the design and analysis of nested case-control studies of early detection biomarkers

Inhibitors of inflammation and endogenous surfactant pool size as modulators of lung injury with initiation of ventilation in preterm sheep

<p>Abstract</p> <p>Background</p> <p>Increased pro-inflammatory cytokines in tracheal aspirates correlate with the development of BPD in preterm infants. Ventilation of preterm lambs increases pro-inflammatory cytokines and causes lung inflammation.</p> <p>Objective</p> <p>We tested the hypothesis that selective inhibitors of pro-inflammatory signaling would decrease lung inflammation induced by ventilation in preterm newborn lambs. We also examined if the variability in injury response was explained by variations in the endogenous surfactant pool size.</p> <p>Methods</p> <p>Date-mated preterm lambs (n = 28) were operatively delivered and mechanically ventilated to cause lung injury (tidal volume escalation to 15 mL/kg by 15 min at age). The lambs then were ventilated with 8 mL/kg tidal volume for 1 h 45 min. Groups of animals randomly received specific inhibitors for IL-8, IL-1, or NF-κB. Unventilated lambs (n = 7) were the controls. Bronchoalveolar lavage fluid (BALF) and lung samples were used to quantify inflammation. Saturated phosphatidylcholine (Sat PC) was measured in BALF fluid and the data were stratified based on a level of 5 μmol/kg (~8 mg/kg surfactant).</p> <p>Results</p> <p>The inhibitors did not decrease the cytokine levels or inflammatory response. The inflammation increased as Sat PC pool size in BALF decreased. Ventilated lambs with a Sat PC level > 5 μmol/kg had significantly decreased markers of injury and lung inflammation compared with those lambs with < 5 μmol/kg.</p> <p>Conclusion</p> <p>Lung injury caused by high tidal volumes at birth were decreased when endogenous surfactant pool sizes were larger. Attempts to decrease inflammation by blocking IL-8, IL-1 or NF-κB were unsuccessful.</p