***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: The present study was carried out to assess the role of and rogen receptor CAG repeat polymorphism and X chromosome inactivation ( XCI ) pattern among Indian PCOS women and controls which has not been hitherto explored and also to test the hypothesis that shorter CAG alleles would be preferentially activated in PCOS .  CAG repeat polymorphism and X chromosome methylation patterns  were compared between PCOS and non-PCOS women . 250 PCOS women and 299 controls were included for this study . And rogen receptor CAG repeat sizes , XCI percentages , and clinical and biochemical parameters were measured . The mean CAG repeat number is similar between the cases ( 18.74±0.13 ) and controls ( 18.73±0.12 ) . The obese PCOS women were significantly more frequent in the < 18 and > 20 CAG repeat category than the lean PCOS women , yielding a highly significant odds ( p = 0.001 ) . Among the women with non-r and om X-inactivation , alleles with < 19 repeats were more frequently activated among cases than controls ( p = 0.33 ) . CAG repeat polymorphism  by itself can not be considered as a useful marker for discriminating PCOS . We observed a trend of preferential activation of the shorter allele among the PCOS cases with non r and om XCI pattern . In the obese PCOS women , this microsatellite variation may account for the hyper and rogenicity to a larger extent than the lean PCOS women Methods of sample size and power calculations are review ed for the most common study design s. The sample size and power equations for these design s are shown to be special cases of two generic formulae for sample size and power calculations . A computer program is available that can be used for studies with dichotomous , continuous , or survival response measures . The alternative hypotheses of interest may be specified either in terms of differing response rates , means , or survival times , or in terms of relative risks or odds ratios . Studies with dichotomous or continuous outcomes may involve either a matched or independent study design . The program can determine the sample size needed to detect a specified alternative hypothesis with the required power , the power with which a specific alternative hypothesis can be detected with a given sample size , or the specific alternative hypotheses that can be detected with a given power and sample size . The program can generate help messages on request that facilitate the use of this software . It writes a log file of all calculated estimates and can produce an output file for plotting power curves . It is written in FORTRAN-77 and is in the public domain Hyper and rogenism is currently thought to be central to the pathogenesis of polycystic ovarian syndrome ( PCOS ) , a common endocrine disorder in premenopausal women characterized by irregular menstruation and anovulatory infertility . Although hyper and rogenism is characteristic , some women with PCOS have normal serum and rogen levels . All and rogens act through the X-linked and rogen receptor ( AR ) , the N-terminal domain of which contains a polyglutamine tract encoded by a highly polymorphic CAG trinucleotide repeat tract . Recently , variations in this CAG microsatellite tract , while remaining within the normal polymorphic range ( 11 - 38 CAGs ) , have been inversely correlated with receptor activity . Thus , short tracts are associated with high intrinsic AR activity and increased severity and earlier age of onset of the and rogen-regulated tumor prostate cancer , whereas longer CAG tracts are associated with low AR activity and oligospermic infertility . To investigate the role of the CAG repeat tract in PCOS , we measured its length in 91 patients with ultrasound diagnosis of polycystic ovaries , irregular menstrual cycles , and anovulatory infertility and compared them to 112 control subjects of proven fertility with regular menses . Fluorescent-labeled DNA fragments containing the CAG repeat tract were amplified from leucocytic DNA , and their lengths were compared with internal size markers on an automated DNA Sequencer . There were no differences in the mean CAG length between patients and controls when both alleles were considered together or separately . Because there is a subset of PCOS patients whose serum and rogens are normal , we compared differences in CAG length between patients whose serum testosterone ( T ) levels were below the normal laboratory mean , to those that were higher . There was a trend for a lower mean CAG biallelic length among anovulatory patients with T less than 1.73 nmol/L compared with those whose T was more than 1.73 nmol/L ( 22.47 + /- 0.36 vs. 23.25 + /- 0.29 ) . This difference in CAG length between patients with low and high T levels ( 20.38 + /- 0.51 vs. 21.98 + /- 0.29 ) was highly significant ( P = 0.004 ) when only the shorter allele of each individual was considered . Ethnic differences were also evident in our data ; Indian subjects had a significantly shorter AR-CAG length compared with Chinese , being 22.08 + /- 0.50 and 23.16 + /- 0.17 , respectively . Our data indicate an association between short CAG repeat length and the subset of anovulatory patients with low serum and rogens , suggesting that the pathogenic mechanism of polycystic ovaries in these patients could be due to the increased intrinsic and rogenic activity associated with short AR alleles CONTEXT Genetically determined heightened and rogen sensitivity may influence the phenotype of polycystic ovary syndrome ( PCOS ) . To date , studies of the and rogen receptor exon 1 polymorphic CAG repeat have produced conflicting results in PCOS . OBJECTIVE We tested the hypothesis that a lower number of CAG repeats is associated with increased odds of PCOS . We also compared X-chromosome inactivation between cases and controls . DESIGN Women with and without PCOS were genotyped for the CAG repeat and assessed for X-chromosome methylation . Association analyses were performed . SETTING Subjects were recruited from the reproductive endocrinology clinic at the University of Alabama at Birmingham ; controls were recruited from the surrounding community . Genotyping took place at Cedars-Sinai Medical Center in Los Angeles . PARTICIPANTS Participants included 330 women with PCOS and 289 controls ( 77 % white , 23 % black ) . MAIN MEASUREMENTS And rogen receptor genotype , X-chromosome methylation , and phenotyping for PCOS were measured . RESULTS A smaller biallelic mean of CAG repeats was associated with increased odds of PCOS . X-chromosome inactivation was not different comparing cases with controls ; however , in the subset with nonr and om inactivation , the chromosome bearing the shorter CAG allele was preferentially active in PCOS women . CONCLUSIONS Association of shorter CAG repeats with PCOS is consistent with in vitro functional studies demonstrating higher activity of and rogen receptors expressed from alleles with fewer CAG repeats , suggesting inherited alteration in and rogen sensitivity may contribute to PCOS . In some women , such heightened sensitivity may also result from preferential expression of and rogen receptors with shorter alleles . Thus , genetic and epigenetic changes may be involved in the pathogenesis of PCOS A quantitative systematic review , or meta- analysis , uses statistical methods to combine the results of multiple studies . Meta-analyses have been done for systematic review s of therapeutic trials , diagnostic test evaluations , and epidemiologic studies . Although the statistical methods involved may at first appear to be mathematically complex , their purpose is simple : They are trying to answer four basic questions . Are the results of the different studies similar ? To the extent that they are similar , what is the best overall estimate ? How precise and robust is this estimate ? Finally , can dissimilarities be explained ? This article provides some guidance in underst and ing the key technical aspects of the quantitative approach to these questions . We have avoided using equations and statistical notations ; interested readers will find implementations of the described methods in the listed references . We focus here on the quantitative synthesis of reports of r and omized , controlled , therapeutic trials because far more meta-analyses on therapeutic studies than on other types of studies have been published . For practical reasons , we present a stepwise description of the tasks that are performed when statistical methods are used to combine data . These tasks are 1 ) deciding whether to combine data and defining what to combine , 2 ) evaluating the statistical heterogeneity of the data , 3 ) estimating a common effect , 4 ) exploring and explaining heterogeneity , 5 ) assessing the potential for bias , and 6 ) presenting the results . Deciding Whether To Combine Data and Defining What To Combine By the time one performs a quantitative synthesis , certain decisions should already have been made about the formulation of the question and the selection of included studies . These topics were discussed in two previous articles in this series [ 1 , 2 ] . Statistical tests can not compensate for lack of common sense , clinical acumen , and biological plausibility in the design of the protocol of a meta- analysis . Thus , a reader of a systematic review should always address these issues before evaluating the statistical methods that have been used and the results that have been generated . Combining poor- quality data , overly biased data , or data that do not make sense can easily produce unreliable results . The data to be combined in a meta- analysis are usually either binary or continuous . Binary data involve a yes/no categorization ( for example , death or survival ) . Continuous data take a range of values ( for example , change in diastolic blood pressure after antihypertensive treatment , measured in mm Hg ) . When one is comparing groups of patients , binary data can be summarized by using several measures of treatment effect that were discussed earlier in this series [ 3 ] . These measures include the risk ratio ; the odds ratio ; the risk difference ; and , when study duration is important , the incidence rate . Another useful clinical measure , the number needed to treat ( NNT ) , is derived from the inverse of the risk difference [ 3 ] . Treatment effect measures , such as the risk ratio and the odds ratio , provide an estimate of the relative efficacy of an intervention , whereas the risk difference describes the intervention 's absolute benefit . The various measures of treatment effect offer complementary information , and all should be examined [ 4 ] . Continuous data can be summarized by the raw mean difference between the treatment and control groups when the treatment effect is measured on the same scale ( for example , diastolic blood pressure in mm Hg ) , by the st and ardized mean difference when different scales are used to measure the same treatment effect ( for example , different pain scales being combined ) , or by the correlation coefficients between two continuous variables [ 5 ] . The st and ardized mean difference , also called the effect size , is obtained by dividing the difference between the mean in the treatment group and the mean in the control group by the SD in the control group . Evaluating the Statistical Heterogeneity of the Data This step is intended to answer the question , Are the results of the different studies similar ( homogeneous ) ? It is important to answer this question before combining any data . To do this , one must calculate the magnitude of the statistical diversity ( heterogeneity ) of the treatment effect that exists among the different sets of data . Statistical diversity can be thought of as attributable to one or both of two causes . First , study results can differ because of r and om sampling error . Even if the true effect is the same in each study , the results of different studies would be expected to vary r and omly around the true common fixed effect . This diversity is called the within- study variance . Second , each study may have been drawn from a different population , depending on the particular patients chosen and the interventions and conditions unique to the study . Therefore , even if each study enrolled a large patient sample , the treatment effect would be expected to differ . These differences , called r and om effects , describe the between- study variation with regard to an overall mean of the effects of all of the studies that could be undertaken . The test most commonly used to assess the statistical significance of between- study heterogeneity is based on the chi-square distribution [ 6 ] . It provides a measure of the sum of the squared differences between the results observed and the results expected in each study , under the assumption that each study estimates the same common treatment effect . A large total deviation indicates that a single common treatment effect is unlikely . Any pooled estimate calculated must account for the between- study heterogeneity . In practice , this test has low sensitivity for detecting heterogeneity , and it has been suggested that a liberal significance level , such as 0.1 , should be used [ 6 ] . Estimating a Common Effect The questions that this step tries to answers are , 1 ) To the extent that data are similar , what is their best common point estimate of a therapeutic effect , and 2 ) how precise is this estimate ? The mathematical process involved in this step generally involves combining ( pooling ) the results of different studies into an overall estimate . Compared with the results of individual studies , pooled results can increase statistical power and lead to more precise estimates of treatment effect . Each study is given a weight according to the precision of its results . The rationale is that studies with narrow CIs should be weighted more heavily than studies with greater uncertainty . The precision is generally expressed by the inverse of the variance of the estimate of each study . The variance has two components : the variance of the individual study and the variance between different studies . When the between- study variance is found to be or assumed to be zero , each study is simply weighted by the inverse of its own variance , which is a function of the study size and the number of events in the study . This approach characterizes a fixed-effects model , as exemplified by the Mantel-Haenszel method [ 7 , 8 ] or the Peto method [ 9 ] for dichotomous data . The Peto method has been particularly popular in the past . It has the advantage of simple calculation ; however , although it is appropriate in most cases , it may introduce large biases if the data are unbalanced [ 10 , 11 ] . On the other h and , r and om-effects models also add the between- study variance to the within- study variance of each individual study when the pooled mean of the r and om effects is calculated . The r and om-effects model most commonly used for dichotomous data is the DerSimonian and Laird estimate of the between- study variance [ 12 ] . Fixed- and r and om-effects models for continuous data have also been described [ 13 ] . Pooled results are generally reported as a point estimate and CI , typically a 95 % CI . Other quantitative techniques for combining 
    Output:
    
No statistically significant results were identified by different comparison models or different cut-off point definitions .
No publication bias was observed in continuous and dichotomous data analysis .
In summary , the current systematic review and meta- analysis found that the AR CAG microsatellite repeat polymorphism is unlikely to be a major determining factor in the development of PCOS